quality tools-asq-london-may-10-2006

Quality Tools

When and Where to Apply Them?

© Omnex All rights reserved

Copyright 2006 Omnex. All rights reserved2

David A. Barber, CQE (ASQ)

• Mr. Barber is the General Manager and Senior Quality Consultant / Trainer for Omnex

Canada Inc. He has been a full time consultant since 1998, and estimates a total of 5,000 hours of in-class instructional training and 3,000 hours of client consultation since that time.

• Mr. Barber has over 20 years experience in the Quality profession with the majority of this time being spent in Quality Director / Manager and Quality Engineering roles. He has a diverse background with experience in the automotive, medical devices, consumer electronic, telecommunications, plastics, machining, and stamping industries. Mr. Barber has assisted companies in the development and implementation of cohesive quality systems that have resulted in the achievement of ISO 9001:1994, QS-9000, TS16949:1999, ISO 9001:2000, TS16949:2002, TL9000, ISO/IEC 17025 and Six Sigma projects.

• Mr. Barber is a fourteen-year senior member of the American Society for Quality (ASQ) and is a Certified Quality Engineer. He received his Quality Assurance Certificate (accredited by the ASQ) from Centennial College of Applied Arts and Technology, has a diploma in Electronics from Radio College of Canada.


Omnex provides training, consulting and software to the international market with offices in the USA, Brazil, Canada, India, Mexico,

Venezuela, China (PRC) and Thailand. Omnex offers over 70 training courses in business and quality management systems worldwide.

Internet email: [email protected]

Web: www.omnex.com


Presentation Overview

• Introduction & Overview• Types of Quality Tools

– Traditional Quality Tools– Commonly Used Additional Tools– Some Other Quality Tools

• Types, or categories of Tools– What are the best use of certain tools

• When to Apply• Where to Apply• Review Some Tools

– Workshop – When and Where to apply a specific tool?


History Quality (Tools)

1920 – Quality was a inspection function

1924 – Statistical Quality Control - Dr.Walter Shewhart

1930 – Acceptance Sample

Late 1940’s – Could not produce quick enough (Japan Struggled)

1950’s – Quality Tools defined (Juran, Feiganbaum, Taguchi, etc.)

1960’s – FMEA’s in Aerospace, COQ loses favor

1980’s – ISO 9000, Six Sigma, (Tools part of business)

1990 to 2002 – Fine tune tools, program management

1970’s – QFD & Team Problem Solving (More Tools)

2006 – How to effectively & efficiently apply the right tools (does it work, at what $)


Traditional Quality Tools (ASQ)

7 Quality Control Tools (will look at some)

• Flowcharts

• Pareto Charts

• Control Charts

• Cause & Effect Diagrams

• Check Sheets

• Scatter Diagrams

• Histograms

7 Quality Control Tools (will look at some)

• Flowcharts

• Pareto Charts

• Control Charts

• Cause & Effect Diagrams

• Check Sheets

• Scatter Diagrams

• Histograms

7 Quality Management & Planning Tools

• Affinity Diagrams

• Tree Diagrams

• Process Decision Program Charts (PDPC)

• Matrix Diagrams

• Interrelationship Digraphs

• Prioritization Matrices

• Activity Network Diagrams

7 Quality Management & Planning Tools

• Affinity Diagrams

• Tree Diagrams

• Process Decision Program Charts (PDPC)

• Matrix Diagrams

• Interrelationship Digraphs

• Prioritization Matrices

• Activity Network Diagrams


Additional Quality Tools• There are many quality tools used today• Some are even call techniques• Have seen as many as 100 tools used for a given

continuous improvement roadmap or Quality / Business Management System

• Which ones to use?• We will look at some more commonly used tools


Commonly Used Additional Quality Tools

Will look at some

• 5W/2H

• Is / Is Not (Difference & Changes)

• Brainstorming (Creative Thinking)

• Failure Modes & Effects Analysis (Process)

• Box Plot

• Design of Experiments

• Run (Trend) Chart

• Control Plans

• Value Stream Map

Will look at some

• 5W/2H

• Is / Is Not (Difference & Changes)

• Brainstorming (Creative Thinking)

• Failure Modes & Effects Analysis (Process)

• Box Plot

• Design of Experiments

• Run (Trend) Chart

• Control Plans

• Value Stream Map


Some Other Quality Tools

• Balanced Scorecard

• Gantt Chart

• Paynter Chart

• Storyboard

• Tree Diagram

• Turtle Diagram / SIPOC

• Value-added Analysis

• ANOVA

• Balanced Scorecard

• Gantt Chart

• Paynter Chart

• Storyboard

• Tree Diagram

• Turtle Diagram / SIPOC

• Value-added Analysis

• ANOVA


Where Did They Come From?

• Area of Quality Engineering and Quality Systems• In what area are the tools taught;

– Quality Control, Assurance, and Engineering– Quality Tools (7 basic)– TQM – Continuous & Continual Improvement– SPC (many levels)– Six Sigma– Lean Manufacturing


Where Did They Come From?Continued

– Program Management– Team Building / Creative Thinking– Team Problem Solving– ISO 9000 / Process Mapping– Individual Core Tools & Competencies

• Advanced Product Quality Planning• Failure Modes & Effects Analysis• Measurement Systems Analysis

– Others?


How to ThinkMost Quality Tools only have two purposes;1. Creative thinking

• Diverge – Look at many possibilities• Expand our thinking• What are all variable• Right brain• Ideate

2. Analytical thinking• Converge – Narrow down and prove out• Focus our thinking• Which is the most important variable• Let the data solve the disagreement• Left brain• Evaluate


Type or Category of Tools

• Tools have been broken down into categories to improve proper application and to understand “how to think” (focus).

• All the quality tools (i.e. 20 to 100) can be grouped into 3 to 6 categories (normally).

• The categories combined the “how to think” with the traditional “Plan, Do, Check, Act” cycle, or a variation thereof.


Type or Category of Tools

Sample 1.

• Project Planning & Implementation

• Idea Creation

• Process Analysis

• Data Collection & Analysis

• Cause Analysis

• Evaluation & Decision Making

Sample 1.

• Project Planning & Implementation

• Idea Creation

• Process Analysis

• Data Collection & Analysis

• Cause Analysis

• Evaluation & Decision Making

Sample 2. (Simplified)

• Identifying and Understanding

• Analyzing

• Implementing and Maintaining

Sample 2. (Simplified)

• Identifying and Understanding

• Analyzing

• Implementing and Maintaining

Source; ASQ.org / OmnexSource; ASQ.org / Omnex


When to Apply the Tool?Or what do we want to do with the tools, or how is the tool used, to;• Identifying and Understanding

– A process– A problem (potential root cause)– A product

• Analyzing– Process or product variation (target, spread, pattern / shape)

– Relationships of multi variables

– Variable data over time

• Implementing and Maintaining– A control method (error-proofing)

– A corrective or preventive action

– Review over time

– A project


Where to Apply the Tool?

There are several different ways to look at this;

A. Where to apply the tool in the quality improvement process (General Steps - Examples);

• Overall Plan• Current State• Opportunities for Improvement• Root Cause• Corrective / Prevent Action (Change)• Standardize



B. Where to apply the tool in the quality improvement process such as Six Sigma;

• Define – improvement opportunities and their value to the company.

• Measure – the current level of performance.• Analyze – the current process to identify improvement

opportunities.• Improve – plan and deploy process improvements.• Control – hold the gains.



C. Where in your organization?

• Which Process(s)?• Product Realization Process• Support Process• Customer Process• Management Process

• Which Department or Function?


Example; Problem Solving Tools

Flowcharts



Having The “Right” Amount Of Detail To Identify The Issues Is Essential

Too little detail will not expose the problem. Too much detail will hide the problem.

Input OutputProcess

I can’t find the issue in all this detail!

Well we can see the issue is the

process, but where?

22

Two basic types of Flow Maps

• Top Level Flow Map– Major Use - to locate critical

features and processes for subsequent processing

– Usually focused on process sequence for a product

– It is used to develop the FMEA and Control Plan

– It is characterized by a linear flow from one process step to the next

– Follows the process sequence, but usually lacks sufficient detail for a new person to successfully complete the task.

• Top Level Flow Map– Major Use - to locate critical

features and processes for subsequent processing

– Usually focused on process sequence for a product

– It is used to develop the FMEA and Control Plan

– It is characterized by a linear flow from one process step to the next

– Follows the process sequence, but usually lacks sufficient detail for a new person to successfully complete the task.

• Detail Process Flow Map– Major Use - to understand

details of how a process works, decision points

– Usually focused on one or two processes in a work area

– It is usually used for Work Instructions

– It is characterized by convoluted decision paths

– Following the map should lead to successful completion of the task

• Detail Process Flow Map– Major Use - to understand

details of how a process works, decision points

– Usually focused on one or two processes in a work area

– It is usually used for Work Instructions

– It is characterized by convoluted decision paths

– Following the map should lead to successful completion of the task


Top Level Flow Map

FirstProcess

SecondProcess

ThirdProcess

• Benefits of Using this Map- Helps us view “the BIG picture” -- interrelationships of processes- This map is product process Sequence focused- Top Level Map has a Product or Process Perspective

• Limitations:- Not very helpful in knowing work content, nor decision points in work

• Benefits of Using this Map- Helps us view “the BIG picture” -- interrelationships of processes- This map is product process Sequence focused- Top Level Map has a Product or Process Perspective

• Limitations:- Not very helpful in knowing work content, nor decision points in work


Top Level Flow Mapping - Basics

Creating a Process Map– Product or Process Focus

– List Key Process Input Variables

– List Key Customer Output Variables

– Identify value-added and non-value added steps in the process. (process steps, inspection / testing, rework, scrap points)

Key Process Input Variables– Controllable Inputs: these can be

changed and we see an effect in the key output variables. These inputs are sometimes called “knob variables”

– Critical Inputs: are shown to statistically have major impact on variability of output variables

– Noise Inputs: Inputs that impact the outputs but are difficult to control.

– Standard Operating Procedures: A standard procedure for running the process


Example of Top Level Flow Map• Note the linear flow

– Note the key inputs and outputs for the process chain– These key inputs and outputs become the basis for FMEA and

Control Plan linkage

• Note the linear flow– Note the key inputs and outputs for the process chain– These key inputs and outputs become the basis for FMEA and

Control Plan linkage

Decision forGreen Belt

Training

Green BeltCandidateSelection

ExecutiveOverview

Work onSelectedProjects

Green BeltTrainingWeek 1

Green BeltTrainingWeek 2

Work onSelectedProjects

CertificationDecision

y Select Participants

y Executive Overview Heldy Certification Criteria Discussedy Projects Selectedy Data Collection System

y Tool Proficiencyy Homework

y Data Collection System Becomes Functional

y ID Key Process Variablesy Understand Variation

C Opportunity IDC Budgeted Time

S OSD Black Belt Consulting

N Level of Skill

C Opportunity IDC Budgeted Time

S OSD Black Belt Consulting

N Level of Skill

y Reduced Variationy Lower Defectives

y Tool Proficiencyy Homework

S Candidates Who Meet Requirements are Certified Green Belts

Y Business ResultsY Tool ProficiencyY MethodologyY OrganizationalY Behavioral


Detail Process Flow Map

• Benefits of using this map:– Logical flow when decision

points are reached– This map is work area and

work content focused– Operator perspective

• Limitations:– Often too much detail to

clearly see interrelationships between processes

– May be limited so that critical FMEA items get missed

Process 1

Decision Process A

Process 2 Process B

No

Yes

Process 3


Apply the Specific Tool

Page No. Application of Tool Answers (Possibilities)

12How to Think (Purpose)? Creative (Expanded) or Analytical (Focus)?

13 - 15

Category of Tool. When to apply the tool? 1. Identifying and Understanding 2. Analyzing 3. Implementing and Maintaining

16 - 18Where to apply the tool? A. / B. Where in the improvement process? C. Where in your organization?

OverallAdditional Comments (i.e Collect variable or attribute data?)

Name of Quality Tool:

Pareto Diagram



Pareto Diagram

• Pareto Diagrams are an essential tool to help prioritize improvement targets. Paretos usually allow us to focus on the 20% of the problems that cause 80% of the poor performance.

• Pareto Diagrams are an essential tool to help prioritize improvement targets. Paretos usually allow us to focus on the 20% of the problems that cause 80% of the poor performance.

Defects Freqs WeekAir Bubble 93 1Air Bubble 81 2Air Bubble 62 3Air Bubble 57 4Weight Dev. 120 1Weight Dev. 132 2Weight Dev. 91 3Weight Dev. 88 4Deformation 18 1Deformation 29 2Deformation 31 3Deformation 42 4Color 24 1Color 42 2Color 39 3Color 27 4

Defects Freqs WeekAir Bubble 93 1Air Bubble 81 2Air Bubble 62 3Air Bubble 57 4Weight Dev. 120 1Weight Dev. 132 2Weight Dev. 91 3Weight Dev. 88 4Deformation 18 1Deformation 29 2Deformation 31 3Deformation 42 4Color 24 1Color 42 2Color 39 3Color 27 4

Deformatio

nColor

Air Bubble

Weight Dev.

12013229343112.313.530.044.2

100.0 87.7 74.2 44.2

1000

500

0

100

80

60

40

20

0

Defect

CountPercentCum %

Pe

rce

nt

Co

unt

Pareto Chart for Defects


Second Level Pareto• A second level Pareto is used to drill down into the

data.

• In Minitab, chart defects data in ‘Flaws,’ BY variable in ‘Period.’

• A second level Pareto is used to drill down into the data.

• In Minitab, chart defects data in ‘Flaws,’ BY variable in ‘Period.’

Flaws PeriodScratch DayScratch DayPeel DayPeel DaySmudge DayScratch DayOther DayOther EveningPeel EveningPeel EveningPeel EveningPeel EveningScratch EveningScratch EveningPeel NightScratch NightSmudge NightScratch NightPeel NightPeel NightPeel NightPeel NightOther NightOther NightScratch NightScratch NightPeel NightScratch NightSmudge NightScratch NightOther NightScratch NightScratch NightPeel WeekendPeel WeekendPeel WeekendSmudge WeekendSmudge WeekendSmudge WeekendOther Weekend

15

10

5

0

Day

Cou

nt

15

10

5

0

Evening

Cou

nt

15

10

5

0

Night

Cou

nt

15

10

5

0

Weekend

Cou

nt

Scratch

Peel

Other

Others

Pareto Chart for Flaws

You should drill down using third level, fourth level, etc., as far as it makes sense in solving your problem.

You should drill down using third level, fourth level, etc., as far as it makes sense in solving your problem.





13 - 15





Histogram



Histogram• Histograms show how data is distributed.• The Output Variable represents 6 days of data collection, 12

hours per day.

• Histograms show how data is distributed.• The Output Variable represents 6 days of data collection, 12

hours per day.

108106104102100989694

10

5

0

DBP

Freq

uenc

y

DBP Time Day 95 1 1100 2 1104 3 1105 4 1108 5 1 99 6 1100 7 1104 8 1101 9 1105 10 1

(The First Ten Observations)





13 - 15





Cause and Effect Diagrams



Cause and Effect Diagrams

• Purpose– The Fishbone Diagram, also known as the Cause and

Effect Diagram or Ishikawa Diagram, is a graphical construct used to identify and explore on a single chart, in increasing detail, the possible causes which lead to a given effect. The ultimate aim is to work down through the causes to identify basic root causes of a problem.

• Purpose– The Fishbone Diagram, also known as the Cause and

Effect Diagram or Ishikawa Diagram, is a graphical construct used to identify and explore on a single chart, in increasing detail, the possible causes which lead to a given effect. The ultimate aim is to work down through the causes to identify basic root causes of a problem.


Benefits of Cause and Effect Diagrams

• Categorize causes of variation• Apply to product and service related functions• Complement the brainstorming process• Retain lasting value• Provide succinct information• Promote teamwork• Clarify understanding• Identify potential problem areas


The Generic Cause and Effect Diagram


Other Cause and Effect Diagrams


Six Sigma - Tools & Concepts

Fishbone Diagram

B

C

AMajor Cause Category 1

Cause

Effect

Major Cause Category 2





Cause

Cause

Cause

Cause

Cause

RootCause

Cause

Cause

Cause

RootCause

Cause

Cause

Cause

Cause

RootCause

CauseCause

Cause

RootCause

Cause

Cause

Cause

RootCause

SecondaryRoot Cause

RootCause

SecondaryRoot Cause

D

E

Fishbon1_001

Six Sigma - Tools & Concepts

Fishbone Diagram

B

C

AMajor Cause Category 1

Cause

Effect






Cause

Cause

Cause

Cause

Cause

RootCause

Cause

Cause

Cause

RootCause

Cause

Cause

Cause

Cause

RootCause

CauseCause

Cause

RootCause

Cause

Cause

Cause

RootCause

SecondaryRoot Cause

RootCause

SecondaryRoot Cause

D

E

Fishbon1_001

Reference:The Memory Jogger P. 23 - 30 Juran Quality Control Handbook P. 22.37 - 22.38

Fishbone Diagram





13 - 15





Failure Modes & Effects Analysis (FMEA)



• A systematic group of activities intended to:

1. Recognize and evaluate the potential failure of a product / process and the effects of that failure,

2. Identify actions that could eliminate or reduce the chance of the potential failure occurring, and

3. Document the entire process.

What is an FMEA


FMEA Sequence

• Review all process operations• What can go wrong?• What are the effect(s)?• How bad is it?• What are the cause(s)?• How often does it happen?• How can this be prevented and detected?• How good are the methods of detection?• What can be done to improve the process?


FMEA SequenceProcessFunction

Require.

Potential

Failure

Mode

Potential

Effect(s) of

Failure

Sev

Class

Potential

Cause(s)/

Mechanism(s)

Of Failure

Occur

Current

Process

ControlsPrevention

Current

Process

Controls

Detection

Detec

RPN

Recomm’d

Action(s)

Responsibility

& Target

Completion

Date

Action Results

Actions

Taken

Sev

Occ

D

e

t

R

P

N

ProcessSteps

What cango wrong?

What are theEffect(s)?

Howbadis it?

What are thecause(s)?

How can this beprevented

anddetected?

Howoften does it happen?

How goodare the

method(s) of detection?

What can be doneto improve

the process?

RiskPriorityNumber

What is the new RPN?

Analysis 1 Analysis 2 Analysis 3


Analysis of a FMEA• A FMEA is like three separate analyses in one• Severity (scaled 1 to 10, where 10 = very Serious)• Occurrence (scaled 1 to 10, where 10 = high likelihood)• Detection (scaled 1 to 10, where 10 = can’t detect)• All three are multiplied

S x O x D = RPN (risk priority number)

• RPN ranges is from 1 to 1000, where lower = better• Must establish a customer agreed maximum RPN

– Common values have been 100, 70, 54, 42


Type of FMEA’s

• Design

• System

• Bulk Material

• Process

• Service

• Machinery (new AIAG guideline)

• Safety





13 - 15





Control Charts



Control Chart Roadmap Select the appropriate variable to control Select the data collection point Select Type of Control Chart Establish basis for rational sub-grouping Determine sample size Determine measurement method/criteria Determine measurement system variation Perform initial capability study to establish control limits Set up forms for charting data Prepare procedures & train personnel Implement & monitor

(identify special causes thru pattern recognition)

Select the appropriate variable to control Select the data collection point Select Type of Control Chart Establish basis for rational sub-grouping Determine sample size Determine measurement method/criteria Determine measurement system variation Perform initial capability study to establish control limits Set up forms for charting data Prepare procedures & train personnel Implement & monitor

(identify special causes thru pattern recognition)


Control Charts for Variable Data• Xbar Chart

– Measures the aim or center of the process– Monitors the change in the Mean of the variable across time

• Range Chart– Measures the gain or loss of uniformity– Monitors variability of the process over time

• Sigma Chart– Similar to Range Chart– Uses the sample estimate of Sigma

• Individuals Chart– Similar to Xbar– Plots individual points instead of the Mean

• Moving Range Chart– Similar to Range chart– New range is plotted with each consecutive point– Used with the Individuals chart

• Xbar Chart– Measures the aim or center of the process– Monitors the change in the Mean of the variable across time

• Range Chart– Measures the gain or loss of uniformity– Monitors variability of the process over time

• Sigma Chart– Similar to Range Chart– Uses the sample estimate of Sigma

• Individuals Chart– Similar to Xbar– Plots individual points instead of the Mean

• Moving Range Chart– Similar to Range chart– New range is plotted with each consecutive point– Used with the Individuals chart


302010Subgroup 0

75

70

65Sam

ple

Mea

n

X=70.13

3.0SL=76.41

-3.0SL=63.85

20

10

0

Sam

ple

Ran

ge

R=10.89

3.0SL=23.03

-3.0SL=0.00E+00

Xbar/R Chart for Length

302010Subgroup 0

75

70

65Sam

ple

Mea

n

X=70.13

3.0SL=76.41

-3.0SL=63.85

20

10

0

Sam

ple

Ran

ge

R=10.89

3.0SL=23.03

-3.0SL=0.00E+00

Xbar/R Chart for Length

Xbar-R Example


Control Charts for Attribute Data• P Chart (Percent or Fraction Defective of units non-conforming/

defective)– Subgroup sample size may not be equal– Good/Bad or Pass/Fail

• NP Chart (Number of units non-conforming/ defective)– Equal subgroup sample sizeExamples: Oil filter does not leak/leaks or Lamp lights/does not

light• C Chart (Number of non-conformities/ defects)

– Equal subgroup sample size– Each unit can have more than one non-conformance/ defect

• U Chart (Number of non-conformities/ defects per unit)– Subgroup sample size may not be the equalExamples: Paint defects on an oil filter or Errors on an invoice

• P Chart (Percent or Fraction Defective of units non-conforming/ defective)– Subgroup sample size may not be equal– Good/Bad or Pass/Fail

• NP Chart (Number of units non-conforming/ defective)– Equal subgroup sample sizeExamples: Oil filter does not leak/leaks or Lamp lights/does not

light• C Chart (Number of non-conformities/ defects)

– Equal subgroup sample size– Each unit can have more than one non-conformance/ defect

• U Chart (Number of non-conformities/ defects per unit)– Subgroup sample size may not be the equalExamples: Paint defects on an oil filter or Errors on an invoice


Chart P Example

3020100

0.05

0.04

0.03

0.02

0.01

0.00

Sample Number

Pro

po

rtio

n

P Chart for FinalTes

P=0.02747

3.0SL=0.04939

-3.0SL=0.005539

3020100

0.05

0.04

0.03

0.02

0.01

0.00

Sample Number

Pro

po

rtio

n

P Chart for FinalTes

P=0.02747

3.0SL=0.04939

-3.0SL=0.005539





13 - 15





Value Stream Mapping



Drawing Current State Maps 1. Draw customer & supplier icons

2. Draw customer and supplier information flows

3. Draw delivery icons with frequency of deliveries

4. Draw & label operation boxes

5. Draw icons for raw, W.I.P. & F.G. inventories

6. Draw scheduling information flows

7. Draw material flows between operations

8. Draw data boxes for each operation

9. Calculate inventory days for each storage location

10. Draw timeline

11. Label and date the map – current state map for….


Current State Mapping Icons

OutsideCustomer or

Supplier

Operation

Operator

I

InventoryStorage Point

I

InventoryPushed Ahead Shipment

Data Box

Electronic Information Flow

Scheduling Information Flow

Customer Delivery

Supplier Delivery


VA & NVA Timelines

Draw value-added time down on the timeline, draw non value-added time up

Inventory Days Inventory Days

Cycle Time Cycle Time Cycle Time

Total N.V.A. Time

Total V.A. Time


PRODUCTIONCONTROL

MRPWeekly PO fax

90/60/30 DayForecast

90/60/30 DayForecasts

Weekly Order

8400 pcs/mo

Skid = 40 pieces

Distributors& Installers

Weekly

Weekly

ISheets4000

I3000

I1000

I800

I2200

I4800

FS = 600 SQ FS = 800 SQ FS = 300 SQ FS = 4000 SQ

9.5 days 7 days 2.4 days 1.9 days 5.2 days 11.4 days

25 seconds 60 seconds 35 seconds 20 seconds 40 seconds

Lead Time = 37.4 DAYS

Production Time = 180 sec

Weekly ShipSchedule

Weekly Schedule2500 Sheets

Michigan Steel Co.

C/T=25 sec.

C/O= 90 sec.

U/T = 95%

QR = 99%

SHEAR PUNCH

1

C/T=60 sec.

C/O= 0

U/T = 95%

QR = 98%

FORM

1

C/T=35 sec.

C/O=40 min.

U/T = 90%

QR = 95%

1

C/T = 20

C/O=0

U/T = 99%

10

C/T=40 sec.

C/O=0

U/T = 95%

SHIPPING

Staging 1

FS = 600 SQ

S. WELD

QR = 98% QR = 97%

PAINT/BOX


Rolled Throughput Yield (RTY) = FTQ (Yield) of all Operations Multiplied

Process Leadtime Days = Value-Added Time + Non Value-Added Time

Process Efficiency % = (VAT / Process Leadtime) x 100%

Inventory Days = Number Pieces divided by Daily Sales (for Raw, WIP & FG).

Floor Space = Total Square Footage of Production + Inventory Stores

Labor Productivity Ratio = Average Daily Sales / # Operators

Availability = Average of Uptime % for all Operations in the Process

Rolled Throughput Yield (RTY) = FTQ (Yield) of all Operations Multiplied

Process Leadtime Days = Value-Added Time + Non Value-Added Time

Process Efficiency % = (VAT / Process Leadtime) x 100%

Inventory Days = Number Pieces divided by Daily Sales (for Raw, WIP & FG).

Floor Space = Total Square Footage of Production + Inventory Stores

Labor Productivity Ratio = Average Daily Sales / # Operators

Availability = Average of Uptime % for all Operations in the Process

Process Scorecard Measures

Process Performance is a lagging indicator – improvements will show on this scorecard after Lean Projects have been successfuly deployed!

Process Performance is a lagging indicator – improvements will show on this scorecard after Lean Projects have been successfuly deployed!


Future State Maps• Future State Maps describe the value stream as it

could be – the ideal state!• The Future State Map is the Blueprint you will use to

plan improvement projects.


Future State Icons

Withdrawalform a

Supermarket

Supermarket

WithdrawalKanban

Leveling

ProductionKanban

KanbanPath

SignalKanban

OXOXKaizenProject

Required

KanbanPost

FIFO

KanbanArriving in

Batches

First-InFirst-Out

Flow

max 50 pcs


PRODUCTIONCONTROL

MRPDaily PO fax

90/60/30 DayForecast

90/60/30 DayForecasts

Daily Order

8400 pcs/mo

Skid = 40 pieces

Distributors& Installers

Daily

Daily Ship Schedule

500 Sheets

Michigan Steel Co.

SHIPPING

Staging

1 Day 2 Days

10

PAINT/BOX

40

Body Cell

2 Days

Daily

C/T = 59 sec.

C/O = 90 sec.

Uptime = 99%

FTQ = 99%

FS = 875

1

FS = 4000 SQ

C/T=40 sec.

C/O=0

U/T = 95%

FTQ = 97% Present State MapDSM Cabinet Body CellNovember 7, 2003

TT = 60 sec.

40

40


Measure Baseline State Present State Improvement

Raw Inventory Days

WIP Inventory Days

F.G. Inventory Days

Process Leadtime

Process Efficiency

Rolled Throughput Yield

Floor Space

E.P.E.I.

Labor Productivity

Process Scorecard for: ____________________DSM Cabinet Body Mfg.

9. 5 Days 2 Days 79%

16. 5 Days 1 Day 94%

11. 4 Days 2 Days 82%37. 4 Days 5 Days 87%

88% 94% + 6%

2300 SQ Ft. * 875 SQ Ft. * -1425 Sq Ft.

2. 4 Days 2.5 hours 85%

105 / OP. * 420 / OP. * 400%

. 02% . 07% 250%

* Mfg. Only.





13 - 15






Summary & Review

• Types of Quality Tools– Traditional Quality Tools– Commonly Used Additional Tools– Some Other Quality Tools

• Types, or categories of Tools– HOW TO THINK

• When & Where to Apply

Thank youQ & A!

quality tools-asq-london-may-10-2006

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