6 sigma glossary

6-Sigma Glossary

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Key Word Description Hyperlink MiniTab Topics

80:20 See Pareto Diagram Pareto Diagram

6-Sigma Glossary

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Yield DOE Symbol Warranty DfSS Process Capability

Gage R&R

Variables & Attributes

Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

H1

MSA / Gage Repeatability & Reproducability

J1

Product & Manufacturing Engineering Terms

6-Sigma Glossary

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# Number Symbol

%GRR Stat>Quality>Gage Gage R&R

%R&R Stat>Quality>Gage Gage R&R

* Symbol

** Symbol

/ Divide by Symbol

^ Symbol

` Symbol

~ Symbol

DOE

5.15*Sigma Stat>Quality>Gage Gage R&R

5M's and a P

6-Sigma

7-Step Method

80:20a Symbol

See ALPHA RISK. Hyp Test

ABC FlowcharterABSCISSA

Hyp Test

Accuracy Gage R&R

ACES-II Warranty

AIAG

Alpha Risk Hyp Test

Measurement system study variation (sMS) divided by total variation (sTOTAL). Assesses data relative to total sample data spread irrespective of spec limits.Range: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. Also known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measruement Capability Index.

Gage R&R


Gage R&R

1. Multiplied by2. An outlier on a box plot.

To the power of. E.g. 3**2 means 3 squared. Alternative nomenclature: ^ (Caret), or superscript e.g. x2

1. The Caret accent on a letter2. The circumflex accent on a letter e.g. ô3. The Mode. E.g. x with a caret represents the Mode of the x values4. To the power of. E.g. 3^2 means 3 squared. Alternative nomenclature: **, or superscript e.g. x**2 or x2

Mode

1. Bar or Overscore character2. The Mean. E.g. X-Bar, R-Bar. The mean of those values.See X-Bar, T-Bar, L-Bar.

Mean

1. The Tilde accent on a letter2. The Median. E.g. x with a tilde represents the Median of the x values

2K Factorials A DOE where each factor has only two levels. This enables rapid early assessment of a large number of factors with relatively few runs. May be used to conribute to further more complex study. See also: Full Factorials, Fractional Factorials, Standard Order. Nomenclature: 2 = # Levels, K = # Factors.Step 1: State the Practical Problem Step 2: State the factors and levels of interest.Step 3: Select the appropriate sample size based on the effect you are trying to detect. Stat>Power&SampleSize>2LevelStep 4: Create the Minitab experimental data sheet with the factors in their respective columns. Randomize the experimental runs in the data sheet. Conduct the experiment. Stat > DOE > Factorial> Create Factorial DesignStep 5: Construct the ANOVA table for the full model: Stat>DOE>Factorial>Analyze-Graphs>EffectsPlots(normal/Pareto)Step 6: Review the ANOVA table and eliminate effects with p-values above .05. Remove these one at a time and then run the reduced model and plot the residual graphs for further analysis Stat>DOE>Factorial>Analyze-Graphs>EffectsPlots-Graphs>ResidualsPlotsStep 7: Analyze the residual plots to ensure we have a model that fits Step 8: Investigate significant interactions (p-value < .05). Assess the significance of the highest order interactions first.For 3-way interactions unstack the data and analyze. Stat > DOE > Factorial> Factorial Plots > Interaction PlotOnce the highest order interactions are interpreted, analyze the next set of lower order interactions.Step 9: Investigate the main effect of significant factors (p-value < .05) whose settings have not been determined by interaction analysis. Stat > DOE > Factorial> Factorial Plots > Main Effects Plot & Cube PlotsStep 10: State the mathematical model obtained. If possible calculate the epsilon squared and determine the practical significance.Step 11: Translate the mathematical model into process terms and formulate conclusions and recommendations.Step 12: Replicate optimum conditions. Plan the next experiment or institutionalize the change.

Full Factorials Stat>DOE>Factorial>Create>2Level

Stat>ANOVA>1way.1wayUnstck..Bal..GLM

Equivalent to range covering 99% of variance. See Gage R & R Gage R&R

Machine (Equipment), Method (Procedures), Measurement system, Materials, Mother Nature (Environment), People. Same as Fish Bone or Cause & Effect diagram. See also MEPEM.

Cause & Effect Diagram

1. A problem solving methodology - To ensure sources of variation in manufacturing and business processes are objectively identified, quantified and controlled or eliminated.2. Process Sigma Level 6 - I.E. 6 Std Deviations between mean & nearest spec limit. Equivalent to 3.4 DPMO = "Pretty Darn Good" Mike Cifaratta

http://www.6-sigma.ford.com/

Process Mapping methodology:1) Define the scope of the process the team needs to map. Start & Stop2) Document all tasks or operations needed in the production of a “good” product or service 3) Document each task or operation above as value add or non value add. 4) List both internal and external Y's at each process step.5) List both internal and external X's at each process step.6) Classify all X's as one or more of : Controllable (C), Standard Operating Procedures (SOP), Noise (N)7) Clearly identify all data collection points.

See Pareto Diagram Pareto Diagram

Greek letter Alpha. See Alpha value, Alpha Risk Alpha Risk

a Risk Alpha Risk

Previous version of MicroGrafx iGrafx process mapping software

The horizontal axis of a graph. See also OrdinateACCEPTANCE REGION

The region of values for which the null hypothesis is accepted;

Closeness of Mean to target. Measured as a bias from a true value. Improved through Calibration to remove measurement system bias. See also Precision.

Precision

Global Automated Claims Editor System. Ford Global warranty claim validation and payment system. Feeds into AWS Analytical Warranty System. Supersedes previous Ford & Jaguar JVW, QIS & SEII systems late 2001 as warranty claims collection system. Both use AWS for analysis and sharing of warranty information.

AWS

Automotive Industry Action Group - A standards body supported by The Big 3, defining quality and other standards for the automotive industry.

The probability of accepting the alternate hypothesis when, in reality, the null hypothesis is true. See also Beta Risk

Beta Risk


6-Sigma Glossary



Alpha Level Hyp Test

Hyp Test

Analyse Phase

ANOVA Stat>ANOVA>…

APEAL

APQP QS

ASQC American Society for Quality Control

Assignable Cause C&E PCM

Attribute Variables Sys Eng

ATTRIBUTE DATA Variables

Attribute R & R Stat>Quality>Gage

AWS Warranty

See BETA RISK. Symbol Hyp Test

Variables

Bartlett Hyp Test

Baseline DPMO

Benchmarking

Beta Risk Hyp Test

Bias Gage R&R

1-(%Confidence). Nomenclature: a. E.g. a = 0.05 for 95% confidence. See also P-Value

Alternative Hypothesis

A tentative explanation which indicates that an event does not follow a chance distribution; What we want to prove/disprove - the contrast to the null hypothesis.Nomenclature: HA.

Null Hypothesis

Identify vital few X’s:Establish baseline capability for key output variables (y’s) To identify and manage high risk input variables (x’s) (e.g. FMEA)To reduce the number of process input variables (x’s) to a manageable number of critical variables (e.g. hypothesis testing, multi-vari studies and ANOVA techniques)Identify potential sources of variationTo plan and document initial improvement activities

Improve

Analysis of Variance studies - A wide range of statistical tools including support for Gage R&R and DOE. Steps include Main Effcts graphical representation, Normality & equal variance determination to support 1 and 2 way ANOVA analysis of means. Steps: Test normality; Test for Equal Variances, ANOVA (n-way according to no of factors), Assess P-Value for H0, assess e2 for extent to which variation is explained by model/factors. Handles multiple sample sets. See also Gage R & R, P/TV, ANOM, MANOVA. If data is non-normal use Kruskal-Wallis or Moods Median Test.

Automotive Performance, Execution & Layout. A J.D. Powers competitor vehicle quality survey- Focuses on the positive aspects of Things Gone Right (TGR) and examines how much consumers like or dislike virtually every aspect of their vehicle.

Advanced Product Quality Planning - The processes by which quality is designed into the product. A structured method for defining and executing the actions necessary to ensure the product satisfies the customer. An integrated, structured team approach to all system, sub-system & component manufacturing. Implemented by PD, mfg & suppliers. APQP has 23 elements from Sourcing through PSW part delivery at IPD - See APQP Elements. APQP uses QS-9000 elements 4.1, 4.2, 4.9 as its' timing structure. It is an automotive extension to QS-9000 sections 4.1-4.20.See: www.quality.ford.com and, for APQP forms: web.bli.ford.com. http://www.jaguar.ford.com/qual/apqp/apqphome.htm?

APQP Elements

A source of variation which is non-random; a change in the source (“Vital Few” variables) will produce a significant change of some magnitude in the response (dependent variable), e.g., a correlation exists; the change may be due to an intermittent in-phase effect or a constant cause system which may or may not be highly predictable; an assignable cause is often signalled by an excessive number of data points outside a control limit and/or a non-random pattern within the control limits; an unnatural source of variation; most often economical to eliminate. May be identifiable and correctable by the operator. Also known as Special Cause. See also Common / Random Cause.

Common Cause

ASSIGNABLE VARIATIONS

Variations in data which can be attributed to specific causes. See also Random Variation

1. A characteristic that may take on only one value, e.g.0 or 1. As opposed to a Variable which takes a measurable value.2. Vehicle attributes e.g. Safety, Security, Cost, Performance.

Variable

Numerical information at the nominal level; subdivision is not conceptually meaningful; data which represents the frequency of occurrence within some discrete category, e.g., 42 solder shorts.

The process for measuring the repeatability & reproducability of the value of an attribute Gage R&R Variables

Analytical Warranty System - Records product problems addressed by dealer fix actions with associated direct costs. Accessing this information requires detail system knowledge. CDA can provide this for you.

http://www.quality.ford.com/aws/aws/

b Risk Beta Risk

BACKGROUND VARIABLES

Variables which are of no experimental interest and are not held constant. Their effects are often assumed insignificant or negligible, or they are randomized to ensure that contamination of the primary response does not occur.

A form of f-Test for > 2 samples of data. Used for showing equal variances for normal data. Use Levene for non-normal data.

F-Test Stat>ANOVA>EqualVariances

The initial or starting value of the process DPMO. Also known as Initial DPMO. See DPMO. See also Baseline Capability.

DPMO

Study & Indetification of best in class process elements across vehicle lines, competotors, related processes and other industry and research sources so as to maximise variability reduction and quality improvement. See also BIC, CAB Events.

CAB Events

The probability of accepting the null hypothesis when, in reality, the alternate hypothesis is true. Beta risk is the converse of Power. See also Alpha Risk.

Alpha Risk

Distance between True Value and the observed value. The measure of Accuracy. See also Linearity, Mallow's C-p Value.

Linearity Stats>QualityStats>GageLinearity

http://www.quality.ford.com/aws/aws/

6-Sigma Glossary



BIC

Big 3 The Big 3 US automotive manufacturers: Ford, Daimler-Chrysler & GM

Black Belt

Blocking Variables Variables DOE

Bonus Parts

BoundaryBPM Eng

BPU Eng

C

C & E Chart C&E

C & E Diagram C&E

C & E Matrix C&E

C CHARTS Charts which display the number of defects per sample.

c2 Symbol

Symbol

CAL Customer Acceptance Line – in plant test of all cars off the production line

CAP

Capability Cp PCM

Cp PCM

Categorical Variable Variables

Category Stat>Quality>Gage Gage R&R

CAUSALITY Variables

CAUSATIVE Variables

Cause Variables C&E

Variables C&E

CC Code

Warranty

CCC

Warranty

CD6S

Best In Class - The highest quality level achievable in this class of process across current industry achievements. See also Benchmarking

A certified full time practicioner of 6-Sigma problem solving methodologies. Will deliver successful projects using the Breakthrough Strategy. Will train and mentor the local organization on Six Sigma. Mentored by a Master Black Belt. May train and/or be supported by Green Belts

Green Belt

A relatively homogenous set of conditions within which different conditions of the primary variables are compared. Used to ensure that background variables do not contaminate the evaluation of primary variables.A Blocking Variable is a factor in an experiment that may have an undesired influence as a source of variability is called a “block. A block can be “lots” of material, operators, days, etc…, which are likely to produce experimental runs that are more homogenous within the block than between the blocks.”A blocking variable is generally one which is not thought to be a Critical Few X, but which may be introducing noise which we want to spread across the sampling rather than having it confound (mask) variation in a specific Critical X.The blocking factor is placed into the DOE in place of the highest level interaction which is usually insignificant. e.g. Put the blocking value in place of ABCD and do not then look for ABCD in the DOE.See also: Confounding.

Confounding Stat > DOE > Create Factorial Design

Surplus parts dropped of left in vehicle e.g. washers in door linings, potentially causing S&R or other TGW

See Process Boundary

Body Processor Module - Electronic control module for body & lighting systems. Also known as BPU

Body Processor Unit - Electronic control module for body & lighting systems. Also known as BPM

1. See Controllable Variable.2. Number of Censored data items. See Censored, F/C.3. The Natural response rate of a Probit reliability analysis.4. Number of defects. See c Chart.5. Current APQP GYR status.6. The Control phase of a DMAIC 6-Sigma project.7. The Characterize phase of a DfSS 6-Sigma project

F/C Variables Reliability QS

Ambiguous reference to C & E Diagram or C & E Matrix Cause & Effect DiagramStat>Quality>Cause&Effect

Cause & Effect diagram - Same as Fish Bone diagram Cause & Effect DiagramStat>Quality>Cause&Effect

Cause & Effect matrix - Same as CT Matrix CT Matrix Stat>Quality>Cause&Effect

May appear in place of c2 if the fonts have not been set right! Change the font of the 'c' to Symbol Font and apply Font Superscript to the '2' to get c2. See Chi-Square

Chi-Square

c2 See Chi Square. Chi-Square

1. Change Acceleration Process - GE's change management methodology. Helps speed & sustain change.2. Cause Action Prevention/Profit (VRT's reaction to out of control situation)

See Process capability. See also Sigma Level, Capability Assessment Process Capability Stat>Quality>Capability

Capability Assessment

Assessing capability to predict true quality levels for products. A guide to the product or process sigma level. See Process Capability.See also: Cp, CPK, Pp, Ppk.

Process Capability Stat>Quality>Capability

A random variable whose value will be one of a limited number of categories in some specified variable.e.g. Day number, Shift, Operator number. As opposed to a Continuous Variable. May be used interchangeably with Discrete Variable

See Distinct Category, Categorial Variable, Cat 'A', Cat '1'/'2'/'3'. Distinct Category

The principle that every change implies the operation of a cause. See X's X's

Effective as a cause. See X's X's

That which produces an effect or brings about a change. An independant variable. See X's X's

Cause & Effect Diagram

A tool used to identify & organise possible causes of a problem. Same as fish bone diagram. Also known as Ishikawa, Fish or Herring Bone diagram or (in FMEA) Cause & Mode Diagram, "5M's and a P", MEPEM. See also X's, Y's

X's Stat>Quality>Cause&Effect

Condition Code - Problem category. The root cause – what the dealer needed to fix to resolve the customer concern. See also CCC

https://web.quality.ford.com/online_ref/index.html

Customer Concern Code - Problem category - TGW Check Box item in GQRS detail verbatims – what the customer thought was wrong. See also CC Code


Consumer Driven Six Sigma - The Ford 6-Sigma improvement initiative focussing on improving Consumer Quality issues



6-Sigma Glossary



CDA

CENTRE LINE DOE

CFC

CharacteriseCHARACTERISTIC A definable or measurable feature of a process, product, or variable.

Check Sheets Forms used to collect data by making tally marks to indicate # occurrences

Chi-Square Hyp Test

CI

CLASSIFICATION Differentiation of variables.CLT

Cluster Sample

CNVQ

COMMON CAUSE C&E PCM

Complexity

Concern CodeCondition CodeConfidence Interval

The confidence / probability that a random variable x lies within a defined interval.

Confounding DOE

CONSUMERS RISK Hyp Test

Continuous Data Variables

A random variable which can assume any value continuously in some specified variable. Variables

1. Customer Data Analysis. Assessing the Voice Of the Customer. Same as CIE, CIA.2. Customer Data Analysis Department. Based at jaguar, Whitley. Provide GQRS data on their website. Provide a service to understand these and to extract AWS data for you. Ford Tel.: +726 4697.

http://www.jaguar.ford.com/eng/web/engpvtw/cdaweb/index2.htm

Central Limit Theorem

Technique to reduce sample variation by processing the means of set of samples. Reduces variation by 1 / Sq Rt n where n is the number of samples in each agregated set.Method: Add sub-group no col; Unstack using sub-group as subscript; Calc row means from unstacked data.

Calc>MakePattern>Simple…Manip>Unstack…Calc>Calc>rmean

CENTRAL TENDENCY

Numerical average, e.g., mean, median, and mode; centre line on a statistical process control chart.

The line on a statistical process control chart which represents the characteristic’s central tendency.

Customer Focus Centre – end of line test in plant + road teat, of selected vehicles for full customer quality assessment.

See Characterize. Characterize

1. A skewed probability distribution and hypothesis tests using this.2. Chi-Square Test for goodness of fit / strength of relationship between discrete variables. 3. ChiSquare Test for Variances4. Chi-Square values (from stats tables) are also used in calculating Confidence Intervals. Calculation: Chi-Sq = (Expected - Observed)^2 / ExpectedNomenclature: c2. See Proportion Test

Proportion Test 2.Stat>Table>Chi-sq.Stat>Basic>2Prop.Calc>Prob>Chi-sq.Calc>Rand>Chi-sq.

3.Stat>Basic>Disply

Confidence Interval e.g. 95% CI Confidence Interval Stat>Regression>Fitted Line Plot

See Central Limit Theorem Central Limit Theorem

Sampling by diving the population into arbitrary cluster groups and drawing a sample from each group. I.e. where no correlation/dependance is expected between variables and cluster.

Customer/Competitive New Vehicle Quality - Customer quality survey. Superceded by GQRS

See RANDOM CAUSE. Variation within process capoability. Not controllable by the operators.

Random Cause

“Complexity” is a measure of how complicated a particular good or service is. Increased process complexity increases opportunities for defects. May be represented by the “Opportunity Count”.

See CCC CCC

See CC Code CC

Reflects the range into which you can expect the mean response to fall. PI = confidence that a point will fall in the range. CI = confednce the mean will fall in the range. See ConfidenceLevel and Confidence Limit.See also Prediction Interval

Prediction Interval Stat>Regression>Fitted Line Plot.Stat>BasicStats>1or2Proportion

CONFIDENCE LEVEL

Stat>Regression>Fitted Line Plot

CONFIDENCE LIMITS

The two values that define the confidence level. Stat>Regression>Fitted Line Plot

Allowing two or more variables to vary together so that it is impossible to separate their unique effects. If an experiment does not vary A & B separately then the effects of the 2 factors cannot be distinguished. Blocking may be used to separate these effects where one is expected to be a critical X and one appears to be noise. The main effects & interactions can be confounded in order to reduce the no of runs in a Fractional Factorial DOE. Same as Alias structure. See also Blocking Variables, Compounding.

Blocking Variables

Probability of accepting a lot when, in fact, the lot should have been rejected (see BETA RISK).

Beta Risk

CONTINUOUS DATA

Numerical information a the interval of ratio level; subdivision is conceptually meaningful; can assume any number within an interval, e.g., 14.652 amps.

Data from continuous measurement e.g. temperature. See M-07 Introduction to Data.ppt for selecvting statistical techniques according to Discrete/Continuous nature of input & output attributes & variables. See also Discrete Data, Attribute

Discrete Data

CONTINUOUS RANDOM VARIABLE


6-Sigma Glossary



Contribution Stat>Quality>Gage Gage R&R

In Control PCM

Control Limit

Control Phase

Control Plan

Specifications called for by the product being manufactured.

Controllable

Variables

COP DOE

COPQ

Correlation Gage R&R

CP

Cp Cp PCM

Cpk Cp PCM

CPL Cp

Contribution (proportion) of variance attributable to one factor. Often refers to Gage Contribution. Minitab field: %Contribution. AIAG standards require: <1% = Ideal, 1-4%=Acceptable, 4-9%=Marginal, >=10%=Unacceptable.

Gage Contribution

A process is in control when its data falls within the upper and lower Control Limits. Control Limit

Upper & lower statistical data limits (UCL & LCL). Always calculated from the data - typically (always) set at +/- 3 Std Deviations (c.99.73%). Measure of whether the process is in control. See Upper & Lower Control Limit. A process is In Control if its' data falls cosistently within its Control Limits. This does not imply the product is within customer specification - it can by In Control but off target and out of spec - See Spec Limit. A process is Out Of Control if data falls outside the Control Limits.

Specification Limit

Optimize, eliminate, automate, and / or control vital few inputsDocument and implement the control planSustain the gains identifiedRe-establish and monitor long-term delivered capabilityImplement continuous improvement efforts (Green Belts at the functional area)

Define

A plan to control CTQ's on an ongoing basis. Defines the Who/Where/When… control methods for each process step. Statistical Process Control Plan specifies data collection on a Control Chart which monitors data in control within Control Limits. Out of control data triggers a Reaction Plan.

http://www.jaguar.ford.com/manuf/prodop1/spc/misc.htm

CONTROL SPECIFICATIONS

Process inputs that you can adjust or control while the process is running (e.g. speed, feed rate, temperature, pressure, etc)

Controllable Variable

Controlled or Controllable Variables: Inputs that you can adjust or control while the process is running (i.e...., speed, feed rate, temperature, pressure, etc). See X's

X's

Conformity Of Production - Tests applied to verify the extent to which goods produced and quality assured do actually meet production specifications

Cost Of Poor Quality - Cost of not being RFT = VCSI plus the hidden: - Cost of failing to meet customer expectations the first time - Opportunity for increased efficiency - Potential for higher profits - Loss in market share - Increase in production cycle time - Labour associated with ordering replacement material - Costs associated with disposing of defects - "A one sigma improvement will result in 20% margin improvement, 12-18% capacity increase, 12% reduction in or redeployment of employees, and 10-30% capital reduction."

A measure of association between two quantitative variables. Correlation measures the degree of linearity between two variables assumed to be completely independent of each other. Correlation coefficient, r, always lies between -1 and +1. May be for linear, quadratic or cubic relationship. Calculated by regression methods e.g. Pearson. Nomenclature: rxy R-Value, R2-Value (R-Sq), R-Sq(adj). Range of R-Values: -1 = negative correlation, 0 = no correlation, 1 = high correlationE.g. |/ shows high linear correlation between x & y.NB Correlation can provide a strong message about correlation between 1 X & 1 Y. It conveys a strong message about NON-correlation of multiple variables. It does not, however, give a strong message about positive correlation between many variables as it works only on individual linear association. For positive messages about correlation of multiple variables always use Regression which takes account of interactions between multiple variables. See also Multicollinearity, Variance Inflation Factor.

Gage R&R Stats>BasicStat>Correlation

Stats>Regression>Fitted Line etc.

Confirmation Prototype - Not to be confused with: Cp (process capability) or C-p (Mallow's C-p Statistic for bias vs. closeness of fit)!

Cp

A measure of Process Capability reflecting how good the process would be, if it were centred on the target. It is ambiguously used for: 1. Short Term Process Capability (6-Sigma & MiniTab). 2. Long Term Process Capability (Ford & Big 3 SPC Reference Manual). Cp takes no account of whether the process is centred. Cpk takes this into account.Pp = (USL-LSL) / 6*s. See also Cpk, Pp, Ppk.Note: Nothing to do with CP or C-p !

Cpk Stat>Quality>Capability

A measure of Process Capability which takes account of both the spread and centering of the data. It is ambiguously used for: 1. Short Term (6-Sigma & MiniTab) and 2. Long Term (Ford SPC Ref Manual). Cpk equals the minimum of CPU & CPL. This is the shortest distance between the mean and the nearest spec limit. If the mean is outside both spec limits Cpk becomes negative but Cp is unaffected. Cpk approximates to Cpk = Z / 3.Cpk=min(Cpk(USL),Cpk(LSL)) Cpk(USL)=(USL-Xbar)/3s Cpk(LSL)=(Xbar-LSL)/3sso: Cpk=2 equivalent to Sigma level of 6 (i.e. 6-Sigma).See also Cp, Pp, Ppk, Sigma Level.

Short Term Process CapabilityStat>Quality>Capability

LSL Cpk. Also shown as Cpk(LSL)=(Xbar-LSL)/3s. See also Cpk Cpk Stat>Quality>Capability

http://www.jaguar.ford.com/manuf/prodop1/spc/misc.htm

6-Sigma Glossary



Cpm Cp

CPU Cp LR

CQIS

Critical X's Variables

CSI

CSI-2CT Matrix C&E

CT TreeCTC

CTDCTPCTQ

CTQ Tree

CTS Critical To Satisfaction - Customer specified critical satisfaction factors of product / service

CT'sCTX Tree

CTY Tree

CUT-OFF POINT The point which partitions the acceptance region from the reject region.d Symbol

d/s Symbol

DATA

Defect

Defect Opportunity See Opportunity

Define Phase

Degrees Of Freedom

Demerits Faults found in a quality inspection

Variables

DetectionDF

DFMEA

Cpm is a capability index that is the ratio of the specification spread (USL - LSL) to the square root of the mean squared deviation from the target.Cpm is available only when a target is specified.

Stat>Quality>Capability

1. USL Cpk. Also shown as Cpk(USL)=(USL-Xbar)/3s See also Cpk.2. Cost Per Unit - Warranty: Cost of repair per unit. See also %CPU. Expressed in USD ($) for Ford / Jaguar. Expressed in GBP pence for Land Rover.3. Cost Per Unit - Tolerance Design: Cost of making improvement.

Cpk Stat>Quality>Capability

Global Common Quality Indicator System - CQIS is a Ford database providing world-wide access to vehicle concern data. A variety of research and analytical tools are available to assist the problem solver and quality analyst in the early identification and definition of product concern issues

http://www.mso.ford.com/fcsd/vsp/tech/gcqis

The "Vital Few" input varibles to control to reduce variation and achieve CTQ's. See also Critical Characteristics.

CTQ

J.D. Powers Consumer Service Index - Designed to provide automobile manufacturers with an objective measure of service usage and customer satisfaction with dealer service during the first three years of ownership, roughly representative of the warranty period.

Revised CSI measurement criteria from 1999MY forwards

Matrix of the CTX (Process) Tree & CTY (Product) Tree. See Y = f(X) and C & E Matrix C & E Matrix

See CTX, CTY, CTQ CTQ

1. Critical To Cost factors - See CTQ2. Concept To Customer. Product development process preceeding World Class Process & FPDS.

CTQ

Critical To Delivery factors - See CTQ CTQ

Critical To Process factors - See CTQ CTQ

Critical To Quality factors - Producer measurable quality factors to meet Customer Satisfaction (CTS) needs (Similarly for CTC, CTD, CTP). Generically may be used to include CTC, CTD, CTP & potentially CTS.

CTQ Tree Diagram showing break down of CTQ factors and causes affecting these processes

CTQ

See CTS; CTC, CTD, CTP, CTQ CTQ

Process & Sub-Process hierarchy tree - "X" inputs. Supports the left hand side of the CT Matrix

CT Matrix

Product System-Sub-System-Part hierarchy tree - "Y" outputs. May be the top line of the CT Matrix

CT Matrix

Customer Concern Code

See CCC CCC

Difference / effect size. See also d/s d/s

The magnitude or size of the difference being tested. This is sometimes called the Test Sensitivity.

Test Sensitivity

Factual information used as a basis for reasoning, discussion, or calculation; often refers to quantitative information.

An event which does not meet the specification of a CTQ. Occurrence is measured in DPU, DPO, DPMO or Sigma Level. So a Defect is an individual non-conformance. A given unit may potentially have any number of Defects - according to the number of Opportunities. A unit with one or more Defects is a Defective. Defects are caused by Errors. Measured using c Chart or u Chart. See also Defectives, Opportunity, Error.

CTQ

Opportunity

The 1st DMAIC project phase. Define the Project Scope, Problem Statement (identify Defects), Objective (reduce Defects), Benefits and Metric (track Defects - % defects, overtime, RTY, etc…)

measure

The number of independent measurements available for estimating a population parameter. Calculating degrees of freedom. Nomenclature: n· DF Total = n - 1, where n = total number of observations· DF Operator = b - 1, where b = the number of operators· DF Part = b(a -1), where a = the number of parts measured by each operator and b = the number of operators· DF Repeatability = n - (a*b)

DENSITY FUNCTION

The function which yields the probability that a particular random variable takes on any one of its possible values.

DEPENDENT VARIABLE

A Response Variable; e.g., y is the dependent or “Response” variable where Y=f (X1…Xn) variable. See Y's

Y's

See D Level, FMEA

See Degrees of Freedom. Nomenclature: n Degrees of Freedom

Design FMEA. Application of FMEA during the design process to assess product relaibility. See also PFMEA.

http://www.mso.ford.com/fcsd/vsp/tech/gcqis

6-Sigma Glossary



DfSS DfSS

Discrete Data Variables

Variables

Discrim Stat>Quality>Gage Gage R&R

Discrimination Stat>Quality>Gage Gage R&R

Discrimination Index Stat>Quality>Gage Gage R&R

dof Degrees of Freedom

Distinct Category Stat>Quality>Gage Gage R&R

Distributions

D-Level FMEA

DMAIC

DOE DOE

DPMO

DPO

DPU

DriftDTSe Symbol

Symbol

ED DOE

EFFECT Variables DOE

Entitlement

EST Engineering Systems Team

Design For Six Sigma - A methodology to apply to the design process to generate RFT quality designs. Should pre-empt the need for DMAIC projects to fix quality issues.

DMAIC

Data from counting categories of conditions e.g. Pass/Fail counts. See M-07 Introduction to Data.ppt for selecvting statistical techniques according to Discrete/Continuous nature of input & output attributes & variables. See also Attribute, Continuous Data

Continuous Data

DISCRETE RANDOM VARIABLE

A random variable which can assume values only from a definite number of discrete values.As opposed to a Continuous Variable. May be used interchangeably with Categorical Variable

See Discrimination Index Discrimination Index

The number of decimal places that can be measured by the system. Increments of measure should be at least one-tenth of the width of the product specification or process variation. Same as resolution. See also Discrimination Index.

Gage R&R

The Discrimination Index provides the number of divisions that the Measurement System can accurately measure across the part (sample) variation. If this index is less than 4, then it is inadequate to provide data for a study. If the index is 4, then it is equivalent to a go/no-go gage. We would like to see the value of 5 or greater. Discrim = 1.41 * sp / sms. Same as number of Distinct Categories

Distinct Category

Degrees Of Freedom

Number of Distinct Categories. Aletrnative (MiniTab) nomenclature for Discrimination Index. Values: 1: Measurement System cannot discriminate between parts2 - 4: Parts can be divided into high/low groups only. Equiv to attribute data. Can provide Insensitive control charts, Coarse estimates of process parameters and capability indices5 or more: The gage system is acceptable, can differentiate between parts and can provide: Control charts, process parameters, and capability indices

Gage R&R

Tendency of large numbers of observations to group themselves around some central value with a certain amount of variation or “scatter” on either side. See Normal, t-, Z-, F- , Weibull Distributions

Normal Distribution

FMEA Detection Level. Rated 1-10 by the Big 3 for Certain, High, moderate, Low, Almost imposible liklihood of detection by process controls before next process step or process completion. An assessment of the likelihood (or probability) that your current controls will detect 1) when the X fails or 2) when the failure mode occurs.

FMEA

Define-Measure-Analyse-Improve-Control - The major phases of a 6-Sigma project, representing the core 6-Sigma methodologies. In contrast to 6-Sigma DFSS methodology. The DMAIC process may be further broken down as:M1 Select Output Characteristic and identify key process input and output variables, M2 Define Performance Standards, M3 Validate Measurement SystemA4 Establish Product Capability, A5 Define Performance Objectives, A6 Identify Variation SourcesI7 Screen Potential Causes, I8 Discover Variable Relationships, I9 Establish Operating TolerancesC10 Validate Measurement System, C11 Determine Process Capability, C 12 Implement Process Controls

DFSS

Design Of Experiments - Systematic study multiple factors & levels to determine their effects & identify CTQ's. Includes: Full Factorials, 2K Factorials, Fractional Factorials.DOE can be used to:- Establish the relationship between the X’s & Y's- Establish the vital few X’s from the trivial many (possible factors)- To establish if several input factors act together to influence the output (Y)- Determine the optimal settings of the input factorsMethod: DARE RIMCY - Define, Analyse, Reduce, e2, Residuals, Interactions, Main, Cube, Y=c+mx... Resource OptimisationSee DOE Worksheet at: ...\SSAnavProcessDataFiles\DOE Worksheet.doc. See also Type Of Experiment. Extensions to DOE include: Parameter Design & Tolerance Design.

DOE Worksheet.doc Stat>DOE>Factorial…Define…Analyse…Plots(Interaction, Main Effects)...

Stat>ANOVA…

Stat>Regression>Residuals

Defects Per Million Opportunities. See also Initial, Baseline & Target DPMO, DPO, DPU. Where opportunities per unit = 1 will be same as PPM.

PPM SixSigma>ProductReport

Defects Per Opportunity. See also DPMO. DPMO SixSigma>ProductReport

Defects Per Unit - A measure of customer impact vs. process capability. See also DPMO. Related to RTY: Yrt = e-DPU

DPMO SixSigma>ProductReport

Changes in quality over time. See also stability, Shift, short & long term sigma values

Durability Tracking Study - TGW survey data for products 12 & 36MIS

A constant: 2.71828182845905. The base of natural logarithms. e is such that the gradient of the function y=ex always equals y.e is defined as the limit of (1 + 1/n)n as n approaches infinity.

e Greek letter Epsilon. See Random Error, Epsilon-Squared Random Error

Experimental Design - See DOE DOE

1. That which was produced by a cause. See Y's. See also Failure Effect.2. In DOE Sample Size: The d difference that the experiment needs to detect. See also DOE, Corner Point, Failure Effect, Main Effects.

1. Y's2. Stat>Power> 2LevelFactorial…

As good as it gets - maximum process potential. Potential (Within) Capability for centred normal data. See Short Term Process Capability.

Short Term Process CapabilityStat>Quality>Capability>Zbench

http://www.jaguar.ford.com/manuf/prodop1/sigma/TrainingMaterials/SSAnavProcessDataFiles/DOE%20Worksheet.doc

6-Sigma Glossary



EWMA Stat>Control>EWMA

EXPERIMENT DOE Hyp Test

Experimental Error

f

Factorials DOE

FACTORS Variables

Failure Cause C&E FMEA

Failure Effect FMEA

Failure Mode FMEA

Fault Code Warranty

FCPA

F-Distribution

Final Yield Yield

First Pass Yield Yield

First Time Yield Yield

Fish bone diagram C&E

FlowCharterFLUCTUATIONS Variances in data which are caused by a large number of minute variations or differences.

FMEA - FMEA

FMEA Plus FMEA

Exponentially Weighted Moving Average - a control chart which smoothes data to emphasize trends. Uses weights to emphasize the importance of the most recent data. See also Moving Average.

A test under defined conditions to determine an unknown effect; to illustrate or verify a known law; o test or establish a hypothesis.

Variation in observations made under identical test conditions. Also called residual error or Random Error. The amount of variation which cannot be attributed to the variables included in the experiment. See Type I, II Errors, Alpha & Beta Risks. See also Error, Defect.

Alpha Risk

Function - Y = f(X) - Outputs are a function of the inputs. See also F, F(t). F

Part of DOE. Includes: Full Factorials, 2K Factorials, Fractional Factorials DOE

Independent variables. See X's X's

FMEA Failure Cause: something which can be corrected or controlled (X) that describes “how the failure mode could have occurred”. Sources of process variation that causes the Failure Mode to occur. What is the root cause of the failure.

FMEA

FMEA Failure Effect: the effect a particular failure mode will have on the customer (attempt to quantify with respect to Y's). The impact on customer requirements. Generally external customer focus, but can also include downstream processes.

FMEA

FMEA Failure Mode: a description of a “non-conformance” at a particular process step (also know as a shop floor defect). The way in which a specific process input fails - if not detected and either corrected or removed, will cause Effect to occur. See also Failure, Failure Type.

Failure

Six character warranty code reflecting 4 character warranty part identification plus 2 character failure mode. Values can be found in the old Jaguar Warranty Manuals. The failure mode = the CC Code. The warranty fault code is shown in the Part Number field of AWS when there has been corrective action but no replaced part. Once JVW has been replaced by ACES-II The fault code is no longer used. The equivalent information is shown by the part number to which the corrective action was applied, and the CC code applied to it.

AWS

Ford Consumer Plant Assessment - Vehicle quality measure. A measure of the the vehicle as would be perceived by the customer. Includes full road test. Replaces NOVA-C

NOVA-C

Fishers-Distribution. The statistical distribution of data being ratio of two Chi-Squared variables

Distributions

Measure of % defect-free output after rework. See Yield for formulasSee also First Pass Yield & Rolled Throughput Yield/First Time Through, Normalised Yield

Normalised Yield

Measure of % defect-free output at any process step before rework. See Yield for formulasSee also Final Yield & Rolled Throughput Yield/First Time Through, Normalised Yield

Final Yield

Same as First Pass Yield. Nomenclature: YFT First Pass Yield

Fish bone shaped map of inputs, sub-inputs and root causes. See Cause & Effect Diagram and "5M's and a P", Also known as Ishikawa or Herring Bone diagram:x\ x\ y x/ x/

Cause & Effect DiagramStat>Quality>Cause&Effect

FIXED EFFECTS MODEL

Experimental treatments are specifically selected by the researcher. Conclusions only apply to the factor levels considered in the analysis. Inferences are restricted to the experimental levels.

MicroGrafx iGrafx process flow charting software

FMEA: Failure Mode Effects Analysis - Systematic method of risk assessment for identifying and preventing product & process problems before they occur. Identify ways the product or process can fail and eliminate or reduce the risk of failure in order to protect the customer. Always done as a team exercise. Process:Select Team, define roles scope product/process; Identify Functions and Failure Modes. Path 1: Define Failure Effects, Seveity & critical actions. PAth 2: Define Causes, Occurence & critiocal Actions. Path 3: Define preventaive (P) & detective (D) Controls & Actions to reduce RPN.See also S, O & D Levels, PFMEA, DFMEA, CFMEA, MFMEA, Failure Mode, -Effect, -Cause, RPN, Boundary Diagram, Interface Matrix, Critical, Significant Characteristic, YC, YS, Effects List, OS, HI, Big 7, Criticality, FMEA Plus

http://www.dearborn2.ford.com/qvtqual2/fmea/index.htm

Ford FMEA software package FMEA

http://www.dearborn2.ford.com/qvtqual2/fmea/index.htm

6-Sigma Glossary



Fractional Factorial DOE

The pattern or shape formed by the group of measurements in a distribution.

F-Test Hyp Test

FTT Yield

Full Factorial DOE

FunnelGage Stat>Quality>Gage Gage R&R

Gage Contribution Stat>Quality>Gage Gage R&R

Gage Precision Stat>Quality>Gage Gage R&R

Gage R&R Stat>Quality>Gage Gage R&R

Gantt Chart Graph showing steps of plan and durations

Gauge Stat>Quality>Gage Gage R&R

GCI Global Customer Insights

GCSA

GQRS Global Quality Research System/Study/Survey/Sample - Results available on CDA website.

Application of screening to reduce no of DOE runs by exploiting the Sparsity of Effects Principle Each resulting effect will be the sum of an individual effect plus an interaction effect which can no longer be distinguished (confounded).May be done using Resolution III, IV or V Designs:III: No main effects are aliased with other main effects. Main effects aliased with 2-factor interactions. 2-factor interactions are aliased with each other.IV: No main effect aliased with other main effects OR with two-factor interactions. 2-factor interactions aliased with other 2-factor interactions.V: No main effect or 2-factor interaction is aliased with any other main effect or 2-factor interaction. 2-factor interactions are aliased with 3-factor interactions.Nomenclature: 2R

k-p. Where k is the number of factors 2k-p is the number of runs & R is the resolution (III/IV/V). See also: Full Factorials, 2K Factorials, Blocking Variables, Resolution, Alias.NB: Fractional factorials will lead to wrong conclusions when interactions are dominant factors e.g. Sweetness from Sugar/Srirring/Cup colour would highlight cup colur rather than the sugar-stirring interaction. Similarly draughts from Door size/Frame Size/Door colour.

2K Factorials Stat>DOE>Factorial >Create…>Designs>1/4, 1/2…

FREQUENCY DISTRIBUTION

Distributions

A type of t-Test. Tests the assumption of equal variance. An F-test is done when you have only two samples; test assumes your data is normally distributed. Use Levene or Bartlett tests for > 2 samples.

t-Test Stat>ANOVA>EqualVariances

First Time Through - Jaguar speak for RTY - Rolled Throughput Yield. RTY

Part of DOE. See also: 2K Factorials, Fractional Factorials, |.Step 1: State the Practical Problem and Objective using the DOE Worksheet.docStep 2: State the factors and levels of interest, Step 3: Select the appropriate sample size.Step 4: Create a Minitab experimental data sheet with the factors in their respective columns. Randomize the experimental runs in the data sheetStat > DOE > Factorial> Create Factorial DesignStep 5: Conduct the experiment.Step 6: Construct the ANOVA table for the full model, use either: Stat>ANOVA>BalANOVA or Stat>DOE>Factorial>AnalyzeStep 7: Review the ANOVA table and eliminate effects with p-values above .05. Run the reduced model to include those p-values that are deemed significant.Step 8: Analyze the residuals of the reduced model to ensure we have a model that fits. Calculate the Fits and Residuals. Stat>ANOVA>Storage. Next create the residual plots Stat>Regression>ResidPlots Step 9: Determine optimal settings by graphically analyzing significant interactions (p-value <.05). Assess the significance of the highest order interactions first.For 3-way interactions unstack the data and analyze. Use either:Stat > ANOVA > Interaction Plot or Stat > DOE > Factorial> Factorial Plots > Interaction PlotOnce the highest order interactions are interpreted, analyze the next set of lower order interactions. Investigate significant Main Effects (p-value <.05) use either. Stat > ANOVA > Main Effects; Stat > DOE > Factorial> Factorial Plots > Main Effects Plot & Cube PlotsStep 10: For practical purposes calculate epsilon square valuesStep 11: Replicate optimum conditions. Plan the next experiment or institutionalize the change.Step 12: Document final report

Fractional Factorials Stat>ANOVA>BalANOVAStat>ANOVA>GLMStat>ANOVA>Balanced

Stat>DOE>Factorial>Create>GenFullFactorial

Process of converging from multiple X's (inputs/causes) to the CTQ's CTQ

American spelling of Gauge. A device for measuring an important product feature or combination of features (attribute or variable) during the manufacturing process. See Gage R & R. See also Jig, Tool, Fixture, Setting Aid.

Gage R&R

Contribution (proportion) of cause attributable to the gage or measurement system. Minitab field: %Contribution. AIAG standards require: <1% = Ideal, 1-4%=Acceptable, 4-9%=Marginal, >=10%=UnacceptableNomenclature: P = s2

ms / s2total or P = 5.15* sms.

See also Gage R & R, Part Contribution

Part Contribution

Same as Gage Contribution Gage Contribution

A method of measuring (gauging) an attribute or varibles Value relative to its' Operational Definition. Further used to determine the Repeatability & Reproducibility of those values. Generically used to mean any measurement system. When quoted as a number this is the P/T ratio. (or in some companies the P/TV).Acceptance criteria: Ideal Accept Marginal FailGage Contibution <1% 1-4% 4-9% >10%%Tol=P/T and %R&R=Study Var <10% <20% 20-30% >30%Discim Index & Distinct Categories >14 >7 7-5 <5Nomenclature: %R&R. %R&R = sMS / stotal .See P/T, P/TV. See also Attribute R & R, Variable R & R, Correlation.

British English spelling of Gage. A device for measuring an important product feature or combination of features (attribute or variable) during the manufacturing process. See Gage R & R. See also Jig, Tool, Fixture, Setting Aid.

Gage R&R

Global Customer Satisfaction Awards - Ford awards to project teams achieving outstanding customer satisfaction improvements



6-Sigma Glossary



Green Belt

GRR Stat>Quality>Gage Gage R&R

Symbol Hyp Test

Symbol Hyp Test

Stat>Control>BoxCox

C&E

NFF

Hidden Factory

HISTOGRAM

Hydropulse

Hypothesis Test Hyp Test

ICCD

iGrafx

Improve Phase

I-MR Chart

Variables

Initial DPMOInput Variables Variables

INSTABILITY Unnaturally large fluctuations in a pattern.INTERACTION DOE

| Pipe. Symbol used in Minitab to indicate all permutaions of factors DOE Symbol

INTERVAL

A certified part time practicioner of 6-Sigma problem solving methodologies. Will deliver successful localized projects using the Breakthrough Strategy. Mentored by and may support a Black Belt.

Black Belt

Same as Gage R & R Gage R&R

H0 Nomenclature for Null Hypothesis Null Hypothesis

Ha Nomenclature for Alternative Hypothesis Alternate Hypothesis

Box Cox Transformation

Means of transforming non-normal data to normal. Uses the equation W=Yl.E.g. W=Y2, W=Y-1i.e.1/Y. W=Y0.5 i.e.sq rt., W=Y0=Ln(Y)An alternative to transforming the data is to try the Weibull distribution.

Normal Distribution

Herring Bone Diagram

Same as Cause & Effect Diagram. See also Fish bone diagram, "5M's and a P". Also known as Ishikawa diagram


No Fault Found. A replaced component, when tested is found to be not at fault. I.E. the component itself was not the cause of the problem. See also TNI.

Scrap & Rework costs inc. Time, Money, Resources, Floor space etc. 6-Sigma seeks to identify and eliminate the Hidden Factory

Vertical display of a population distribution of terms of frequencies; a formal method of plotting a frequency distribution.

Homogeneity Of Variance

The variances of the groups being contrasted are equal (as defined by statistical test of significant difference). Use Bartlett test if data Normal, or Levene if any variables are non-Normal. Also known as Equal Variance.

Normal Distribution Stat>Basic>GraphThen Stat>ANOVA> TestEqVar OrStat>Basic>2Var

Vehicle test rig which vibrates vehicle to check for squeaks & rattles. Supervised at B/L by Alex Djordjevic

Statistical test showing whether data sets are from same statistical population. T-Tests can be used to test hypotheses relating to Continuous data. Chi-Square tests can be used on discrete data. See Null Hypothesis, Alternate Hypothesis.Variable Data:* Normal Data + Means Tests: 1 Sample t-Test, 2 Sample t-Test, Paired t-Test General Linear Model (for >2 factors); Correlation; Regression + Variance Tests: Chi2, 2 Variances, ANOVA Equal Variances * Non-Normal Data + Means:Correlation,Sign Test,Wilcoxon,Mann-Whitney,Kruskal-Wallis,Mood's, Friedman. + Variance: ANOVA > Equal VariancesAttribute Data: See Chi2 contingency tables, Proportion testing.

Null Hypothesis Stat>Basic>...orStat>ANOVA>...

Intensified Customer Concern Definition - Global customer concerns database. 30 days in service telephone survey which asks in depth questions on specific concerns.

Software package provided by MicroGrafx for process mapping. Also known as FlowCharter

Discover variable relationships between x’s and y’s (e.g. DOE, regression)Utilize critical variables to achieve the desired objective such as reducing variation or shifting the mean Establish operating tolerances (e.g. realistic tolerancing)

Control

The I-MR chart is a combined chart consisting of:· An Individuals (I) chart, which plots the values of each individual observation, and provides a means to assess process center.· A Moving Range (MR) chart, which plots the range calculated from artificial subgroups created from successive observations, and provides a means to assess process variation.Use an I-MR chart to draw a combined control chart for assessing whether process center and variation are in control when your data are individual observations. When subgroups are available use an X-Bar & R Chart.

X-bar Chart Stats >Control >I-MR. >MovingRange. >Indiv.

INDEPENDENT VARIABLE

A controlled variable; a variable whose value is independent of the value of another variable.See X's, Parameter.

X's

The process DPMO currently. Also known as Baseline DPMO. See DPMO DPMO

See X's, Parameter. X's

When the effects of a factor A are not the same at all levels of another factor B. The tendency of two or more variables to produce an effect in combination which neither variable would produce if acting alone. If the Interaction Plot lines are close to parallel then there is NO significant interaction. Note: If 3-way interaction is significant then MTB cannot show 3-way interaction plot. To represent this unstack data and produce multiple interaction plots - See carpet1.mtw example file. Examples:2-Way: Sugar & Stirring effect on sweetness. Door & Farme size on goodness of fit.3-Way: Wet weather, Lawn mowing & Fire drill on grass on carpet!

Stat>ANOVA>InteractionsPlot

Stat>ANOVA>Balanced

Numeric categories with equal units of measure but no absolute zero point, i.e., quality scale or index.

6-Sigma Glossary



IQS

IQS2

Ishikawa Diagram C&E

J.D. Powers

JDS Jaguar Diagnostic System - System for testing vehicle electrical functionality

JRS Jaguar Rating System. 0-10 rating of goodness in the customers perception.

JVW Warranty

BTB Warranty

KCA

KPIVKPOV

Krytox

LCL Lower Control Limit

Levene Hyp Test

Likert Scale

LINE CHARTS

Linearity Gage R&R

Cp

LSLSymbol

Master Back Belt

Master Value

Material Yield Goods in / Goods out. Measurable of scrap, but not rework Yield

Mean

Mean Square

Measure Phase

Stat>Quality>Gage Gage R&R




MedianMicroGrafx

MiniTab Software package for statistical analysis

MIS

J.D. Powers Initial Quality Survey at 3MIS. Measured in PP100 faults per 100 not TGW/1000. Offers analysis on reported defects during the first 90 days of ownership, providing insight and information on 135 problem issues affecting vehicle quality across nine vehicle systems.

Latest version of J.D. Powers Initial Quality Survey - See IQSSame as Cause & Effect Diagram. See also Fish bone diagram, "5M's and a P". Also known as herring bone diagram


Customer quality survey organisation producing critical industry J.D. Powers benchmeark reports - See ISQ, APEAL, SSI, VDI, CSI

Jaguar Vehicle Warranty Claims system. Warranty claims system superceeded by ACES-II late 2001. Both use AWS for analysis and sharing of warranty information.

ACESII

Bumper-To-Bumper. Warranty claims relating to all components and systems but exclduing other causal factors such as emissions, corrosion, safety etc.

AWS

Knowledge Centred Activity - wisely obtaining and using knowledge of the organisation and its' processes

Key Process Input Variables. Same as X's X's

Key Process Output Variables. Same as Y's Y's

Proprietary anti-squeak lubricant – used by some suppliers (e.g. Valeo on door handle) and by dealers to address S&R complaints. Tends to be a temporary solution as the treatment wears out with time

Lower Control Limit

A form of f-Test for > 2 samples of data. Used for showing equal variances for non-normal data. Use Bartlett for normal data.

F-Test Stat>ANOVA>EqualVariances

Discrete ordinal measurement scale e.g. 5-Point Grading A-C, 7-Point numerical rating, Verbal rating (Poor, Good Excellent etc.)

Charts used to tract the performance without relationship to process capability of control limits.

A measure of the difference in Accuracy or Precision over the range of instrument capability. Measured as the gradient of a line through the averages. We seek for that line to be horizontal. Therefore Good data has a low (close to zero) measure of variance due to linearity. See also Bias

Bias Stats>QualityStats>GageLinearity

Long Term Sigma Level

Long Term Process Sigma Level capability. = Short Term Sigma Level - ZShift (typically 1.5). Nomenclature: ZLT. Note: In Excel ZLT = NORMSINV(1-DPO)

Long Term Process Capability

LOWER CONTROL LIMIT

Control Limit

Lower Specification Limit. The lower limit of customer acceptability of CTQ. CTQ

m See Population Mean Population Mean

An advanced expert in 6-Sigma methodologies. Trains, mentors and certifies Black Belts

The reference standard used to represent the True Value. E.g. the National Physics Laboratory official 1 metre rule; or a master gauge.

Arithmetic average i.e. sum / number. Nomenclature: m for sample mean and X-Bar for population mean. See also sample mean, population mean, Total Mean, Mode, Median

Mode

A measure of Variance. Calculated as the Sum of Squares (SS) divided by Degrees of Freedom

Variance

Document the existing process (using a Process map, C&E Matrix, and an FMEA)Identify key output variables (Y’s) and key input variables (X’s)Establish a data collection system for your X’s and Y’s if one does not exist to determine the performance baselineEvaluate measurement system for each key output variable (e.g. Gage R&R)

Analyse

Measurement System

Measurement System - Any method of measuring values relative toi the Operational Definition. Analysis of MS should lead to creative continuous measurement of CTQ's

Gage R&R

Measurement System Analysis

See Gage R & R Gage R&R

Measurement System Mean

Accuracy / mean of the measurement system. Nomenclature mmeasurement. See also Product Mean, Total Mean

Gage R&R

Measurement System Variance

Measurement System Variance (MSV). The variance within or introduced by the measurement system. Consists of the repeatability variance plus the reproducability variance. As opposed to the True Variance of the defect itself. See also Total Variance.MSV is also the QS9000 term for the whole Gage R&R process.

Gage R&R

Middle value in sample. See also Mean, Mode Mean

Supplier of iGrafx Flowcharter process mapping software. Previously known as ABC Flowcharter

Months In Service. See also TIS, YIS, MPP.

6-Sigma Glossary



Contain elements of both the fixed and random effects models.

Symbol

Mode

Moment Of Truth

MOP Month Of ProductionSymbol

MR

MS Stat>Quality>Gage Gage R&R

MS Variance Stat>Quality>Gage Gage R&R

MSA Stat>Quality>Gage Gage R&R

Symbol

Multi-Vari

Stat>Multivariate>…

MY Model Yearn Symbol

n Sample size

N

N/A

NA

NGT

Noise

NOMINAL

A unit which does not conform to one or more specifications, standards, and/or requirements.

NONCONFORMITY

Non-Value added step

Normal Distribution

Normalised Yield Yield

NOVA-C

MIXED EFFECTS MODEL

mmeasurement See Measurement System Mean

Most frequently occuring value in sample. Nomenclature: ^. See also Mean, Median, Failure Mode.

Median

Point of customer contact with product / process. Initial point of formulation of positive or negative impression.

mproduct See Product Mean

Moving Range chart. See I-MR I-MR Chart Stats>Control>MovingRange.Stats>Control>I-MR

1. Measurement System2. Mean Square

Gage R&R

See Measurement System Variance Gage R&R

Measurement System Analysis. See Measurement System, Gage R & R Gage R&R

mtotal See Total Mean

Multi-Vari analysis: A graphical tool which, through logical sub-grouping, analyzes the effects of categorical X’s on continuous Y’s. A great graphical tool to help visual how various X’s (both controllable and noise) impact our response Y. A technique to reduce the number of process input variables (x’s) to a manageable number of critical variables Part of the Analyse project phase. Where data is continuous more specific correlation techniques can be applied - see Correlation and Multivariate Regression.See also ANOVA, Hypothesis Testing, Main Effects.

Stat>Quality>Multi-Vari

Stat>ANOVA>Main Effects

Multi-Variant Regression

A multivariate regression model is a functional representation of the relationship between a response variable and several predictors. When there is one response and one factor, the regression model simply represents a line in a two-dimensional space. When there is one response and two factors, the regression model can be graphed by a curve in two-dimensions. When there is one response and two-factors, the regression model can be graphed by a curve in three-dimensions. See also Multicollinearity, VIF.See also Multi-Vari for mapping categorical variables

Multicollinearity

Greek letter Nu. Degrees of Freedom. Degrees Of Freedom

1. Noise.2. Population Size.

Noise

1. North American market or specification. 2. Normally Aspirated. 3. Not Available. 4. Not Applicable. Alternative nomenclature: NA

1. North American market or specification i.e. USA, Canada, Mexico. 2. Normally Aspirated (as opposed to Super Charged). 3. Not Available. 4. Not Applicable. See also OSNA, N/A, S/C

Nominal Group Technique - A method for teams to quickly combine individual importance ratings to reach concensus

Factors affecting responses that are difficult, impossible, or expensive to control (e.g. ambient temperature or humidity, operator training). Inner Noises: part-to-part variation & variation over time. Outer Noises: Conditions of use & Operating Environment.Also known as Uncontrollable Variables. See also X's.

X's

Unordered categories which indicate membership or non-membership with no implication of quantity, i.e., assembly area number one, part numbers, etc.

Nominal Group Technique

See NGT NGT

NONCONFORMING UNIT

A condition within a unit which does not conform to some specific specification, standard, and/or requirement; often referred to as a defect; any given nonconforming unit can have the potential for more than one nonconformity.

A Process Map step which is not an operation which transforms the product in a way that is meaningful to the customer. A process step for which the customer would not be willing to pay.

A continuous, symmetrical density function characterized by a bell-shaped curve. e.g., distribution of sampling averages. Fully defined by the Mean and the Standard Deviation. Non-normal distributions may be transformed to normality by the application of e.g. the Box Cox Transformation.

Distributions Stat>BasicStats>Normality TestCalc>RandomData>Normal

Average process Yield. Commonally calculated as DPO. Can also be calculated as the nth root of RTY, where n = # process steps (i.e. nÖYRT)

DPO

New Overall Vehicle Assurance-Concern. Vehicle quality / defect measurement system. Now replaced by FCPA.

FCPA

6-Sigma Glossary



Null Hypothesis Hyp Test

N-VAÖ Symbol

Occurrence FMEA

O-Level FMEA

The value of a parameter which has an upper bound and a lower bound, but not both.

Gage R&R

Opportuity Count

Opportunity

Optimise DOE

ORDINAL

ORDINATEORT Ongoing Reliability Testing

OSNAOutput Variables Variables

P Gage R&R

p Chart Stat>Control>

P to T Stat>Quality>Gage Gage R&R

P to TV Stat>Quality>Gage Gage R&R

P/T Stat>Quality>Gage Gage R&R

P/T Ratio Stat>Quality>Gage Gage R&R

P/TV Stat>Quality>Gage Gage R&R

Paired t-Test Stat>Basic>Paired-t

Parameter Variables

ParatoParato AnalysisPARETO DIAGRAM Stat>Quality>Pareto

Part Contribution Stat>Quality>Gage Gage R&R

The basic proposal of the status quo, no effect - that nothing has changed. The explanation which indicated that a chance distribution is operating; Nomenclature: H0. In contrast to the Alternative Hypothesis

Alternate Hypothesis

Non-Value Added. A Non-Value Added process stepSquare Root. Öx means the number which multipled by itself equals x

See O Level, FMEA, Criticality. FMEA

FMEA Occurrence Level. Rated 1-10 by the Big 3 for Remote, Low, Moderate, High probability of failure. An assessment of the frequency with which the failure cause occurs. “How often does this X fail in a specific way”? See also Criticality.

FMEA

ONE-SIDED ALTERNATIVE

Operational Definition

The precise description that defines the Defect free condition. Defines the defect free product in specific and concrete criteria. Defines how to measure those criteria (CTQ's)

Gage R&R

Count of the total number of opportunities for defects from the process. A measure of process complexity

Opportunity for a defect. Any event which can be measured that provides a chance of not meeting a CTQ customer requirement. Number of ways you may not meet the customer requirements (CTQ's). I.e. multiple opportunities for defects per unit produced. E.g. length, weight & material may all be opportunities for defects on a component. See also Defect.

Defect

See Optimize. Optimize2.Stat>DOE>Factorial>ResponseOptimizer

Ordered categories (ranking) with no information about distance between each category, i.e., rank ordering of several measurements of a output parameter.

The vertical axis of a graph.See also Abscissa

Out Side North America - All markets except North America. See also NA

See Y's Y's

1. Probability - liklihood of occurrence. 0=never, 1=Certain. See also P-Value2. Proportion Defective - P = Population percent, p = Sample percent. See also p Chart.3. Gage Contribution.4. Parameter - See P Diagram.5. Preventative Control - See FMEA.6. Physically touching interface in FMEA Interface Matrix.

P-Value

An attribute Run Chart for measuring Proportion of Defectives. A simple chart used to track the proportion of defectives per lot or subgroup. p Charts can be used when the sample size is either constant or not constant. More flexible than np charts. Use if sample size is variable. Can also be used to compare two or more similar processes. See also np Chart. Note. Nothing to do with P Diagram.

Defectives

See P/T P/T

See P/TV P/TV

Precision-to-Tolerance Ratio. “P to T” is used to qualify a measurement system as capable of measuring to a given product specification. P/T = 5.15 * sMS / ToleranceRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. Also known as: %Tolerance, P/T, SV/Toler, Tolerance Ratio.See also Gage R & R.

Gage R&R

See P/T P/T

Precision to product/process Total Variation.Measurement system study variation (sMS) divided by total variation (sTOTAL). Assesses data relative to total sample data spread irrespective of spec limits.Range: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. Also known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measruement Capability Index.

Gage R&R

A t-Test where there is a known relationship between equivalent samples from a two sample survey. t-Test

1. A constant defining a particular property of the density function of a variable.2. A factor, X, Input or Variable (Note 6-Sigma does not generally use this term) - See P Diagram.

P Diagram

See Parato Diagram Pareto Diagram


A chart which ranks, or places in order, common occurrences. Used to break a big problem down to its' major parts and causes. May be applied on a strict 80:20 basis, or more generically to highlight highest impact/priority items.

Contribution (proportion) of variation attributable to the part, product or process. See also Gage R & R

Gage R&R

6-Sigma Glossary



Part Number

PDGPDI

PDU Portable Dignostic Unit - Dealer hand held electrical test equipment Vehicle Test

Pearson

PERTURBATION A non-random disturbance.PFMEA FMEA

PI

Pilot A small scale test of a solution to learn how to acjieve an effective full scale implementation

PM Preventative Maintenance

PoE

POFD Point Of Fit Delivery – delivery of parts/assemblies to point of fit on track.

POPULATION

Population Mean

Power Hyp Test

Pp Cp

PP100

Ppk Cp

PPL Cp

PPM

PPU Cp

PQR Plant Quality Review - Weekly or Monthly review of top plant quality issues

Precision Stat>Quality>Gage Gage R&R

Prediction Interval

PREVENTION

The major independent variables used in the experiment. Variables

1. Engineering part number as released2. Service / warranty part number - an abbreviated / cross-referenced version of the above3. In AWS, a warranty Fault Code if no actual part replaced

See 6-SigmaPre-Delivery Inspection. See also PoE. PoE

A correlation coefficient from the Pearson regression method. See Correlation. Nomenclatrue: R.

Correlation

Process FMEA. Application of FMEA to assess manufacturing process relaibility. See also DFMEA.

FMEA

1. Product Investigation - The department which gathers and processes AWS information. Provides Jaguar interface to dealers on problem solving. Interprets service part numbers. Land Rover Single Product Investigation Team.2. Prediction Interval

Prediction Interval

Port Of Entry - Point of entry into overseas territory following shipment from plant. Another potential measurement point in the total order fulfilment process. See also PDI

PDI

A group of similar items from which a sample is drawn. Often referred to as the universe.

Average (Sum divided by the population size) across entire population. Nomenclature: m (Greek letter Mu). See also Sample Mean

The Power of an experiment is the probability of rejecting the null hypothesis when it is false and accepting the alternate hypothesis when it is true. = 1 - b Risk. Power & Sample Size Proportion Test shows Beta risk. Use Proportion Test to show the Alpha likelihood.

Beta Risk Stat>Power&SampleSize>…See also: Stat>BasicStat>1/2Proportion

A measure of Process Capability reflecting how good the process would be, if it were centred on the target. It is ambiguously used for: 1. Long Term Process Capability (6-Sigma & MiniTab). 2. Short Term Process Capability i.e. Preliminary or Potential Process capability. (Ford & Big 3 SPC Reference Manual). Pp takes no account of whether the process is centred. Ppk takes this into account.Pp = (USL-LSL) / 6*s. See also Ppk, Cp, Cpk.

Ppk Stat>Quality>Capability

Problems Per 100 Vehicles - J.D. Powers TGW analysis unit of customer surveyed problems reported per 100 vehicles. Note: J.D. Powers measures per 100 in contrast to other TGW measures which are per 1000.

Actual Process Capability

Same as Long Term Process Capability. Also known as Between Capability, Overall Capability.


A measure of Process Capability which takes account of both the spread and centering of the data. It is ambiguously used for: 1. Long Term (6-Sigma & MiniTab) and 2. Short Term (Ford SPC Ref Manual). Ppk equals the minimum of PPU & PPL. This is the shortest distance between the mean and the nearest spec limit. If the mean is outside both spec limits Ppk becomes negative but Pp is unaffected. Ppk approximates to Ppk = Z / 3.Ppk=min(Ppk(USL),Ppk(LSL)) Ppk(USL)=(USL-Xbar)/3s Ppk(LSL)=(Xbar-LSL)/3sPpk approximates to Ppk = Z / 3 so Ppk=1.5 equivalent to Sigma level of 4.5 or Z 6See also CP, PP, Cpk, Sigma Level

Long Term Process CapabilityStat>Quality>Capability

LSL Ppk. Also shown as Ppk(LSL)=(Xbar-LSL)/3s. See also Ppk Stat>Quality>Capability

Parts Per Million defective. E.g. if an assembly which has 40 parts and 4 process steps, has, on average has one defect. This equates to 1,000,000/160 = 6,210 PPM. Same as DPMO (?)

DPMO SixSigma>ProductReport

USL Ppk. Also shown as Ppk(USL)=(USL-Xbar)/3s. See also Ppk Stat>Quality>Capability

Spread / variance of values. Improved through addressing Repeatability & Reproducability. See Gauge R&R. See also Accuracy.

Accuracy

The prediction bands (or prediction intervals, PI) illustrate the range of likely values for new observations. They represent a series of prediction intervals that span the range of observed density values. PI = confidence that a point will fall in the range. CI = confednce the mean will fall in the range. See also Confidence Interval(CI)

Confidence Interval Stat>Regression>Fitted Line Plot

The practice of eliminating unwanted variation of priori (before the fact), e.g., predicting a future condition from a control chart and when applying corrective action before the predicted event transpires.

PRIMARY CONTROL VARIABLES

6-Sigma Glossary



PROBABILITY

The number of successful events divided by the total numbers of trials.

Probe 1 MIS customer satisfaction telephone survey

PROBLEM A deviation from a specified standards.

Problem Variables

PROCESS

Process BoundaryProcess Capability Cp PCM

PCM

Process Entitlement Cp

Process Map

Process Sigma Level

PROCESS SPREAD Gage R&R

PRODUCERS RISK Hyp Test

Product Mean Gage R&R

Product Variance Gage R&R

PROJECT A problem, usually calling for planned action.Project Champion

Proportion Hyp Test

Proportion Test Hyp Test

PTAR

The chance of something happening; the percent or number of occurrences over a large number of trials.

PROBABILITY OF AN EVENT

1. An independant variable. See X's2. A Defect

X's

PROBLEM SOLVING

A process of solving problems; the isolation and control of those conditions which generate or facilitate the creation of undesirable symptoms.

A particular method of doing something, generally involving a number of steps or operations.May also ambiguously be used to reference the data resulting from a process. May be represented by a P Diagram.

P Diagram

PROCESS AVERAGE

The central tendency of a given process characteristic across a given amount of time or a specific point in time.

In Process mapping, the start and end point of the process being mapped.

The measure the capability of a process to meet its' requirements (specification).There are a variety of measures and a diversity of terminology applied to these:

The capability may be estimated from the data within a single sample. This is referred to as the Within Capability. Also known as the Short Term or Potential Process Capability, the Process Entitlement, Preliminary Process Capability, Performance Index or the Process Potential.Where the capability is determined from data between many samples over a longer period of time it is referred to as the Between Capability. Also known as the Long Term or Overall Process Capability or the Capability Index.

The indexes of capability may be quoted irrespective of process centering. These are the Cp & Pp indexes. The indexes of process capability which reflect the off-centre measure of the data are the Cpk & Ppk indexes.The units of measure of these indices are Sigma Levels - the number of standard deviations between the mean and the Specification Limits.See Short & Long Term Process Capability, Cp, Cpk, Pp, Ppk, Sigma Level.


PROCESS CONTROL

See STATISTICAL PROCESS CONTROL, Process Control Chart. SPC

PROCESS CONTROL CHART

Any of a number of various types of graphs used for SPC upon which data are plotted against specific control limits. Often X-Bar and R Charts. See Control Chart.

Control Chart Stat>QualityTools>CapabilityAnalysis

First time assessment of a process/operation over limited data or time period. Same as Short Term Process Capability. Also known as Within Capability, Potential Capability, Preliminary Process Capability, Performance Index, Process Potential, Process Entitlement.


A diagram showing process flow including: sub-processes, value added & non-value added steps, inputs (X's), outputs (Y's), measures, decisions, bottlenecks, re-work loops, control points. Typically has three representations: How you thought it was; how it actually is; how it should be. Graphically represented using iGrafix. See also SIPOC, "7 Step Method", Process Boundary

SIPOC

See Sigma Level.

The range of values which a given process characteristic displays; this particular term most often applies to the range but may also encompass the variance. The spread may be based on a set of data collected at a specific point in time or may reflect the variability across a given amount of time.

Gage R&R

Probability of rejecting a lot when, in fact, the lot should have been accepted (see ALPHA RISK).

Alpha Risk

Accuracy / mean of the product itself. Nomenclature mproduct. See also Measurement System Mean, Total Mean

Gage R&R

Precision / variance of the product itself. Same as True Variance. See also Total Variance Gage R&R

Strategic business leader and agent of change. Manages 6-Sigma project resources, mentors Black Belts, provides infrastructure support. Advises on project priorities.

Proportion of sample meeting criteria. Testing of proportion hypothesis for discrete data can be done with the 1 or 2 Propotion Tests. See also P.

Propotion Test Stat>BasicStat>1/2Proportion

Test for whether 1 or 2 discrete samples reflect equal proportions. Note a 2 Proportion test is equivalent to a Chi-Squared test with 1 degree of freedom. Proportion Test shows the Alpha likelihood. Use Power & Sample Size Proportion Test to show Beta risk.

Chi-Square Test Stat>BasicStat>1/2ProportionStat>Power>1/2Proportion

Plan-Train-Apply-Review - The training process breakthrough strategy of the Black Belt certification program. A closed-loop feed-back system.

6-Sigma Glossary



PTS

P-Value Hyp Test

PVQA

Q1

Q3 The third quartile. The point covering the lowest 75% of the data

QA Quality Assurance

QC

QFD Sys Eng

QFTF Quality Focused Test Fleet

QIDM

QLQPSQRT

Quality LevelR & R Stat>Quality>Gage Gage R&R

R Chart Stat>Control>R Gage R&R

r/1000 Repairs per thousand Symbol

Gage R&R Symbol

RANDOM

RANDOM CAUSE C&E PCM

Random Error Stat>ANOVA>…

RANDOM SAMPLE One or more samples randomly selected from the universe (population).

A variable which can assume any value of a set of possible values. Variables

RANDOMNESS

RANGE

RANKS Values assigned to items in a sample to determine their relative occurrence in a population.

Project Tracking System - Ford web based database of all 6-Sigma projects. New projects are registered, project numbers assigned, latest project status and reports are stored. Search here for details of other projects potentially related to yours.

Probability Value. Range 0-1. Likelihood of the Null Hypothesis. Generally P < 0.05 implies statistically significant."If P is Low, H0 must go"NB: In the Anderson-Darling Normality test P > 0.05 implies likely Normal Distribution. i.e. The Null Hypothesis is that the data is Normal. See also Alpha & Beta Risks, Type I & II Errors, P.

P Stat>BasicStat>Corr'nStats>BasicStats>Disp. Desc. Stats (for Normaility test)

Yet another quality measurement system. Used by Ford audit. Superceded by FCPA1. A Ford & Big 3, plant and supplier quality standard (and Flag!)2. The first quartile. The point covering the lowest 25% of the data

1. Quality Control2. Quality Characteristics

Quality Function Deployment - Techniques & matrices to highlight process issues - Taking customer needs through the design process to ensure the product meets customer needs - inc Cause & Effect Matrices. Now known as ACF.

ACF

Quality Investment Decision Model - Method of calculating ongoing $ savings of a TGW based project.

T:\\man6sig3\Templates\QIDMtemplate.xls

The Quality Level measure of FCPA. A function of demerits counted. FCPA

Quality Process Sheet. See also OMS, Operational Method Sheet, Process Instruction. OMS

Quality Responsible Team - a cross-functional organizational team responsible for the quality of a particular vehicle system.


The Quality Level measure of FCPA. A function of demerits counted. FCPA

Repeatability & Reproducibility. See Gage R & R Gage R&R

Measure of variability within subgroups. Plot of the difference between the highest and lowest in a sample. Range control chart. When greater than 10 samples available then S Chart can provide addition standard Deviation data. When no subgroups are available use MR Chart. See also X-Bar Chart, I-MR Chart.

Gage R&R

R2-Value The proportion of the variability in the response that is explained by the correlation equation. Square of the Pearson measure of correlation. Range 0 to 1. See also R-Value

Correlation

Random Sampling: Selecting a sample so each item in the population has an equal chance of being selected; lack of predictability; without pattern.See also other applications: Random*

Calc>Random Data>Normal

A source of variation which is natural / random and is inherent in the process; a change in the source (“trivial many” variables) will not produce a highly predictable change in the response (dependent variable), e.g., a correlation does not exist; any individual source of variation results in a small amount of variation in the response; cannot be economically eliminated from a process; an inherent natural source of variation. Not controllable by the operator. Also known as Common Cause variation. See also Special / Assignable Cause.

Special Cause

RANDOM EFFECTS MODEL

Experimental treatments are a random sample from a larger population of treatments. Conclusions can be extended to the population. Interferences are not restricted to the experimental levels.

Errors not accounted for by idendified factors. Also called Experimental Error or Residual Error. See also Error, Defect. Nomenclature:e

Experimental Error

RANDOM VARIABLE

RANDOM VARIATIONS

Variations in data which result from causes which cannot be pinpointed or controlled. See also Assignable Variation

A condition in which any individual event in a set of events has the same mathematical probability of occurrence as all other events within the specified set, i.e., individual events are not predictable even though they may collectively belong to definable distribution.

In an X-Bar & R Chart this is the difference between the highest and lowest values in a set of values or “subgroup.”In an I & MR Chart the Moving Range is the difference between value and the next.


6-Sigma Glossary



RATIO

Regression Gage R&R

REJECT REGION The region of values of which the alternate hypothesis is accepted. Hyp Test

Reliability

Repeatability Stat>Quality>Gage Gage R&R


Replication DOE

Reproducability Stat>Quality>Gage Gage R&R


RESEARCH

RESIDUAL ERROR DOE

Resolution Gage R&R DOE

Response Variables

RFT Right First Time Yield

FMEA

Robustness

ROIC Return On Investment CapitalYield

RPN FMEA

RSM

R-Sq Gage R&R Symbol

R-Sq(adj) Gage R&R Symbol

Numeric scale which ha a absolute zero point and equal units of measure through, i.e., measurements of an output parameter, i.e., amps.

Linear Regression: Models the linear relationship between variables when one response variable depends on the others. Regression is used with a continuous response variable (Y) and one or more predictor variables that are most often continuous, but can be categorical. When used on two X's regression calculates correlation. When used on X & Y values regression can be used for prediction. See also Correlation, Multi-Variant Regression, Multicollinearity, Variance Inflation Factor, Best Subsets, C-p.

Correlation Stat>Regression>Fitted Line etc.

Consistency of output across time. The probability that an item will adequately perform for a specified time, under specific environmental conditions. Robustness over time. See also Repeatability, Reproducability, Reliability Function, R(t), Robustness (to Noise), Homeostasis, Durability (throughout product life).

Durability Stat>Reliability>DistOverView

Gage R&R Reliability

Consistency of output across different machines. Same as Equipment Variation.See also Reproducability, Reliability, Repetition, Assessor Variation.

Reliability

Repeatability Variance

Precision / variance of the machine. See also Total Variance Gage R&R

Observations made under identical test conditions.Replication: Replicating the entire experiment in a time sequence.Replication & Repetition help reduce noise in a designed experiment.Both can be used in the same experiment & each contributes to sample size required.

Repetition DOE>Factorial>Define…

REPRESENTATIVE SAMPLE

A sample which accurately reflects a specific condition or set of conditions within the universe.

Consistency of output across different operators. Same as Assessor Variation.See also Repeatability, Equipment Variation, Reliability.

Repeatibility

Reproducability Variance

Precision / variance of the people operating the machines/process. See also Total Variance Gage R&R

Critical and exhaustive investigation or experimentation having for its aim the revision of accepted conclusions in the light of newly discovered facts.

See EXPERIMENTAL ERROR. Check for: Normal Residual plot approximating to straight line, I Chart of Residuals showing process in control, Residuals vs Fits being random +/- the zero line (Histogram of residuals is for info only - no special test on this). See Breyfogel p.367. See also Fits, Residual Error, Regression, Correlation.

Experimental Error StatANOVA>1way>StoreRes&FitsThen ...Stat>Regression>ResidualPlots

1. Resolution: The number of decimal places that can be measured by the system. Increments of measure should be at least one-tenth of the width of the product specification or process variation. In Gage R&R this is the same as discrimination.2. Resolution III, IV, V Designs: Used to assess what needs to be lost, in terms of our ability to estimate higher order interactions, in order to reduce DOE Fractional Factorials no of runs. A Fractional factorial distinguishing: III Main effects only, IV Main effects & 1st order interactions. V Main effects and 1st & 2nd order interactions. Nomenclature: R.

1. Gage R&R

2. Stat>DOE>Factorial>Create>DisplayAvailDesigns

A dependant variable. The output of a process. See Y's. See also P Diagram. Y's

Risk Priority Number

See RPN RPN

Maintainance of required functionality across operating range despite all noise factors. The conditions or state in which a response parameter exhibits hermeticity to external cause of a non-random nature; i.e., impervious to perturbing influence. See also Reliability, Durability, Parameter Design, Homeostasis.

Reliability

Rolled Throughput Yield

See RTY RTY

FMEA Risk Priority Number: = Severity X Occurrence X Detection. Rated 1-1000.Used to prioritize recommended actions. Special consideration should be given to high Severity ratings even if Occurrence and Detection are low.

FMEA

Response Surface Method is a technique to find maximum or minimum condition.The basic strategy is to consider the graphical representation of the yield as a function of the two significant factors. This graphic could be considered to be similar to the contours on topographical maps. The higher the “hill”, the better the yield. The idea is to gather data to enable us to plot the contours. Once done, we can use the resulting map to find the path of steepest ascent or descent to the maximum or minimum, respectively. See also Method of Steepest Ascent/Descent, Region of Curvature, Central Composite Design, Star Points, Multiple Response Optimizer.

Stat>DOE>Respnse...

The proportion of the variability in the response that is explained by the correlation equation. See R2-Value and Correlation. See also R-Sq(adj)

Correlation Stat>Regression>FittedLine

The adjusted R (R-Sq(adj)) takes into account the fact that R tends to overestimate the actual amount of variation accounted for in the population. See R2-Value and Correlation

Correlation Stat>Regression>FittedLine

6-Sigma Glossary



RTY Yield

Run Chart Stat>Quality>Run

R-Value Gage R&R Symbol

Symbol

s Symbol

S Symbol

s Symbol

S&R Squeaks & Rattles Symbol

S/C Symbol

S/N Gage R&R Symbol

Symbol

Gage R&R Symbol

Gage R&R Symbol

Gage R&R Symbol

Gage R&R Symbol

Gage R&R Symbol

Gage R&R Symbol

Gage R&R Symbol

Gage R&R Symbol

Symbol

SAMPLE

Sample Mean

Sampling

SE Mean Stat>Quality>Gage Gage R&R

Severity

Shift

Shop Floor DefectCp

Sigma

Sigma Level Cp

Rolled Throughput Yield - The product of the First Pass Yields of all process steps / stations - Known at Jaguar as First Time Through (FTT). Probability that a product will pass through the entire process without rework and without defects. Identifies the hidden factory. Nomenclature: Yrt Related to DPU: Yrt = e-DPU

e.g. Stn 1 90%, Stn 2 90%, Stn 3 90% gives 73% RTY. See also Yield, Final Yield, First Pass Yield.

First Pass Yield

Provides a plot of Measures of problem vs. Time plus analysis to look for evidence of patterns in your process data and perform two tests for non-random behavior. See also Time Series Plot for basic plot of Measures vs. Time with no analysis. See also I-MR, R, S, X, X-bar, np, p, c, u Charts.

Time Series Chart

Pearson measure of correlation. Range 0 to 1. See also R2-Value and R-Sq(adj) Correlation

rxy See Correlation Correlation

Greek letter lower case sigma. See Sigma, Z. Sigma

Greek letter capital sigma. Sum of. e.g. S Xn = sum of the sample values X1, … Xn

Sample Standard Deviation or Sigma Sigma

Supercharged cf. Normally Aspirated See NA

The Signal-to-Noise Ratio (S/N Ratio) relates the product variation to the measurement system variation. The S/N Ratio should be as large as possible. S/N ratio = sp / sMS. S/N = Energy used to produce intended result / Energy for unintended results. S/N = 10Log10 (m/s)2 For Static System: Nominal is BestS/N = -10Log10 1/n Sy2 For Static System: Smaller The BetterS/N = -10Log10 1/n S(1/y)2 For Static System: Larger The BetterS/N = 10Log10 (b2/s2) For Dynamic Systems.These give S/N in decibels, so *6 is equivalent to double output. Also known as SNR.When the response is proportional to the change in any of the factors then S/N is a better measure of noise than Standard Deviation or Variance. (Advocated by Taguchi).

Gage R&R

s2 The Variance

s2 The Variance Variance

s2MS Variance due to the Measurement System Variance

s2ms Variance due to the Measurement System Variance

s2product Variance due to the Product Variance

s2repeatability Variance due to lack of Repeatability Variance

s2reproducability Variance due to lack of Reproducability Variance

s2total See Total Variance Total Variance

s2true See True Variance True Variance

s4 Smarter Six Sigma Solutions - The Forrest W. Breyfogle III methodology for wisely applying 6-Sigma process principles to improve the bottom line

One or more observations drawn from a larger collection of observations or universe (population). Samples seek to be random, independent and unbiased. See Simple Random, Stratified, Cluster & Systematic Sample

Stat>Power&SampleSize>…Calc>Random Data>…

Average (Sum divided by the sample size) across sample. Nomenclature: X with a line above it. See also X-Double Bar. See also Population Mean, X-Bar, SE Mean, Tr Mean

The process of taking a sample from the populationSCATTER DIAGRAMS

Charts which allow the study of correlation, e.g., the relationship between two variables.

Standard Error of the Mean. Calculated as the Std Dev divided by the Square Root of n.

Sensitivity See Test Sensitivity, d/s d/s

See S Level, FMEA, Criticality. S-Level

Changes in quality over time. This will incorporate a growing number of general cause variation which broaden the overall data spread. Data typically shifts 1.5 Sigma Levels between short term & long term. See also stability, drift, short & long term sigma values

See Failure Mode

Short Term Sigma Level

Short Term Process Sigma Level capability. = Long Term Sigma Level + Z-Shift (typically 1.5). Nomenclature: ZBench or Zst

Short Term Process Capability

1. A letter in the Greek alphabet2. Standard Deviation = Root of sum of squares of deviations from the mean3. Sigma Level measure of population process capability. E.G. 6-Sigma.

Sigma Level

Key measure of process capability of the population. The number of Std Deviations between the mean and the nearest Spec Limit (LSL or USL). Also known as Process Sigma Level or Z-Score ("Zee Score").May be Short Term Sigma Level or Long Term Sigma Level. Typically reported as Short Term Sigma Level, See also Capability, CpNote: Nothing to do with S-Level

Short Term Sigma LevelSixSigma>ProductReport

6-Sigma Glossary



Signal Variables

Significance Level

SIL

SIP ???

SIPOC

S-Level Symbol FMEA

SNR

SOP Variables

SPC

Spec LimitSpecial Cause C&E PCM

Specification Limit

SPIT Warranty

Square Root

SS Cp

SSA

SSAN

SSI

Stability

STABLE PROCESS A process which is free of assignable causes, e.g., in statistical control.

Stack Loss ???Standard Deviation

Variables

Std Dev

What the customer does to make the process work. The desired product characteristic. The input to a process. E.g. customer selects parameter values, and applies trigger force to switch. See also Y's, SNR, P Diagram.Nomenclature: M (i.e. Modulated Signal) in the Ideal Function model y=bM.

Y's

a Alpha level Alpha Level

Satisfaction Inhibitor List. Same as SSA

Simple Random Sample

Sampling where all samples of n objects is equally likely - such unbiased independent samples are reare in the real world.

Suppliers - Inputs - Process steps (with action verb) - Outputs - Internal & External Customers - Variables - A business process mapping methodology. Presented in textual rather than graphical form. See also P Diagram.

Process Map

FMEA Severity Level. Rated 1-10 by the Big 3 for Minor, Low, Moderate, High, Hazardous effect of failure. An assessment of the seriousness of the failure effect on the customer. The customer can be the end customer and / or the next process operation. (Note: Nothing to do with Sigma Level). See also Criticality, Occurrence, Detection.

FMEA

Signal to Noise Ratio. Also known as S/N.

Standard Operating Procedures: E.G. Jaguar procedures, Work or Process Instructions, Operational Method Sheets etc. In process mapping: Common sense items that one should execute because it makes sense. The goal here is to make sure that we document the true procedure (E.G. cleaning, safety, loading of components, setup, etc) . See OMS, X's

OMS

Statistical Process Control - Methods of improving and controlling process capability by statistical analysis and monitoring of quality inputs. A Control Plan specifies data collection on a Control Chart which monitors data in control within Control Limits. Out of control data triggers a Reaction Plan.

Quality System

See Specification Limit Specification Limit

See ASSIGNABLE CAUSE, Assignable Variation Assignable Cause

Limit of customer acceptable range. See also USL, LSL, Control LimitsNB. Data within Spec Limit does not necessarily imply process within Control Limits and vice versa

Control Limit

Single Point In Time vehicle warranty analysis - comparison of quality levels across vehicle lines at same model year

AWS Documentation

The number which multipled by itself equals x. Nomenclature: Öx means Square Root of x

1. Six Sigma - Measure of process variation - See 6-Sigma. This particular abbreviation is used in the formula: SStotal = SSbetween + SSwithin. i.e. Total variation = variation between subgroups + variation within subgroups2. Sum of Squares - The summation of the squared deviations relative to zero, or to the mean of an experiment. See also Variance and Standard Deviation3. Swallow Sidecars - The name of the company founded by Sir WIlliam Lyons which later became Jaguar Cars Ltd.

1. SixSigma>…2. Stat>ANOVA>…

1. Satisfaction Single Agenda. Top product issues list to which 6-Sigma is applied to improve product quality & customer satisfaction. Same as SIL2. Six Sigma Acadamy - The organisation sponsoring and leading the 6-Sigma processes.

http://www.jaguar.ford.com/eng/web/engpvtw/cdaweb/

Six Sigma Acadamy Navigator - The delivery toolkit for the Six Sigma Acadamy teaching/training/Reference material

J.D. Powers Sales Satisfaction Index - Focuses on what is important to the consumer--process/transaction-related factors. It highlights to what contributes most to customers' satisfaction with dealer/retailer.

The distribution of measurements remains constant and predictable over time for both mean and standard deviationNo drifts, sudden shifts, cycles, etc… Lack of shift / drift over time.

A statistical index of variability which describes the spread. See also Variance.NB: S/N may be a better measure of spread/noise, especially where there is a response changes proportionally with any of the factors.

Variance

Standard Operating Procedure

See SOP

STATISTICAL CONTROL

A quantitative condition which describes a process that is free of assignable/special causes of variation, e.g., variation in the central tendency and variance. Such a condition is most often evidenced on a control chart, i.e., a control chart which displays an absence of non-random variation.

STATISTICAL PROCESS CONTROL

The application of statistical methods and procedures relative to a process and a given set of standards. See SPC.

SPC

See Standard Deviation Standard Deviation

http://www.quality.ford.com/aws/aws/documentation/SPIT.xls

http://www.jaguar.ford.com/eng/web/engpvtw/cdaweb/

6-Sigma Glossary



Stratified Sample

Study Variation Stat>Quality>Gage Gage R&R

SUBGROUP

SV Stat>Quality>Gage Gage R&R

SYMPTOM That which serves as evidence of something not seen.

Symptom Variables

SYSTEM

Systematic Sample

A pattern which displays predictable tendencies. Variables

t

T

DOE Symbol

Tally Point Measurement / Inspection point in a process

TargetTarget DPMO

t-Distribution

Test Of Significance

TGR

TGW

THEORY A plausible or scientifically acceptable general principle offered to explain phenomena.

Tolerance

Tolerance ratio Stat>Quality>Gage Gage R&R

Total Mean Stat>Quality>Gage Gage R&R

Total Variance Stat>Quality>Gage Gage R&R

Total Variation Stat>Quality>Gage Gage R&R

TPC Technical, Political & Cultural change factors

TQM

Tr Mean

Sampling by diving the population into homogenous groups and drawing a sample from each group. I.e. where some correlation/dependance is expected between variables and cluster.


Gage R&R

A logical grouping of objects or events which displays only random event-to-event variations, e.g., the objects or events are grouped to create homogenous groups free of assignable or special causes. By virtue of the minimum within group variability, any change in the central tendency or variance of the universe will be reflected in the “subgroup-to-subgroup” variability.

See Study Variation Study Variation

A dependant variable. See Y's Y's

A set of connected things or parts; an organized body. That which is connected according to a scheme. A regularly interacting or interdependent group of items forming a unified whole, a network serving a common purpose.An Engineered System Model may be represented in a P Diagram.A Viable System is one able to maintain a separate existence, surviving in a given environment.See Static System, Dynamic System, Purposive System, Viable System Model.

P Diagram

Sampling by select every nth object from a selected starting point.

SYSTEMATIC VARIABLES

t Value. See stats tables for values by sample size. E.g. sample size 10 t=2.26, for 100 t=1.98 for 1000 t=1.96. See t-Distribution

1. See Target. 2. T-Statistic. A measure of impact in ANOVA used in pareto & normal charts.3. T-Bar. In Parameter design used for grand average of responses (y-Double Bar) for S/N & b only.4. Minitab Error messages may use a 'T' as an arrow to show the point in the input command string which is in error. This has nothing to do with 'T' itself, or any reference to 'T' in the command string! Confusing eh!!See also t (t-Value), t, (Treatment).

2. Stat>DOE>Analyse>>Graphs>Normal, Pareto

t Greek letter Tau. See Treatment Treatment

Desired value - Sits between USL & LSL.

The process target DPMO at the completion of the project. See DPMO DPMO SixSigma>ProductReport

A series of bell shaped normal distributions. The higher the sample size, the higher and narrower the bell. The t value used in calcuating this is also used in calculating Confidence Intervals

Distributions

A procedure to determine whether a quantity subjected to random variation differs from a postulated value by an amount greater than that due to random variation alone.

Test Sensitivity The magnitude or size of the difference being tested. Nomenclature: d/sThings Gone Right - Measured in J.D. Powers APEAL survey. Defined as achieving 9/10 on an individual product attribute. See also TGW.

TGW

Things Gone Wrong - Customer surveyed product problems - Recorded in GQRS. Usually quoted in TGW/1000 except for J.D. Powers which quotes TGW/100


USL - LSL. See Tolerance Ratio, %Tolerance, Study Tolerance, Tolerance Design, Tolerancing.

Tolerance Ratio Gage R&R Tol Design

Measurement System variation (5.15*sMS) divided by Tolerance. Also known as: %Tolerance, P/T, SV/Toler, Tolerance Ratio.

Gage R&R

Accuracy / mean of the product plus that of the measurement system. Nomenclature mtotal. See also Measurement System Mean, Product Mean

Precision / variance of the product plus that of the people and machines. Total Variance = MS Variance + True Variance. See also Product, Repeatability & Reproducability Variances

Variance

Total Variation -TV (5.15*sTotal)

Total Quality Management - Previous Ford/Jaguar quality / process improvement methodology

Trimmed Mean. The mean of the sample values excluding outlaying values. The 5% Tr Mean is the mean of the sample excluding the highest and lowest 5% of values.


6-Sigma Glossary



Treatment DOE

True Value Gage R&R

True Variance Gage R&R

t-Test Hyp Test

TV Gage R&R

The values of a parameter which designate an upper and lower bound.

TYPE I ERROR Hyp Test

TYPE II ERROR Hyp Test

TYPE III ERROR Answering the wrong question (!) Hyp Test

u Symbol

UCL Upper Control LimitVariables

Universe

USLVA Sys Eng

Value added step

VarComp Gage R&R

Variable Variables

Variable R & R Stat>Quality>Gage

VARIABLES DATA Variables

Variance Gage R&R

Variation

VCATSVCSI

VDI

Verbatims Warranty

VFG Vehicle Function Group - 3 digit code for organisational area responsible for vehicle area Warranty

VFG OwnerVital Few Variables

VRT Variability Reduction Team

Changes to factors to improve outputs. Nomenclature:t. See also Treatment Combinations, tc's.

The target value of the CTQ CTQ

True Variance. The variance of the defect. Excludes measurement system variance. Same as Product Variance

Variance

Method for analyzing the difference between a sample mean and a target valueMethods for analyzing the difference between means obtained from two samplesMethod for determining whether significant differences in variance exist between two or more samples

Stat>Basic>1/Paired/2-t.Stat>Basic>2Var.Stat>ANOVA>EqualVariances.

See Total Variance Total Variance

TWO-SIDED ALTERNATIVE

See ALPHA RISK. Alpha Risk

See BETA RISK. Beta Risk

1. Proportion of defectives - See u Chart.2. English letter "u". Sometimes used in lieu of Greek letter Mu (m) for Mean

u Chart

Upper Control Limit

Uncontrollable Variable

Noise (N): things you cannot control due to cost or difficulty (ambient temperature or humidity, operator training). See X's

X's

Same as PopulationUNNATURAL PATTERN

Any pattern in which a significant number of the measurements do not group themselves around a centre line; when the pattern is unnatural, it means that outside disturbances are present and are affecting the process.

UPPER CONTROL LIMIT

A horizontal line on a control chart (usually dotted) which represents the upper limits of process capability. See Control Limit

Control Limit

Upper Specification Limit. The lower limit of customer acceptability of CTQ. CTQ

1. Value Added. A Value Added process step.2. Value Analysis.

Value Analysis

A Process Map step which is an operation which transforms the product in a way that is meaningful to the customer. A process step for which the customer would be willing to pay.

MiniTab report of components of Total Variance Total Variance

A characteristic which may take different values and is measurable - as opposed to an attribute which has only a binary value. See also X's, Y's, Parameter.

Attribute

The process for measuring the repeatability & reproducability of the value of a variable Gage R&R Variables

Numerical measurements made at the interval or ratio level; quantitative data, e.g., ohms, voltage, diameter; subdivisions of the measurement scale are conceptually meaningful, e.g., 1.6478 volts.

The average squared deviation of each individual data point from the mean. Nomenclature: s2 or s2. See also Standard Deviation. NB: S/N may be a better measure of spread/noise, especially where there is a response changes proportionally with any of the factors.

Standard Deviation Stat>ANOVA>EqualVariances.

Any quantifiable difference between individual measurements, such differences can be classified as being due to common causes (random) or special causes (assignable).See also Study Variation, Total Variation, %Variation.

VARIATION RESEARCH

Procedures, techniques, and methods used to isolate one type of variation from another (for example, separating product variation from test variation).

System for testing vehicle electrical functionality on the assembly line. Part of JDS.Value of Customer Satisfaction Improvement - The direct costs associated with finding and fixing defects. See also COPQ

J.D. Powers Vehicle Dependability Index - Designed to provide manufacturers and consumers information on the long-term durability of cars sold in the U.S.

Customer problem statement. Survey verbatioms recorded in GQRS. Customer warranty verbatioms sometimes recorded alongside dealer service engineer problem statement in AWS.


Vehicle Function Group Manager responsible for vehicle area under investigation

See Critical X's Critical X's




6-Sigma Glossary



WCC Warranty

X & R CHARTS Stat>Control>Xbar-R

X-Bar Variables Symbol

X-Double Bar Variables Symbol

X's Variables

y=f(x) Variables

Yield Symbol

Yield Symbol

Yield Yield

YIS Years In ServiceYield Symbol

Yield Symbol

Yield Symbol

Y's Variables

Z Symbol

Cp Symbol

Z-Distribution

ZeeZee Score Cp

Zee Shift Cp

Zee Transformation Cp

Zero Bias

Zero Variance

Cp Symbol

Cp Symbol

Z-Score Cp Symbol

Z-Shift Symbol

Cp Symbol

Z-Transformation Cp Symbol

Balanced ANOVAProcess Mean

Warranty Component Classification code. 4 digit AWS warranty codeA code identifying warranty component classification a logical grouping representing the area of the vehicle repaired. A code used to classify parts and pseudo parts for the purpose of concern definitionWCC Major Code: 1st of WCC code: identifies major vehicle systems e.g. engine carburettor starter etcWCC Group Code: the 1st & 2nd characters of the WCC code. Identifies vehicle subsystems e.g. Engine cooling manual transmission etc. Goto hyperlink for full listing. Contact Bronni Lindsey for further details of WCC codes, 313- 84-50136, BLINDSA1


A control chart combining an X-Bar Chart and an R Chart, which is a representation of process capability over time; displays a variability in the process average and range across time. Use an R or S Chart before performing an X-Bar Chart. Also known as a Shewhart Control Chart. See also X-Bar Chart, R Chart, S Chart.

The Sample Mean. Nomenclature:`X i.e. X with a line above it. See also X & R Chart, X-Double Bar.

Stat>Control Chart>X-Bar…

The Mean of a set of sample means (X-Bar's). Nomenclature: X with two lines above it.

Process Inputs, Causes, Problems. Also known as Inputs, KPIV's, Parameters or Independent Variables. X's may be categorised as Controllable Variables, Uncontrollable Variables (Noise) and Standard Operating Procedures (SOP's). See also Critical X's

Critical X's

The CT Matrix. The transfer functions (f) which translate of customer needs (Y's) into product requirements (X's)

YFINAL Yield of the final step in the process

YFT 1. First Time Yield. 2. First Time Yield of a process step. i.e. (input-scrap-rework)/input3. Further ambiguously may be used as Final Test Yield YFINAL

% defect free Yield = e-DPU (Using Poisson Approximation - accurate only for small DPU e.g. 0.1DPU=0.9Yield but 0.9DPU¬=0.4Yield)Or Yield = (1-DPO) # of Opportunities

- See also Final Yield, First Pass Yield & Rolled Throughput Yield/First Time Through, Normalised Yield, DPO, DPU

RTY

Yn Yield of the nth step in the process

YNORM See Normalised Yield

Yrt Nomenclature for Rolled Throughput Yield - See RTY. Note:Yrt = e-dpu

Outputs, Effects, Dependent variables, Responses, Symptoms. Also known as Outputs or KPOV's. See also X's

X's

Same as Sigma, but for Sample rather than Population. See also Z-Score, Zbench, ZLT, ZST. Sigma

Za Z-Alpha. The alpha value of the Z-Distribution

ZBench Process Capability Sigma Level. May refer to either:1. General Sigma Level in which case it may be qualified as short or long term value.2. Specifically the sigma level of the sample rather than the population. If so Zbench = Short Term (ZST)


Another statistical distribution of data. Values may be used in hypothesis testing or the determining of sample sizes.

Distributions

American pronounciation of the letter "Z". Same as Sigma

Same as Process Sigma Level. Typically reported as Short Term Sigma Level Short Term Sigma Level

Expected shift in Process Sigma Level over the long term. Typically 1.5

See Z-Transformation Z-Transformation Stat>Quality>Capability

The ideal measurement system which will produce “true” measurements every time it is used. i.e. Measurement System Variance = zero. Also known as Zero Variance

MS Variance

The ideal measurement system which will produce “true” measurements every time it is used. i.e. Measurement System Variance = zero. Also known as Zero Bias

MS Variance

Zindividual Individual data values transformed using the Z-Transformation. Z-Transformation Stat>Quality>Capability

ZLT Nomencalture for Long Term Process Sigma Level of the sample. Long Term Sigma Level

Same as Process Sigma Level of a sample. Typically reported as Short Term Sigma Level. Nothing to do with Z-Value.


Expected shift in Process Sigma Level over the long term. Typically 1.5

ZST Same as Short Term Process Sigma Level of the sample & ZBENCH Short Term Sigma Level

This “transform” converts any normal distribution (given a sample mean and sample sigma) to a standard normal distribution which always has a mean=0 and sigma=1.

Stat>Quality>Capability

ANOVA on two columns of data of equal length. Where data is missing use GLM. ANOVA

Same as Population Mean Population Mean


6-Sigma Glossary


Key Word Description Hyperlink MiniTab TopicsSymbol Hyp Test

Bimodal

Unimodal

Kurtosis Stat>Basic>Display

Success Stat>Basic>1Prop

Failure FMEA

C-p

F-Ratio

Parato Chart Stat>Quality>Pareto

EDA Exploratory data Analysis - preliminary high level data analysis e.g. Pareto, Run charts etc.

S Chart

X-Bar Chart Stat>Control>Xbar

XDbar6 Pack Gage R&R

Six Pack Gage R&R

Capability Six Pack Cp Gage R&R

Within Capability Cp PCM

Potential Capability Cp PCM

Overall Capability Cp PCM

l Symbol Reliability

Weibull Distribution

OBD On Board Diagnostics Vehicle Test

Epsilon-Squared Stat>ANOVA>1way DOE

Symbol

Equal Variance

OFAT DOE

H1 Alternative nomenclature for Alternative Hypothesis. Preferred nomenclature: Ha Null Hypothesis

A statistical distribution which has two dominant peaks. This is visible on the baisic stats histogram graph. There is no commonly available standard test to reflect the significance of potential bimodal trends. See also Uni-Modal

Unimodal Stat>Basic>Display>graph...No numerical test available

A statistical distribution which has single dominant peaks. E.g. The Normal Distribution. See also Bi-Modal

Bimodal

Kurtosis refers to how sharply peaked a distribution is. A kurtosis statistic is provided with the graphical summary:· Values close to 0 indicate normally peaked data· Negative values indicate a distribution that is flatter than normal· Positive values indicate a distribution with a sharper than normal peak

An observation that has the characteristic of interest. NB. The characteristic of interest may well be real world failures! See also Failure

Failure

1. An observation that does not demonstrate the characteristic of interest. See Success2. Just a plain ordinary failure to meet required standard of performance

Success

Mallow's C-p Statistic

Mallow's C-p Statistic. A measure of bias vs. closeness of fit of subsets of variables in multiple regression. The lower the C-p the better.Cp = (SSEp/MSEm) - (n - 2P) where:SSEp is SSE for the model with p parameters (including the intercept term)MSEm is the mean square error for the model with all m predictorsP is the number of parameters in your model.In general, we look for models where C-P is small and close to P. e.g. for a model with three predictors that includes the constant (or intercept), you would like for a model with C-P close to four.

Regression Stat>Regression>BestSubsets

See Mallow's C-p Statistic. Mallow's C-p Statistic Stat>Regression>BestSubsets

SS / MS. Sum od Squares divided by the Mean Square. Used to calculate P in ANOVA. ANOVA Stat>ANOVA>1-Way…


Stat>EDA...Stat>Quality>Pareto, Run…

Standard Deviation Chart. A Control Chart of subgroup standard deviations. Can be used in preference to R Charts when subgroups have 10 or more data points enabling standard deviations of sub-groups to be calculated. See also X & R Charts.

X & R charts Stat>Control>S or Xbar-S

A Control Chart which measures variability between subgroups. Use an R Chart or S Chart first. See X & R Charts.

X & R charts

Same as X-Double Bar. X-Double Bar

See Capability Six Pack Capability Six Pack Stat>Quality>CapabilitySixPack

See Capability Six Pack Capability Six Pack Stat>Quality>CapabilitySixPack

A combined process capability report including: X-Bar & R Chart, Last 25 Subgroups, Capability Histogram, Normal probability plot and process tolerance capability plot.

X & R charts Stat>Quality>CapabilitySixPack

Same as Short Term Process Capability.Best Potential process capability within subgroups. I.e. Capability within the one sampled sub-group.Also know as Potential or Short Term Capability. See also Between Capability, Overall Capability.

Short Term Process CapabilityStat>Quality>Capability...

Same as Short Term Process Capability.Best Potential process capability within subgroups. Also known as Short Term, or Within Capability. See also Between, Overall Capability.


Overall Long term process capability across subgroups. This is the sum of the Within Capability and the Between Capability.

Long Term Process CapabilityStat>Quality>Capability...

1. Greek letter Lamda. 2. Used in the Box Cox Transformation to achieve normality of data.3. In Reliability l = 1/MTBF.

Box Cox Transformation

A non-normal data distribution. Process capability analysis of the Weibull distribution may be performed as an alternative to transforming data to a normal distribution. Weibull values are determined by the Shape (b), and the Scale (h).See also Extreme Value Distribution, Box Cox Transformation, Normal Distribution, Weibull Analysis.

Box Cox TransformationStat>Quality>Capability..Weibull

Proportion of behaviour is explained by the factor. Part of ANOVA One Way analysis - Sum of Squares (SS) of Factor over SS Total I.e. SS(Factor) / SS(Total). Nomenclature: e2. Units: Expressed as a %Note: Minitab shows SS values, but does not calculate e2 for you - its DIY!

e2 See Epsilon-Squared Epsilon-Squared

See HOMOGENEITY OF VARIANCE Homogeneity Of VarianceStat>Basic>GraphThen Stat>ANOVA> TestEqVar OrStat>Basic>2Var

One Factor At a Time. See DOE. DOE

6-Sigma Glossary



Type of Experiment DOE

Inference Space DOE

Validity DOE

Internal Validity DOE

External Validity DOE

GLM Stat>ANOVA>GLM DOE

E-Squared DOE Symbol

Standard Order DOE

K DOE

Level

Randomized Order DOE

Coded DOE

Uncoded DOE

Center Points DOE

Centre Points DOE

Block DOE

Screening DOE

tc's DOE

DOE

Repetition DOE

Alias DOE

Reduced Model DOE

Compounding DOE

Design Resolution DOE

Fold Over DOE

DOE types in descending order of effectiveness and resource required to execute:- Response Surface Methods- Full Factorials with Replication- Full Factorials with Repetition- Full Factorials without Replication or Repetition- Screening or Fractional Designs- One Factor At a Time (OFAT)

DOE

Part of DOE. The area within which you can draw your conclusions. Two classifications: Broad and Narrow:-Narrow Inference: Experiment focused on specific subset of overall operation. Examples: Only one shift, one operator, one machine, one batch, etc. Narrow inference studies are not as affected by Noise variablesBroad Inference: Usually addresses entire process (all machines, all shifts, all operators, etc.). Generally, more data must be taken over a longer period of time. Broad Inference studies are affected by Noise variables

DOE

Ensuring Validity of DOE. See Internal Validity, External Validity. Internal Validity

Part of ensuring DOE Validity. Randomization of experimental runs “spreads” the noise across the experimentBlocking insures Noise is part of the experiment and can be directly studiedHolding Noise Variables constant eliminates the effect of that variable but limits Broad Inferences. See also External Validity.

External Validity

Part of ensuring DOE Validity. Include representative samples from possible Noise Variables. Examples: Insure experimental units represent supplier variability.Do experiment across shifts and days. Include different product families. See also Internal Validity.

Internal Validity

General Linear Model. A DOE analysis where there are missing value(s) which prevent a balanced ANOVA analysis

Balanced ANOVA

See Epsilon-Squared Epsilon-Squared

A standard pattern of Low/High factor levels in a 2K Factorial DOE. -1 and 1 are used to represent the low and high levels of the factors. e.g. for 1 replication:-1 -1 1 -1-1 1 1 1

The converse of Standard Order is Randomized order

2K factorials Stat>DOE>Factorial>CreateFactorialDesign>Options>RandomizeRuns(Off)

1. The number of Factors in a 2K Factorial DOE. 2. 1000's or multiple of 1024.

2K factorials

The value of a Factor. E.g. Temperature may have a high and a low setting. In this case the factor temperature has two levels.See also Alpha Levels, Sigma Levels etc etc.

Conducting a data collection expreiment sampling readings in a random sequence of factors and levels. In contrast to Standard Order.

Standard Order

Arbitrary -1 and +1 values to represent Low and High levels for a factor e.g. in a 2K Factorials DOE. In contrast to Uncoded real life actual values.

2K factorials Stat>DOE>Factorial>Define>LowHigh

Representative real life actual factor levels e.g. in a 2K Factorials DOE. In contrast to Coded values.

Coded Stat>DOE>Factorial>Define>LowHigh

Centre points, midway between the Low and High values of a two level factor. This can be done to reduce the number of experimental runs needed by adding extra levels. There will only ever be one centre point - being the mid point of all factors. Reference to 'Number of Center Points' means number of repetitions of the same centre point.E.g. 3 factor 2 levels plus a center point needs 9 runs (2*2*2 + 1); 3 factor 3 levels needs 27 runs (3*3*3)

Stat>DOE>Factorial>Create>Designs

Stat>Power>Power

See Center Points. Center Points

See Blocking Variables. Blocking Variables

Investigation of Main Effects and/or lower order factors/interactions. Application of Fractional Factorials to reduce DOE no of runs

Fractional Factorials

Treatment Combinations - Number of DOE runs with different permutations of factors.

Treatment Combinations

Number of DOE runs with different permutations of factors. Also called tc's. See Treatment. Treatment

Repetition: Running more than one sample of a single run.Replication & Repetition help reduce noise in a designed experiment.Both can be used in the same experiment & each contributes to sample size required.

Replication

The Alias structure identifies those main effects & interactions that are confounded in order to reduce the no of runs in a Fractional Factorial DOE. Same as Confounding.


Simplification of the factors to the critical few using DOE analysis.

Combining a number of factors / variables into a single measure. See broader use under Confounding.

Counfounding

A Roman numeral notation that allows you to describe the confounding scheme associated with the design of an experimant. See Resolution, Fractional factorials.

The ability to add sequential factorial experiments to an existing fractional experiment with the intention of estimating specific main effects or interactions free of particular confounding problems. Fractional Factorials can be “folded over” to add to the design. Example: A half-fraction folded over can become a full factorial with two blocks. Folding over is just changing the signs of the original fraction and rerunning the experiment.See Fractional Factorials.


6-Sigma Glossary


Key Word Description Hyperlink MiniTab TopicsDOE

Projective Property DOE

DOE

DARE RIMCY DOE

Corner Point DOE

OTA

OTR

I

I-Chart Stat>ControlCht

PDF Reliability

Reliability

Censoring Reliability

Right Censoring Stat>Reliability… Reliability

Arbitrary Censoring Stat>Reliability… Reliability

Survival Reliability

Hazard Reliability

Reliability Function Reliability

R(t) Reliability

Survival Function Reliability

Anderson-Darling Reliability

A-Squared Reliability

H(t) Reliability

AD* Reliability

MTTF Stat>Reliability… Reliability

MTBF Stat>Reliability… Reliability

Hazard Function Reliability

B Life Stat>Reliability… Reliability

B5 Life Time at which 5% of parts are expected to have failed Stat>Reliability… Reliability

Tally Stat>Tables>Tally

Cross Tabulation Stat>Tables>Xtab

Balanced data Stat>Tables>Xtab

Value Ordering Selecting which order to sort data in. e.g. Alphabetical, Numeric, Days, Months etc. RMB>Col>value…

Order RMB>Col>value… DOE

Sparsity of Effects Principle

A concept of Fractional Factorial Experiments:Processes are usually driven by Main Effects and Low-order interactions. Higher order interactions are extremely unlikely to be significant.See also Fractional Factorials, DOE, Projective Property, Sequential Experimentation.


A concept of Fractional Factorial Experiments:Fractional Factorials can represent full-factorials once some effects demonstrate weaknessSee also Fractional Factorials, DOE, Sparsity of Effects, Sequential Experimentation.


Sequential Experimentation

A concept of Fractional Factorial Experiments:Fractional Factorials can be combined into more powerful designsHalf-Fractions can be “folded over” into a full factorialBy eliminating uninteresting Input Variables, fractions can become full factorials.See also Fractional Factorials, DOE, Sparsity of Effects, Projective Property


Acronym for the Deisgn Of Experiment (DOE) methodology: Define, Analyse, Reduce, e2, Residuals, Interactions, Main, Cube, Y=c+mx…Response Optimisation

DOE

As in a Cube Plot: No Factors * Levels. Part of DOE sample size. See also Effect. Stat>Power&SampleSize>2-Factorial etc

Off Track Area. Production area off the main assembly line. Often for rectification work. See OTR.

OTR

Off Track Recification. A production area off the main assembly line for product rectification work. See also OTA.

OTA

1. Individual data point(s). See I-Chart.2. Information Interface in FMEA Interface Matrix.3. The Improve phase of a DMAIC 6-Sigma project.4. The Identify phase of the SSA DfSS project methodology.

I-Chart

Run Chart of Individual data points. See also I-MR Chart, X-Bar Chart. XbarChart

See Probability Density Function. Probability Density Function

Probability Density Function.

One of several functions that can be used to define a distribution. The distribution of failures over time. It indicates when a new item is likely to fail. Also known as PDF. Can also be expressed as a CDF - Cumulative Distribution Function.

Cumulative Distribtion Function

In Reliability Testing: allowing for the effects of parts that have survived the test time - See Right Censoring; and allowing for the intervals at which failure readings are taken (Arbitrary Censoring).

Right Censoring Stat>Reliability>DistributionIdPlot>Censoring…

In Reliability Testing: allowing for the effects of parts that have survived the test time. I.e. You only know that the failure occurred after a particular time. NB: To perform reliability Analysis where there is no censoring use Right Censoring techniques. See also Arbitrary Censoring, Left Censoring, Interval Censoring.

Arbitrary Censoring

In Reliability Testing: allowing for the intervals at which failure readings are taken . See also Right Censoring.

Right Censoring

Endurance of capability prioir to failure. See Survival Function. Survival Function Stat>Reliability>DistOverView

Probability of failure at a given point in time. See Hazard Function. Hazard Function Stat>Reliability>DistOverView

Same as Survival Function. Survival Function Stat>Reliability>DistOverView

The Reliability or Survival Function. Survival Function Stat>Reliability>DistOverView

Plot of probability of survival vs time. R(t) = Successes/Total Units. Where MTBF is constant this = e-t/MTBF .Where t=time period. See also Hazard Function.

Hazard Function Stat>Reliability>DistOverView

Test of conforminty of data to a statistical distribution e.g. Test for Normality or other goodness of fit. Expressed as A-Squared or AD* value or as P-Value (0-1).

P-Value Stat>Reliability>DistributionIdPlot

Anderson-Darling test value of conformity of data to a distribution. The lower the A-Squared value the closer the conformity. See also P-Value.


The Hazard Function. Hazard Function Stat>Reliability>DistOverView

Anderson-Darling test value of conformity of data to a distribution. The lower the A-Squared value the closer the conformity. Same as A-Squared value. See also P-Value.


Mean Time To Failure. The average life of the product. Also known as Mean Life (ML). MTBF

1. Mean Time Before Failure. Same as MTTF & ML.2. Mean Time Between Failures. MTBF=t*U/F Where t = time e.g. No Days, U=Units e.g. No Parts & F = No Failures. (Note: MTBF is not a Minitab supported measure).

MTTF

Plot of probability of failure vs time. Nomenclature: F(t). See also Survival Function. Survival Function Stat>Reliability>DistOverView

Time at which x% of parts are expected to have failed. E.g. B5 Life = time at which 5% of parts have failed. Nomenclature originates in the Bearings industry and related to Bearing life expectancy.

B5 Life

B Life

Count of subtotals of each (text) value of a factor. See also Cross Tabulation. Cross Tabulation

A very usefule check for balanced data. See also Tally. Tally

A fully matching set with equal numbers of data values for all permutations of all factors. See also Cross Tabulation, Tally.

Cross Tabulation

1. The no. of factors interacting e.g. factors A*B*C interacting is a 3rd order interaction.2. See Value Ordering, Standard Order, Randomised Order.

Value Ordering

6-Sigma Glossary


Key Word Description Hyperlink MiniTab TopicsReliability

Reliability

F Reliability

Interval Censoring Stat>Reliability… Reliability

Location Reliability

Scale Reliability

Shape Reliability

B10 Life Time at which 10% of parts are expected to have failed Stat>Reliability… Reliability

Reliability

h Stat>Reliability… Reliability

Bayes Analysis Reliability

WeiBayes Stat>Reliability… Reliability

F/C Stat>Reliability… Reliability

b

IQR

Nu Symbol

Probit Analysis Stat>Reliability>Probit Reliability

Binomial Distribution Stat>Reliability… Reliability

Stat>Reliability… Variables Reliability

Stress Coefficient Stat>Reliability>Probit Reliability

Intercept Coefficient Stat>Reliability>Probit Reliability

Stat>Reliability>Probit Reliability

QIS Warranty

SEII Warranty

FCSD

ACSG

Lot Rot Product failures / deteriation pre-sales whilst on dealer lot.

Time Series Chart

Character Graph

FPV

Parametric Distribution

Reliability analysis having established the data distribution e.g. Normal. In contrast to Non-Parametric Distribution tools which are used where the data distribution cannot be determined.

Non-Parametric DistributionStat>Reliability>DistributionIdPlotStat>Reliability>Parametric…

Non-Parametric Distribution

Reliability analysis where the data distribution is not assumed or cannot be established. In contrast to Parametric Distribution tools which are used where the data distribution has been determined to be e.g. Normal.

Parametric Distribution Stat>Reliability>DistributionIdPlotStat>Reliability>NonParametric…

1. Number of Failures. See Failures, F/C.2. The F-Statistic. Used by ANOVA in calculating P-Value from adjusted mean squares.See also f.

f 1. Stat>Reliability> Parametric…2. ...

Same as Arbitrary Censoring. Arbitrary Censoring

Reliability Testing: Generically, an average e.g the mean of a Normal Distribution. This mayl be a value other than the mean for non-normal distributions. See also Scale, Shape.

Scale Stat>Reliability>Parametric…

Reliability testing: A measure of the spread of the data. E.g. the std dev of a normal distribution. See also Location, Shape.

Shape Stat>Reliability>Parametric…

Reliability testing: The shape of the Weibull or Exponential distribution. See also Location, Scale. Nomenclature: b

Location Stat>Reliability>Parametric…

B Life

Extreme Value Distribution

A logarithmic distribution releated to the Weibull distribution which can be useful for reliability testing of failures relkated to loads and strength e.g. bridges!

Stat>Reliability>Parametric>Extreme

1. Greek letter Eta ???2. The Scale of the Weibull Distribution.

Weibull Distribution

Conducting a reliability data analysis using an assumed shape and/or scale. Stat>Reliability>Parametric…

The application of Bayes Analysis to Weibull Distribution data. Bayes Analysis

Number of Failures and number of items Censored. E.G. F/C = 27/3 means that the Reliability test measured 27 failures and 3 parts which survived.

1. The Greek Letter Beta2. Beta Risk3. The Shape of the Weibull Distribution.4. The Intercept (b0) and Stress (b1) Coefficients of a Probit Analysis.5. The slope of the Ideal Function: y=bM

Beta Risk Symbol Hyp Test Reliability

Inter-Quartile Range. The difference between the 1st Quartile (Q1) and 3rd Quartile (Q3) values.

Q1

Greek letter Nu: n. Degrees of Freedom. Degrees Of Freedom

Regression analysis which examines the relationship between a binomial response variable (I.e. two possible outcomes, success & failure) and a continuos stress variable.P = C + (1-C)*F(b0 + b1*Stress) Where C = the natural response rate, b0 is the intercept coefficient and b1 is the stress coefficient.

Binomial Response Variable

The distribution of a Binomial Response variable. I.e. one which has two possible outcomes: success & failure



A binomial response variable is an outcome variable that has only two possible outcomes. These outcomes are typically defined as a success or a failure. The distribution, or pattern, of these outcomes follows a binomial distribution.

Binomial Distribution

A parameter of the probability function for a Probit Reliability analysis. Noemclature: b1.

Probit Analysis

A parameter of the probability function for a Probit Reliability analysis. Noemclature: b0.

Probit Analysis

Natural Response Rate

A parameter of the probability function for a Probit Reliability analysis. Noemclature: C.

Probit Analysis

1. Ford of Europe warranty system prior to implementation of Ford Global AWS Analystical Warranty System2. Quarters In Service - See MIS.

AWS

Ford North America warranty system prior to implementation of Ford Global AWS Analystical Warranty System

AWS

Ford Customer Service Division. Former name of ACSG Automotive Customer Services Group. Includes processing of warranty claims.

ACSG

ACSG Automotive Customer Services Group. Includes processing of warranty claims.. Formerly known as FCSD.

Provides a basic plot of Measures of problem vs. time etc. Use Run Chart where more detailed analysis is required. See also Trend Chart.

Trend Chart Graph>TimeSeriesPlot orStat>TS>TSplot

A Minitab function to produce graphs for text only output devices by plotting the required graph using only text characters instead of lines/vectors etc. Very useful if you only have a teletype for output. Not much use now since these became obsolete around 1980!

Graph>CharacterGraphs

1. Faults Per Vehicle2. Functional Proveout Vehicle 3. Financial Planning Volume

6-Sigma Glossary



Daniel Plot DOE

Half Normal Plot DOE

DOE

Balanced Array DOE

DOE

DOE

Geometric Array DOE

DOE

Pipe DOE Symbol

Response Optimizer DOE

Collinearity

R Resolution

III Resolution

IV Resolution

V Resolution

PLEX EVOP

EVOP PLEX

Response Surface

Region of Curvature

Star Points

Test for significance. Named after industrial statistician Cuthbert Daniel. Maps absolute values of Effect of each factor and each Interaction vs RH half of a normal distribution. Insignificant factors will be small and in a line from the origin. Significant factors will be higher. Also known as Half Normal Plot. Minitab equivalent is the Normal Effects Plot with the axes reversed. See FTEP Experimental Design course notes.

Normal Distribution No reference in MiniTab or Stat Guide

Same as Daniel Plot. Daniel Plot No reference in MinitTab or Stat Guide

LF L to the F. No of Runs for a Full Factorial experiment = No Levels (L) to the power of no. Factors (F). See also 2K.

Full Factorials Stat>DOE>Factorial>Define

For use in an Experiment the array of factors and levels must be Balanced. To satisfy this there must be: 1. Equal numbers of levels in each column2. The Sum Product of each pair of columns equals zero (Taking levels as "+" & "-").Balanced arrays may be Geometric or Non-Geometric Arrays.

Geometric Array Stat>DOE>Factorial>Define

The some sum of the products of a set of values. See Balanced Array. Balanced Array

L8 Latin Square orthogonal array for an 8 run experiment (And similarly for other numbers of runs). Array filled with the permutations of factors and levels for a factorial experiment.

Full Factorials Stat>DOE>Factorial>Define

A Balanced Array for a Factorial Experiement where: the product of each pair of columns is another column in the table. E.g. L8. Geometric arrays confound each 2-way & many-way interaction onto individual Main Effects factors/columns. See also Non-Geometric Array.

Non-Geometric Array Stat>DOE>Factorial>Define

Non-Geometric Array

A Balanced Array for a Factorial Experiement where: the product of each pair of columns is not another column in the table. E.g. L12. Non-Geometric arrays spread the confounding of each 2-way & many-way interaction across many individual Main Effects factors/columns. See also Geometric Array.

Array Stat>DOE>Factorial>Define

The Pipe Symbol is used in Minitab to indicate all permutaions of factors.Nomenclature: |

Stat>ANOVA>Balanced

Minitab Response Optimizer. Tool for optimising paramters in a reduced model. Apply after Defining, Analysing & Reducing DOE.

Stat>DOE>Factorial>ResponseOptimizer

Collinearity means that within the set of input variables, some of the variables are (nearly) totally predicted by other variables. When input variables are correlated in this way, there are problems in estimating regression coefficients. Often referred to as Multicollinearity. See also Variance Inflation Factor.

Multicollinearity

See Range, Regression Coefficient, Repeatability, Reproducibility, Resolution ...

Resolution III. A Fractional factorial distinguishing only the main effects. Resolution

1. Resolution IV. A Fractional factorial distinguishing main effects & 1st order interactions.2. Input Variable.

Resolution

1. Resolution V. A Fractional factorial distinguishing main effects and 1st & 2nd order interactions.2. The Verify phase of a DfSS 6-Sigma project.

Resolution

PLant EXperimentation. A process improvement tool for on-line use in full-scale production. Uses simple factorial designs, two-level designs in two or three factors. Usually, requires several iterations of experimental design, analysis, and interim improvements. Goal is to minimize disruption to production, but make big enough changes to quickly see effects on output variables. See also EVOP.

EVOP

EVolutionary OPerations. A process improvement tool used while the process is running in the production mode for the optimization of plant performance. Method that uses 22 or 23 factorials with replicates and center points. Empowers operators to conduct experiment with minimal engineering support during normal operations. Each experimental run is called a CYCLE: One cycle is one of the following: (0,0)=>(1,1)=>(1,-1)=>(-1,-1)=>(-1,1)Eliminate randomization to minimize disruption & document effect estimates at the end of each cycle. Cycle continues in the hopes of collecting “sufficient evidence” of significant change in the Y for the various levels of X. See also PLEX.

PLEX

Response Surface Method

See RSM. RSM Stat>DOE>Response...

The surface represented by the expected value of an output modeled as a function of significant inputs (variable inputs only): Expected (Y) = f(x1, x2, x3,…xn)

RSM Stat>DOE>Response...

Method of Steepest Ascent

A procedure for moving sequentially along the direction of the maximum increase (steepest ascent) or maximum decrease (steepest descent) of the response variable using the following first order model: Y (predicted) = b0 + S bi Xi


Method of Steepest Descent

Same as Method of Steepest Ascent. Method Of Steepest AscentStat>DOE>Response...

The region where one or more of the significant inputs will no longer conform to the first order model. Once in this region of operation most responses can be modeled using the following fitted second order model: Y (predicted) = b0 + S bi Xi + S bii XiXi + S bij XiXj


Central Composite Design

A common DOE matrix used to establish as valid second order model. Formed by adding Star Points to a standard Cube/Square+Centre Point design.


Enhancing design for a Response Surface Method by adding additional Star Points to a standard Cube/Square+Centre Point design in a circluar/spherical pattern. These will be points with factor levels of +/1 root 2 (1.414)


6-Sigma Glossary



D Symbol

RS

Box-Behnken

Delta Symbol

Response Optimizer

D

Desirability

Weight

Importance

Linear Regression

Fits

Residuals DOE

Best Stats

Multicollinearity Stat>Regression>...

Delta. Step size in one of the process variables in a Response Surface Method DOE or EVOP. Note: D must be estimated heuristically based on process understanding and potential risks. See also Inverted Delta (Ñ).


Response Surface. See RSM. RSM Stat>DOE>Response...

A form of Response Surface Method. See RSM. RSM Stat>DOE>Response...

1. d - Difference / effect size2. D - Step size in one of the process variables in a Response Surface Method DOE or EVOP. Note: D must be estimated heuristically based on process understanding and potential risks.See also Inverted Delta (Ñ).


Multiple Response Optimizer

A Minitab tool to estimate optimal input settings to achieve target/min/max outpuit variable values. See also Desirability, Weight, Importance.

RSM Stat>DOE>MultipleResponseOptStat>DOE>Factorial>RespOptStat>DOE>ResponseSurface>RespOpt

See Multiple Response Optimizer. Multiple Response OptimizerStat>DOE>...l>RespOpt

1. See Desirability. 2. See FMEA, Detection.3. The Define phase of a DMAIC 6-Sigma project.4. The Define phase of the Ford DfSS 6-Sigma project methodology.5. The Design phase of the SSA DfSS 6-Sigma project methodology.See also Multiple Response Optimizer and Response Surface Method.

Desirability Stat>DOE>ResponseSurface>RespOpt

An output measure fromMultiple Response Optimizer and Response Surface Method. A desirability function translates each response scale to a zero-to-one desirability scale. The most desirable values of the response have desirability one. The least desirable values have desirability zero.Nomenclature: D=0-1. See also Weight, Importance.

Weight Stat>DOE>ResponseSurface>RespOpt

An input to Multiple Response Optimizer and Response Surface Method. The weight defines the shape for each response. Select a weight (from 0.1 to 10) to emphasize or de-emphasize the target. A weight <1 places less emphasis on the target; =1 places equal importance on the target and the bounds; > 1 places more emphasis on the target The weight determines how the desirability is distributed over the interval between the lower (or upper) bound and the target. It determines the shape of the desirability function that is used to translate the response scale to the zero-to-one desirability scale to determine the individual desirability of a response. You can think of a weight of one as a neutral setting. Increasing the weight requires the response to move closer to the target to achieve a given desirability. Decreasing the weight has the opposite effect. See also Multiple Response Optimizer, Desirability, Importance.

Importance Stat>DOE>ResponseSurface>RespOpt

An input to Multiple Response Optimizer and Response Surface Method. Determines the relative importance of multiple response variables. Often, there is no factor setting that simultaneously maximizes the desirability of the individual responses. That is why we maximize the composite desirability. The importance determines how much influence each response has on the composite desirability.See also Multiple Response Optimizer, Desirability, Weight.

Multiple Response OptimizerStat>DOE>ResponseSurface>RespOpt

See Regression. Regression Stat>Regression>FittedLine, Residual...

The place on the best fit line where the Y value was expected to be for this X. See also Residuals, Regression, Correlation.

Residuals Stat>Regression>FittedLine, Residual...

The difference between individual points and best fit line. Note: DOE, ANOVA etc will do a fitted line as part of their analysis. See also Fits, Residual Error, Regression, Correlation.

Fits Stat>Regression>FittedLine, Residual...

A Regression method for selecting most significant factors ... Regression Stat>Regression>BestSubsets

Multicollinearity occurs when two or more predictor variables are highly correlated with one another. For example, say we are trying to use the number of hours of sunshine and the amount of rainfall to predict the yield of a particular crop. Since hours of sunshine will decrease as the amount of rainfall increases, these two variables will be correlated. Using both variables in a regression model will result in what is known as multicollinearity. Note: Collinearity and multicollinearity are used synomymously. See also VIF.

Regression

Stepwise Regression

A Regression method for selecting most significant factors on a stepwise basis taking the most significant and then adding others ...

Regression Stat>Regression>Stepwise

6-Sigma Glossary



Quality System

OMS

Poka-Yoke

Mistake Proofing

Joiner Triangle

Lean

5S Lean

Visual Factory Lean

Standardized Work Lean

A Quality System is an organization’s agreed upon method of doing business.It is not to be confused with a set of documents that are meant to satisfy an outside auditing organization (i.e. ISO 900x, QS9000, TS16949). This means a Quality System represents the actions, not the written words of an organization. Includes: Quality Policy; Organization for Quality (does not mean Quality Department!); Management review of Quality; Quality Planning (APQP) (how to launch and control products and processes); Design Control; Data Control; Purchasing: Approval of materials for on-going production, Evaluation of suppliers, Verification of Purchased Product (does not mean incoming inspection!); Product identification and traceability; Process Control; Inspection and Testing; Control of Inspection , Measuring, and Test Equipment (Calibration, MSA); Control of Nonconforming Product; Corrective and Preventative Action; Handling, Storage, Packaging, Preservation, and Delivery; Control of Quality Audits (do what we say we do?); Training; Service; Use of Statistical Techniques. See OMS, SPC, Poka-Yoke, Joiner Traingle.

OMS

Operational Method Sheets. Standard Operating Procedures (SOP's), Process Instructions. Quality Process Sheet (QPS). See also Quality System, SPC, Poka-Yoke...

Poka-Yoke

Operational Method Sheets


Poka-Yoke

To avoid (yokeru) inadvertent errors (poka). A mistake proofing method. Ensuring benefits introduced are sustained by ongoing control methods. Developed by Shigeo Shingo to achieve zero defects. Multiple ingenious devices to prevent the processing of bad parts or materials. Human errors are inadvertent - Think of potential ways to “Mistake Proof” the process with a Poka-Yoke device. Helps build quality into the product. Allows only good product to go to the next operator or customer. Can’t build it wrong ... Can only build one way ... If wrong, detected 100% ... Can’t be built into next assy. Strategies: Good: Detects defects before they continue to the next operationBetter: Allows for the detection of errors during processingBest Strategy: Makes occurrence of errors impossible

SPC

Ensuring benefits introduced are sustained by ongoing control methods. Methods to prevent Errors and, therefore, Defects. See also Poka-Yoke.

Poka-Yoke

Process Instruction Sheets


Poka-Yoke

A 4th generation Quality System control management process. Consists of:Quality - Understanding that quality is defined by the customer; developing an obsession for delighting customers. This understanding is no longer the special domain of special groups, it is shared with everyone.Scientific Approach - Learning to manage the organization as a system, developing process thinking, basing decisions on data, and understanding variation.All One Team - Believing in people; treating everyone in the organization with dignity, trust, and respect; working toward win-win instead of win-lose for all stakeholders (customers, employees, shareholders, suppliers, and our communities)

Quality System

Toyota Production System

Toyota's production quality system achieving world leading consistant quality through the following elements, each building on each other: 5S, Visual Factory, Standardized Work, Kaizen, Kan Ban.

5S

Sorting, Storage, Shining, Standardise, Sustain. “5S Standards are the foundation that supports all the Phases of Lean Manufacturing.” The system can only be as strong as the foundation it is built on. The foundation of a production system is a CLEAN and SAFE work environment. It’s strength is contingent upon the employees/company committed to maintaining it. Derived from TPS Japanese:Japanese Literal Translation English EquivalentSeiri Clearing Up SortingSeiton Organizing StorageSeiso Cleaning ShiningSeketsu Standardizing StandardizeShitsuke Training & Discipline SustainingSee Sorting, Storage, Shining, Standardise, Sustain.See also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan.

Sorting

Processes to ensure we can readily identify: Downtime issues; Scrap issues; Changeover problems; Line balancing problems; Excessive Inventory Levels; Extraneous Tools & Supplies; If you CANNOT readily identify any of these opportunities through a visual glance of the area, then the team should seek to establish a means of immediate identification.See also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan.

Standardized Work

The “one best way” to perform each operation identified and agreed upon through general consensus (not majority rules). This becomes the “Standard” work procedure. The affected employees should understand that once they have defined the standard, they will be expected to perform the job according to that standard.See also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan.

Kaizen

6-Sigma Glossary



Kaizen Lean

Kan Ban Lean

Basic Quality

Control Chart Stat>Control>… PCM

Reaction Plan

MR Chart

CUSUM Stat>Control>CUSUM

Moving Average

Defectives

np Chart Stat>Control>

c Chart Stat>Control>

u Chart Stat>Control>

Pre-Control

C&E

Lean Manufacturing Lean

Waste Lean

Kai=Change, Zen=For the Better. The philosophy of continual improvement, that every process can and should be continually evaluated and improved in terms of time required, resources used, resultant quality, and other aspects relevant to the process.Rules: Keep an open mind to change, Maintain a positive attitude, Never leave in silent disagreement, Create a blameless environment, Practice mutual respect ever day, Treat others as you want to be treated, One person , one vote -- no position, no rank, No such thing as a dumb question, Understand the thought process and then Do It.Elements: Takt Time, Cycle Time, Work Sequence, Standard WIP.See also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan.

Takt Time

Kan Ban a Japanese word for communication signal or card. It is a technique used to “pull” products and material through and into the lean manufacturing system. The actual “Kan Ban” can be a physical signal such as an empty container or a small card. Kan Ban provides production conveyance, and delivery information. In it’s purest form the system will not allow any goods to be moved within the facility without the appropriate Kan Ban (signal) attached to the goods.See also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan.

Musts: CTQ's which must be satisfied. Part of the Kano Model. See also Performance Quality, Excitement Quality.

Kano Model

A graphical rendition of a characteristic’s performance across time in relation to its natural limits and central tendency. Records measurements / counts and highlights variation within and outside Control Limits (not Spec Limits).A Run Chart with Centre Line, USL, LSL. Quickly detects specific types of out of control process changes. Statistical Process Control Control Plan specifies data collection on a Control Chart which monitors data in control within Control Limits. Out of control data triggers a Reaction Plan.

Reaction Plan

Statistical Process Control Control Plan specifies data collection on a Control Chart which monitors data in control within Control Limits. Out of control data triggers a Reaction Plan.

SPC

Moving Range Chart. See I-MR Chart. I-MR Chart Stat>Control>I-MR or >MovingRange

Cumulative Sum (CUSUM) a control chart which detects abrupt but slight changes in the process mean past data are combined with current data

A control chart which smoothes data to emphasize trends. Each point includes effect of current value plus a specified number of past points. Assumes past and present data equally important.

Stat>Control>MovingAverage

Defectives are nonconformities in products or services that render the product or service unusable. You have defectives when data can take only one of two values, such as pass/fail, go/no-go, or present/absent.The binomial distribution characterizes defectives data. So a Defect is an individual non-conformance. A given unit may potentially have any number of Defects - according to the number of Opportunities. A unit with one or more Defects is a Defective. Measured using np or p Chart. See also Defect, Opportunity.

Defect Stat>Control>np Chart, p chart

An attribute Run Chart for measuring Defectives. A simple chart used to track the number of nonconforming units (percentage of defective parts) assuming the sample size is constant. See also p Chart.

Defectives

An attribute Run Chart for measuring Defects. A simple chart used to track the number of defects sampled (not the percent defective) assuming the sample size is constant. Requires a constant inspection sample size. See also u Chart.

Defect

An attribute Run Chart for measuring number of Defects per Unit. A simple chart used to track the number of defects per unit sampled (not the percent defective). U charts can be used when the sample size is either constant or not constant. See also c Chart.

Defect

A Red-Yellow-Green Traffic light process which can flag up data approaching the control limits so as to help keep processes cntred and in control. See SSA Navigator for details.

Cause & Effect Matrix

A Simple QFD (Quality Function Deployment) matrix to emphasize the importance of understanding the customer requirements. Method: 1. List the output variables (Y's) along the top section of the matrix. These are outputs which the team and / or the customer deem to be important. These may be a subset of the list of Y's identified on the process map. 2. Rank each output numerically using an arbitrary scale (possibly 1-10). The most important output receives the highest number. 3. Identify all potential inputs or causes (X's) that can impact the various Y's and list these along the left hand side of the matrix. 4. Numerically rate (correlate) the effect of each X on each Y within the body of the matrix. This is based on the experience of the team. 5. Use the totals column to analyze and prioritize where to focus your effort when creating the preliminary FMEA. NB: LH Column contains all potential causes. Top Rows contains all Customer CTQ's - not just the TGW and Warranty Claim headings; and their importance to the customer. Matrix elements are correlation measures (0-10) between the causes & the CTQ's.

Definition: A systematic approach to manufacturing which based on the premise that anywhere work is being done, waste is being generated. A vehicle through which organizations can identify and reduce waste. A manufacturing methodology which will facilitate and foster a living quality system. The GOAL: Total elimination of waste through: Defining Waste, Identifying the sources, Planning for Waste elimination, Establish “PERMANENT” control to prevent its reoccurrence.See 5S>Visual Factory>Standardized Work>Kaizen>KanBan, TPS, Waste.

There are 7 elements of Waste, they are Waste of: Correction, Overproduction, Processing, Conveyance, Inventory, Motion, Waiting. Elimination of Waste is a key objective of Lean Manufacturing. Also known as the 7 Sinds of Muda.

Lean Manufacturing

6-Sigma Glossary



JIT Lean

High level process mapping. Lean

Sorting Lean

Storage Lean

Shining Lean

Standardize Lean

Sustaining Lean

Red Tagging Lean

TPS Lean

Takt Time Lean

Cycle Time Lean

Work Sequence Lean

Standard WIP Lean

Yamazumi Board Lean

Lean

Machine Cycle Time Lean

SMED Lean

Error

Red Flag

Error ProofingOE5Y's

VOC

EAR

Just-In-Time manufacturing. A key element of Lean Manufactring. Full use of all the resources to produce the right product, at the right time, in the right quantities, based on customer demand. 1. Continuous one piece flow, 2. Kan Ban demand pull scheduling, 3. Simplify to get rid of waste

Value Stream Mapping

Deciding what is needed. Part of 5S. To sort out necessary and unnecessary items. To store often used items at the work area, infrequently used items away from the work area and dispose of items that are not needed. Removes waste, Safer work area, Gains space, Easier to visualize the process. See also Red Tagging, Storage, Shining, Standardise, Sustain.

Storage

Arrangement of items needed straight up the work place. Part of 5S. To arrange all necessary items. To have a designated place for everything. A place for everything and everything in its place. Visually shows what is required or is out of place. More efficient to find items/ documents (silhouettes/labels). Saves time, not having to search for items. Shorter travel distances. See also Sorting, Shining, Standardise, Sustain.

Shining

Sweep and cleanliness. Part of 5S. To keep your area clean on a continuing basis. A clean workplace is indicative of a quality product and process. Dust and dirt cause product contamination & potential health hazards. A clean workplace helps to identify abnormal conditions. See also Sorting, Storage, Standardise, Sustain.

Standardize

Part of 5S. To maintain the workplace at a level which uncovers and makes problems obvious. To continuously improve our plant by continuous assessment & actions. To sustain sorting, storage and shining activities every day. See also Sorting, Storage, Shining, Sustain.

Sustaining

Training & Disciplined Culture. Part of 5S. To maintain our discipline, we need to practice and repeat until it becomes a way of life. To build 5-S’s into our every day process. See also Sorting, Storage, Shining, Standardise.

5S

The process of marking potentially superfluous items with Red Tags before removing them from the work place. A key enabler for Sorting and the 5S strategy.

Sorting

See Toyota Production System. Toyota Production System

A geman acronym for: Time available to produce a unit. I.e. Time available/period divided by no. units needed to meet customer requirements. Part of Kaizen Lean Manufacturing method. See also Cycle Time, Work Sequence, Standard WIP.

Cycle Time

Time to complete a manufacturing operation. Incorporates Operator Cycle Time & Machine Cycle Time. Can be mapped in a Yamazumi Board to identify Value added and non-value added steps. Part of Kaizen Lean Manufacturing method. See also Takt Time, Work Sequence, Standard WIP.

Work Sequence

??? Part of Kaizen Lean Manufacturing method. See also Takt Time, Cycle Time, Standard WIP.

Standard WIP

??? Part of Kaizen and an enabler for Kan Ban Lean Manufacturing method. See also Takt Time, Cycle Time, Work Sequence.

Kaizen

Cycle Time mapping process to identify Value added and non-value added steps. Part of Kaizen Lean Manufacturing method.

Operator Cycle Time

Operator Cycle Time - The total time required for a worker to complete once cycle of an operation. Includes: Manual Operations, Walking, Inspecting, Loading and machines and gauging. Does not include waiting for the equipment/machine auto cycle to finish.Together with Machine Cycle Time contributes to total Cycle Time.

Machine Cycle Time

Machine Cycle Time - The total time for a machine to finish one complete cycle.Together with Operator Cycle Time contributes to total Cycle Time.

Operator Cycle Time

Single Minute Exchange of Dies. Processes to minimise press die change over times by maximising offline preparation.

Errors are the cause of defects…an error occurs when the conditions for successful processing are either incorrect or absent. See also Experimental Error, Random Error. 10 most common errors: 1. Processing omissions, 2. Processing errors, 3. Error in setting up job, 4. Assembly omissions ( missing components), 5. Inclusion of incorrect component, 6. Incorrect job, 7. Operation error, 8. Measurement error, 9. Tool or equipment error, 10. Defects in job components10 common human errors: 1. Forgetfulness, 2. Misunderstanding, 3. Identification, 4. Lack of effective training, 5. Willful errors, 6. Inadvertent errors, 7. Delay in task execution, 8. Lack of standards, 9. Inability to compensate for new situations, 10. Intentional errorsSee also Error Proofing, Error State, Defect.

Defect

Red flag conditions in the process will contribute to error and defect generation. Watch for and react to Red flag conditions! Ideal Applications for Poka-Yoke! e.g. 1. Frequent changes to a job, 2. Complex processes, 3. Lack of standards, 4. Lack of measurement systems, 5. Lack of training, 6. Long cycle times, 7. Infrequent jobs, 8. High output, 9. Environmental conditions, 10. Attitude ( Motivation)

Poka-Yoke

See Mistake Proofing. Mistake Proofing

OE documents (Acronym for ???) - Process Instructions, SOP, QPS etc. SOP

Ask the question "Why?" five times to peel the onion of causes down to root causes to address. See also Stair Stepping.

Voice Of the Customer - Ensuring that customer requirements drive process & product improvement. CTS driven definition of the big Y's. See also VOP.

VOP

Engineering Action Request - Generic term for product change request. Follow Ford/Jaguar AIMS & WERS processes to introduce product change.

AIMS

6-Sigma Glossary



WERS

AIMS

PSW

Type IType IIType 1 Gauge R&R Gage R&R

Type 2 Gauge R&R Stat>Quality>Gage Gage R&R

Glossary

MSV Gage R&R

CBGFlier

IC Important Characteristic (quality/customer related characteristic) FMEA

Nominal Desired Engineering location within specification.

OOC Out of control (see out of control guidelines)

OOS Out of specification (measurement exceeding Engineering specification)

Preliminary

SC SC Significant characteristic derived from FMEA (quality/customer related characteristic) FMEA

Process StabilityMSE Gage R&R

Sgauge Gage R&R

Cg Gage R&R

ACF Sys Eng

CFE Sys Eng

QQFD Sys Eng

Traceability Sys Eng

Cascading Breaking down high level factors to lower level factors (little x's) Sys Eng

CAFÉ Corporate Average Fuel Economy ratings

FMVSS Federal Motor Vehicle Safety Standards

CMVSS Canadian Motor Vehicle Safety Standards

Coefficient of Drag

VDS Sys Eng

SDS Sys Eng

CDS Sys Eng

Targets Sys Eng

Ford World-wide Engineering Release System for managing product change approval and Bill Of Material updates. See AIMS for management of product issues prior to introduction of product change.

AIMS

Ford Automated Issues Management System for managing product issues and change requirements. See also WERS for introducing resulting product changes.

WERS

Parts Submission Warrant - A measure of part quality readiness for vehicle build, special focus during vehicle launch. The supplier warrants that the customers requirements can be met usining fully production tools, processes, feedrates etc. The supplier provides evidence of this and it is approved by the customer. This concludes the Production Part Approval Process and releases payment for tooling investment.

PPAP

See Type I Error or Type 1 Gauge R&R. Type I Error

See Type II Error or Type 2 Gauge R&R. Type II Error

A first level Gage R&R propounded by Jaguar SPC department. Determines the capability of a measuring system to be used by a single operator repeatably measure a single part. Type 1 gauge capability Cg = TotalTolerance*0.15 / 6*Sgauge.For acceptance Cg must be <=15%.Cg is equivalent to %Tolerance in a Type 2 Gauge R&R.

Type 2 Gauge R&R

A full scale Gage R&R which determines the capability of a measuring system to measure a variety of parts with good repeatability, when measured by a variety of operators (reproducability). Nomenclature: Known generically within 6-Sigma processes as a Gage R&R. Identified as Type 2 by Jaguar SPC department so as to differentiate it from a Type 1 R&R.

Gage R&R

Other 6-Sigma, Statistics and/or SPC glossaries can be found at:Minitab>Help>Help>GlossaryMinitab>Help>Search Stat Guidehttp://www.animatedsoftware.com/statgloshttp://www.jaguar.ford.com/manuf/prodop1/spc/reports/glossary.pdfImplementing Six Sigma, F. W. Breyfogle III, Glossary

http://www.animatedsoftware.com/statglosHelp>Help>GlossaryHelp>Search Stat Guide

MSV is the QS9000 term for Gage R&R. See Measurement System Variance.

Consumer Business Group. See also GEC. GEC

Term used for a point plotted onto control chart that exceeds control limits and is considered a one-off.

First time assessment of a process/operation over limited data or time period. See Pp, Ppk.

Pp

Significant Characteristic

See Stable Process. Stable Process

1. Measurement System Evaluation. See MSA.2. Mean Square Error. The mean of the squares of the residuals.

MSA

Standard Deviation of Type 1 Gauge R & R readings. Same as sMS. Type 1 Gauge R&R

Capability of Gauge. Type 1 Gauge R & R capability.Cg = TotalTolerance*0.15 / 6*Sgauge.%Variation = 15 / Cg. For acceptance %Variation must be <=15%.

Type 1 Gauge R&R

Advanced Consumer Focus. Latest embodiment of QFD. The process of converting customer requirement statements into technical design specifications. Refer David Hill, TDA, FTEP training. 3Q01 reduced to a half day CBT module. May be re-introduced as a instructor lead course in '02.

QFD www.fdi.ford.com

Customer/Consumer Focused Engineering. Previous name for QFD and, now, ACF.Terminology still used in CIPE.

ACF

Quick Quality Function Deployment. A quick version of the Ford FTEP QFD process training.

Linking of requirements to sub-requirements. All little x's and y's are traceable back to high level customer CTQ's.

Cd

Vehicle Design Specification. Level 1 Systems Engineering specification. Includes: Interfaces, Targets and Attributes, Preliminary Cascade to SDS & CDS and Verification methods.

SDS

System Design Specification. Level 2 Systems Engineering specification. Includes: Interfaces, Targets and Attributes, Preliminary Cascade to CDS and Verification methods. Cascaded from VDS.

http://ctisweb.ta.ford.com/sds/sds-main-menu.html

Component Design Specification. Level 3 Systems Engineering specification. Includes: Interfaces, Targets and Attributes and Verification methods. Cascaded from VDS & SDS.

VDS

Attribute targets set in the Systems Engineering VDS, SDS & CDS specifications. Under FPDS, Targets become Objectives when they have been verified through the Systems Engineering target cascade process.

Objectives

http://www.animatedsoftware.com/statglos

http://www.fdi.ford.com/

http://ctisweb.ta.ford.com/sds/sds-main-menu.html

6-Sigma Glossary



Objectives Sys Eng

Sys Eng

Design Synthesis Sys Eng

Verification Sys Eng

Sys Eng

SE Sys Eng

Partitioning Sys Eng

Trade-Off Study Sys Eng

DVP Sys Eng

DVP&R Sys Eng

FDVS Sys Eng

DVP-SOR Design Verification Plan & Sign-Off Report Sys Eng

SSO Sys Eng

ESSE Sys Eng

%Variation Stat>Quality>Gage Gage R&R

Orthogonal DOE

Durability Reliability

%Failure Reliability

FRG Reliability

DIF-DOF Reliability

Reliability

Stat>Reliability… Reliability

Infant Mortality Early Life failures. Reliability

Early Life Failures Reliability

Wear Out Failures Reliability

Useful Life Failures Reliability

Bathtub Curve Reliability

Reliability

SSI Reliability

End Of Life Failures Reliability

Premature Failures Reliability

ROCOF Reliability

Hazard Interval Reliability

Targets that have been verified through the Systems Engineering target cascade process. Targets

Requirements Analysis

The Systems Engineering process of gathering customer, legislative & corporate musts & wants and providing feasibility feedback. Iterates with Design Synthesis & Verification at vehicle, system & component levels. Gathers requirements, translates into procise terms & develops verification requirements.

Design Synthesis

The Systems Engineering process which generates & evaluates alternative designs using the Trade-Off process. Iterates with Requirements Analysis & Verification at vehicle, system & component levels.

Verification

The Systems Engineering process of verifying completeness of requirements and that design meets customer requirements. Iterates with Requirements Analysis & Design Synthesis at vehicle, system & component levels.


Systems Engineering

The core FPDS process of engineering systems with all their interfaces and dependancies so as to optimse vehicle design by cascading requirements from Vehicle to System to Sub-System to Component levels and then verification of those back up the levels. Each stage of systems engineering iterations are made of the three bubbles: Requirements Analysis, Design Synthesis, Verification.


See Systems Engineering. Systems Engineering

Systems Engineering process of clarifying interfaces between functions.

Systems Engineering process of assessing the capability of alternative solutions to meet the system requirements.

Design Verification Plan. Managed in FDVS. The planned analysis and vehicle/rig tests to confirm achievement of design specification. Achievement is approved in the DVP Sign-Off Report (DVP&R or DVP-SOR).

FDVS

Design Verification Plan & Report. Same as DVP-SOR. DVP-SOR

Ford Design Verification System - manages VDS, SDS, CDS & DVP's. Web access is provided by the eFDVS interface.

DVP

DVP

Ford Strategic Standards Organisation. Now ESSE. ESSE

Ford Engineering Standards & Systems Engineering department. Where all generic VDS, SDS, CDS's are stored. Formerly SSO.

Type 1 Gauge R & R capability. Data range divided by tolerance.%Variation = 6*Sgauge / TotalTolerance. For acceptance %Variation must be <=15%.%Variation in a Type 1 study is equivalent to %Tolerance in a Type 2 study.see also Study Variation, Total Variation, Random Variation, Assignable Variation.

Type 1 Gauge R&R

Same number of replications of each level of each factor and independence of variability of factors. Minitab always generates orthogonal designs of experiment.

Reliability over the specified life-time of the product. See also Robustness, Homeostasis, DQR.

Robustness

A measure of un-reliability. See also Failure. Reliability

Ford Reliability Guide: Methodology to achieve 10yrs/150,000m reliability through phases: Define Requirements, Design for Robustustness, Verify Design, Maintain / Improve Quality in Production.

http://www.quality.ford.com

Design In Function; Design Out Failure. Complimentary strategies to achieve quality and reliability

F(t) The Hazard Function. The distribution of Failures over time. The inverse of the Reliability Function R(t). See also F, f.

Hazard Function

Bq Life Time at which q% of parts are expected to have failed. E.g. B5 Life = time at which 5% of parts have failed.

B Life

Early Life Failure

Failures early in product life. Generally manufacturing problems. See also Useful Life Failures, Wear Out Failures, Bathtub Curve.

Useful Life Failure

Late in life product failures due to end of life. Generally determined by design life expectancy. NB: These can be identified by increasing frequency with time. This may occur before the target life expectancy of the part/product, thus representing premature wear. See also Early Life Failures, Useful Life Failures, Bathtub Curve.

Bath Tub Curve

Failures after initial introduction and before end of design life. Generally random effects of stress / strength / usage extremes outside of design usage specification.See also Early Life Failures, Wear Out Failures, Bathtub Curve.

Wear Out Failure

The sum of all reliability failures over time. Consists of the effects of Early life failures, Usefule life failures and Wear out failures.

Early Life Failure

Stress/Strength Interference

Mapping the potential interference between the distribtution of the customer stress on the product with the product strength - where stress & strength can represent any CTQ characteristics. Overlap between the two represents Useful Life failures where stress exceeds strength.

Stress/Strength Interference charts. Stress / Strength Interference

See Wear Out Failures. Wear Out Failure

See Wear Out Failures. Wear Out Failure

Rate OF Change OF Failure - Relibality Analysis of failure types to assess where in the Bathtub Curve failures are occuring and, therefore, where root cause(s) are likely to be.

Bath Tub Curve

No Failures / No units (opportunities) during this period sampling without replacement. Same as Hazard Rate, Failure Rate. See also Cumulative Hazard.

Cumulative Hazard

http://www.quality.ford.com/

6-Sigma Glossary



Hazard Rate Reliability

Failure Rate Reliability

Cumulative Hazard Reliability

Hazard Plot Reliability

GCQISTNI Reliability

R & M Reliability

TED Reliability

EAM Reliability

Reliability

Failure Type Reliability FMEA

P Diagram Reliability

P2P Piece-to-Piece or Part-to-Part variation. Reliability

RWUP Reliability

Use Cases Reliability

MTTR Reliability

Maintainability Reliability

Availability Reliability

LCC Life Cycle Costing Reliability

Fault Tree Analysis Reliability

FTA Reliability

MEPEM Reliability

Fault Tree Reliability

Success Tree Reliability

Weibull Analysis Reliability

Reliability

CDF Reliability

Median Rank Reliability

No Failures / No units (opportunities) during this period sampling without replacement. Same as Hazard Interval, Failure Rate. See also Cumulative Hazard.

Cumulative Hazard

No Failures / No units (opportunities) during this period sampling without replacement. Same as Hazard Interval, Hazard Rate. See also Cumulative Hazard.

Cumulative Hazard

Sum of Hazard Intervals to date. Also = Cumulative Hazard up to previous interval + Hazard Interval for this period. Used in Hazard Plot to determine Failure Type.

Hazard Interval

A Log-Log plot of Cumulative Hazard vs Time. It's shape and slope can be used to identify where the issues are in the Bathtub Curve. Early Life failures will appear as sharply risung then trailing off. Useful Life failures will appear as a straight line at gradient = 1 i.e. 45o. Wear Out failures will appear as a steep straight line or may feather off if late submission of readings occurs. To differentiates between Early Life and Wear Out failures observe readings for successive collection months. If the trend is followed failures are Early Life. If the feathering moves up the failures are Wear Out failures.

Bath Tub Curve

Global Common Quality Indicator System - See CQIS. CQIS

Trouble Not Identified - Failure cause not found due to non-representative tests, destruction of information, intermittent failures etc. See also NFF.

NFF

Manufacturing Reliability & Maintainanbility.

Things Engineers Do - See Engineering Activity Matrix. Engineering Activity Matrix

See Engineering Activity Matrix. Engineering Activity Matrix

Engineering Activity Matrix

Five stage matrix of engineering activities ("Things Engineers Do"). Covers Analysis and Diagnosis of reliability; Define Requirements; Design for Robustness; Design Verification; Maintenance & Improvement of Quality in Production.

Type of failure according to:-1. Point of failures in product lifes cycle. Shown by the Bathtub Curve or Hazard Plot to be Early Life, Usefule Life or Wear Out failures.2. Partial, Intermittent, Total loss, Degradation or Excessive Function3. Severity: Dependability/Safety, High: Confidence, Irritaion, Cost of ownership

Hazard Plot

Parameter Diagram of a system. Mapping Inputs (Signals), Outputs (Responses & Errors) and Parameters - Noises and Controllable factors for a system. An extension of a process block diagram to study and improve robustness. _v_Noises_v_Signal->|___Process__|-->Intended Responses ^ Controls ^ \->Error StatesNote: Nothing to do with p Charts, Pareto Diagrams. See also Process Block Diagram, Static System, Dynamic System, VSM.

Process Block Diagram

Real World Customer Usage Profile - the full scope of customer product utilisation covering normal use, misuse and abuse conditions.

Process mapping of usage cases. See RWUP. RWUP

Mean Time To Repair. The measure of Maintainability. See also MTBF, MTTF, Availability.

Maintainability

Expectation of a product or machine being restored to full function in a specified period of time. Measured by MTTR. See also Availability.

MTTR

Proportion of total time that item is available for use.Availability = MTBF / (MTBF + MTTR).See also OEE, Performance Efficiency, Quality Yield.

Performance Efficiency

Fault Tree Analysis. A graphical representation of the causes of a problem. Structured using boolean And & Or gates. Captures interactions as well as individual factors. Frequency of occurrence can then be assessed & prioritised. A more powerful representation than a Cause & Effect Diagram. A useful tool to assist preparation of an FMEA which also incorporates Severity & Detection.

See Fault Tree Analysis. Fault Tree Analysis

Cause factors: Materials, Equipment, People, Environment, Methods. An alternative representation of 5M's & a P which additionally categorises Measurement System related failures. See also Process Block Diagram.

5M's & a P

See Fault Tree Analysis. Fault Tree Analysis

The converse of a Fault Tree. See Fault Tree Analysis. Fault Tree Analysis

A tool for predicting population behaviour from small samples. A Log-Log representation of the Cumulative Distribution Function (CDF). Process involves: Rank sample failure times/cycles; calculate Median Rankings; Produce Weibull Plot of Ln time vs. Ln-Ln Median Rank. Determine failure type from Weibull Slope (b): <1 Early Life, =1 Useful Life, >1 EOL; Get Weibull figure for mean value proportion according to slope/shape. Measure this from Weibull plot to get MTBF. Use 5% & 95% figures to get MTBF CI. See also Weibull Distribution.



Cumulative Distribtion Function. The cumulative representation of the Probability Density Function (PDF). When plotted on Log scale for time/cycles and Log-Log scale for Median Ranked frequency becomes a straight line slope in the Weibull Analysis.

Weibull Analysis

See Cumulative Distribtion Function. Cumulative Distribtion Function

Adjusting averaged values to reflect point on curve / sequence. Used on sequenced sample to reflect representative proportion of population represented by the sample. May be approximated by (J-0.3)/(N+0.4) where J is the ranking of the test and N is the sample size. Usually expressed as a %. Used in Weibull Analysis.

Weibull Analysis

6-Sigma Glossary



MLR Reliability

Key Life Tests Reliability

KLT Reliability

Bogey Testing Reliability

Test to Bogey Reliability

Accelerated Testing Reliability

QLF

Plan Do Study Act

PDSA

Quality Yield Yield

OEE Yield Reliability

Reliability

ÑSymbol FMEA

C&E FMEA

Inverted Delta Symbol FMEA

SOD FMEA

CFMEA FMEA

MFMEA FMEA

Function FMEA

Function Trees FMEA

CIPE Sys Eng

Quality DashboardBoundary Diagram FMEA

Interface Matrix FMEA

FMEA

Mean Life Ratio. The ratio of the MTBF's of two samples in a Weibull Analysis.

Key Life Tests - Design verification through key life tests representing key factors which drive loss of function during Real World Usage Profiles.

RWUP

See Key Life Tests. Key Life Tests

Gather data by testing to a set no. of cycles / duration. Measure state at end of test period. A low cost data collection method gleaning limited data. Nomenclature said to originate from Golf where Bogey is one over par - maybe representing testing to one measure beyond specified life expectancy.

See Bogey Testing. Bogey Testing

Methods to accumulate large volumes of test data intended tp represent RWUP over a short period of time. It can be challenging to correlate accelerated conditions to RWUP.

RWUP

See Quality Loss Function. Quality Loss Function Reliability Tol Design

Quality Loss Function

Determining the relationship between deteriation of product characteristics (X) and the quality loss of that to the customer (Y). Methods to seek to reduce variability in Y for given variability in X, thus minimising costs of tolerance control whilst maximising customer satisfaction. i.e. reducing the slop of the signal to response graph, thereby reducing the effects of variability. Movement away from target increases waste to Society: loss of material, energy, labour, customer satisfaction, performance etc. Used to optimise parameters to maximise quality rather than simply setting go/go-go tolerances.`L(y)=k[s2

y+(y-t)2]. See L, L-Bar.

L-Bar Reliability Tol Design

Deming-Shewhart Cycle

See Plan Do Study Act. Plan Do Study Act

The Deming-Shewhart Cycle. Plan to achieve objectives. Do: Execute the Plan. Study: Analyse the results to determine next course of Action.

See Plan Do Study Act. Plan Do Study Act

Performance Efficiency

Ideal Cycle Time / Actual Cycle Time.See also OEE, Availability, Quality Yield.

Quality Yield

No. Good Parts / Total Parts. See Yield. See also OEE, Availability, Performance Efficiency. Yield

See Overall Equipment Effectiveness. Overall Equipment Effectiveness

Overall Equipment Effectiveness

Availability x Perfomance Efficiency x Quality Yield. Availability

Inverted Delta. Classification of the Effect of a Failure Mode which is Critical to Quality, Safety or Regulator compliance.

Cause & Mode Diagram

The FMEA version of a Cause & Effect or Fish Bone Diagram. In FMEA the granularity of the analysis matches Failure Causes to Failure Modes. Only via them is the link established to Failure Effects.

Inverted Delta. Classification of the Effect of a Failure Mode which is Critical to Quality, Safety or Regulator compliance. Nomenclature: ÑSeverity x Occurrence x detection = Risk Priority Number (RPN).

Concept FMEA. Application of FMEA to solution independent concept design . See also DFMEA, PFMEA, MFMEA.

FMEA

Machinery FMEA. Application of FMEA to the design and development of new plant machinery. See also DFMEA, PFMEA, CFMEA.

FMEA

1. y=f(x) - Outputs are a function of the inputs.2. In FMEA: Measurable and actionable statements of all product / process deliverables to meet customer needs and wants. Expressed as verb & noun. May be gathered or grouped in a Function Tree.3. See also Density Function, Probability Density & Cumulative Distribution Function, Reliability, Hazard & Survival Function, Quality Loss Function etc.

FMEA

A hierarchical representation of product/process Functions. Typically showing around 5 primary functions with lower level functions below that. Contructed by breaking down primary functions by asking the question: How is this function achieved? And verified in reverse direction with the question: Why does this function contribute or lead to the higher level function?

FMEA

Continuous Improvements in Powertrain Engineering. A suite of 9 quality tools including FMEA, RWUP, KLT, QPQP. CIPE provides baseline data to cover all systems and attributes with common metrics and language. Formerly known as Quality Dashboard.

A suite of quality tools. Now known as CIPE. CIPE

A functional block diagram showing a procust/process and it's interfaces to: Assembly, Use, Service and other parts, subsystems and systems. Interfaces are described. A dotted line is then drawn around those elements and interfaces which are incluided in scope. Part of the FMEA process. See also Interface Matrix & P Diagram.

FMEA

A Robustness tool providing input to the Design FMEA. A matrix of all sub-systems, showing the positive or negative effects of any interface between them. Interfaces are identified as: Physically touching (P), Energy Transfer (E), Information Exchange (I) or Material Exchange (M). Interaction values are set as: +2 = Interaction necessary for function; +1 = Beneficial; 0 = No effect; -1 negative effect; -2 Prevents function.These 4 values are shown in a square for each interaction. See also P Diagram.

FMEA

Critical Characteristic

A characteristic affecting safety. Identified in an FMEA as having a severity of 9 or 10 (YC). If confirmed on review of process controls will be designated with an Inverted Delta (Ñ). See also Significant Characteristic.


6-Sigma Glossary


Key Word Description Hyperlink MiniTab TopicsFMEA

YC FMEA

YS FMEA

DCP Dynamic Control Plan FMEA

PVP FMEA

Effects List FMEA

OS FMEA

HI Reliability FMEA

CAA Component Application Agreement

Big 7 FMEA

Criticality Severity x Occurrence. FMEA

EQUIP

FTEP

M FMEA

E FMEA

PMIE FMEA

Performance Quality

SAQ

6M'sLambda Symbol

Global 8D 8D

G8D 8D

TOPS 8D 8D

ERA 8D

D0 8D

D1 8D

D2 8D

D3 8D

D4 8D

D5 8D

D6 8D

D7 8D

D8 8D

ICA 8D

PCA 8D


A characteristic significantly affecting customer satisfaction. Identified in an FMEA as having a severity of 5-8 & an occurrence >3 (YS). If confirmed on review of process controls will be designated with an Inverted Delta (Ñ). See also Critical Characteristic.

Critical Characteristic

Yes, Critical Charateristic. See also YS, OS, HI. Critical Characteristic

Yes, Significant Charateristic. See also YC, OS, HI. Significant Characteristic

FMEA

Process Validation Plan. The Process equivalent of a DVP. FMEA

List of Failure Effects as the impact:DFMEA: Part, Assy, System, Veh/Product, Customer, Regs, Other.PFMEA: Op Safety, Next user, Downstream user, veh Operation, Int & Ext cust, Regs.See also Big 7.

FMEA

Operator Safety. An additional Critical Characteristic classification in Power Train FMEA's.See also YC, YS, HI.

FMEA

1. High Impact. An additional Significant Characteristic classification in Power Train FMEA's. See also YC, YS.2. hi: Hazard Interval.3. Hi: Cumulative Hazard.

FMEA

Safety and 7 other key areas of Failure Effects in a PFMEA:- Breakdown, Setup & Adjustment, Idling/minor stoppages, Reduced Cycle, Start Up, Defective Parts, Tooling.

FMEA

FMEA

Engineering Quality Improvement Programme. Ford quality improvement initiative which generated FTEP engineering quality training modules.

FTEP

Ford Technical Education Program. Ford Design Institute program of quality training modules. See also EQUIP.

EQUIP

1. Material Exchange interface in FMEA Interface Matrix.2. The Modulated (I.e. changeable) Signal in the Ideal Function. Y=bM.3. The Measure phase of a DMAIC 6-Sigma project.

Interface Matrix

Energy Exchange interface in FMEA Interface Matrix. Interface Matrix

Physically touching (P), Energy Transfer (E), Information Exchange (I) & Material Exchange (M) interfaces in FMEA Interface Matrix.

Interface Matrix

Desires: CTQ's where better performance gives more satisfaction. Part of the Kano Model. See also Basic Quality, Excitement Quality.

Kano Model

Single Agenda for Quality. The Ford consumer TGW driven prioritisation of 6-Sigma projects. http://www.6-sigma.ford.com/

No longer Politically Correct. See 5M's and a P. 5M's & a P

Greek letter Lambda; See l. Lamda

Ford Global Problem Solving methodology applying 9 (!) Disciplines: See D0-D8. D0

See Global 8D. Global 8D

The 8 Discipline problem solving methodology which predated Ford Global 8D. Global 8D

Emergenct Response Action. Immediate action to isolate the customer from the symptom of the problem. Part of Global 8D step D0. See also ICA, PCA.

ICA

A Discipline of the Global 8D problem solving methodology.Preparing for the Global 8D. Includes: Defining & quantifying the Symptoms and taking Emergency Response Actions ERA.

D1

A Discipline of the Global 8D problem solving methodology.Includes: Establishing the Team.

D2

A Discipline of the Global 8D problem solving methodology. Includes: Describe The Problem.Problem statement: Object & defect. Problem Description: IS/IS NOT description. Observations (Not Conclusions), Sub-divide complex problems. Sheet 1 of Problem Solving Worksheet.

D3

A Discipline of the Global 8D problem solving methodology. Includes: Develop Interim Containment Actions ICA.

D4

A Discipline of the Global 8D problem solving methodology. Includes: Define & Verify Cause & Escape PointComparative Analysis from IS/IS NOT problem description; Sheet 2 of Problem Solving Worksheet; Conclusions …

D5

A Discipline of the Global 8D problem solving methodology. Includes: Choose & verify Permanent Corrective Actions PCA.Decision Making Worksheet: Givens, Wants, How Important & Good (1-10). Risk Analysis (mini FMEA) to Givens, Wants, Universal factors.

D6

A Discipline of the Global 8D problem solving methodology. Includes: Implement & validate Permanent Corrective Actions PCA.

D7

A Discipline of the Global 8D problem solving methodology. Includes: Lessons Learned & Preventing Recurrence - Control.

D8

A Discipline of the Global 8D problem solving methodology. Includes: Team & Individual Recognition.

Global 8D

Interim Containment Action. Short term action to remove immediate risk or impact pending full long term solution. A verified refinement of the Emergency Response Action. Preceeds determination of the Permanent Corrective Action. Part of Global 8D step D3. See ERA, PCA.

PCA

Permanent Corrective Action. Action which resolves the root cause of the problem. Part of Global 8D step D5/6. See also ERA, ICA.

ERA


6-Sigma Glossary



Paynter Chart 8D

Ice Cube Chart 8D

Trend Chart Stat>TS>Trend 8D

Escape Point 8D

Stair Stepping 8D

Repeated Whys 8D

Is / Is Not 8D

Something Changed 8D

Never Been There 8D

Day 1 Deviation 8D

MBWA 8D

Escape 8D

Escape Path 8D

RAPID

Ford2000

8D

8D

8D

8D

FFT Fit, Focus & Timing. 8D

Main Effects

ANOM

Analysis of Variance Stat>ANOVA>…

Analysis of Means

DQR

PCM

A matrix of problems / faults / failure types vs occurrence e.g. weeks/months. On this Emergercy Response Actions (ERA), Interim Containment Actions (ICA), & Permanent Corrective Actions (PCA) and their effects can be marked.Nomenclature: Named after its' originator, Marv Paynter, Ford, Dearborn.

ERA

Alternative name for a Paynter Chart. Nomenclature: Chart looks like an ice-cube tray. Paynter Chart

A chart mapping values of criteria varying over time. Often referred to as a Run Chart or Time Series Plot or I-Chart.

Run Chart

The earliest location in the process, closest to root cause, where the problem should have been detected but was not. See also Escape, Escape Path.

Escape

To fully define a problem: "What's wrong with what?" by repeatedly ask "Why?". See also : 5Y's.

5Y's

See Stair Stepping. Stair Stepping

Defining the characteristics of the defective product/process and a similar non-defective product. Categorise factors as: IS, COMMON or IS NOT. Only those factoprs which are unique to the IS defective condition that can be the cause. The What, Where, When & How Big characteristics are recorded on sheet 1 or 4 of the Problem Solving Worksheet. This is then followed by the question: What's Changed? - See Something Changed.

Something Changed

98.5% of all problems are caused by something which has changed. In contrast Never Been There problems which have always been there.

Never Been There

1.5% of problems where the cause has been there all along but has only now been detected. Also known as Day 1 Deviation. See also Something Changed problems.

Something Changed

See Never Been There problems. Never Been There

Management By Walking About. Visual inspection of product, process, site, defects to uncover potential causes.

A control system failure allowing the progress of sub-standard parts. See also Escape Point, Escape Path.

Escape Path

The flow of the product / process from the causal point through to the point the product is declared OK. See also Escape, Escape Point.

Escape Point

Process Re-Engineering

Part of the Ford2000 product and process improvement strategies. Radical long term changes to improve core processes. See also Focused Improvement, RAPID.

Ford2000

Focused Improvement

Part of the Ford2000 product and process improvement strategies. Medium term process streamlining. See also Process Re-Engineering, RAPID.

Process Re-engineering

Part of the Ford2000 product and process improvement strategies. Short term Rapid Action Process Improvement Deployment actions eliminating waste and reducing bureaucratic drag.

Focused Improvement

A set a product & process improvement strategies introduced by Alex Trottman across Ford in the lead up to the millenium. See RAPID, Focused Improvement, Process Re-Engineering.

RAPID

Emergency Response Action

See ERA. ERA

Interim Containment Action

See ICA. ICA

Permanent Corrective Action

See PCA. PCA

Problem Solving Worksheet

A 4 page spreadsheet used in the Global 8D problem solving process. Sheet 1: The IS/IS NOT problem description completed in step D2. Sheet 2: The Comparative Analysis of Differences & Changes between the IS & IS NOT conditions. Sheets 2-4 are completed in step D4.Sheet 3: Theorise Possible Causes: How could "this change" cause "the problem stmt".Sheet 4: Test causes for probability (+, -, ?).

Global 8D

Main Effects plot of a Multi-Vari analysis. Shows the effects of each factor on the output. See also DOE, RSM.

Multi-Vari Stat>Quality>Multi-VariStat>ANOVA>Main Effects

Analysis Of Means. Test to see if the means of two data samples are from the same population. See also ANOVA - Analysis of Variance.

ANOVA Stat>ANOVA>AnalysisOfMeans

Test to see if the variances of two data samples are from the same population. See ANOVA. See also ANOM.

ANOVA

Analysis Of Means. Test to see if the means of two data samples are from the same population. Nomenclature: ANOM. See also ANOVA - Analysis of Variance.

ANOVA Stat>ANOVA>1WayStat>ANOVA>AnalysisOfMeansStat>Basic>2SamT

World-class Durability, Quality, Reliability. One of Jaguar's Core Marque Values. See also Durability, Reliability.

Durability

Process Block Diagram

Process modelling. A graphical representation of a process. Mapping Inputs (The Materials of MEPEM), Outputs (Good & Bad product plus Process & Control Information) and Controls (The Methods of MEPEM) and Resources (The Equipment, People & Environment of MEPEM). _v_Controls_v_Inputs->|___Process___|->Outputs ^ Resources^ Note: Nothing to do with p Charts. Confusiningly similar to p Diagrams.See also MEPEM.

P Diagram

6-Sigma Glossary



Process Model PCM

PCM

PCM

Team Roles

Control PCM

Level of Control PCM

FPDS

World Class Procss

WCP

Out Of Control PCM

PCM

X-Bar & R Chart Stat>Control>Xbar-R PCM

PCM PCM

PCM

np PCM

PIST PCM

PIPC PCM

PPAP PCM

CC

Probability Paper PCM

Between Capability Cp PCM

Short Term Cp PCM

See Process Block Diagram. Process Block Diagram

Demings 14 Management Principles

1. Create Constancy of Purpose for improvement of products and services2. New Philosophy (Right First Time Defect Prevention)3. Cease Dependence on Mass Inspection (Make processes capable)4. End awarding contracts on 'lowest tender' only5. Constantly & forever improve systems & processes6. Institute Training on the Job7. Institute Leadership / Supervision which helps people & machines do a better job8. Drive Out Fear - Encourage effective 2-Way communication9. Break Down Barriers between organisations10. Eliminate Exhortations - numerical goals, slogans & posters11. Eliminate Targets - arbitrary work standards, numeric quotas & goals12. Permit Pride in Workmanship - remove barriers to the hourly worker13. Encourage Education - Institute a vigorous program of education & training14. Top management Commitment to daily push these principles. Taking action to accomplish transformation.

14 Management Principles

See Demings 14 Management Principles. Demings 14 Management Principles

Team Process Review

A process for improving team effectiveness:1. How do you feel? 2. What did you notice (facts)3. What would you like to change? 4. Who did / will do what? (Roles & Responsibilities, actions)

Key team roles include:Leader, Scribe, Time Keeper, Facilitator, team member.

1. See Levels of Control, Control Chart, -Limit, -Phase, -Plan, -Specifications, Controllable Variable.2. The Control phase of a DMAIC 6-Sigma project. Where sustainability is built in to the process to ensure that improvement benefits are sustained. This includes: mistake proofing, control metrics (SPC/SPM), long term MSA, update SOP's & validation of control.

1. Autonomic Operation2. Automatic Control e.g. Termostatic temperature control3. Self-Regulation i.e. Verification by operator4. Regulation by others

Ford Product Development System. The Ford global product development process. Superceeds World Class Process.

Ford Product Development System. The Ford global product development process. Superceeds CTC and superceded by FPDS.

FPDS

See World Class Process. World Class Process

Concept To Customer

See CTC. CTC

A process is in control when some of its data falls outside of the upper and lower Control Limits.

Control Limit

Shewhart Control Chart

An X-Bar and R Control Chart. Control Chart

A control chart combining an X-Bar Chart and an R Chart, which is a representation of process capability over time; displays a variability in the process average and range across time. Use an R or S Chart before performing an X-Bar Chart. Also known as a Shewhart Control Chart. Often referred to simply as an X & R Chart. See also X-Bar Chart, R Chart, S Chart.

Process Control Methods - See SPC. SPC

Attribute Control Charts

Control Charts for attribute data. For constant sample size see: np Chart (# Defective Units), c Chart (# Defects/Non-conformities), For constant sample size see: p Chart (Proportion Defective), u Chart (Proportion Defects/Non-conformities).

Number of Parts defective. See np Chart. np Chart

Percentage of Inspection Points which satisfy tolerance. = No of measured characteristics / Total no characteristics to be measured * 100. Part of PPAP. See also PIPC.

PIPC

Percentage of Indices which are Process Capable. Part of PPAP. See also PIST. PPAP

Production Part Approval Process. Process of tracking suppliers progress to achieving conformity of production. Completed by the authorisation of the Parts Submission Warrant (PSW). Part of QS-9000 & APQP. See also PIST, PIPC.

PIST

See Critical Characteristic. Critical Characteristic

A linear representation of cumulative normal distribution. A visual way of identifying % out of spec from sample data; and of calculating Cp & Cpk.

Cp

Process Capability between subgroups or samples. The additional component of variability, which added to the Within/Potential/Short Term Process Capability determines Overall/Long Term Process Capability.


1. Short Term Process Capability - Generally measured as Cp.2. Short Term Sigma Level - The Sigma Level of the Short Term Process Capability.


6-Sigma Glossary


Key Word Description Hyperlink MiniTab TopicsCp PCM

Cp PCM

Long Term Cp PCM

7 Sins of MudaElimination of Waste

Shewhart

Cancian Dip

CR Cp

PR Cp

Cp

Performance Index Cp

Capability Index Cp PCM

Cp

Process Potential Cp

Bench Cp Symbol

Cp

Quality Data Cube

Data CubeAnalysis CubeCube

QITD Quality is In The Detail!

Excitement Quality

Big 5 The Big 5 European automotive/economic markets: UK, Germany, France, Italy, Spain.

SMART Objectives


Best Potential process capability within subgroups with all other sources of variability removed. Generally measured as Cp/Pp, or, when taking account of how centred the process is, Cpk/Ppk. Min acceptable value: 1.67 or Sigma Level 5. (See Pp or Cp for explanation of terminology usage).Data constitues a Short-term sample where it: is free of assignable causes; represents the effect of random causes only; is collected across a narrow inference space; across one shift of production; using only one machine; with one operator; using raw components from only one lot of raw material, etc…Also known as Within Capability, Potential Process Capability, the Process Entitlement or the Performance Index. See also Cp, Cpk, Process Capability, Long Term Process Capability.



Overall Process Capability between subgroups and over time. This is the sum of the Within Capability and the Between Capability.Generally measured as Pp/Cp, or, when taking account of how centred the process is, Ppk/Cpk. Min acceptable value: 1.33 or Sigma Level 4.(See Cp or Pp for explanation of terminology usage).Data constitutes Long-term data where: it reflects the influence of random causes as well as assignable phenomena; is taken across a broad inference space; across many shifts of production; using many machines; with many operators; using many lots of raw material.Also known as: Between Capability, Overall Process Capability, Actual Process Capability or the Capability Index. See also Process Capability, Short Term Process Capability.

Process Capability Stat>Quality>Capability...

1. Long Term Process Capability - Generally measured as Pp.2. Long Term Sigma Level - The Sigma Level of the Long Term Process Capability.


Elimination of Waste. Muda = Japanese for Waste. See waste for the 7 areas of waste. Waste

See Waste. Waste

Dr. Walter Shewhart. A lead innovator of SPC techniques in the 1920's at Bell Laboratories. See Deming-Shewhart Cycle, Shewhart Control Chart.

A dip in the map of degrees of innovation vs. socio-economic class. High income early adopters tend to be followed by low income groups seeking to replicate their life styles before middle income groups.

Capability Ratio. The reciprocal of Cp. I.E. 1/Cp Cp

Performance Ratio. The reciprocal of Pp. I.E. 1/Pp Pp

Performance Capability

Same as Short Term Process Capability. Best Potential process capability within subgroups. Also known as Within Capability, Process Entitlement, Preliminary Process Capability, Performance Index or Process Potential.See also Between, Overall Capability.


Same as Short Term Process Capability. Best Potential process capability within subgroups. Also known as Within Capability, Process Entitlement, Preliminary Process Capability or Process Potential.See also Between, Overall Capability.


Same as Long Term Process Capability. Also known as Between Capability, Overall Capability.


Preliminary Performance





See ZBench. Same as Short Term Sigma Level & ZST ZBench

Preliminary Process Capability

First time assessment of a process/operation over limited data or time period. Same as Short Term Process Capability. Also known as Within Capability, Potential Capability, Performance Index, Process Potential, Process Entitlement.


Customer data analysis incorporating both AWS warranty data and GQRS TGW data. An open web based easy to access application providing information to support 6-Sigma projects.

http://www.quality.ford.com/gqi/

Quality Analysis Cube

See Quality Data Cube. Quality Data Cube



1. See Quality Data Cube.2. See Ice Cube Chart.

Quality Data Cube

Delights: CTQ's which are over and above customer expectations and which have a wholey positive effect when present. Part of the Kano Model. See also Basic Quality, Performance Quality.

Kano Model

Project objectives which are: Specific & Stretch, Measurable, Aligned with business objectives, Realistic and Time targetted.


6-Sigma Glossary



PC

VOCSTollgates

GRPI

VOPWBS

4P's

CalibrationLET

Tuckman's Model

FSNP

Forming

Storming

Norming

Performing


NIH "Not Invented Here". An key aversion to change factor.

Transition Curve

Versatility Matrix Maps present competencies, skills & knowledge of production operators.

OTJ On The Job training.

%Tolerance Stat>Quality>Gage Gage R&R

%Contribution Stat>Quality>Gage Gage R&R

SV/Toler Stat>Quality>Gage Gage R&R

Study Tolerance Stat>Quality>Gage Gage R&R

%Study Tolerance Stat>Quality>Gage Gage R&R

%SV Stat>Quality>Gage Gage R&R

Quality

1. Project Champion.2. Project Charter.3. Personal Computer.

Value Of Customer Satisfaction improvement. See VCSI. VCSI

Project management points where Go/No-Go decisions are made against a set of deliverables. Tollgate meetings may be held at the start or end of each DMAIC project phase.

Goals, Roles, Processes & Interpersonal relationships. The original 6-Sigma Leadership Team Management Model. Now referred to as 4P's. See Goals, Roles & Repsonsibilities, Processes, Interpersonal Relationships.

Goals

Voice of the Process. CTQ/CTC/CTD driven view of the Critical X's. See also VOC. VOC

Work Breakdown Structure. Breaking large project phases down into smaller tasks and sub-tasks in order to efficiently manage the project to successful completion.

Purpose, People/Roles, Process, Personal Relations. New 6-Sigma Team Management Model in 2002. Succeeds GRPI following the departure of Jacques Nasser who had sponsored the former.

GRPI

Processes to ensure the Accuracy of a tool or gauge. Accuracy

Logic, Emotion & Threat: 3 key Negotiating leverage criteria. e.g.:"The reasons to do this are A, B & C""The [feeling related] consequences of doing this are …""If you don't do this the consequences are …"

Tuckman's model of team development stages. See Forming, Storming, Norming, Performing. Ref: Leadership 2 PG_TM…ppt

Forming

Forming, Storming, Norming, Performing. Tuckman's model of team development stages. Tuckman's Model

Stage 1 in Tuckman's model of team development. Includes: establishment of purpose, objectives & method and cautiously exploring the boundaries of acceptable group behaviour.

Storming

Stage 2 in Tuckman's model of team development. The struggle as the team realises the scale of the challenges ahead. Highlights the need for collaboration, for adherence to process & to focus energy on the purpose. Often expressed in impatience. Conflict resolution is a key team skill in this stage.

Norming

Stage 3 in Tuckman's model of team development. The process of coming together as a team, building relationships and establishing an effective foundation. Sperad the word on the team's progress. This is the springboard for Performing.

Performing

Stage 4 in Tuckman's model of team development. The exercising of effective team roles with energy focused collaboratively on achievement of the goals. Pay attention to detail, to planning towards effective completion. Build stakeholder support for implementation.

Tuckman's Model

Measurement Capability Index


Gage R&R

Stages in the management of change. See Change Management. Change Management

Change Management

Stages in the management of intruducing change form a Transition Curve including: Shock, Denial, Awareness, Acceptance, Testing, Search for Meaning, Integration.


Gage R&R

See Contribution. Contribution


Gage R&R


Gage R&R


Gage R&R


Gage R&R

Quality is defined by the customer. The customer wants products and services that meet, or exceed, their needs, throughout their life, at a cost that represents value.

6-Sigma Glossary



Parameter Design DOE

Taguchi

Tolerance Design DOE Tol Design

DOE

Error State DOE

Array DOE

Inner Array DOE

Outer Array DOE

Energy Transfer DOE

Sys Eng

Ideal Function DOE

PE Eng

KE Eng

Static System Sys Eng

Dynamic System Sys Eng

Noise Control DOE

T-Bar DOE

Y-Double Bar

Level Contribution DOE

Level Average Average of values for a given factor level. DOE

Gain Improvement in target resonse (Y) over baseline.

EOQ European Organisation for Quality

IQA

L Tol Design

L-Bar Tol Design

Tol Design

Tol Design

Symbol

Improving Robustness by making changes to parameters that can be changed easily and cheaply. Parameter Design optimises performance by identifying which Noise factors do/don't interact with the Control Factors and optimising these. An extension of Experimental Design. One of the ideas implemented by Dr. Genichi Taguchi. Should be pursued before considering Tolerance Design. Methodology: 1. Define project & Team 2. Formulate Engeneered System:- Ideal Function 3. - Parameters4. Assign Control Factors to Inner Array 5. Assign Noise Factors to Outer Array.6. Measure: Conduct experiment & collect data. 7. Analyse data & select optimal design 8. Predict Improvement & Confirm (... Control)

Tolerance Design

Dr. Genichi taguchi. Japanese exponent and developer of Quality processes. E.g. Parameter Design, measurement of Energy Transformation, experimental design improvements measured from current state (1,2 level values), Use of S/N as preferred measurement of noise.

Parameter Design

Top down design to set parameters to optimise performance according to what the customer will tolerate. Steps: Team; Identify Function/Response; Identify Factors; Set + & - values as nominal +/- 1s, Measure response (DOE) gives sensitivity of response to the variability of this factor, Calculate Cost/Benefit.Adjusting tolerances and upgrading materials to improve Quality. Tolerance Design should be pursued after implementing effective concept design, experimental design and Parameter Design.See also Tolerancing, Quality Loss Function, L, L-Bar.

Parameter Design

Energy Transformation

The means by which a process generates responses & error states from signal input.Also known as Energy Transfer. See also P Diagram.

P Diagram

An unintended response from a process. See P Diagram. P Diagram

A matrix of values. See Balanced Array, Geometric Array, Non-Geometric Array, Inner Array, Outer Array, L8, L12.

Inner Array

The Array of Control Factors & Levels used in Experimental Design & Paramater Design. See also the Outer Array where Noise factors are added. See also Balanced Array, Geometric Array, Non-Geometric Array, L8, L12.

Outer Array

The Array of significant Noise Factors & Levels used in Experimental Design & Paramater Design. These are added to the Inner Array where Control factors are stored. See also Balanced Array, Geometric Array, Non-Geometric Array, L8, L12.

Balanced Array

The means by which a process generates responses & error states from signal input.Also known as Energy Transformation. See also P Diagram.

P Diagram

Engineered System Model

A representation of a System as shown in a P Diagram. P Diagram

The Response as function of the Signal. Y=bM. Where y=Response, M=Signal, b=Slope. If the signal and response are measured in energy units the ideally y=M. i.e. b=1.

Potential Energy: PE = mgh. See also KE, Energy Transfer. KE

Kinetic Energy: 0.5mv2. (+ rotational energy Iw2). See also PE, Energy Transfer. PE

A System where there is a fixed Signal. E.g. ignition switch. In contrast to a Dynamic System which has a variable Signal. E.g. Accelerator.

Dynamic System

A System which has a variable Signal. E.g. Accelerator. In contrast to a Static System where there is a fixed Signal. E.g. ignition switch. A system where there is interaction between factors. A dynamic system with a highly complex logical relation of function to purpose may be referred to as a Purposive System.

Purposive System

A prime objective of Parameter Design which seeks to optimise factors so as to maximise resilience to noise.

Parameter Design

In Parameter design used for grand average of responses (y-Double Bar) for S/N & b only. Nomenclature: `Tb, `TS/N.

Parameter Design

The grand average of the avergaes of the responses for different settings of the parameters (X's)

S/N or b contribution from a given factor level. Level Average - T-Bar Parameter Design

Parameter Design

Institute of Quality Assurance - "the UK's leading body for the advancementof quality practices"

Quality Loss Function. See also Average Quality Loss Function L-Bar.L(y)=k(y-t)2. Where: k is the coefficient of Loss, y is the current response, t is the target response.


Average Quality Loss Function. Quality loss function plus effects of variation.`L(y)=k[s2

y+(y-t)2]. Where: k is the coefficient of Loss, y is the current response, t is the target response, s2

y is the variance of the response.


y50 The Estimate of the point where the average customer returns for repair. i.e. where the customer can no longer tolerate the fault.(NB. Not the point where 50% of the customers complain.)

$fix Average cost to fix a fault in the product. May (in the West) be taken as the quality loss function. Should include all elements of costs - time, resource, environment etc as well as external costs. See also y50.


`X See X-Bar. X-Bar

6-Sigma Glossary



Tolerancing Tol Design

Symbol Tol Design

Symbol

IR Tol Design

r Percent Symbol

Jig Eng

Fixture Eng

Tool Eng

Setting Aid Eng

CETP Corporate Engineering Test Procedure

FAQ Frequently Asked Questions

F&T Facilities and Tooling - Capitalized F & T Spending

GECKBE Knowledge Base Engineering

MEARS Mechanized Appropriation Request System

LVT Local Vehicle Teams

PBT Profit Before Tax

PVT Plant Vehicle Teams

SPM Statistical Process Measurement

TAR Time Adjusted Return

WCR Worldwide Customer Requirements

TTT

Kano Model

SL

3.0SL-3.0SLS Limits

Sigma Limit

UBLBMoving Range

CLSymbol

Alpha Symbol

Beta Symbol

Chi Symbol

Tau Symbol

Epsilon Symbol

Mu Symbol

Ro Symbol

Parametric Test Stat>NonParametric

Bottom up assessment of Geometric tolerance design. Cf. Customer Tolerance Design - top down design based on what response the customer will tolerate.

Tolerance Design

`L See L-Bar. L-Bar

`T See T-Bar. T-Bar

Improvement Ratio. IR = Benefit/Cost (CPU). IR>1.0 shows positive benefit. IR<1.0 reflects costs > benefits

A device used to hold a part in place and to guide via bushings, dowels etc a drill / boring bar etc. in a specific relationship to it. See also Fixture, Tool, Setting Aid, Gauge.

Fixture

A device used to hold a part in place during the manufacturing process e.g. in relationship to a cutting tool, e.g. milling – turning etc. See also Jig, Tool, Setting Aid, Gauge.

Tool

An object used in the manufacturing process in conjunction with a jig or fixture, e.g. cutting tools, hand tools / power tools. See also Jig, Fixture, Setting Aid, Gauge.

Setting Aid

A device used to set the relationship of a cutting tool to the fixture and part. Or to set two or more specific parts in relationship to each other during assembly. See also Jig, Fixture, Tool, Gauge.

Gage R&R

Global Excellence Center. See also CBG. CBG

Train-The-Trainer. Training for those who will be delivering e.g. Green Belt 6-Sigma training. Also called T3.

T3 Train-The-Trainer. Training for those who will be delivering e.g. Green Belt 6-Sigma training. Also called TTT.

A graphical representation of quality achievement in the three categories of:- Musts: Basic Quality - CTQ's which must be satisfied.- Desires: Performance Quality - CTQ's where better performance gives more satisfaction- Delights: Excitement Quality - CTQ's which are over and above customer expectations and which have a wholey positive effect when present. J|/ ---|--> / | /

Not generally used in isolation. Used as part of two very different concepts:USL & LSL refer to the Upper & Lower Specification Limit.3.0SL & -3.0SL are alternative nomenclature for the Upper & Lower Control Limits.

Upper Control Limit

The Upper 3 Sigma Limit. A former MiniTab nomenclature for Upper Control Limit. Upper Control Limit

The Lower 3 Sigma Limit. A former MiniTab nomenclature for Lower Control Limit. Lower Control Limit

An alternative nomenclature for Sigma Limit as used in Upper & Lower Control Limits to designate the range of data variability.Not to be confused with Sigma Level which is a measure of Process Capability.

Upper Control Limit

Sigma Limits are used in Upper & Lower Control Limits to designate the range of data variability. Not to be confused with Sigma Level which is a measure of Process Capability.

Upper Control Limit

Upper Bound. Same as the Upper Control Limit. Upper Control Limit

Lower Bound. Same as the Lower Control Limit. Lower Control Limit

In an I & MR Chart the Moving Range is a chart of the difference between each value and the next.

I & MR chart

Control Limit. See Upper & Lower Control Limits, UCL & LCL. Upper Control Limit

s Level See Sigma Level. Sigma Level

Greek Letter a. aGreek Letter b. bGreek Letter c. See Chi-Square for Chi distribution and values. See Chi-Square Test for Chi-Squared Tests.

Chi-Square

Greek Letter t. tGreek Letter e. eGreek Letter m. mGreek Letter r. rA statistical test applicable to data demonstrating normal distribution. Normal Distribution

Non-Parametric Test

A statistical test applicable to data demonstrating non-normal distribution. Normal Distribution

6-Sigma Glossary



1 Sample t-test Stat>NonParametric

2 Sample t-test Stat>NonParametric

1 Sample Sign Test Stat>NonParametric

Stat>NonParametric

Sign Test Stat>NonParametric

Wilcoxon Test Stat>NonParametric

Mann-Whitney Test Stat>NonParametric

Kruskal-Wallis Test Stat>NonParametric

Moods Median Test Stat>NonParametric

Outlier

Box Plot Graph>Box Plot

Whisker The first of last quartile of the data on a box plot. Graph>Box Plot

Quartile Graph>Box Plot

Inter Quartile Range Graph>Box Plot

1P1PP First Production Proveout vehicle build

2R's Random & Representative sampling

APEL Assessment for Prior Learning & Experience

BLI Ford Business Leadership Initiative. Teaches Business Concepts of Shareholder Value

BLST Business Leadership Steering Team

CDSS

CEA Constant Employee Awareness

Contingency Table

CTADOPUdpmFDIFIRTFT Fix It Right The First Time

IPORES Things In Process Done More Than Once

A USA quality institution.

NQC National Quality Council - a USA quality institution.

FTDC

OCOS

OCIS

ICOS

ICIS

4M's an E & a PR&R Gage R&R DfSS

Scedasticity

Heteroscedasticity

Homoscedasticity

Q101

A t-Test comparing one sample with a fixed value. If data is non-normal use 1 Sample Sign Test or 1 Sample Wilcoxon Test.

t-Test

A t-Test comparing two samples of data. If data is non-normal use Mann-Whitney Test. t-Test

Non-parametric test equivalent to 1 Sample t-Test. Used in lieu of Wilcoxon test if distribution is non-symmetrical.

One Sample Wilcoxon Test

1 sample Wilcoxon Test

Non-parametric test equivalent to 1 Sample t-Test. Used when data is symmetrical. For non-symmetrical data use the 1 Sample Sign Test.

One Sample Sign Test

See 1 Sample Sign Test. One Sample Sign Test

See 1 Sample Wilcoxon Test. One Sample Wilcoxon Test

Non-parametric test equivalent to 2 Sample t-Test. Samples should have same shape and distribution.

2 Sample t-Test

Non-parametric test equivalent to ANOVA. Samples should have same shape and distribution. If not, then use Moods Median Test.

Moods Median Test

Non-parametric test equivalent to ANOVA. Used in lieu of Kruskal-Wallis Test where samples have different shape or distribution.

Kruskal Wallis Test

An extreme data point. Shown on Box Plots as an asterix (*) beyond the end of the whisker when the whisker is longer than 1.5 times the inter-quartile range (IQR).

Box Plot

A graphical representation of the spread of data like: --[.|]-- *Consists of whiskers at each end, being the initial & final 25% of the data. The box shows the 1st & 3rd quartiles of the data.The line within the box is the median.The mean may be shown as a dot within the box.Outliers beyond 1.5 * IQR are shown by asterix (*).

IQR

Box Plot

The points differentiating the 1st, and 3rd quarters of the data distribution. Shown on Box Plots. See also Q1, Q3, IQR.

IQR

The data between the 1st and 3rd quartiles. Also known as IQR. Shown on Box Plots. Quartile

Person/people - See 5M's & a P. 5M's and a P

Consumer Driven Six Sigma - The Ford 6-Sigma improvement initiative focussing on improving Consumer Quality issues

a two-dimensional table constructed for classifying count data, the purpose of which is to determine whether two variables are dependent (or contingent) on each other.

Cycle Time Analysis. See Cycle Time. Cycle Time

Defect Opportunities Per Unit. See also DPU, DPMO. DPMO

Defects Per Million. See DPMO. DPMO

Ford Design Institute. The authors of the FTEP training material. FTEP

Inputs, Processes & Outputs. See also SIPOC. SIPOC

National Quality Council

Fairlane Training & Development Center. A Ford Dearborn training establishment where much quality training is delivered.

Out of Control and Out of Specification. See Out of Control, Control Limits, Specification Limits.

Control Limit

Out of Control but In Specification.See Out of Control, Control Limits, Specification Limits.

Control Limit

In Control but Out of Specification. See Out of Control, Control Limits, Specification Limits.

Control Limit

In Control and In Specification. See Out of Control, Control Limits, Specification Limits.

Control Limit

Another rendering of 5M's and a P. 5M's and a P

1. In Gage R&R: Repeatability & Reproducability2. In Sampling: Random & Representative3. In DfSS: Reliability & Robustness

Gage R&R

The relationship between the value of input variables and the variation (rather than the mean) of the output. Where variation changes with the input variable (e.g. variation increases over time) then the process demonstrates heteroscedasticity. Where variation is constant the process demonstrates homoscedasticity.

A process where variation changes with the input variable (e.g. variation increases over time). See also homoscedasticity.

Homoscedasticity

A process where variation remains constant with change in the input variables. See also Heteroscedasticity.

Heteroscedasticity

A previous and pre-requisite quality standard to achieving the current Q1 standard. Q1

6-Sigma Glossary



IPD In Plant Date. Date of delivery of Production Parts to plant.

APQP Elements QS

QS

ISO 9000 QS

ISO 9001 QS

QS-9000 QS

TS16949 QS

PDCA23 Elements The 23 areas of application of the APQP process. QS

24 Elements QS

MS-9000 QS

TE Supplement QS

TE-9000 QS

ISO 9002 QS

MQRR QS

QSA QS

QSA-TE QS

7 Pack QS

9 Pack QS

QS9000 7 Pack QS

MH & PE QS

NMP New Model Program.

2PP Second Production Proveout vehicle build

Prove Out Build QS

AMP-PE Advanced Manufacturing Pre-Program Engineering.

Packaging

Production Trial Run QS

QS

SIM Supplier Improvement Metrics

GYR QS

RYG

The 23 areas of application of the APQP process: 1. Sourcing. 2. Customer requirements. 2A. Craftsmanship. 3. DFMEA. 4. Design Review. 5. DVP. 6. Subcontractors 7. Facilities, Tools & Gauges. 8. Proto build control plan. 9. Proto build. 10. Drawings & specs. 11. Team feasibility commitment. 12. Mfg process flow chart. 13. PFMEA. 14. MSA.15. Pre-launch control plan. 16. Process Instructions. 17. Packaging spec. 18. Production trial run. 19. Control plans. 20. Process capability study. 21. Production validation testing. 22. Production part approval (PSW). 23. PSW part delivery at IPD.Later versions renumber 2A as 3 and merge 22 & 23 to maintain the original number of elements. See also Focus Elements.

APQP

APQP Status Reporting

The Ford specific implementation of the reporting tool for APQP. See GYR status, PND. APQP

Series of Quality processes standards. Ethos: Document what you do. Do what you've documented. Demonstrate that you do. Implemented in ISO 9001 & ISO 9002. Later known as QS-9000. Now being superceded by TS16949.

TS16949

Quality processes standard for product producers. Ethos: Document what you do. Do what you've documented. Demonstrate that you do. Later known as QS-9000. Now being superceded by TS16949.

TS16949

Quality processes standards. Consists of 7 standards and 2 supplements.Ethos: Document what you do. Do what you've documented. Demonstrate that you do. Formerly known as ISO 9000. Now being superceded by TS16949. See also 7-Pack, MS-9000, QSA, APQP, PPAP, MSA, FMEA, SPC, TS-9000 / TE Supplement, QSA-TE.

TS16949

The quality process standard. Supercedes QS-9000 / ISO 9000. QS-9000

Plan Do Check Act. See Plan Do Study Act. Plan Do Study Act

APQP Elements

The 23 areas of application of the APQP process by the Big 3 plus, from Jaguar, Element 2A Craftsmanship. Later versions of Ford APQP renumber 2A as 3 and merge 22 & 23 to maintain the original number of elements. Note Volvo also have 1A Environmental & 12A Logistics.

APQP Elements

Materials management Systems requirement. Ford supplementary guideline to QS-9000, to suppliers on material management / shipment packaging engineering responsibilities.

QS-9000

Tooling & Equipment Supplement to QS-9000. Also known as TE-9000. QS-9000

Tooling & Equipment Supplement to QS-9000. Also known as TE Supplement. QS-9000

Quality processes standard for service providers. Ethos: Document what you do. Do what you've documented. Demonstrate that you do. Later known as QS-9000. Now being superceded by TS16949.

TS16949

Machinery Qualification Runoff Requirements. Equipment proveout standard in TE-9000. A machinery equivalent of PPAP.

TE-9000

Quality System Assessment standard - the process for verifying compliance to a standard. Part of QS-9000.

QS-9000

Quality System Assessment standard - the process for verifying compliance to the QS-9000 Tooling & Equipment standard TE-9000 / TE Supplement.

QS-9000

The full set of 7 QS-9000 standards and 2 supplements: QS-9000, QS-9000 QSA, APQP, PPAP, MSA, FMEA, SPC and TE Supplement with its' QSA-TE. Also known as the 9 Pack!

QS-9000

The full set of 7 QS-9000 standards and 2 supplements: QS-9000, QS-9000 QSA, APQP, PPAP, MSA, FMEA, SPC and TE Supplement with its' QSA-TE. Also known as the QS-9000 7 Pack!

QS-9000

The full set of 7 QS-9000 standards and 2 supplements: QS-9000, QS-9000 QSA, APQP, PPAP, MSA, FMEA, SPC and TE Supplement with its' QSA-TE. Also known as the 9 Pack!

QS-9000

Material Handling & Package Engineering. Engineering of physical delivery packaging & logistics.

Series of vehicle builds after the last prototype build and before volume production build. A representative production trial run using production tools, processes, volumes, rates, tolerances, operators etc. Same as Production Trial Run.

1. Product design of the packaging of systems and sub-systems within the vehicle.2. Design of the shipment packaging and stillages for delivery of parts to plant.

Same as Prove Out Build. Prove Out Build

Part Submission Warrant

See PSW. PSW

Green-Yellow-Red traffic light status e.g. in APQP or FPDS deliverables. Green = On target deliverable. Red = Off target, no recovery plan. Yellow = Off target, but with recovery plan in place.

Red-Yellow-Green traffic light status e.g. in APQP or FPDS deliverables. Green = On target deliverable. Red = Off target, no recovery plan. Yellow = Off target, but with recovery plan in place.

6-Sigma Glossary



PND QS

H QS

Focus Elements QS

Inviolable Elements QS

8 Elements QS

9 Elements QS

QOE Quality Of Event QS

PBCP Prototype Build Control Plan QS

PLCP Pre-Launch Control Plan QS

OPI Operator Process Instructions QS

PCP QS

DCOV DfSS

O DfSS

Define DfSS

Measure

Analyse

Improve

Characterize DfSS

Optimize DOE DfSS

Verify DfSS

Systems V Sys Eng

Targets Cascade Sys Eng

QPAT Eng Sys Eng

Jacques Nasser

AR&R Reliability DfSS

Cat 'A' Eng DfSS

ARR Reliability DfSS

enCORE DfSS

Program Need Date. The last possible date the element can be completed without adversely affecting quality, cost or time to the program. NB: target dates should be earlier than PND in order to manage risk. Part of APQP Status Reporting.

Historic APQP GYR status.

The 8 elements of th 1996 APQP which were deemed most critical above the other essential elements, plus one Jaguar element. NB. 2001 APQP treats all as Focus!These get a 0-3 Quality Of Event rating as well as GYR. 0 = 0-50% complete, 1=51-99% complete. 2=100% complete. 3=100% complete plus extra actions. Focus Elements are: DFMEA, DVP, PBCP, Mfg Porcess Flow, PLCP, OPI & PCP(Jag)cf. FPDS Inviolable Elements.

APQP Elements

Those elements of the FPDS deliverables which are deemed to be most critical over and above the other mandatory elements. Cf. APQP Focusu Elements.

FPDS

See Focus Elements. Focus Elements

See Focus Elements. Focus Elements

Focus Elements

Focus Elements

Focus Elements

Production Control Plan. Control Plan

DfSS project phases: Define, Characterize, Optimise, Verify. DfSS

The Optimize phase of a DfSS 6-Sigma project.

The DMAIC and DfSS 6-Sigma project phase which identifies the scope of the project and its' objectives. This phase includes: Identify Critical to Satisfaction (CTS) drivers (Y's).For DMAIC also: process map & metrics - See Define Phase.For DfSS also: Establish operating window for chosen Y’s for new and aged conditions (a£Yi£b).

The Measure phase of a DMAIC 6-Sigma project. This includes: Identify KPIV's, develop data collection plan, MSA/gauge R&R, FMEA, Baseline DPMO/Sigma Level and data collection.

The Analyse phase of a DMAIC 6-Sigma project. This includes: Micro process map, quantify KPIV's & improvement opportunities, root cause andalysis & performance objectives.

The Improve phase of a DMAIC 6-Sigma project. This includes: Performance objectives, identify alternate solutions, select optimal solution, update FMEA, cost/benefit analysis, pliot, implement & validate improvements.

1. The Measure & Analyse phases of a 6-Sigma DMAIC project. Focuses on Y's - outputs. 2. The Characterize of a DfSS DCOV 6-Sigma project. In this phase we:Flow CTS Y’s down to lower level y’s, e.g., Y -> y1, y2, … ynRelate y’s to CTQ parameters (x’s and n’s), y = f(x1, … xk, n1, … nj)Characterize robustness opportunities (including high mileage opportunities)See also Optimize.

Optimise

1. The Improve & Control phases of a 6-Sigma DMAIC project. Focuses on X's - inputs. See also Characterise. 2. Minitab Response Optimizer.3. The Optimize of a DfSS 6-Sigma project. In this step weUnderstand capability and stability of present processesUnderstand the high time-in-service robustness of the present productMinimize product sensitivity to noise, as requiredMinimize process sensitivity to product and manufacturing variations, as required

Characterise2.Stat>DOE>Factorial>ResponseOptimizer

The Verify of a DfSS 6-Sigma project. In this step weAssess actual performance, reliability, and manufacturing capabilityDemonstrate customer-correlated performance over product life

The FPDS Systems Engineering process shown graphically as: `\_/`Represented on the FPDS gateway timeline as: <KO>`<SI>\<PA>_<AA>/<PR>`<J1>&<FS>.The downward slope represents the target cascade systems engineering process.The upward slop is the verification process.

FPDS

The FPDS process of cascading targets from Systems to sub-systems to components before they are converted from targets to objectives. Part of the Systems V timing.

Systems V

Quality Program Activity Team. The team on a vehicle program which progresses the program quality deliverables and reports to the program steering team (PST).

Former Ford CEO (c.1999-2002) who spearheaded the deployment of 6-Sigma across the organisation.

Analytical Reliability & Robustness. See also enCORE, SLAM, Auto-SLAM, QMC, DACE, EMARS.

http://www.arr.ford.com/

The top 30 or so critical systems in a vehicle program. These are assessed by a points scoring method regarding factors such as: Customer needs, Safety, Engineering complexity, New technology, Legislative developments.Also used in other contexts to represent highest priority items generically.

Analytical Reliability & Robustness. See also enCORE, SLAM, Auto-SLAM, QMC, DACE, EMARS.


A web based software toolkit to assist the Analytical Reliability & Robustness process. http://www.arr.ford.com/




6-Sigma Glossary



SLAM DfSS

Auto-SLAM DfSS

QMC DfSS

DACE DfSS

DCE DfSS

ACE DfSS

AOSCE DfSS

EMARS DfSS

CPMT Component Program Module Team Eng

Transfer Function Variables DfSS

MIL Malfunction Indicator Light Eng

Q&R Reliability

DfSS

AQP QS

QSF Quick Service Fix

QDC

Chi-Square Test

Individuals Chart

PDMA DfSS

CIE DfSS

CIA DfSS

CDPM DfSS

CVA Brand/Consumer Value Added DfSS

PD/MA DfSS

DTD DfSS

TRV Target Reference Vehicle - target baseline vehicle design. DfSS

CAB Events DfSS

TCM Total Cost Management DfSS

DfSS

TRIZ DfSS

DfSS

SLAM: Successive Linear Approximation Method: a Web-based program to perform probabilistic analysis and AR&R assessment.

Auto-SLAM: SLAM with integrated Web-based interface to perform AR&R using external code or explicit equation.

QMC: Quasi Monte Carlo with integrated Web-based interface to perform AR&R using external code or explicit equation.

Design and Analysis of Computer Experimentation (DACE) for building Nonlinear Emulator Model. Part of AR&R. Includes: Design of Computer Experiment: AOSCEAnalysis of Computer Experiment: EMARS

Design of Computer Experiment. See AOSCE. Part of DACE & AR&R.

Analysis of Computer Experiment: See EMARS. Part of DACE & AR&R.

LHS:Adaptive Optimal Sampling for Computer Experimentation: A Web-based program to adaptively generate optimal samples for computer experiments. Part of DACE & AR&R.

EMARS:Enhanced Multivariate Adaptive Regression Spline. Part of DACE & AR&R.

Y=f(x). Identifying the material relationship between measurable inputs and customer critical to satisfaction (CTS) key charactyeristics.

Quality & Reliability. See also R&R. R&R

Probabilistic Optimisation

Selecting optimal x value which provides maximum worst case Y response having giben consideration to to the variability of x. I,e, select so as to maximise Y=f(x) for all x +/- n-Sigma.

Advanced Quality Planning. See APQP. APQP

Quality Data Cube: Customer data analysis incorporating both AWS warranty data and GQRS TGW data. An open web based easy to access application providing information to support 6-Sigma projects.


1. Test for goodness of fit between two discrete variables

2. Statistical Test for variance of a process to target variance.H0: s1=st. HA: s1¹st. If target s falls between confidence intervals for s then fail to reject H0.

Chi-Square 1.Stat>Table>Chi-sq.Stat>Basic>2Prop.Calc>Prob>Chi-sq.Calc>Rand>Chi-sq.

2.Stat>Basic>Disply

A Run Chart of individual values. Often displayed with a Moving Range chart. The "I" in an IMR chart.

IMR chart

Product Development/Marketing Alignment. Alignment of PD and marketing brand positioning strategy. May be written as PD/MA.

Customer Information Evaluation. Same as CIA, CDA. CDA

1. Customer Information Analysis. Assessing the Voice of the Customer. Same as CIE, CDA.2. Central Intelligence Agency

CDA

Consumer Driven Predictive Modelling. GQRS ranking analysis. A DfSS step feeding into the Kano model.

Kano Model

Product Development/Marketing Alignment. Alignment of PD and marketing brand positioning strategy. May be written as PDMA.

1. Demand To Delivery. The full product realisation process from concept through design and development to product delivery.2. Dock to dock time.

Cut-Away Benchmarking. Partial teardowns of Ford & comp[etitive vehicles to cost or weight saving opportunities or improved system functionality.

Benchmarking

Structured Inventive Thinking

Structured Inventive Thinking (SIT) is a creative structured problem solving technique. The SIT methodology deals with conceptual solutions to technological problems. Its purpose is to focus the problem solver on the essence of the problem, to overcome psychological barriers to creative thinking, to enable the discovery of inventive solutions.Uses Closed World Method or Particles Method to derive uniqueness from selected objects. Dimensionality, Pluralization & Redistribution are then used to define solution concepts for input to Generification. For origins see TRIZ.

http://www.srl.ford.com/sitteam/sit.htm

Russian acronym for "Theory of Inventive Problem Solving". The forerunner of SIT (Structured Inventive Thinking). Developed in the 1950's by Henry Altshuller in the Soviet Union. Revised & simplified in Israel, enabling swifter learning with less reliance on external databases. See SIT.


Closed World Method

An element of Structured Inventive Thinking (SIT). Use of Closed-World Diagram & Qualitative Change graphs to gain understanding of the system e.g. to identify conflicts to be resolved. Used before applying the SIT solution techniques of Uniqueness, Dimensionality, Pluralization & Redistribution. Used in preference to the Particles Method where there is a known solution technology to improve rather than a clean sheet approach.



http://www.srl.ford.com/sitteam/sit.htm

6-Sigma Glossary



Particles Method DfSS

Uniqueness DfSS

Dimensionality DfSS

Pluralization DfSS

Redistribution DfSS

Generification DfSS

SIT DfSS

DfSS

DfSS

And/Or Tree DfSS

PDL Product Direction Letter. A formal statement of vehicle program scope, targets and financials.

eFDVSDVM Design Verification Method DfSS

RDM Reliability Demonstration Matrix DfSS

2 Variances A Hypothesis Test for assessing homogeneity of the variances of 2 samples.

Baseline Capability Cp

Final Capability Cp

Z-Value

GPMS

LR Land Rover LR

PCM LR

ECM LR

GBOM LR

SAM Single Agenda for Manufacturing LR

SLI Safety Leadership Initiative

LRCPA LR

REA LR

ODI

CCD

NHTSA US Department of Transportation National Highway Traffic Safety Administration

An element of Structured Inventive Thinking (SIT). Applying Particles to Initial & Final States, before drawining And/Or Tree to identify the necessaryproperties of the things that can be used to solve the problem. Used before applying the SIT solution techniques of Uniqueness, Dimensionality, Pluralization & Redistribution. Used in preference to the Closed World Method where there is free choice of solution technology rather than an existing solution to improve. The particles method requires some understanding of how to go about identifying the solution. Where this is not the case, the Closed World Method may be more effective.


An Structured Inventive Thinking solution technique. Follows use of the Closed World Method or the Particles Method. Used with the other SIT solution techniques of Dimensionality, Pluralization & Redistribution.


An Structured Inventive Thinking solution technique. Follows use of the Closed World Method or the Particles Method. Used with the other SIT solution techniques of Uniqueness, Pluralization & Redistribution.


An Structured Inventive Thinking solution technique. Follows use of the Closed World Method or the Particles Method. Used with the other SIT solution techniques of Uniqueness, Dimensionality & Redistribution.


An Structured Inventive Thinking solution technique. Follows use of the Closed World Method or the Particles Method. Used with the other SIT solution techniques of Uniqueness, Dimensionality & Pluralization.


An element of Structured Inventive Thinking (SIT). Structured Inventive Thinking

See Structured Inventive Thinking. Structured Inventive Thinking

Closed World Diagram

An element of Structured Inventive Thinking (SIT). Part of the Closed World Method. Structured Inventive Thinking

Qualitative Change Graph


An element of Structured Inventive Thinking (SIT). Part of the Particles Method. Structured Inventive Thinking

The web interface to the FDVS system. FDVS

Hypothesis Test Stat>Basic>2 Variances

Starting Process capability. Before process improvements are made. Same as Zbench. May bes expressed as Short or long term capability. See also Final Capability, Baseline DPMO.


Process capability after process improvements are made. Same as Z???. May bes expressed as Short or long term capability. See also Baseline Capability.


The Z-value is the normal approximation test statistic used in the one-proportion testing procedure. Nothing to do with Z-Score.

Z-score

Land Rover Group Problem Management System. LR Warranty & problem management system. GPMS was developed initially as a standalone problem logging, reporting and control system. It is used in support of the Problem Definition and Closure steps of the overall process (i.e. the front and back end of the change management process). See also PCM, ECM.

PCM

Land Rover Problem and Change Management has two meanings. In the context of a business process it is used to refer to the entire change management cycle and as such GPMS is a supporting IT tool to the PCM process. In the context of an IT Tool. PCM is the name given to the Solution Management elements of the process and is interfaced electronically with GPMS and GBOM (Group Bill of Material). See also ECM.

ECM

Electronic Change Management is the term developed to resolve the confusing abiguity described above and to herald the shift from paper based change management to a paperless process. ECM therefore describes the new business process, reserving the PCM and GPMS labels for the IT support tools.

GPMS

Land Rover Group Bill Of Material system. Equivalent of Ford WERS system. WERS

Land Rover Consumer Product Audit: method of identifying product concerns at the assembly plants that could negatively impact customer satisfaction. LR equivalent of FCPA.

FCPA

Reliability Evaluation Assessment (?) as measured by the Reliability Evaluation Index. A TGW equivalent measure of customer satisfaction at 0, 6,000 and 15,000 miles in service.

NHTSA Office of Defects Investigation Consumer Complaints Database http://www.nhtsa.dot.gov/cars/problems/complain/index.cfm

NHTSA Office of Defects Investigation Consumer Complaints Database http://www.nhtsa.dot.gov/cars/problems/complain/index.cfm

http://www.nhtsa.dot.gov/

http://www.nhtsa.dot.gov/cars/problems/complain/index.cfm

http://www.nhtsa.dot.gov/cars/problems/complain/index.cfm

http://www.nhtsa.dot.gov/

6-Sigma Glossary



DOT US Department of Transportation

CtW LR

WAA Land Rover Warranty Analysis Application LR

MQ LR

CQTS LR

CAVIAR Previous Rover warranty system LR

SRO Standard Repair Operations. A description of the work undertaken by the dealer. LR

B/S Batch Sample LR

Market Build LR

World Build LR

WR Warranty Returns LR

Cat '1' LR

Cat '2' LR

Cat '3' LR

MPP LR

FPU LR

%FPU LR

%CPU LR

Veh Comp LR

%Incidence LR

PCV LR

Profiles01 LR

CPV LR

sfag LR

IDOV DfSS

Identify DfSS

Design DfSS

MAIC DfSS

QCS Quality and Customer Satisfaction. Top level Ford Global Quality meeting.

QCS&PM Ford Financial Quality, Customer Satisfaction & Process Management

CRT Component Review Team

Soft Failure

Hard Failure

DVP&PV Design Verification, Production and Process Validation Sys Eng

DPV Production Daily Planning Volumes

QRSOW

LEA Lead Engineering Activity Eng

QRP Quality Review Process

Cost to Warranty. LR COPQ measure of %CPU. CtW = 1.3*PartCost + Handling + Markup + Supplement + CPW handling + Landing + Labour + Other charges!

Land Rover Marque: LN=Freelander, LD=Defender, LJ/LT=Discovery 1/2, LH/LP=Range Rover Classic, LM=New Range Rover (L322). See also sfag.

sfag

Customer Quality Tracking Survey (?) by Research International. Previous Rover customer survey. Similar to GQRS.

The number of vehicles despatched to a given market regardless of whether they have been sold. See also World Build.

The number of vehicles despatched to all markets regardless of whether they have been sold. See also Market Build.

LR warranty return assessed as the reponsibility of the supplier. See also Cat '2', Cat '3'.

LR warranty return assessed as the reponsibility of the producer (Land Rover). See also Cat '1', Cat '3'.

LR warranty return assessed as No Fault Found (NFF). See also Cat '1', Cat '2'.

Months Past Production. A BMW, and hence, Land Rover, measure of Time In Service. See also MIS.

Faults Per Unit. LR FPU = # incidents / sample size. See also %FPU, %Incidence.

Faults Per Unit expressed as a percentage of all vehicles produced.LR %FPU = (# incidents / sample size) * 100See also %Incidence.

Cost Per Unit. LR Warranty: Cost of repair per unit. See also CPU. Expressed in GBP pence for Land Rover. E.g. CPU 33 = 33p per vehicle. %CPU = (Sum of costs on incidents (£) / sample size) * 100.

Vehicles Completed. Number of vehicles despatched to a market which have reached a given time in service (MIS) in a LR warranty report.

% of vehicles on which a particular problem occurs. E.g. %Incidence = 3% for fault type '6S1' @ 3 MIS means that 3% of that vehicle type are suffering from a '6S1' failure by 3 MIS. Same as %FPU, IPV.

Percentage Clean Vehicles. % of vehicles built which have no warranty faults at given time in service. PCV = 100 * (Vehicles built - Vehicles with claims) / Vehicles built .See also Yield, RTY, FTT.

Yield

A tabulated report of LR warranty data for a given marque & fault code. For each build month and TIS (0, 3, 6, 9, 12 MIS etc) this shows three figuresfor the chosen report out:FPU: Incidents, Veh Comp, FPU.CPU: Cost (£), Veh Comp, %CPU (pence)

Cost Per Vehicle. In Land Rover this is expressed as Cost in Pence pr Vehicle. Same as CPU.

CPU

Land Rover market code. eg 500=UK, 265=USA, 10=Australia, 32=Canada, 128=Japan.See also MQ.

MQ

DfSS project phases: Identify, Design, Optimise, Verify. SSA definition of the DfSS project phases. See DCOV for Ford DfSS project phases.

DCOV

The first phase of the SSA DfSS IDOV project methodology. IDENTIFY represents market needs, customer requirements (CTS), regulatory requirements, process optimization requirements, sub-system design requirements, etc. It can occur in a variety situations and phases. The data should be in a ranking format (Ordinal scale) to permit some cost and performance trade-off decisions to be made. Proceeds the Design, Optimization & Verification phases. In the Ford DfSS methodology the 1st step is Define and the 2nd step is Characterize.

Design

The second phase of the SSA DfSS IDOV project methodology. DESIGN, represents both the initial concept generation or any re-designs required accommodating technology limitations.Follows Identify and proceeds the Optimization & Verification phases. In the Ford DfSS methodology the 1st step is Define and the 2nd step is Characterize.

Optimize

The formally defined steps in the DMAIC process. DMAIC

Failures can be either SOFT (degraded performance to an unacceptable level) and/or HARD (product function ceases).

Failures can be either SOFT (degraded performance to an unacceptable level) and/or HARD (product function ceases).

DVP

Quality / Reliability Statement Of Work. Detailed expectations of supplier in meeting Ford quality processes.

6-Sigma Glossary



PCC

P-DiagramVSM

Beer

Viable Sys Eng

Recursion Sys Eng

Self-Reference Sys Eng

Homeostasis Sys Eng

Invariant Sys Eng

Cybernetics Sys Eng

p Symbol

Value Analysis Sys Eng

VE Sys Eng

Value Engineering Sys Eng

VA/VE Value Analysis / Value Engineering. Sys Eng

LST Sys Eng

Purposive System Sys Eng

System Model Sys Eng

System In Focus Sys Eng

Sys Eng

Reliability

Reliability

VIF

MANOVAM1000 Vehicle mileage at time of fault, in 1000's of miles. Eng

ML Reliability

Mean Life Reliability

LOWESS Graph>e.g.Histogram

IPVBaseline

QAS

Proactive Customer Call. A form of customer satisfaction quality survey. See also GQRS. GQRS

See P Digram. P Diagram

Viable System Model developed by Stafford Beer. See also Viable, Recursion, Self-Reference, Homeostasis, Invariant, P Diagram.

Viable

1. Stafford Beer, developer of the Viable System Model (VSM).2. An intoxicating beverage. The cause of much variability in human behaviour.

VSM

Viable System Model

Viable System Model developed by Stafford Beer. See also Viable, Recursion, Self-Reference, Homeostasis, Invariant, P Diagram.

Viable

A system able to maintain a separate existence. An organization is viable if it can survive in a given environment.Part of the Viable System Model (VSM) developed by Stafford Beer. See also Recursion, Self-Reference, Homeostasis, Invariant, P Diagram.

Recursion

The situation where a sub-system, organization has an identical system model to the parent and is itself viable. E.g. A viable foetus within its' mother's womb.Part of the Viable System Model (VSM) developed by Stafford Beer. See also Viable, Self-Reference, Homeostasis, Invariant, P Diagram.

Self-Reference

The property of a system whose logic closes in on itself: each part makes sense precisely in terms of the other parts. Self-reference supports Identity & self-awareness, facilitiates self-repair and explains recursion.Part of the Viable System Model (VSM) developed by Stafford Beer. See also Viable, Recursion, Homeostasis, Invariant, P Diagram.

Homeostasis

The stability of a system's internal environment, despite having to cope with an unpredictable external environment. The same principle as Robustness.Part of the Viable System Model (VSM) developed by Stafford Beer. See also Viable, Recursion, Self-Reference, Homeostasis, Invariant, P Diagram.

Invariant

A factor which is unaffected by all the changes around it. I.e. one which demonstrates total homeostatsis / robustness. e.g. the speed of light or p!Part of the Viable System Model (VSM) developed by Stafford Beer. See also Viable, Recursion, Self-Reference, Homeostasis, Invariant, P Diagram.

VSM

The science of effective organization and management. The manager's perogative: To organize effectively.See also: Viable System Model.

VSM

1. The greek letter Pi.2. The ratio of the circumferance of a circle to its diameter. p = 3.14159265358979...

Assessing the value functions of a system or component design vs. their costs. Used to support Value Engineering.

Value Engineering

Value Engineering. Value Engineering

Using the output from Value Analysis to determine the optimal cost / function effective engineered solution for a component or system.

Value Analysis

Value Analysis

Living Systems Theory of organisational behaviour by James Miller. See also VSM, Cybernetics.

VSM

A Dynamic System with a highly complex logical relation of function to purpose. See also Static System.

Static System

1. See Engineered System Model, P Diagram.2. See Viable System Model.

Engineered System Model

The Viable System which is the focus of an organizational or system study. There will be other chains of systems above and below this.

Viable System Model

Recursive Dimension

A chain of systems which, in the Viable Systems Model, demonstrate recursion of the system model at each level. More generically, the inputs & outputs at each level may be equated to the Y=f(X) cascade.

Viable System Model

hi Hazard Interval. See also Hi: Cumulative Hazard. Hazard Interval

Hi Cumulative Hazard. See also hi: Hazard Interval. Cumulative Hazard

See Variance Inflation Factor. Variance Inflation Factor

Variance Inflation Factor

A measure of the magnitude of multicollinearity. VIF quantifies how much the variance of an estimated regression coefficient increases if the predictors are correlated. Regression coefficients are copnsidered poorly estimated if VIF exceeds 5 or 10. Collect additional data or use alternative predictors to overcome multicollinearity. For variable i: VIFi = 1 / (1-R2

1,2,...i) where R is the correlation matrix of input variables.

Multicollinearity

Multi-variate ANalysis Of Variariances. See ANOVA. ANOVA

Mean Life. Average product life before failure. Same as Mean Time Before / To Failure (MTTF / MTBF). See also MLR.

MTBF

Average product life before failure. Same as Mean Time Before / To Failure (MTTF / MTBF). See also MLR.

MTBF

Locally Weighted Scatter plot Smoother. An algorithm for smoothing the graphical display of data. Generally helpful on gently sloping data.

Incidence in Percent of Vehicles. Same as %incidence. See also FPV. %Incidence

The initial or starting value e.g. of the Process Capability or DPMO. A commonly used term in American English, less common in general usage in British English.

Baseline Capability

Quality Audit Survey. 12 & 36 MIS customer satisfaction / TGW survey. See GQRS. GQRS

6-Sigma Glossary



Goals

Processes

FTPM Ford Total Preventative Maintenance

Particle DfSS

Smart Little People DfSS

Dumb Little People DfSS

Magic Devices DfSS

ELP DfSS

Evil Little People DfSS

DfSS

Function Diagram DfSS

DfSS

And/Or Diagram DfSS

CW DfSS

EV Gage R&R

AV Gage R&R

Equipment Variation Gage R&R

Assessor Variation Gage R&R

Non-Parametric

Nonparametric

Parametric

NIST



The 1st element of the GRPI leadership strategy:GOALS: Core Mission of Team - Performance Objectives* Goals are clear and people are committed to themSee also Roles & Responsibilities, Processes, Interpersonal Relationships, 4P's, GOAL.

Roles & Responsibilities

Roles & Responsibilities

The 2nd element of the GRPI leadership strategy:ROLES & RESPONSIBILITIES: Allocation of Work - Role Responsibility & Work Processes* The work is organized in a way which clearly leads to accomplishing the teams goals* There is maximum use of the different resources of individuals on the team. * Everybody is clear about what responsibilities they have and the jobs they're supposed to do. * The leadership is shared.See also Goals, Processes, Interpersonal Relationships, 4P's.

Processes

The 3rd element of the GRPI leadership strategy:PROCESSES: Team Processes - Decision Making, Problem Solving, Conflict Management & Communications* Decisions are made based on who has the expertise and best information, not on hierarchy or authority* Conflict on the team is confronted openly and constructively.See also Goals, Roles & Responsibilities, Interpersonal Relationships, 4P's.

Interpersonal Relationships

Interpersonal Relationships

The 4th element of the GRPI leadership strategy:INTERPERSONAL RELATIONSHIPS:* There is trust and openness in communication and relationships.* Time is taken to examine our process (how we are relating to each other, communicating, handling conflict, etc.) in order to improve the way we work. * Flexibility, sensitivity to the needs of others, and creativity are encouraged. See also Goals, Roles & Responsibilities, Processes, 4P's.

GRPI

Magic' devices invoked to find conceptual problem solutions. Used in the SIT Particle Method. Helps capture vague ideas. Supports the thinking process that Function Follows Form

Particles Method

TRIZ name for Particles. An SIT technique. Particle

A more apt desciption of Particles than TRIZ's Smart Little People Particle

Same as Particles. An SIT technique. Particle

Evil Little People. An extension of the SIT Particles Technique technique to diagnose root causes.

Particles Method

An extension of the SIT Particles Technique technique to diagnose root causes. Particles Method

Functional Relationship Diagram

See SIT Closed World Method. Structured Inventive Thinking

See SIT Oval Wheels notes. Structured Inventive Thinking

Qualitative Change Diagram


An element of Structured Inventive Thinking (SIT). Part of the Particles Method. Structured Inventive Thinking

Closed World. See Closed World Method & Closed World Diagram. An SIT concept. Closed World Method

Equipment Variation in a Gage R&R study. Same as Repeatability. See also Assessor Variation.

Repeatability

Assessor Variation in a Gage R&R study. Same as Reproducability.See also Equipment Variation.

Reproducability

Equipment Variation in a Gage R&R study. Same as Repeatability.See also Assessor Variation.

Repeatability

Assessor Variation in a Gage R&R study. Same as Reproducability.See also Equipment Variation.

Reproducability

Data or tests which do not assume any specific distribution. See Non-Parametric Distribution, Non-Parametric test.


Data or tests which do not assume any specific distribution. See Non-Parametric Distribution, Non-Parametric test.


Data or tests which are based on given distribution - usually the Normal Distribution.See also Non-Parametric Distribution, Non-Parametric test.


US National Institute of Standards and Technology. Founded in 1901, NIST is a non-regulatory federal agency within the U.S. Commerce Department's Technology Administration. NIST's mission is to develop and promote measurements, standards, and technology to enhance productivity, facilitate trade, and improve the quality of life. See also Malcolm Baldrige National Quality Award. www.nist.gov

Measurement System Evaluation

Same as Measurement System Analysis. See Gage R & R. Gage R&R

Pooled Standard Deviation

A weighted average of the standard deviation of each subgroup. The pooled standard deviation provides an overall measure of the average spread of individual data points about their subgroup mean. Used as Gage R&R Reppeatability measure.E.g. The average std dev of the following samples is the same a "I" rather than wider:- | I I |-----I--------- | I I

Gage R&R

http://www.nist.gov/

6-Sigma Glossary



GOAL

Baldrige

Malcolm Baldrige

RIMES

EOS

Back To Mark Eng

Crack On Eng

RTV Room Temperature Vulcanizing. A type of silicon rubber sealant. Eng

PUB No LR Power Unit Build Number: Engine derivative / configuration code Eng

QAT LR Quality Action Team

Growth Opportunity Alliance of Lawrence. Publishers of the 6-Sigma and Quality Tools "Memory Jogger"

The Baldrige National Quality Program and the Malcolm Baldrige National Quality Award of the US National Institute of Standards and Technology (NIST). An endeavour to promote performance excellence among U.S. manufacturers, service companies, educational institutions, and health care providers; conducts outreach programs and recognizes performance excellence and quality achievement; www.nist.gov

The Baldrige National Quality Program and the Malcolm Baldrige National Quality Award of the US National Institute of Standards and Technology (NIST). An endeavour to promote performance excellence among U.S. manufacturers, service companies, educational institutions, and health care providers; conducts outreach programs and recognizes performance excellence and quality achievement; www.nist.gov

Rover Integrated Manufacturing Engineering System. Land Rover system for managing process sheets (Engineering Operation Standards / EOS's) and process assignment information.

EOS

Engineering Operation Standard. Land Rover manufacturing process sheet. Managed in RIMES.

RIMES

Engineering Operation Standard

EOS. Land Rover manufacturing process sheet. Managed in RIMES. RIMES

A bolt torque measuring method by detorquing "Back to Mark":Mark the current position on the bolt and surround. Loosen bolt. Retighten "back to mark"Record torque needed to get back to original position. This is the most informative detorque reading. See also Crack On.

Crack On

A bolt torque measuring method by Cracking On beyond the current position.Apply incremental torque until bolt moves.This is a more resource efficient method but is less informative of the existing relaxed torque condition than is the Back to Mark method.

Back To Mark



6-Sigma Glossary


Yield DOE Symbol Warranty DfSS

Symbol

Gage R&R

Gage R&R

Symbol

Symbol

Symbol

Symbol

Symbol

Symbol

DOE

Gage R&R

Symbol

Hyp Test

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Warranty

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Process Capability

Gage R&R


Engineering

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Gage R&R


Engineering

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Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Hyp Test

Hyp Test

C&E

Variables Sys Eng

Variables

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Symbol Hyp Test

Variables

Hyp Test

Hyp Test

Gage R&R

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Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Variables DOE

Eng

Eng

Variables Reliability

C&E

C&E

C&E

Symbol

Symbol

Cp

Cp

Variables

Gage R&R

Variables

Variables

Variables C&E

Variables C&E

Warranty

Warranty

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Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

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DOE

Hyp Test

C&E

DOE

Hyp Test

Variables

Variables

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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

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DOE

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Cp

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Cp

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Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

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Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Cp

Cp

LR

Variables

C&E

Symbol

Symbol

Variables

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DfSS

Variables

Variables

Gage R&R

Gage R&R

Gage R&R

Gage R&R

DOE

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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

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Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DOE Hyp Test

DOE

Variables

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Yield

Yield

Yield

C&E

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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DOE

Hyp Test

Yield

DOE

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Gage R&R

Symbol Hyp Test

Symbol Hyp Test

C&E

Hyp Test

Variables

Variables

DOE

DOE Symbol

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

C&E

Warranty

Warranty

Hyp Test

Gage R&R

Cp

Symbol

Yield

Gage R&R

Gage R&R

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Gage R&R

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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

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Gage R&R

Gage R&R

Gage R&R

Symbol

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Yield

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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Hyp Test

Symbol

Gage R&R

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Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

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Gage R&R

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Vehicle Test

Hyp Test

Cp

Cp

Cp

Cp

Gage R&R

Variables

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Variables

Cp

Cp

Gage R&R

Hyp Test

Gage R&R

Gage R&R

Hyp Test

Hyp Test

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Hyp Test

Sys Eng

Gage R&R

Gage R&R

Symbol

Gage R&R Symbol

C&E

Variables

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Gage R&R

Hyp Test

Gage R&R Reliability

Gage R&R

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Gage R&R

Gage R&R

DOE

Gage R&R DOE

Variables

Yield

Yield

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Gage R&R Symbol

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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Yield

Gage R&R Symbol

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Gage R&R Symbol

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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Variables

Symbol

Variables

C&E

Warranty

Cp

Variables

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Gage R&R

Gage R&R

Variables

Variables

DOE Symbol

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DOE

Gage R&R

Gage R&R

Hyp Test

Gage R&R

Hyp Test

Hyp Test

Hyp Test

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Variables

Sys Eng

Gage R&R

Variables

Gage Variables

Variables

Gage R&R

Warranty

Warranty

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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Warranty

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Variables Symbol

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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Symbol Hyp Test

Gage R&R

Gage R&R

Cp Gage R&R

Cp

Cp

Cp

Symbol Reliability

Vehicle Test

DOE

Symbol

DOE

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DOE

DOE

DOE

DOE

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DOE Symbol

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H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DOE

DOE

DOE

DOE

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Reliability

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Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

DOE

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Symbol Hyp Test Reliability

Symbol

Reliability

Reliability

Variables Reliability

Reliability

Reliability

Reliability

Warranty

Warranty

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DOE

DOE

DOE

DOE

DOE

DOE

DOE

DOE

DOE Symbol

DOE

Resolution

Resolution

Resolution

Resolution

EVOP

PLEX

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Symbol

Symbol

DOE

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Lean

Lean

Lean

Lean

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Lean

Lean

C&E

Lean

Lean

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

Lean

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Sys Eng

Sys Eng

Sys Eng

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Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Gage R&R

DOE

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

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Reliability

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Reliability

Yield

Yield Reliability

Reliability

Symbol

C&E

Symbol

Sys Eng

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Reliability

Symbol

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Cp

Cp

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Cp

Cp

Cp

Cp

Cp

Cp

Cp

Cp

Cp

Cp

Cp Symbol

Cp

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DOE

DOE

DOE

DOE

DOE

DOE

DOE

DOE

Sys Eng

DOE

Eng

Eng

Sys Eng

Sys Eng

DOE

DOE

DOE

DOE

Symbol

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Symbol

Symbol

Symbol

Eng

Eng

Eng

Eng

Symbol

Symbol

Symbol

Symbol

Symbol

Symbol

Symbol

Symbol

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Gage R&R DfSS

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DfSS

DfSS

DfSS

DfSS

DOE DfSS

DfSS

Sys Eng

Sys Eng

Eng Sys Eng

Reliability DfSS

Eng DfSS

Reliability DfSS

DfSS

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

Eng

Variables DfSS

Eng Reliability

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

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Cp

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LR

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6-Sigma Glossary



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Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

LR

LR

LR

LR

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LR

LR

LR

LR

LR

LR

LR

LR

LR

LR

LR

LR

LR

LR

LR

LR

LR

Sys Eng

Eng

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Symbol

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Sys Eng

Reliability

Reliability

Eng

Reliability

Reliability

H1


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6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

DfSS

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

Gage R&R

H1


J1


6-Sigma Glossary



Gage R&R


Engineering

Vehicle Test

Hypothesis Testing

Reliability Testing

Cause & Effect

Lean Mfg

Systems Eng

Land Rover

Eng

Eng

Eng

Eng

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J1


6 sigma glossary

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