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Managing Business Process Flows

- Managing Flow Variability Process Control and

Capability

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

Managing Business Process Flows

Managing Business Process Flows

All Products Services VARY in Terms Of

Cost

Quality

Availability

Flow Times

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

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.

9.1 Performance Variability

- 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

9.1 Performance Variability

- 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.

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.

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.

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.

Example 9.1

Check Sheets

- Pros
- Easy to collect data
- Cons
- Not very enlightening
- No numerical characteristics

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.

Example 9.2

Pareto Charts

- Pros
- Ranks problems
- Shows relative size of quantities
- Cons
- No numerical characteristics
- Only categorizes data
- No comparison process information

9.2.3 Histograms

- A histogram is a bar plot that displays the

frequency distribution of an observed performance

characteristic.

Example 9.3

Histograms

- Pros
- Visualizes data distribution
- Shows relative size of quantities
- Cons
- No numerical characteristics
- Dependant on category size
- No focus on change over time

Table 9.1

Raw Data

- Pros
- Actual information
- Specific numbers
- Cons
- Not intuitive
- Does not help with understanding of relationships

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

Example 9.4

Run Charts

- Pros
- Shows data in chronological order
- Displays relative change over time (trends,

seasonality) - Cons
- Erratic graph
- No numerical characteristics

9.2.5 Multi-Vari Charts

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

values of performance measurement sampled over

time.

Example 9.5

Table 9.2

Multi-Vari Charts

- Pros
- Shows numerical range and average
- Displays relative change over time
- Cons
- Erratic graph
- No numerical characteristics
- Lacks distribution information
- Does not provide guidance for taking actions

9.3 Process Control

- Goal ? Actual Performance vs. Planned Performance
- Involves ?
- Tracking Deviations
- Taking Corrective Actions
- Principle of feedback control of dynamical

systems

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

Process Control

- Two types of variability
- 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

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.

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

9.3.4 Control Charts Continued

- ? - expected value of the performance
- UCL and LCL
- Standard Deviation ?
- Assign z

- LCL ? - z? UCL ? z?
- The smaller the value of z, the tighter the

control

9.3.4 Control Charts Continued

- 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

9.3.4 Control Charts Continued

- Acceptable
- Frequency
- z too small ? unnecessary investigation

additional cost - z to large ? accept more variations, less costly

- In practice, a value of z 3 is used
- 99.73 of all measurements will fall within the

normal range

9.3.4 Control Charts Continued

- 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

9.3.4 Control Charts Continued

- New, Improved equations for UCL and LCL are
- LCL ?A - zs/?n and UCL ?A zs/?n

Sample Variances

Calculate?V -- the average variance of the sample

variances ?V (V1 V2VN) / N

(N of samples) Also calculate SV -- the

standard deviation of the variances

Variance Control Limits

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

9.3.4 Control Charts Continued

- Average and Variation Control Charts
- Garage Door Example revisited

Ex A1 (81 73 85 90 80) / 5 81.8

kg Ex V1 (90 - 73) 17 kg

9.3.4 Control Charts Continued

- Average and Variation Control Charts
- Average Weights of Garage Door Samples

9.3.4 Control Charts Continued

- Average and Variation Control Charts

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!

9.3.4 Control Charts Continued

- Average and Variation Control Charts

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

9.3.4 Control Charts Continued

- Extensions

Continuous Variables - Garage Door Weights,

Processing Costs, Customer Waiting Time

Use Normal distribution

Discrete Variables - Number of Customer

Complaints, Whether a Flow Unit is Defective,

Number of Defects per Flow Unit Produced

Use Binomial or Poisson distribution

Control Limit formula differs, but basic

principles is same.

9.3.5 Cause-Effect Diagrams

- Cause-Effect Diagrams

Sample Observations

Plot Control Charts

Abnormal Variability !!

Now what?!!

Brainstorm Session!!

Answer 5 WHY Questions !

9.3.5 Cause-Effect Diagrams Continued

- Why? Why? Why?

Our famous Garage Door Example

9.3.5 Cause-Effect Diagrams Continued

- Fishbone Diagram

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

Scatter Plot

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

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 s (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

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)

9.4.1 Fraction of Output within Specifications

cont

- Let Z standard normal variable with µ 0 and s

1, we can use the standard normal table in

Appendix II to compute - AT US
- Prob (W 85) in terms of
- Z (W-µ)/ s
- 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,t

rue) .037073) - THEN
- Prob (75W85)
- .724 - .0367 .6873

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 theyll get doors that are either TOO

LIGHT or TOO HEAVY!!!

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 3s 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)/3s (.1984 as calculated later)
- and (µ -LS)/3s (.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)/3s .1984 and define a single measure of

process capabilities as - Cpk min(US-µ/)3s, (µ -LS)/3s (.1984, as

calculated later)

9.4.2 Process Capability Ratios (C pk and Cp)

- 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)/3s or (µ -LS)/3s
- As both are equal for a centered process.

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)/6s (.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 -3s

from the µ. - Consequently, 6s is sometimes referred to as the

natural tolerance of the process. - Ex 9.8
- Cpk min(US- µ)/3s , (µ -LS)/3s
- min (85-82.5)/(3)(4.2), (82.5-75)/(3)(4.2)
- min .1984, .5952
- .1984

9.4.2 Process Capability Ratios (C pk and Cp)

- If the process is correctly centered at µ 80kg

(between 75 and 85kg), we compute the process

capability ratio as - Cp (US-LS)/6s
- (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.

Its 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

Cp1.3 (see Table 9.4) Test s

(85-75)/(6)(1.282) 1.300kg

Table 9.4

9.4.3 Six-Sigma Capability

- Sigma measure
- S min(US- µ /s), (µ -LS)/s
- ( 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)/2s
- Ex 9.9
- Currently the sigma capability of door making

process is - Smin(85-82.5)/(4.2), (82.5-75)/4.2 .5952
- By centering the process correctly, its sigma

capability increases to - Smin(85-75)/(2)(4.2) 1.19
- THUS, with a 3s that is correctly centered, the

US and LS are 3s away from the mean, which

corresponds to Cp1, and 99.73 of the output

will meet the specifications.

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 6s from the mean each. - Extraordinary high degree of precision.
- Corresponds to Cp2 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 - s (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)

Table 9.5

9.4.3 Six-Sigma Capability cont

- 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.

9.4.3 Six-Sigma Capability cont

- 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

9.4.3 Six-Sigma Capability cont

- Safety capability
- We may also express process capabilities in

terms of the desired margin (US-LS)-zs 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

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.

9.5 Process Capability Improvement

- How do we improve the process capability?
- Shift the process mean
- Reduce the variability
- Both

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

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.

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 lt W lt 85) - Prob (W lt 85) Prob (W lt 75)
- Z (weight process mean)/standard deviation
- Z (85 80)/4.2 1.19
- Z (75 80)/4.2 -1.19

Ex 9.10 Continued

- from table A2.1 on page 319
- Z 1.19

.8830 - Z -1.19 (1 - .8830)

.1170 - Prob (W lt 85) Prob (W lt 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

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

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

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 lt W lt 85) - Prob (W lt 85) Prob (W lt 75)
- Z (weight process mean)/standard deviation
- Z (85 80)/2.5 2.0
- Z (75 80)/2.5 -2.0

Ex 9.11 Continued

- from table A2.1 on page 319
- Z 2.0

.9772 - Z -2.0 (1 - .9772)

.0228 - Prob (W lt 85) Prob (W lt 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

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

9.6 Product and Process Design

- Reducing the variability from product and process

design - simplification
- standardization
- mistake proofing

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

Standardization

- Use standard parts and procedures
- reduces operator discretion, ambiguity, and

opportunity for making mistakes

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

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 customers

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

9.6 Product and Process Design

- 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.

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