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Quality Management “ It costs a lot to produce a bad product. ” Norman Augustine. Cost of quality. Prevention costs Appraisal costs Internal failure costs External failure costs Opportunity costs. What is quality management all about?.

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slide1

Quality Management

“It costs a lot to produce a bad product.”Norman Augustine

slide2

Cost of quality

  • Prevention costs
  • Appraisal costs
  • Internal failure costs
  • External failure costs
  • Opportunity costs
slide3

What is quality management all about?

Try to manage all aspects of the organization in order to excel in all dimensions that are important to “customers”

Two aspects of quality:

features: more features that meet customer needs = higher quality

freedom from trouble: fewer defects = higher quality

the quality gurus edward deming
The Quality Gurus – Edward Deming
  • Quality is “uniformity and dependability”
  • Focus on SPC and statistical tools
  • “14 Points” for management
  • PDCA method

1900-1993

1986

the quality gurus joseph juran
The Quality Gurus – Joseph Juran
  • Quality is “fitness for use”
  • Pareto Principle
  • Cost of Quality
  • General management approach as well as statistics

1904 - 2008

1951

slide6

History: how did we get here…

  • Deming and Juran outlined the principles of Quality Management.
  • Tai-ichi Ohno applies them in Toyota Motors Corp.
  • Japan has its National Quality Award (1951).
  • U.S. and European firms begin to implement Quality Management programs (1980’s).
  • U.S. establishes the Malcolm Baldridge National Quality Award (1987).
  • Today, quality is an imperative for any business.
slide7

Technical

Tools

(Process Analysis, SPC, QFD)

Customer

Cultural

Alignment

What does Total Quality Management encompass?

  • TQM is a management philosophy:
  • continuous improvement
  • leadership development
  • partnership development
slide8

Design quality

Dimensions of quality

Conformance quality

Developing quality specifications

Design

Input

Process

Output

six sigma quality
A philosophy and set of methods companies use to eliminate defects in their products and processes

Seeks to reduce variation in the processes that lead to product defects

The name “six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs

Six Sigma Quality
six sigma roadmap dmaic

Define

    • Customers, Value, Problem Statement
    • Scope, Timeline, Team
    • Primary/Secondary & OpEx Metrics
    • Current Value Stream Map
    • Voice Of Customer (QFD)
  • Measure
    • Assess specification / Demand
    • Measurement Capability (Gage R&R)
    • Correct the measurement system
    • Process map, Spaghetti, Time obs.
    • Measure OVs & IVs / Queues
  • Analyze (andfix the obvious)
    • Root Cause (Pareto, C&E, brainstorm)
    • Find all KPOVs & KPIVs
    • FMEA, DOE, critical Xs, VA/NVA
    • Graphical Analysis, ANOVA
    • Future Value Stream Map
  • Improve
    • Optimize KPOVs & test the KPIVs
    • Redesign process, set pacemaker
    • 5S, Cell design, MRS
    • Visual controls
    • Value Stream Plan
  • Control
    • Document process (WIs, Std Work)
    • Mistake proof, TT sheet, CI List
    • Analyze change in metrics
    • Value Stream Review
    • Prepare final report

Validate Project $

Validate Project $

Validate Project $

Validate Project $

Six Sigma Roadmap (DMAIC)

Next Project

Celebrate Project $

quality improvement
Quality Improvement

Continuous Improvement

Quality

Traditional

Time

slide14

Plan

Do

Act

Check

Continuous improvement philosophy

  • Kaizen: Japanese term for continuous improvement. A step-by-step improvement of business processes.
  • PDCA: Plan-do-check-act as defined by Deming.
  • Benchmarking : what do top performers do?
slide16

Performance

Time

Tools used for continuous improvement

2. Run Chart

slide17

Tools used for continuous improvement

3. Control Charts

Performance Metric

Time

slide18

Machine

Man

Environment

Method

Material

Tools used for continuous improvement

4. Cause and effect diagram (fishbone)

slide20

Frequency

Tools used for continuous improvement

6. Histogram

slide21

Tools used for continuous improvement

7. Pareto Analysis

100%

60

75%

50

40

Frequency

50%

Percentage

30

20

25%

10

0%

A

B

C

D

E

F

slide22

Summary of Tools

  • Process flow chart
  • Run diagram
  • Control charts
  • Fishbone
  • Check sheet
  • Histogram
  • Pareto analysis
slide23

Case: shortening telephone waiting time…

  • A bank is employing a call answering service
  • The main goal in terms of quality is “zero waiting time”

- customers get a bad impression

- company vision to be friendly and easy access

  • The question is how to analyze the situation and improve quality
slide24

Customer B

The current process

Operator

Receiving

Party

Customer A

How can we reduce waiting time?

slide25

Absent receiving party

Working system of operators

Absent

Too many phone calls

Lunchtime

Out of office

Makes customer wait

Not at desk

Absent

Not giving receiving party’s coordinates

Does not understand customer

Lengthy talk

Does not know organization well

Complaining

Takes too much time to explain

Leaving a message

Customer

Operator

Fishbone diagram analysis

slide26

Reasons why customers have to wait

(12-day analysis with check sheet)

slide27

Frequency

Percentage

87.1%

300

250

71.2%

200

49%

150

100

0%

A

B

C

D

E

F

Pareto Analysis: reasons why customers have to wait

slide28

Ideas for improvement

  • Taking lunches on three different shifts
  • Ask all employees to leave messages when leaving desks
  • Compiling a directory where next to personnel’s name appears her/his title
slide29

Percentage

Percentage

Frequency

Frequency

100%

87.1%

300

300

71.2%

Improvement

200

200

49%

100

100

100%

0%

0%

A

B

C

D

E

F

B

C

A

D

E

F

Results of implementing the recommendations

…After

Before…

slide30

In general, how can we monitor quality…?

By observing

variation in

output measures!

  • Assignable variation: we can assess the cause
  • Common variation: variation that may not be possible to correct (random variation, random noise)
slide31

Statistical Process Control (SPC)

Every output measure has a target value and a level of “acceptable” variation (upper and lower tolerance limits)

SPC uses samples from output measures to estimate the

mean and the variation (standard deviation)

Example

We want beer bottles to be filled with 12 FL OZ ± 0.05 FL OZ

Question:

How do we define the output measures?

slide32

In order to measure variation we need…

The average (mean) of the observations:

The standard deviation of the observations:

average variation example
Average & Variation example

Number of pepperoni’s per pizza: 25, 25, 26, 25, 23, 24, 25, 27

Average:

Standard Deviation:

Number of pepperoni’s per pizza: 25, 22, 28, 30, 27, 20, 25, 23

Average:

Standard Deviation:

Which pizza would you rather have?

slide34

High

Incremental

Cost of

Variability

Zero

Lower

Tolerance

Target

Spec

Upper

Tolerance

Traditional View

When is a product good enough?

a.k.a

Upper/Lower Design Limits

(UDL, LDL)

Upper/Lower Spec Limits

(USL, LSL)

Upper/Lower Tolerance Limits

(UTL, LTL)

The “Goalpost” Mentality

but are all good products equal

High

Incremental

Cost of

Variability

Zero

Lower

Spec

Target

Spec

Upper

Spec

But are all ‘good’ products equal?

Taguchi’s View

“Quality Loss Function”

(QLF)

LESS VARIABILITY implies BETTER PERFORMANCE !

slide36

Capability Index (Cpk)

It shows how well the performance measure fits the design specification based on a given tolerance level

A process is ks capable if

slide37

Capability Index (Cpk)

Another way of writing this is to calculate the capability index:

Cpk < 1 means process is not capable at the ks level

Cpk >= 1 means process is capable at the ks level

slide38

Accuracy and Consistency

We say that a process is accurate if its mean is close to

the target T.

We say that a process is consistent if its standard deviation

is low.

slide39

LTL

UTL

X

Example 1: Capability Index (Cpk)

X = 10 and σ = 0.5

LTL = 9

UTL = 11

slide40

Example 2: Capability Index (Cpk)

X = 9.5 and σ = 0.5

LTL = 9

UTL = 11

LTL

UTL

X

slide41

Example 3: Capability Index (Cpk)

X = 10 and σ = 2

LTL = 9

UTL = 11

LTL

UTL

X

example
Example
  • Consider the capability of a process that puts pressurized grease in an aerosol can. The design specs call for an average of 60 pounds per square inch (psi) of pressure in each can with an upper tolerance limit of 65psi and a lower tolerance limit of 55psi. A sample is taken from production and it is found that the cans average 61psi with a standard deviation of 2psi.
  • Is the process capable at the 3s level?
  • What is the probability of producing a defect?
solution
Solution

LTL = 55 UTL = 65 s = 2

No, the process is not capable at the 3s level.

solution44
Solution

P(defect) = P(X<55) + P(X>65)

=P(X<55) + 1 – P(X<65)

=P(Z<(55-61)/2) + 1 – P(Z<(65-61)/2)

=P(Z<-3) + 1 – P(Z<2)

=G(-3)+1-G(2)

=0.00135 + 1 – 0.97725 (from standard normal table)

= 0.0241

2.4% of the cans are defective.

example contd
Example (contd)

Suppose another process has a sample mean of 60.5 and

a standard deviation of 3.

Which process is more accurate? This one.

Which process is more consistent? The other one.

slide46

Upper Control Limit

Central Line

Lower Control Limit

Control Charts

Control charts tell you when a process measure is exhibiting abnormal behavior.

slide47

Two Types of Control Charts

  • X/R Chart
  • This is a plot of averages and ranges over time (used for performance measures that are variables)
  • p Chart
  • This is a plot of proportions over time (used for performance measures that are yes/no attributes)
slide48

Statistical Process Control with p Charts

When should we use p charts?

  • When decisions are simple “yes” or “no” by inspection
  • When the sample sizes are large enough (>50)
slide49

Statistical Process Control with p Charts

Let’s assume that we take t samples of size n …

slide51

UCL = 0.117

p = 0.066

LCL = 0.015

Statistical Process Control with p Charts

slide52

Statistical Process Control with X/R Charts

When should we use X/R charts?

  • It is not possible to label “good” or “bad”
  • If we have relatively smaller sample sizes (<20)
slide53

X is the mean for each sample

Statistical Process Control with X/R Charts

Take t samples of size n (sample size should be 5 or more)

R is the range between the highest and the lowest for each sample

slide54

X is the average of the averages.

R is the average of the ranges

Statistical Process Control with X/R Charts

slide55

Statistical Process Control with X/R Charts

define the upper and lower control limits…

Read A2, D3, D4 from

Table TN 8.7

slide57

Example: SPC for bottle filling…

Calculate the average and the range for each sample…

slide58

Then…

is the average of the averages

is the average of the ranges

slide59

Finally…

Calculate the upper and lower control limits

slide60

X = 12.00

The X Chart

UCL = 12.10

LCL = 11.90

slide61

UCL = 0.32

R = 0.15

LCL = 0.00

The R Chart

slide62

UCL

X

UCL

LCL

R

LCL

The X/R Chart

What can you conclude?