1 chapter 9 managing flow variability managing flow variability: process control and capability...

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Chapter 9 Managing Flow Variability

Managing Flow Variability: Process

Control and Capability

Managing Business Process Flows:

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Managing Flow Variability

9.1 Performance Variability

9.2 Analysis of Variability

9.3 Process Control

9.4 Process Capability

9.5 Process Capability Improvement

9.6 Product and Process Design

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Great year…….Great Products!Service!Reputation!

Congratulations!!

Good Job everyone!

Sorry to burst the bubble... Butwe are not doing well. You’re

Fired

I heard customers are not satisfied with our products and services

Hhhmmm… we need hard data.

We need to identify, correct and prevent future problems!

Yikes…more work

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All Products & Services VARY in Terms Of


CostCostQualityQuality AvailabilityAvailability

FlowFlowTimesTimes

Variability often leads to Customer Dissatisfaction

Chapter covers some geographical/statistical methods for measuring, analyzing, controlling & reducing variability in product & process performance to improve customer satisfaction

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§ 9.1 Performance Variability

All measures of product & process performance (external & internal) display Variability. External Measurements - customer satisfaction, relative product

rankings, customer complaints (vary from one market survey to the next) Possible sources: supplier delivery delays or changing tastes

Internally - flow units in all business processes vary with respect to cost, quality & flow times

Possible sources: untrained workers or imprecise equipment

Example 1 ~ No two cars rolling off an assembly line are identical. Even under identical circumstances, the time & cost required to produce the same product could be quite different.

Example 2 ~ Cost of operating a department within a company can vary from one quarter to the next.

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Variability refers to a discrepancy between the actual and

the expected performance.

Can be due to gap between the following: What customer wants and what product is designed for

What product design calls for and what process for making it is capable of producing

What process is capable of producing and what it actually produces

How the produced product is expected to perform and how it actually performs

How the product actually performs and how the customer perceives it

This often leads to:

higher costs, longer flow times, lower quality &

DISSATISFIED CUSTOMERS

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Processes with greater performance variability are generally judged

LESS satisfactory than those with consistent, predictable

performance.

Variability in product & process performance, not just its average,

Matters to consumers!

Customers perceive any variation in their product or service from

what they expected as a LOSS IN VALUE.

In general, a product is classified as defective if its cost, quality,

availability or flow time differ significantly from their expected values,

leading to dissatisfied customers.

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Quality Management Terms

BOOK COVERS A FEW QUALITY MANAGEMENT TERMS:

Quality of Design: how well product specifications aim to meet customer

requirements (what we promise consumers ~ in terms of what the product can do)

Quality Function Deployment (QFD): conceptual framework for translating

customers’ functional requirements (such as ease of operation of a door or its

durability) into concrete design specifications (such as the door weight should be

between 75 and 85 kg.)

Quality of conformance: how closely the actual product conforms to the chosen

design specifications (how well we keep our promise in terms of how it actually

performs)

Measures: fraction of output that meets specifications, # defects per car,

percentage of flights delayed for more than 15 minutes OR the number of

reservation errors made in a specific period of time.

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§ 9.2 Analysis of Variability

To analyze and improve variability there are diagnostic tools to help us:

Monitor the actual process performance over time

Analyze variability in the process

Uncover root causes

Eliminate those causes

Prevent them from recurring in the future

Again we will use MBPF Inc. as an example and look at how their

customers perceive the experience of doing business with the company &

how it can be improved.

– Need to present raw data in a way to make sense of the numbers,

track change over time, or identify key characteristics of the data set.

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§ 9.2.1 Check Sheets

A check sheet is simply a tally of the types and

frequency of problems with a product or a service

experienced by customers.

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Example 9.1

Type of Complaint Number of Complaints

Cost IIII IIII

Response Time IIII

Customization IIII

Service Quality IIII IIII IIII

Door Quality IIII IIII IIII IIII IIII

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Check Sheets

Pros

Easy to collect data

Cons

Not very enlightening

No numerical characteristics

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§ 9.2.2 Pareto Charts

A Pareto chart is simply a bar chart that plots

frequencies of occurrences of problem types in

decreasing order.

The 80-20 Pareto principle states that 20% of problem

types account for 80% of all occurrences.

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Example 9.2

0

5

10

15

20

25

Door Quality Service Quality Cost Response Time Customization

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Pareto Charts

Pros

Ranks problems

Shows relative size of quantities

Cons


Only categorizes data

No comparison process information

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§ 9.2.3 Histograms

A histogram is a bar plot that displays the frequency

distribution of an observed performance characteristic.

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Example 9.3

0

2

4

6

8

10

12

14

72 74 76 78 80 82 84 86 88 90 92

Weight (kg)

Fre

qu

en

cy

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Histograms

Pros

Visualizes data distribution

Shows relative size of quantities

Cons


Dependant on category size

No focus on change over time

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Table 9.1

Day

Time 1 2 3 4 5 6 7 8 9 10

9:00 AM 81 82 80 74 75 81 83 86 88 82

11:00 AM 73 87 83 81 86 86 82 83 79 84

1:00 PM 85 88 76 91 82 83 76 82 86 89

3:00 PM 90 78 84 75 84 88 77 79 84 84

5:00 PM 80 84 82 83 75 81 78 85 85 80

Day

Time 11 12 13 14 15 16 17 18 19 20

9:00 AM 86 86 88 72 84 76 74 85 82 89

11:00 AM 84 83 79 86 85 82 86 85 84 80

1:00 PM 81 78 83 80 81 83 83 82 83 90

3:00 PM 81 80 83 79 88 84 89 77 92 83

5:00 PM 87 83 82 87 81 79 83 77 84 77

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Raw Data

Pros

Actual information

Specific numbers

Cons

Not intuitive

Does not help with understanding of relationships

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§ 9.2.4 Run Charts

A run chart is a plot of some measure of process

performance monitored over time

Advantage is that it is dynamic

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Example 9.4

70

75

80

85

90

95

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

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Run Charts

Pros

Shows data in chronological order

Displays relative change over time (trends, seasonality)

Cons

Erratic graph


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§ 9.2.5 Multi-Vari Charts

A multi-vari chart is a plot of high-average-low values of

performance measurement sampled over time.

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Example 9.5

70

75

80

85

90

95

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

High

Low

Average

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Table 9.2

Day

1 2 3 4 5 6 7 8 9 10

High 90 88 84 91 86 88 83 86 88 89

Low 73 78 76 74 75 81 76 79 79 80

Average 81.8 83.8 81.0 80.8 80.4 83.8 79.2 83.0 84.4 83.8

Day

11 12 13 14 15 16 17 18 19 20

High 87 86 88 87 88 84 89 85 92 90

Low 81 78 79 72 81 76 74 77 82 77

Average 83.8 82.0 83.0 80.8 83.8 80.8 83.0 81.2 85.0 83.8

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Multi-Vari Charts

Pros

Shows numerical range and average

Displays relative change over time

Cons

Erratic graph


Lacks distribution information

Does not provide guidance for taking actions

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§ 9.3 Process Control

Goal Actual Performance vs. Planned Performance Involves

Tracking Deviations

Taking Corrective Actions Principle of feedback control of dynamical systems

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Plan-Do-Check-Act (PDCA)

Process planning and process control are similar to the Plan-Do-

Check-Act (PDCA) cycle.

PDCA cycle…

“involves planning the process, operating it, inspecting its

output, and adjusting it in light of the observation.”

Performed continuously to monitor and improve the process

performance

Main Problems

When to Act ….

Variances beyond control …

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Process Control

Two types of variability

1. Normal variability

– Statistically predictable

– Structural variability and

stochastic variability

– Variations due to random

causes only (worker

cannot control)

– PROCESS IS IN

CONTROL

– Process design

improvement

2. Abnormal variability

– Unpredictable

– Disturbs state of statistical

equilibrium of the process

– Identifiable and can be

removed (worker can

control)

– Abnormal - due to

assignable causes

– PROCESS IS OUT OF

CONTROL

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Process Control

The short run goal is:

Estimate normal stochastic variability.

Accept it as an inevitable and avoid tampering

Detect presence of abnormal variability

Identify and eliminate its sources

The long run goal is to reduce normal variability by improving

process.

When is observed variability normal and abnormal???

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§ 9.3.3 Control Limit Policy

Control Limit Policy

Control band

Range within variation in performance normal

Due to causes that cannot be identified or eliminated in short run

Leave alone and do not tamper

Variability outside this range is abnormal

Due to assignable causes

Investigate and correct

Applications

Inventory, Process Flow

Cash management

Stock trading

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9.3.4 Control Charts … Continued

LCL = - z UCL = + z

The smaller the value of “z”, the tighter the control

- expected value of the performance

UCL and LCL

Standard Deviation Assign z

Process Control Chart:

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Within the control band Performance variability is

normal

Outside the control band Process is “out of control”

Data Misinterpretation

Type I error, : Process is “in control”, but data outside

the Control Band

Type II error, : Process is “out of control”, but data inside

the Control Band

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Acceptable

Frequency

“z” too small

unnecessary

investigation;

additional cost

“z” to large

accept more

variations, less

costly

Optimal Degree of Control

In practice, a value of z = 3 is used

99.73% of all measurements will fall within the “normal” range

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Average and Variation Control Charts

- To calculate:

Calculate the average value, A1, A2….AN

Calculate the variance of each sample, V1, V2….VN

A = /n (n = sample size)

LCL = - z/n and UCL = + z/n

Take it one step further:

Estimate by the overall average of all the sample averages, A A = (A1+ A2+…+AN) / N (N = # of samples)

Also estimate by the standard deviation of all N x n observations, S

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New, Improved equations for UCL and LCL are:

LCL = A - zs/n and UCL = A + zs/n

CalculateV -- the average variance of the sample variances

V = (V1+ V2+…+VN) / N (N = # of samples)

Also calculate SV -- the standard deviation of the variances

Sample Variances

LCL = V - z sV and UCL = V + z sV

If fall within this range Process Variability is stable

If not within this range Investigate cause of abnormal variations


Variance

Control Limits

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Garage Door Example revisited…

Ex: A1 = (81 + 73 + 85 + 90 + 80) / 5 = 81.8 kg

Ex: V1 = (90 - 73) = 17 kg

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Average Weights of Garage Door Samples:

A = 82.5 kg V = 10.1 kg

Std. Dev. of Door Weights: s = 4.2 kgStd. Dev. of Sample Variances: sV = 3.5 kg

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Average Weight Control Chart

74

76

78

80

82

84

86

88

90

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Days

Ave

rag

e W

t. (

Kg

)

`

UCL = 88.13

LCL = 76.87

Let z = 3 Sample Averages

UCL = A + zs/n = 82.5 + 3 (4.2) / 5 = 88.13

LCL = A - zs/n = 82.5 – 3 (4.2) / 5 = 76.87

Process is Stable!

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Let z = 3 Sample Variances

UCL = V + z sV = 10.1 + 3 (3.5) = 20.6

LCL = V - zs sV = 10.1 – 3 (3.5) = - 0.4

Variance Control Chart

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Days

Var

ian

ce (

ran

ge)

of

Wt.

(K

g)

UCL = 20.6

LCL = 0

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Extensions

Continuous Variables - Garage Door Weights, Processing Costs, Customer Waiting Time

Discrete Variables - Number of Customer Complaints, Whether a Flow Unit is Defective, Number of Defects per Flow Unit Produced

Use Normal distribution

Use Binomial or Poisson distribution

Control Limit formula differs, but basic principles is same.

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9.3.5 Cause-Effect Diagrams

Cause-Effect Diagrams

Now what?!!

Answer 5 “WHY” Questions !

Sample

Observations

Plot

Control Charts

Abnormal

Variability !!

Brainstorm Session!!

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9.3.5 Cause-Effect Diagrams … Continued

Why…? Why…? Why…?

Our famous “Garage Door” Example:

1. Why are these doors so heavy? Because the Sheet Metal was too ‘thick’.

2. Why was the sheet metal too thick? Because the rollers at the steel mill were

set incorrectly.

3. Why were the rollers set

incorrectly?

Because the supplier is not able to meet

our specifications.

4. Why did we select this supplier? Because our Project Supervisor was too

busy getting the product out – didn’t have

time to research other vendors.

5. Why did he get himself in this

situation?

Because he gets paid by meeting the

production quotas.

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9.3.5 Cause-Effect Diagrams … Continued

Fishbone Diagram

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9.3.6 Scatter Plots

The Thickness of the Sheet Metals

Change Settings on Rollers

Measure the Weight of the Garage Doors

Determine Relationship between the two

Plot the results on a graph:

Roller Settings & Garage Door Weights

0

5

10

15

20

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Roller Setting (mm)

Do

or

Wei

gh

t (

Kg

)

Scatter Plot

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9.3 Section Summary

Process Control involves

– Dynamic Monitoring

– Ensure variability in performance is due to normal random causes only

– Detect abnormal variability and eliminate root causes

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9.4 Process Capability

Ease of external product measures (door operations and durability) and

internal measures (door weight)

Product specification limits vs. process control limits

Individual units, NOT sample averages - must meet customer specifications.

Once process is in control, then the estimates of μ (82.5kg) and σ (4.2k) are

reliable. Hence we can estimate the process capabilities.

Process capabilities - the ability of the process to meet customer

specifications

Three measures of process capabilities:

9.4.1 Fraction of Output within Specifications

9.4.2 Process Capability Ratios (Cpk and Cp)

9.4.3 Six-Sigma Capability

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9.4.1 Fraction of Output within Specifications

To compute for fraction of process that meets customer specs:

Actual observation (see Histogram, Fig 9.3)

Using theoretical probability distribution

Ex. 9.7:

US: 85kg; LS: 75 kg (the range of performance variation that

customer is willing to accept)

See figure 9.3 Histogram: In an observation of 100 samples, the process

is 74% capable of meeting customer requirements, and 26%

defectives!!!

OR:

Let W (door weight): normal random variable with mean = 82.5 kg and

standard deviation at 4.2 kg,

Then the proportion of door falling within the specified limits is:

Prob (75 ≤ W ≤ 85) = Prob (W ≤ 85) - Prob (W ≤ 75)

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9.4.1 Fraction of Output within Specifications cont…

Let Z = standard normal variable with μ = 0 and σ = 1, we can use the

standard normal table in Appendix II to compute:

AT US:

Prob (W≤ 85) in terms of:

Z = (W-μ)/ σ

As Prob [Z≤ (85-82.5)/4.2] = Prob (Z≤.5952) = .724 (see Appendix II)

(In Excel: Prob (W ≤ 85) = NORMDIST (85,82.5,4.2,True) = .724158)

AT LS:

Prob (W ≤ 75)

= Prob (Z≤ (75-82.5)/4.2) = Prob (Z ≤ -1.79) = .0367 in Appendix II(In Excel: Prob (W ≤ 75) = NORMDIST(75,82.5,4.2,true) = .037073)

THEN:

Prob (75≤W≤85)

= .724 - .0367 = .6873

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9.4.1 Fraction of Output within Specifications cont…

SO with normal approximation, the process is capable of producing 69% of

doors within the specifications, or delivering 31% defective doors!!!

Specifications refer to INDIVIDUAL doors, not AVERAGES.

We cannot comfort customer that there is a 31% chance that they’ll get doors

that are either TOO LIGHT or TOO HEAVY!!!

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9.4.2 Process Capability Ratios (C pk and Cp)

2nd measure of process capability that is easier to compute is the process capability ratio (Cpk)

If the mean is 3σ above the LS (or below the US), there is very little chance of a product falling

below LS (or above US).

So we use:

(US- μ)/3σ (.1984 as calculated later)

and (μ -LS)/3σ (.5952 as calculated later)

as measures of how well process output would fall within our specifications.

The higher the value, the more capable the process is in meeting specifications.

OR take the smaller of the two ratios [aka (US- μ)/3σ =.1984] and define a single measure of

process capabilities as:

Cpk = min[(US-μ/)3σ, (μ -LS)/3σ] (.1984, as calculated later)

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Cpk of 1+- represents a capable process

Not too high (or too low)

Lower values = only better than expected quality

Ex: processing cost, delivery time delay, or # of error per transaction process

If the process is properly centered

– Cpk is then either:

(US- μ)/3σ or (μ -LS)/3σ

As both are equal for a centered process.

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9.4.2 Process Capability Ratios (C pk and Cp) cont…

Therefore, for a correctly centered process, we may simply define the

process capability ratio as:

– Cp = (US-LS)/6σ (.3968, as calculated later)

Numerator = voice of the customer / denominator = the voice of the

process

Recall: with normal distribution:

Most process output is 99.73% falls within +-3σ from the μ.

Consequently, 6σ is sometimes referred to as the natural tolerance of the

process.

Ex: 9.8

Cpk = min[(US- μ)/3σ , (μ -LS)/3σ ]

= min {(85-82.5)/(3)(4.2)], (82.5-75)/(3)(4.2)]}

= min {.1984, .5952}

=.1984

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If the process is correctly centered at μ = 80kg (between 75 and 85kg), we

compute the process capability ratio as

Cp = (US-LS)/6σ

= (85-75)/[(6)(4.2)] = .3968

NOTE: Cpk = .1984 (or Cp = .3968) does not mean that the process is capable of

meeting customer requirements by 19.84% (or 39.68%), of the time. It’s about

69%.

Defects are counted in parts per million (ppm) or ppb, and the process is assumed

to be properly centered. IN THIS CASE, If we want no more than 100 defects per

million (.01% defectives), we SHOULD HAVE the probability distribution of door

weighs so closely concentrated around the mean that the standard deviation is

1.282 kg, or Cp=1.3 (see Table 9.4) Test: σ = (85-75)/(6)(1.282)] = 1.300kg

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Table 9.4

Table 9.4 Relationship Between Process Capability Ratio and Proportion DefectiveDefects (ppm) 10000 1000 100 10 1 2 ppbCp 0.86 1 1.3 1.47 1.63 2

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9.4.3 Six-Sigma Capability

Sigma measure

S = min[(US- μ /σ), (μ -LS)/σ]

(= min(.5152,1.7857) = .5152 to be calculated later)

S-Sigma process

If process is correctly centered at the middle of the specifications,

S = [(US-LS)/2σ]

Ex: 9.9

Currently the sigma capability of door making process is

S=min[(85-82.5)/(4.2), (82.5-75)/4.2] = .5952

By centering the process correctly, its sigma capability increases to

S=min(85-75)/[(2)(4.2)] = 1.19

THUS, with a 3σ that is correctly centered, the US and LS are 3σ away from the

mean, which corresponds to Cp=1, and 99.73% of the output will meet the

specifications.

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9.4.3 Six-Sigma Capability cont…

Correctly centered six-sigma process has a standard deviation so small that

the US and LS limits are 6σ from the mean each.

Extraordinary high degree of precision.

Corresponds to Cp=2 or 2 defective units per billion produced!!! (see Table

9.5)

In order for door making process to be a six-sigma process, its standard

deviation must be:

σ = (85-75)/(2)(6)] = .833kg

Adjusting for Mean Shifts

- +-1.5 standard deviation from the center of specifications.

- Producing an average of 3.4 defective units per million. (see table 9.5)

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Table 9.5

Table 9.5 Fraction Defective and Sigma MeasureSigma S 3 4 5 6Capability Ratio Cp 1 1.33 1.667 2Defects (ppm) 66810 6210 233 3.4

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Why Six-Sigma?

– See table 9.5

– Improvement in process capabilities from a 3-sigma to 4-sigma = 10-fold

reduction in the fraction defective (66810 to 6210 defects)

– While 4-sigma to 5-sigma = 30-fold improvement (6210 to 232 defects)

– While 5-sigma to 6-sigma = 70-fold improvement (232 to 3.4 defects, per

million!!!).

Average companies deliver about 4-sigma quality, where best-in-class

companies aim for six-sigma.

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Why High Standards?

The overall quality of the entire product/process that requires ALL of

them to work satisfactorily will be significantly lower.

Ex:

If product contains 100 parts and each part is 99% reliable, the chance

that the product (all its parts) will work is only (.99)100 = .366, or

36.6%!!!

Also, costs associated with each defects may be high

Expectations keep rising

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Safety capability

We may also express process capabilities in terms of the desired

margin [(US-LS)-zσ] as safety capability

It represents an allowance planned for variability in supply and/or

demand

Greater process capability means less variability

If process output is closely clustered around its mean, most of the

output will fall within the specifications

Higher capability thus means less chance of producing defectives

Higher capability = robustness

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9.4.4 Capability and Control

In Ex. 9.7: the production process is not performing well in terms of MEETING THE

CUSTOMER SPECIFICATIONS. Only 69% meets output specifications!!! (See 9.4.1:

Fraction of Output within Specifications)

Yet in example 9.6, “the process was in control!!!”, or within us & ls limits.

Being in control and meeting specifications are two different measures of performance.

The former indicates internal stability, the latter indicates the ability to meet the customers

specifications.

Observation of a process in control ensures that the resulting estimates of the process

mean and standard deviation are reliable so that our measurement of the process

capability is accurate.

The final step is to improve process capability, so it is satisfactory from the customers

viewpoint as well.

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9.5 Process Capability Improvement

How do we improve the process capability?

Shift the process mean

Reduce the variability

Both

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9.5.1 Mean Shift

Examine where the current process mean lies in comparison to the

specification range (i.e. closer to the LS or the US)

Alter the process to bring the process mean to the center of the specification

range in order to increase the proportion of outputs that fall within

specification

66


Ex 9.10

MBPF garage doors (currently)

specification range: 75 to 85 kgs

process mean: 82.5 kgs

proportion of output falling within specifications: .6873

The process mean of 82.5 kgs was very close to the US of 85 kgs (i.e. too

thick/heavy)

To lower the process mean towards the center of the specification range the

supplier could change the thickness setting on their rolling machine.

67


Ex 9.10 Continued

Center of the specification range: (75 + 85)/2 = 80 kgs

New process mean: 80 kgs

If the door weight (W) is a normal random variable, then the proportion of

doors falling within specifications is: Prob (75 =< W =< 85)

Prob (W =< 85) – Prob (W =< 75)

Z = (weight – process mean)/standard deviation

Z = (85 – 80)/4.2 = 1.19

Z = (75 – 80)/4.2 = -1.19

68


Ex 9.10 Continued

[from table A2.1 on page 319]

Z = 1.19 .8830

Z = -1.19 (1 - .8830) .1170 Prob (W =< 85) – Prob (W =< 75) =

.8830 - .1170 = .7660

By shifting the process mean from 82.5 kgs to 80 kgs, the proportion of

garage doors that falls within specifications increases from .6873 to .7660

69


9.5.2 Variability Reduction

Measured by standard deviation

A higher standard deviation value means higher variability amongst outputs

Lowering the standard deviation value would ultimately lead to a greater

proportion of output that falls within the specification range

70


9.5.2 Variability Reduction Continued

Possible causes for the variability MBPF experienced are:

old equipment

poorly maintained equipment

improperly trained employees

Investments to correct these problems would decrease variability however

doing so is usually time consuming and requires a lot of effort

71


Ex 9.11

Assume investments are made to decrease the standard deviation from 4.2

to 2.5 kgs

The proportion of doors falling within specifications: Prob (75 =< W =<

85)

Prob (W =< 85) – Prob (W =< 75)

Z = (weight – process mean)/standard deviation

Z = (85 – 80)/2.5 = 2.0

Z = (75 – 80)/2.5 = -2.0

72


Ex 9.11 Continued

[from table A2.1 on page 319]

Z = 2.0 .9772

Z = -2.0 (1 - .9772) .0228 Prob (W =< 85) – Prob (W =< 75) =

.9772 - .0228 = .9544

By shifting the standard deviation from 4.2 kgs to 2.5 kgs and the process

mean from 82.5 kgs to 80 kgs, the proportion of garage doors that falls within

specifications increases from .6873 to .9544

73


9.5.3 Effect of Process Improvement on Process Control

Changing the process mean or variability requires re-calculating the control

limits

This is required because changing the process mean or variability will also

change what is considered abnormal variability and when to look for an

assignable cause

74



Reducing the variability from product and process design

simplification

standardization

mistake proofing

75


Simplification

Reduce the number of parts (or stages) in a product (or process)

less chance of confusion and error

Use interchangeable parts and a modular design

simplifies materials handling and inventory control

Eliminate non-value adding steps

reduces the opportunity for making mistakes

76


Standardization

Use standard parts and procedures

reduces operator discretion, ambiguity, and opportunity for making

mistakes

77


Mistake Proofing

Designing a product/process to eliminate the chance of human error

ex. color coding parts to make assembly easier

ex. designing parts that need to be connected with perfect symmetry or

with obvious asymmetry to prevent assembly errors

78


9.6.2 Robust Design

Designing the product in a way so its actual performance will not be affected

by variability in the production process or the customer’s operating

environment

The designer must identify a combination of design parameters that protect

the product from the process related and environment related factors that

determine product performance

79



Summary

Variability is inevitable. It is a problem when it creates process instability,

lower capability, and customer dissatisfaction.

The goal of this chapter has been to study how to measure, analyze, and

minimize sources of this variability.

The point of this it to improve consistency in product process and

performance, which will hopefully lead to…

Total customer satisfaction, and..

A better competitive position.

1 chapter 9 managing flow variability managing flow variability: process control and capability...

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