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Managing Flow Variability: Process Control and Capability

Amber Young

Sam Parduhn

Paresh Sinha

Chapter 9

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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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Introduction ~ MBPF Example

MBPF, Inc. - High-tech manufacturer of steel doors• This company prided themselves on:

– High Quality Products– Professional after-sales service– Solid reputation (15% share of market)

• Recently were celebrating their successes during a holiday company party– Numerous Speeches; Executives congratulating one

another on successes/accomplishments

• Company believed they were headed in the right direction & that all was operating smoothly.

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MBPF Example (continued)The celebration was short lived & had a quick change of

pace when a sales manager spoke up:“Ladies & Gentlemen, I do not wish to spoil your mood, but

I have some disturbing news! Lately I have been talking to some of our major customers, and I have found, much to my surprise, that many of them are less than satisfied with our products and service…”

“…Although we think our products are great & that our service is unsurpassed, if what I’m hearing is right, it is only a matter of time before we lose our loyal customer base to the competition, which is working hard to provide newer & better products, cheaper & faster.”

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MBPF Example (continued)Wasn’t the case at MBPF as their CEO was a true leader &

was interested in these findings and asked for elaboration:

CUSTOMER DISSATISFACTIONS/COMPLAINTS:• Door quality in terms of safety, durability & ease of use• Costs are much more than competitors• Difficulty getting orders in on-time• Customer Service when something went wrong with

installation/operation*All very valid complaints for a company in their type of

business.

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MBPF SolutionCEO listened carefully to all complaints & decided it was

time to be PROACTIVE:• Since the sales managers observations were primarily

subjective, the CEO recognized the need for something solid as opposed to mere hearsay or intuition.

NEXT LOGICAL STEP:• COLLECT & ANALYZE SOME HARD DATA

– Assigned a team to analyze the concrete data on critical performance measures that drive customer satisfaction

GOAL:• To IDENTIFY, CORRECT & PREVENT sources of future

problems

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Variability often = Customer Dissatisfaction

All Products & Services VARY in Terms Of:

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.

CostCost QualityQuality AvailabilityAvailabilityFlowFlow

TimesTimes

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§ 9.1 Performance Variability• All measures of product & process performance (internal

& external) display Variability.– External Measurements - customer satisfaction, relative product

rankings, customer complaints (vary from one market survey to the next)

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

Example 1 ~ No 2 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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§ 9.1 Performance Variability

• Sources of Variability– Internal: imprecise equipment, untrained

workers, and lack of standard operating procedures

– External: inconsistent raw materials, supplier delivery delays, changing consumer tastes & requirements, and changing economic conditions

In general, variability refers to a discrepancy between the actual and the expected performance.

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§ 9.1 Performance Variability

A discrepancy between the actual and the expected performance often leads to:– higher costs, longer flow times, lower quality &

DISSATISFIED CUSTOMERS

• 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.

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Quality Management Terms• 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.

**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.)

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Quality Management Terms

• 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: # defects per car, fraction of output that

meets specifications

• Example: Airline conformance can be measured in terms of the 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:1. Monitor the actual process performance over time2. Analyze variability in the process3. Uncover root causes4. Eliminate those causes5. 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.

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§ 9.2 Analysis of Variability

• 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

Good

• Easy to collect data

Bad

• 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

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Door Quality Service Quality Cost Response Time Customization

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Pareto Charts

Good

• Ranks problems

• Shows relative size of quantities

Bad

• No numerical characteristics

• 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

ency

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Histograms

Good

• Visualizes data distribution

• Shows relative size of quantities

Bad

• No numerical characteristics

• Dependant on category size

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Table 9.1Day

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

Good

• Actual information

• Specific numbers

Bad

• 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.

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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

Good

• Shows data in chronological order

• Displays relative change over time

Bad

• Erratic graph

• No numerical characteristics

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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

Good

• Shows numerical range and average

• Displays relative change over time

Bad

• Erratic graph

• No numerical characteristics

• Lacks distribution information

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Process Management

Two aspects to process management

• Process planning

• Process control

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§ 9.3 Process Planning

It involves

• Structuring the process

• Designing operating procedures and

• Developing key competencies such as process capability, flexibility, capacity, and cost efficiency.

Its goal is to produce and deliver products that satisfy targeted customer needs.

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§ 9.3 Process Control

Involves:• Tracking deviations between the actual

and the planned performance and taking corrective actions to identify and eliminate sources of these variations.

• There could be various reasons behind variation in performance.

• Its goal is to ensure that actual performance conforms to the planned performance.

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§ 9.3.1 The Feedback Control Principle

• Process performance management is based on the general principle of feedback control of dynamical systems.

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The Feedback Control Principle

Applying the feedback control principle to process control..“involves periodically monitoring the actual

process performance (in terms of cost, quality, availability, and response time), comparing it to the planned levels of performance, identifying causes of the observed discrepancy between the two, and taking corrective actions to eliminate those causes.”

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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.

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Problems in Process Control

• Performance variances are determined by comparison of the current and previous period’s performances.

• Decisions are based on results of this comparison.

• Some variances may be due to factors beyond a worker’s control.

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Process Control

• According to W. Edward Deming, incentives based on factors that are beyond a worker’s control is like rewarding or punishing workers according to a lottery.

• Two categories of performance variability– Variability due to factors within a worker’s control.– Variability due to factors beyond a worker’s control.

• Two types of variability1. Normal variability

2. Abnormal variability

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§ 9.3.2 Types and Cause of Variability

Two types of variability

• Normal variability is statistically predictable and includes both structural variability and stochastic variability.

• Abnormal variability is unpredictable and disturbs the state of statistical equilibrium of the process by changing parameters of its distribution in an unexpected way.

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Normal Variability

• Statistically predictable.

• Contains structural variability & stochastic variability.

• Random causes have unpredictable effect, and cannot be removed easily.

• Not in worker’s control.

• Can be removed only by process re-design, more precise equipment, skilled workers, better quality material etc.

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Abnormal Variability

• Unpredictable• Disturbs statistical equilibrium in

unexpected way.• Implies that one or more performance

affecting factors may have changed.• Due to causes superimposed externally or

process tampering.• Within worker’s control.• Can be identified and removed easily

therefore worker’s responsibility.

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Process Control• If observed performance variability is

– Normal - due to random causes - process is in control

– Abnormal - due to assignable causes - process is out of control

• The short run goal is:1. Estimate normal stochastic variability.2. Accept it as an inevitable and avoid tampering3. Detect presence of abnormal variability4. Identify and eliminate its sources

• The long run goal is to reduce normal variability by improving process.

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§ 9.3.3 Control Limit Policy

• How to decide whether observed variability is normal or abnormal?

• Control Limit Policy– Control band - A range within which any

variation in performance is interpreted as normal due to causes that cannot be identified or eliminated in short run.

– Variability outside this range is abnormal.– Lower limit of acceptable mileage, control

band for house temperature.

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Process Control

• Process control is useful to control any type of process.

• Application of control limit policy– Managing inventory, process capacity and flow time.– Cash management - liquidate some assets if cash

falls below a certain level.– Stock trading - purchase a stock if and when its price

drops to a specific level.

• Control limit policy has usage in a wide variety of business in form of critical threshold for taking action

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Questions?(Applause)