09 quality and jit spring06

8/8/2019 09 Quality and JIT Spring06

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Topic 9:

Quality and the Toyota System

1. Quality Costs2. Statistical Process Control

3. Six Sigma

4. Just in Time Production


2/47

Philip Crosby

Former VP of quality control at ITT corp.

Wrote Quality is Free: The Art of Making Quality Certain

Proposed: Zero Defects as the goal for quality

Consider the AQL you would establish on the product you buy.

Would you accept an automobile that you knew in advance was 15%

defective? %5? 1%? 1/2%? How about nurses that care for newborn

babies? Would an AQL of 3% on mishandling be too rigid?

Mistakes are caused by lack ofknowledge and lack ofattention


3/47

Crosbys Quality Postures

0

2

4

6

8

10

12

14

16

18

20

Re por te dAc tual

Unc e r tA w a k e

Enlight

Wisdo

Ce r tai

Uncertainty

We dont know why we have problemswith quality

Awakening

It is absolutely necessary to always have

problems with quality

Enlightenment

Through management commitment andquality improvement we are identifying

and resolving our problems

Wisdom

Defect prevention is a routine part of our

operation

Certainty

We know why we dont have problems

with qualityCostof

Qualit y

asa%

ofsa

les


4/47

Categories of Quality Costs

Prevention costs Costs associated with preventing

defects

Appraisal costs

Costs associated with assessing

quality within a productive system Internal failure costs

Costs associated with losses from

disposal of or fixing quality

problems

External failure costs

Costs associated with releasing

poor quality into the demand

stream

Cost of yield loss

cost to send your employees toquality training

warranty costs associated with

unplanned product repair

cost of a new automated quality

testing device

cost of rework loss of market share due to a

national product purity scandal

litigation cost due to product

defect


5/47

Rework / Elimination of Flow Units

Step 1 Test 1 Step 2 Test 2 Step 3 Test 3

Rework



Rework:

Defects can be corrected

by same or other resource

Leads to variability

Loss of Flow units:

Defects can NOT be corrected

Leads to variabilityTo get X units, we have to

start X/y units


6/47

Calculation of Yield Loss

B(1-d1)(1-d2)(1-d3)(1-dn) = m Thus: B=m/(1-d1)(1-d2)(1-d3)(1-dn)

Where:

di = proportion of defectives generated by operation i

n = number of operations

m = number of finished products

B = raw material started in process

Example:

1000 finished product needed from a flow cell

4 operations generating 2%,3%,5%,3% proportion

defective respectively.

How many units must be started in the process?


7/47

Quality Costs

2% 5%3% 3% 1000

1142 1119 1086 1031

31553323


8/47

Not just the mean is important, but also the variance

Need to look at the distribution function

The Concept of Consistency:

Who is the Better Target Shooter?


9/47

Common Cause Variation (low level)

Common Cause Variation (high level)

Assignable Cause Variation

Need to measure and reduce common cause variation

Identify assignable cause variation as soon as possible

Two Types of Causes for Variation


10/47


11/47

SPC Objectives

Insure high quality production by reducing

and controlling process variation.

Identify types of process variation.

Common cause variation: small, randomforces that continually act on a process

Special cause: variation that may be

assigned to abnormal, unpredictable forces Take action whenever a process is judged to

have been influenced by special causes.


12/47

A General SPC Procedure

Periodically select from the process a sample

of items, inspect them, and note the result.

Because ofcommon orspecial causes, the

results of every sample will vary. Determinewhether the cause of the variation is common

or special.

Take action depending on what was

determined in step 2.

This procedure is enacted through the use of control charts


13/47

Time

Process

Parameter

Upper Control Limit (UCL)

Lower Control Limit (LCL)

Center Line

Track process parameter over time

- mean

- percentage defects

Distinguish between- common cause variation

(within control limits)

- assignable cause variation

(outside control limits)

Measure process performance:

how much common cause variation

is in the process while the process

is in control?

Statistical Process Control: Control Charts


14/47

Charting Continuous Variables

The Xbar-R Chart: tracks the mean and

range of a variable calculated from a fixed

sample

The Xbar-S Chart: tracks the mean and

standard deviation of a variable calculated

from a large sample


15/47

The Xbar-R Chart

Collect sample data by sub-group (normally containing 2 - 5 data points): recordthe continuous variable under study.

Compute the mean and range for each sub-group:

Calculate average mean and average range

Compute and draw control limits:

Plot mean and range for each subgroup.

RAxLCLUCLxx 2

/ =

smallestestl

n

xxRn

xxx

x=

+++=

arg

21 ...

RDLCL

RDUCL

R

R

3

4

=

=


16/47

Number of

Observations

in Subgroup

(n)

Factor for X-

bar Chart

(A2)

Factor for

Lower

control Limit

in R chart

(D3)

Factor for

Upper

control limit

in R chart

(D4)

Factor to

estimate

Standard

deviation, (d2)

2 1.88 0 3.27 1.128

3 1.02 0 2.57 1.6934 0.73 0 2.28 2.059

5 0.58 0 2.11 2.326

6 0.48 0 2.00 2.534

7 0.42 0.08 1.92 2.704

8 0.37 0.14 1.86 2.847

9 0.34 0.18 1.82 2.97010 0.31 0.22 1.78 3.078

Parameters for Creating X-bar Charts


17/47

Example of an Xbar-R Chart

Sub-group Obs1 Obs2 Obs3 Obs4 Obs5 Mean Range

1

2

3

456

7

89

10.

25

14.0

13.2

13.5

13.913.013.7

13.9

13.414.4

13.3

13.3

12.6

13.3

12.8

12.413.012.0

12.1

13.612.4

12.4

12.8

13.2

12.7

13.0

13.312.112.5

12.7

13.012.2

12.6

13.0

13.1

13.4

12.8

13.112.212.4

13.4

12.412.4

12.9

12.3

12.1

12.1

12.4

13.213.312.4

13.0

13.512.5

12.8

12.2

Total

Mean

13.00

12.94

12.90

13.1812.7212.60

13.02

13.1812.78

12.80

12.72

323.50

12.94

1.9

1.3

1.1

1.51.21.7

1.8

1.22.2

0.9

1.1

33.80

1.35

Each data point

is the pulling

force applied toa glass strand

before breaking

For 5 obs.

D3=0

D4=2.114

A2=0.577


18/47

Example (cont)

For this example,

the control

limits reduce to:

0)35.1(0

86.2)35.1(114.2

16.12&72.13

35.1)577(.94.12/

==

==

=

=

R

R

xx

LCL

UCL

LCLUCL

1

Sub-group

2

3

Range

12

Sub-group

13

14

Mean


19/47

The Xbar-s Chart

Similar to Xbar-r chart except that a larger sample is taken.

The calculation of control limits may include a sample

standard deviation as an estimate of the population standard

deviation.

Control limits are calculated :

sAxLCLUCLxx 3

/ =sBLCL

sBUCL

s

s

3

4

=

=


20/47

Process capability measure

Estimate standard deviation: Look at standard deviation relative to specification limits Dont confuse control limits with specification limits: a process can be out of

control, yet be incapable

=R/d2

3

Upper

Specification

Limit (USL)

Lower

Specification

Limit (LSL)

X-3 A X-2 A X-1 A X X+1 A X+2

X+3 A

X-6 B X X+6 B

Process A

(with st. dev A)

Process B

(with st. dev B)

6

LSLUSLCp

=

x Cp P{defect} ppm

1 0.33 0.317 317,000

2 0.67 0.0455 45,500

3 1.00 0.0027 2,700

4 1.33 0.0001 63

5 1.67 0.0000006 0,6

6 2.00 2x10-9 0,00

The Statistical Meaning of Six Sigma


21/47

Control Limits and Specification Limits

Control limits of a quality characteristic represent

natural variation in a process

Specification limits indicate acceptable variation set

by the customer

The process capability index is useful in comparison:

The capability index may be adjusted to to consider

how well the process is centered within the limits

6

LSLUSLCp

=

)1( kCC ppk =

K=2 |design target - process average | / specification range


22/47

Process Capability Example

USL=10

LSL=9.5

= .02167.4

)02(.6

5.910=

=pC

9.5 10.0

8334.)8.1(167.4 ==pkC

K=2 |9.75 - 9.95| / .5 = .8


23/47

PC Example (cont)

USL=10

LSL=9.5

= .02167.4

)02(.6

5.910=

=pC

9.5 10.0

917.3)16.1(167.4 ==pkC

K=2 |9.75 - 9.79| / .5 = .16


24/47

Charting Discrete Attributes

Charts that track the number of units

defective

P Chart: fraction of a sample that is defective

given different sample sizes

NP Chart: fraction of a sample that is

defective given constant sample sizes

Att ib t B d C t l Ch t Th h t


25/47

pUCL= + 3 pLCL= - 3

SizeSample

pp )1( =

Estimate average defect percentage

Estimate Standard Deviation

Define control limits

Divide time into:

- calibration period (capability analysis)

- conformance analysis

1 300 18 0.060

2 300 15 0.050

3 300 18 0.060

4 300 6 0.020

5 300 20 0.067

6 300 16 0.053

7 300 16 0.053

8 300 19 0.063

9 300 20 0.067

10 300 16 0.053

11 300 10 0.033

12 300 14 0.047

13 300 21 0.070

14 300 13 0.043

15 300 13 0.04316 300 13 0.043

17 300 17 0.057

18 300 17 0.057

19 300 21 0.070

20 300 18 0.060

21 300 16 0.053

22 300 14 0.047

23 300 33 0.110

24 300 46 0.153

25 300 10 0.033

26 300 12 0.040

27 300 13 0.043

28 300 18 0.060

29 300 19 0.063

30 300 14 0.047

p =0.052

=0.013

=0.091

=0.014

Period n defects p

Attribute Based Control Charts: The p-chart


26/47

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0.180

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Attribute Based Control Charts: The p-chart


27/47

Example of a P Chart

Sub-groupNumber

Sub-groupSize (n)

Number ofDefectives

(np)

PercentDefective

(np/n)100

UCL LCL

1

23

45

6.

Total

115

220210

65220

255

5925

15

1823

518

15

610

13.0

8.211.0

7.78.2

5.9

10.3

18.8

16.516.6

21.616.5

16.0

1.8

4.14.0

0.04.1

4.6

Note: control limits calculated assuming z=3

Quantities of lightbulbs are tested to

see if they function


28/47

Example of a P Chart (cont)

For this example, the control

limits reduce to:)304(.

3103.

0924.3103.

nn=

5

1015

20

25

Percen t

Defective

UCL

LCL

p

Sub-group


29/47

The NP Chart

Similar to the P Chart except assumes

constant sample size

Calculation of the control limits must be

performed only once

nplineCenter =

)1(/ pnpznpLCLUCL =


30/47

Discrete Attributes (cont)

Charts that track the number of defects in

one or more units

U Chart: defects in a variable sized sample

volume

C Chart: defects in a fixed sized sample


31/47

The U Chart

Collect sample data: for each sample record the

number of units sampled (n) and the number of

defects (c)

Compute the number of defects per unit for eachsample sub-group: (u = c/n)

Calculate the mean defects per unit:

Compute and draw control limits

Plot u

n

cu

=

n

uzuLCLUCL =/


32/47

The C Chart

Similar to the U Chart except assumes

constant sample size

Calculation of the control limits must beperformed only once

ClineCenter =

CzCLCLUCL =/


33/47

Example of a C Chart

Sub-groupNumber

Num ber ofDefects

Sub-groupNumber

Num ber ofDefects

123

456789

10

753

438234

3

111213

141516171819

20

Total

632

724742

3

82

Note: control limits calculated assuming z=3

In this example,

a data pointrepresents the

number of rips

found in 5 yards

of nylon fabric


34/47

Example of a C Chart

For this example, the control

limits reduce to: 1.431.4/

1.4

=

=

LCLUCL

C

5

10

Defec

tives

UCL

C

Sub-group


35/47

We assume the process is in an

in control state when:

Points are within the control limits

Consecutive groups of points do not take a particular form.

Runs on one side of the central line (7 out of 7, 10 out of 11,

or 12 out of 14) Trends of a continued rise or fall of points (7 out of 7)

Periodicity or same pattern repeated over equal interval

Hugging the central line (most points within the center half of

the control zone)

Hugging the control limits (2 out of 3, 3 out of 7, or 4 out of

10 points within the outer 1/3 zone)

S C


36/47

Statistical Process Control

Capability

AnalysisConformance

Analysis

Investigate for

Assignable Cause

Eliminate

Assignable Cause

Capability analysis What is the currently "inherent" capability of my process when it is "in control"?

Conformance analysis SPC charts identify when control has likely been lost and assignable cause

variation has occurred

Investigatefor assignable cause Find Root Cause(s) of Potential Loss of Statistical Control

Eliminate or replicate assignable cause

Need Corrective Action To Move Forward


37/47


38/47

CUSTOMER FOCUS

CONTINUOUS

IMPROVEMENT

MANAGEMENT

COMMITMENT

& LEADERSHIP

EMPLOYEE

INVOLVEMENT

ANALYTICAL

PROCESS

THINKING

MGT BY FACTEMPOWERMENT

PLA

NNING

TR

AINING

A Systems View

of Total Quality

Management


39/47

Toyota Production System

Pillars:

1.just-in-time, and

2.autonomation, or automation with a human touch

Practices: setup reduction (SMED)

worker training

vendor relations

quality control

foolproofing (baka-yoke)

many others


40/47

JIT Implementation

Adopt goal to eliminate all forms of waste

Improve workplace cleanliness and order

Promote flow manufacturing Level production requirements

Improve and standardize all process steps


41/47

The Seven Zeros

Zero Defects: To avoid delays due to defects. (Quality at the

source)

Zero (Excess) Lot Size: To avoid waiting inventory delays.

(Usually stated as a lot size of one.)

Zero Setups: To minimize setup delay and facilitate small lot sizes.

Zero Breakdowns: To avoid stopping tightly coupled line.

Zero (Excess) Handling: To promote flow of parts.

Zero Lead Time: To ensure rapid replenishment of parts (very

close to the core of the zero inventories objective).

Zero Surging: Necessary in system without WIP buffers.


42/47

Cross Training and Plant Layout

Cross Training:

Adds flexibility to inherently inflexible system

Allows capacity to float to smooth flow

Reduces boredom

Fosters appreciation for overall picture

Increase potential for idea generation


43/47

Plant Layout:

Promote flow with little WIP

Facilitate workers staffing multiple machines

U-shaped cells

Maximum visibility Minimum walking

Flexible in number of workers

Facilitates monitoring of work entering and leaving cell

Workers can conveniently cooperate to smooth flow andaddress problems


44/47

U-Shaped Manufacturing Cell

Inbound Stock Outbound Stock


45/47

Kanban

Definition: A kanban is a sign-board or card in Japanese and

is the name of the flow control system developed by Toyota.

Role:

Kanban is a tool for realizing just-in-time. For this tool to work

fairly well, the production process must be managed to flow asmuch as possible. This is really the basic condition. Other

important conditions are leveling production as much as possible

and always working in accordance with standard work methods.

Ohno 1988

Push vs. Pull: Kanban is a pull system Push systems schedule releases

Pull systems authorizereleases


46/47

One-Card Kanban

Outboundstockpoint

Outboundstockpoint

Production

cards

Completed parts with cardsenter outbound stockpoint.

When stock is

removed, placeproduction cardin hold box.

Productioncard authorizesstart of work.


47/47

The Lessons of JIT

The production environment itself is a control

Operational details matter strategically

Controlling WIP is important

Speed and flexibility are important assets

Quality can come first

Continual improvement is a condition for survival

09 quality and jit spring06

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