1 quality management and control presented by: mohammad saleh owlia, visiting professor, university...

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1 Quality Management Quality Management and Control and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive Advantage, Eleventh Edition (2006) Richard B. Chase, F. Robert Jacobs and Nicholas J. Aquilano

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Page 1: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Quality ManagementQuality Managementand Controland Control

Presented by:Mohammad Saleh Owlia, Visiting Professor, University of Malaya

Adopted from:Operations Management for Competitive Advantage, Eleventh Edition (2006) Richard B. Chase, F. Robert Jacobs and Nicholas J. Aquilano

Page 2: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

22

What is Quality?What is Quality?

Page 3: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

33

Garvin’s Product Quality DimensionGarvin’s Product Quality Dimension Garvin’s Product Quality DimensionGarvin’s Product Quality Dimension

Performance

Features

Reliability

Conformance

Durability

Serviceability

Aesthetics

Perceived Quality

                                                                                                                                                     

              

                                                                                                                                     

Page 4: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Service Quality DimensionsService Quality Dimensions Service Quality DimensionsService Quality DimensionsParasuraman, Zeithamel, and

Berry’s Service

Quality Dimensions

Tangibles

Service Reliability

Responsiveness

Assurance

Empathy

                     

                        

Page 5: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Total Quality ManagementTotal Quality Management

TQMTQM may be defined as managing the may be defined as managing the

entire organization so that it excels on entire organization so that it excels on

all dimensions of products and services all dimensions of products and services

that are important to the customer.that are important to the customer.

Page 6: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Quality SpecificationsQuality Specifications

Design quality (consumer’s view)Design quality (consumer’s view)– inherent value of the product in the inherent value of the product in the

marketplace and therefore, has strategic marketplace and therefore, has strategic implications.implications.

CConformance quality (producer’s view)onformance quality (producer’s view)– degree to which the product or servicedegree to which the product or service

design specifications are metdesign specifications are met

Page 7: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Costs of QualityCosts of QualityAppraisal costsAppraisal costs

Prevention costs

Internal failure costs

External Failure costs

inspection and testing

scrap, rework, yield loss, downtime

complaint adjustment, allowances, warranty work

quality planning and training

Page 8: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

Co

st

pe

r g

oo

d u

nit

of

pro

du

ct

0 100%Quality level (q)Optimum

quality level

TotalqualitycostsInternal

and externalfailurecosts

Minimumtotal cost

Preventionand appraisalcosts

Quality Cost: Traditional ViewQuality Cost: Traditional View

Page 9: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Phases of Quality Assurance

Acceptancesampling

Processcontrol

Continuousimprovement

Inspectionbefore/afterproduction

Correctiveaction duringproduction

Quality builtinto theprocess

The leastprogressive

The mostprogressive

Page 10: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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PDCA Cycle (Deming Wheel)PDCA Cycle (Deming Wheel)

1. Plan a change aimed at improvement.

1. Plan

2. Execute the change.

2. Do

3. Study the results; did it work?

3. Check

4. Institutionalize the change or abandon or do it again.

4. Act

Page 11: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Ishikawa’s Basic Tools of QualityIshikawa’s Basic Tools of QualityIshikawa’s Basic Tools of QualityIshikawa’s Basic Tools of Quality

HistogramHistogramHistogramHistogram

Pareto ChartsPareto ChartsPareto ChartsPareto Charts

Cause & Effect Cause & Effect DiagramsDiagrams

Cause & Effect Cause & Effect DiagramsDiagrams

Check SheetsCheck SheetsCheck SheetsCheck Sheets

Scatter Scatter DiagramsDiagramsScatter Scatter

DiagramsDiagrams

FlowchartsFlowchartsFlowchartsFlowcharts

Control ChartsControl ChartsControl ChartsControl Charts

Page 12: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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HistogramsHistogramsN

um

be

r o

f Lo

ts

Data Ranges

Defectsin lot

0 1 2 3 4

Can be used to identify the frequency of quality defect occurrence and display quality performance.

Can be used to identify the frequency of quality defect occurrence and display quality performance.

Graphical representation of data in a bar chart format Graphical representation of data in a bar chart format

Page 13: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Can be used to find when 80% of the problems may be attributed to 20% of thecauses.

Can be used to find when 80% of the problems may be attributed to 20% of thecauses.

Assy.Instruct.

Fre

quen

cy

Design Purch. Training Other

80%

Pareto ChartsPareto ChartsPareto ChartsPareto Charts

Page 14: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Pareto ChartsPareto ChartsPareto ChartsPareto Charts

The Steps Used in Pareto Analysis The Steps Used in Pareto Analysis Include:Include:– Gathering categorical data relating to Gathering categorical data relating to

quality problems.quality problems.– Drawing a histogram of the data.Drawing a histogram of the data.– Focusing on the tallest bars in the Focusing on the tallest bars in the

histogram first when solving the problemhistogram first when solving the problem

Page 15: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Cause and Effect Cause and Effect DiagramsDiagrams

Cause and Effect Cause and Effect DiagramsDiagrams

Cause and Effect (or Fishbone or Cause and Effect (or Fishbone or Ishikawa) DiagramIshikawa) Diagram– A diagram designed to help workers A diagram designed to help workers focus focus

on the causes of a problem rather than on the causes of a problem rather than the symptoms.the symptoms.

– The diagram looks like the skeleton of a The diagram looks like the skeleton of a fish, with the problem being the head of the fish, with the problem being the head of the fish, major causes being the “ribs” of the fish, major causes being the “ribs” of the fish and subcauses forming smaller fish and subcauses forming smaller “bones” off the ribs.“bones” off the ribs.

Page 16: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Cause & Effect DiagramCause & Effect Diagram

Effect

ManMachine

MaterialMethod

Environment

Possible causes:Possible causes: The results or effect

The results or effect

Can be used to systematically track backwards to find a possible cause of a quality problem (or effect)

Can be used to systematically track backwards to find a possible cause of a quality problem (or effect)

Page 17: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Cause and Effect Cause and Effect DiagramsDiagrams

Cause and Effect Cause and Effect DiagramsDiagrams

Page 18: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Billing Errors

Wrong Account

Wrong Amount

A/R Errors

Wrong Account

Wrong Amount

Monday

Can be used to keep track of defects or used to make sure people collect data in a correct manner.

Can be used to keep track of defects or used to make sure people collect data in a correct manner.

Check SheetsCheck SheetsCheck SheetsCheck Sheets

Page 19: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Check SheetsCheck SheetsCheck SheetsCheck Sheets

Setting Up a Check SheetSetting Up a Check Sheet– Identify common defects occurring in the Identify common defects occurring in the

process.process.– Draw a table with common defects in the left Draw a table with common defects in the left

column and time period across the tops of column and time period across the tops of the columns to track the defects.the columns to track the defects.

– The user of the check sheet then places The user of the check sheet then places check marks on the sheet whenever the check marks on the sheet whenever the defect is encountered.defect is encountered.

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Check SheetsCheck SheetsCheck SheetsCheck Sheets

Page 21: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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1012

0 10 20 30

Hours of Training

De

fect

s

Can be used to illustrate the relationships between variables (Example: quality performance and training).

Can be used to illustrate the relationships between variables (Example: quality performance and training).

Scatter DiagramsScatter DiagramsScatter DiagramsScatter Diagrams

Page 22: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Scatter DiagramsScatter DiagramsScatter DiagramsScatter Diagrams

Used to examine the relationships Used to examine the relationships between variables:between variables:Steps in Setting Up a Scatter PlotSteps in Setting Up a Scatter Plot– Determine your X (independent) and Y Determine your X (independent) and Y

(dependent) variables.(dependent) variables.– Gather process data relating to the variables Gather process data relating to the variables

identified in step 1.identified in step 1.– Plot the data on a two-dimensional Plot the data on a two-dimensional

Cartesian plane.Cartesian plane.– Observe the plotted data to see whether Observe the plotted data to see whether

there is a relationship between the variables.there is a relationship between the variables.

Page 23: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Scatter DiagramsScatter DiagramsScatter DiagramsScatter Diagrams

Prevention in Costs and Conformance

Page 24: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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FlowchartsFlowchartsFlowchartsFlowcharts

Flowcharts:Flowcharts:Picture of a processPicture of a process

Allows a company to see process weaknessesAllows a company to see process weaknesses

Sometimes the first step in many process Sometimes the first step in many process improvement projects to see how the process improvement projects to see how the process existsexists

““You have to be able to know the process You have to be able to know the process before you can improve it”before you can improve it”

Page 25: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Example: Process Flow Chart Example: Process Flow Chart

No, Continue…

Material Received

from Supplier

Inspect Material for

Defects Defects found?

Return to Supplier for

Credit

Yes

Can be used to find quality problems.

Can be used to find quality problems.

Page 26: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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FlowchartsFlowchartsFlowchartsFlowcharts

Basic Flowcharting Symbols

Page 27: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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FlowchartsFlowchartsFlowchartsFlowcharts

Steps in Flowcharting IncludeSteps in Flowcharting Include– Settle on a standard set of flowcharting Settle on a standard set of flowcharting

symbols to be used.symbols to be used.– Clearly communicate the purpose of the Clearly communicate the purpose of the

flowcharting to all the individuals involved in flowcharting to all the individuals involved in the flowcharting exercise.the flowcharting exercise.

– Observe the work being performed by Observe the work being performed by shadowing the workers performing the work.shadowing the workers performing the work.

– Develop a flowchart of the process.Develop a flowchart of the process.

Page 28: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Control ChartsControl ChartsControl ChartsControl Charts

Control ChartsControl Charts– Control charts are used to determine Control charts are used to determine

whether a process will produce a product or whether a process will produce a product or service with consistent measurable service with consistent measurable properties. properties.

– Control charts are discussed in detail in Control charts are discussed in detail in Technical Note 7.Technical Note 7.

Page 29: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

Example: Run ChartExample: Run Chart

0.440.460.48

0.50.520.540.560.58

1 2 3 4 5 6 7 8 9 10 11 12Time (Hours)

Dia

me

ter

Can be used to identify when equipment or processes are not behaving according to specifications.

Can be used to identify when equipment or processes are not behaving according to specifications.

Page 30: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

Example: Control ChartExample: Control Chart

970

980

990

1000

1010

1020

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

LCL

UCL

Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality.

Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality.

Page 31: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Six Sigma QualitySix Sigma Quality

A philosophy and set of methods A philosophy and set of methods companies use to eliminate defects in companies use to eliminate defects in their products and processestheir products and processes

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

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

6

Page 32: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Six Sigma Quality (Continued)Six Sigma Quality (Continued)

Six Sigma allows managers to readily Six Sigma allows managers to readily describe process performance using a describe process performance using a common metric: Defects Per Million common metric: Defects Per Million Opportunities (DPMO)Opportunities (DPMO)

1,000,000 x

units of No. x

unit per error for iesopportunit ofNumber

defects ofNumber

DPMO

Page 33: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Six Sigma Quality (Continued)Six Sigma Quality (Continued)

Example of Defects Per Million Example of Defects Per Million Opportunities (DPMO) calculation. Opportunities (DPMO) calculation. Suppose we observe 200 letters Suppose we observe 200 letters delivered incorrectly to the wrong delivered incorrectly to the wrong addresses in a small city during a addresses in a small city during a single day when a total of 200,000 single day when a total of 200,000 letters were delivered. What is the letters were delivered. What is the DPMO in this situation?DPMO in this situation?

Example of Defects Per Million Example of Defects Per Million Opportunities (DPMO) calculation. Opportunities (DPMO) calculation. Suppose we observe 200 letters Suppose we observe 200 letters delivered incorrectly to the wrong delivered incorrectly to the wrong addresses in a small city during a addresses in a small city during a single day when a total of 200,000 single day when a total of 200,000 letters were delivered. What is the letters were delivered. What is the DPMO in this situation?DPMO in this situation?

000,1 1,000,000 x

200,000 x 1

200DPMO

000,1 1,000,000 x

200,000 x 1

200DPMO

So, for every one million letters delivered this city’s postal managers can expect to have 1,000 letters incorrectly sent to the wrong address.

So, for every one million letters delivered this city’s postal managers can expect to have 1,000 letters incorrectly sent to the wrong address.

Cost of Quality: What might that DPMO mean in terms of over-time employment to correct the errors?

Cost of Quality: What might that DPMO mean in terms of over-time employment to correct the errors?

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Six Sigma Quality: DMAIC Six Sigma Quality: DMAIC

Cycle (Continued)Cycle (Continued) 1. Define (D)

2. Measure (M)

3. Analyze (A)

4. Improve (I)

5. Control (C)

Customers and their priorities

Process and its performance

Causes of defects

Remove causes of defects

Maintain quality

Page 35: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Second PartSecond Part

Statistical Process ControlStatistical Process Control

Page 36: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Statistical ThinkingStatistical Thinking

All work occurs in a system of All work occurs in a system of interconnected processesinterconnected processes

Variation exists in all processesVariation exists in all processes

Understanding and reducing variation are Understanding and reducing variation are the keys to successthe keys to success

Page 37: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Sources of Variation in Production Sources of Variation in Production ProcessesProcesses

Materials

Tools

Operators Methods Measurement Instruments

HumanInspectionPerformance

EnvironmentMachines

INPUTS PROCESS OUTPUTS

Page 38: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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VariationVariation

Many sources of uncontrollable Many sources of uncontrollable variation exist (common causes)variation exist (common causes)

Special (assignable) causes of Special (assignable) causes of variation can be recognized and variation can be recognized and controlledcontrolled

Failure to understand these differences Failure to understand these differences can increase variation in a systemcan increase variation in a system

Page 39: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Problems Created by Problems Created by VariationVariation

Variation increases unpredictability. Variation increases unpredictability.

Variation reduces capacity utilization. Variation reduces capacity utilization.

Variation makes it difficult to find root causes. Variation makes it difficult to find root causes.

Variation makes it difficult to detect potential Variation makes it difficult to detect potential problems early. problems early.

Page 40: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Importance of Importance of Understanding VariationUnderstanding Variation

time

PREDICTABLE

?UNPREDECTIBLE

Page 41: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Two Fundamental Two Fundamental Management MistakesManagement Mistakes

1.1. Treating as a special cause any fault, complaint, Treating as a special cause any fault, complaint, mistake, breakdown, accident or shortage when it mistake, breakdown, accident or shortage when it actually is due to common causesactually is due to common causes

2.2. Attributing to common causes any fault, complaint, Attributing to common causes any fault, complaint, mistake, breakdown, accident or shortage when it mistake, breakdown, accident or shortage when it actually is due to a special causeactually is due to a special cause

Page 42: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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• Number or percent of defective items in a lot.• Number of defects per item.• Types of defects.• Value assigned to defects (minor=1, major=5, critical=10)

• Length• Weight• Time

• Diameter• Tensile Strength• Strength of Solution

• Height• Volume• Temperature

Types of Data

“Things we count”

Variables Data

Attribute Data

“Things we measure”

Page 43: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Process Control ChartsProcess Control Charts Process Control ChartsProcess Control Charts

Variables and Attributes

Variables Attributes

X (process population average) P (proportion defective)

X-bar (mean for average) np (number defective)

R (range) C (number conforming)

MR (moving range) U (number nonconforming)

S (standard deviation)

Page 44: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Process Control ChartsProcess Control Charts Process Control ChartsProcess Control Charts

X-barX-bar and and RR Charts Charts– The The X-barX-bar chart is a process chart used to chart is a process chart used to

monitor the average of the characteristics being monitor the average of the characteristics being measuredmeasured. To set up an . To set up an X-barX-bar chart select chart select samples from the process for the characteristic samples from the process for the characteristic being measured. Then form the samples into being measured. Then form the samples into rational subgroups. Next, find the average rational subgroups. Next, find the average value of each sample by dividing the sums of value of each sample by dividing the sums of the measurements by the sample size and plot the measurements by the sample size and plot the value on the process control the value on the process control X-barX-bar chart. chart.

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Process Control ChartsProcess Control Charts Process Control ChartsProcess Control Charts

X-barX-bar and and RR Charts (continued) Charts (continued)– The The RR chart is used to monitor the variability or chart is used to monitor the variability or

dispersion of the processdispersion of the process. It is used in . It is used in conjunction with the conjunction with the X-barX-bar chart when the chart when the process characteristic is variable. To develop an process characteristic is variable. To develop an RR chart, collect samples from the process and chart, collect samples from the process and organize them into subgroups, usually of three to organize them into subgroups, usually of three to six items. Next, compute the range, six items. Next, compute the range, RR, by taking , by taking the difference of the high value in the subgroup the difference of the high value in the subgroup minus the low value. Then plot the minus the low value. Then plot the RR values on values on the the RR chart. chart.

Page 46: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Process Control ChartsProcess Control Charts Process Control ChartsProcess Control Charts

X-bar and R Charts

Page 47: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Example of x-Bar and R Charts: Example of x-Bar and R Charts: Required DataRequired Data

Sample Obs 1 Obs 2 Obs 3 Obs 4 Obs 51 10.682 10.689 10.776 10.798 10.7142 10.787 10.86 10.601 10.746 10.7793 10.78 10.667 10.838 10.785 10.7234 10.591 10.727 10.812 10.775 10.735 10.693 10.708 10.79 10.758 10.6716 10.749 10.714 10.738 10.719 10.6067 10.791 10.713 10.689 10.877 10.6038 10.744 10.779 10.11 10.737 10.759 10.769 10.773 10.641 10.644 10.72510 10.718 10.671 10.708 10.85 10.71211 10.787 10.821 10.764 10.658 10.70812 10.622 10.802 10.818 10.872 10.72713 10.657 10.822 10.893 10.544 10.7514 10.806 10.749 10.859 10.801 10.70115 10.66 10.681 10.644 10.747 10.728

Page 48: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Example of x-bar and R charts: Step 1. Example of x-bar and R charts: Step 1. Calculate sample means, sample ranges, mean Calculate sample means, sample ranges, mean

of means, and mean of ranges.of means, and mean of ranges.

Sample Obs 1 Obs 2 Obs 3 Obs 4 Obs 5 Avg Range1 10.682 10.689 10.776 10.798 10.714 10.732 0.1162 10.787 10.86 10.601 10.746 10.779 10.755 0.2593 10.780 10.667 10.838 10.785 10.723 10.759 0.1714 10.591 10.727 10.812 10.775 10.73 10.727 0.2215 10.693 10.708 10.79 10.758 10.671 10.724 0.1196 10.749 10.714 10.738 10.719 10.606 10.705 0.1437 10.791 10.713 10.689 10.877 10.603 10.735 0.2748 10.744 10.779 10.11 10.737 10.75 10.624 0.6699 10.769 10.773 10.641 10.644 10.725 10.710 0.13210 10.718 10.671 10.708 10.85 10.712 10.732 0.17911 10.787 10.821 10.764 10.658 10.708 10.748 0.16312 10.622 10.802 10.818 10.872 10.727 10.768 0.25013 10.657 10.822 10.893 10.544 10.75 10.733 0.34914 10.806 10.749 10.859 10.801 10.701 10.783 0.15815 10.660 10.681 10.644 10.747 10.728 10.692 0.103

Averages 10.728 0.220400

Page 49: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Example of x-bar and R charts: Step 2. Example of x-bar and R charts: Step 2. Determine Control Limit Formulas and Determine Control Limit Formulas and

Necessary Tabled ValuesNecessary Tabled Values

x Chart Control Limits

UCL = x + A R

LCL = x - A R

2

2

R Chart Control Limits

UCL = D R

LCL = D R

4

3

n A2 D3 D42 1.88 0 3.273 1.02 0 2.574 0.73 0 2.285 0.58 0 2.116 0.48 0 2.007 0.42 0.08 1.928 0.37 0.14 1.869 0.34 0.18 1.82

10 0.31 0.22 1.7811 0.29 0.26 1.74

Page 50: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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Example of x-bar and R charts: Steps 3&4. Example of x-bar and R charts: Steps 3&4. Calculate x-bar Chart and Plot ValuesCalculate x-bar Chart and Plot Values

10.601

10.856

=).58(0.2204-10.728RA - x = LCL

=).58(0.220410.728RA + x = UCL

2

2

10.550

10.600

10.650

10.700

10.750

10.800

10.850

10.900

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Sample

Mea

ns

UCL

LCL

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Example of x-bar and R charts: Steps 5&6. Example of x-bar and R charts: Steps 5&6. Calculate R-chart and Plot ValuesCalculate R-chart and Plot Values

0

0.46504

)2204.0)(0(R D= LCL

)2204.0)(11.2(R D= UCL

3

4

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Sample

RUCL

LCL

Page 52: 1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive

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UCL

LCL

Samples over time

1 2 3 4 5 6

UCL

LCL

Samples over time

1 2 3 4 5 6

UCL

LCL

Samples over time

1 2 3 4 5 6

Normal Behavior

Possible problem, investigate

Possible problem, investigate

•Interpreting Control Charts

•Interpreting Control Charts

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Process Control ChartsProcess Control Charts Process Control ChartsProcess Control Charts

Implications of a Process Out of ControlImplications of a Process Out of Control– If a process loses control and becomes If a process loses control and becomes

nonrandom, the process should be nonrandom, the process should be stopped immediately. stopped immediately.

– In many modern process industries where just-in-In many modern process industries where just-in-time is used, this will result in the stoppage of time is used, this will result in the stoppage of several work stations. several work stations.

– The team of workers who are to address the The team of workers who are to address the problem should use a structured problem solving problem should use a structured problem solving process.process.

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Process Control ChartsProcess Control ChartsProcess Control ChartsProcess Control Charts

Control Charts for AttributesControl Charts for Attributes– We now shift to charts for attributes. These We now shift to charts for attributes. These

charts deal with binomial and Poisson charts deal with binomial and Poisson processes that are not measurements. processes that are not measurements.

– We will now be thinking in terms of defects and We will now be thinking in terms of defects and defectives rather than diameters or widths.defectives rather than diameters or widths.

A defect is an irregularity or problem with a larger A defect is an irregularity or problem with a larger unit. unit.

A defective is a unit that, as a whole, is not A defective is a unit that, as a whole, is not acceptable or does not meet specifications.acceptable or does not meet specifications.

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Process Control ChartsProcess Control Charts Process Control ChartsProcess Control Charts

pp Charts for Proportion Defective Charts for Proportion Defective– The The pp chart is a process chart that is used to chart is a process chart that is used to

graph the proportion of items in a sample that graph the proportion of items in a sample that are defective (nonconforming to specifications)are defective (nonconforming to specifications)

– pp charts are effectively used to determine when charts are effectively used to determine when there has been a shift in the proportion there has been a shift in the proportion defective for a particular product or service. defective for a particular product or service.

– Typical applications of the Typical applications of the pp chart include things chart include things like late deliveries, incomplete orders, and like late deliveries, incomplete orders, and clerical errors on written forms.clerical errors on written forms.

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Process Control ChartsProcess Control Charts Process Control ChartsProcess Control Charts

npnp Charts Charts– The The npnp chart is a graph of the number of chart is a graph of the number of

defectives (or nonconforming units) in a defectives (or nonconforming units) in a subgroup. The subgroup. The npnp chart requires that the chart requires that the sample size of each subgroup be the same sample size of each subgroup be the same each time a sample is drawn. each time a sample is drawn.

– When subgroup sizes are equal, either the When subgroup sizes are equal, either the pp or or npnp chart can be used. They are essentially chart can be used. They are essentially the same chart.the same chart.

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Example of Constructing a Example of Constructing a pp-Chart: -Chart: Required DataRequired Data

1 100 42 100 23 100 54 100 35 100 66 100 47 100 38 100 79 100 1

10 100 211 100 312 100 213 100 214 100 815 100 3

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Statistical Process Control Formulas:Statistical Process Control Formulas:Attribute Measurements (Attribute Measurements (pp-Chart)-Chart)

p =Total Number of Defectives

Total Number of Observations

ns

)p-(1 p = p

p

p

z - p = LCL

z + p = UCL

s

s

Given:

Compute control limits:

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1. Calculate the sample proportions, p (these are what can be plotted on the p-chart) for each sample.

Sample n Defectives p1 100 4 0.042 100 2 0.023 100 5 0.054 100 3 0.035 100 6 0.066 100 4 0.047 100 3 0.038 100 7 0.079 100 1 0.01

10 100 2 0.0211 100 3 0.0312 100 2 0.0213 100 2 0.0214 100 8 0.0815 100 3 0.03

Example of Constructing a Example of Constructing a pp-chart: -chart: Step 1Step 1

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2. Calculate the average of the sample proportions.

0.036=1500

55 = p

3. Calculate the standard deviation of the sample proportion

.0188= 100

.036)-.036(1=

)p-(1 p = p n

s

Example of Constructing a Example of Constructing a pp-chart: -chart: Steps 2&3Steps 2&3

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4. Calculate the control limits.

3(.0188) .036

UCL = 0.0924LCL = -0.0204 (or 0)

p

p

z - p = LCL

z + p = UCL

s

s

Example of Constructing a Example of Constructing a pp-chart: -chart: Step 4Step 4

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Example of Constructing a Example of Constructing a pp-Chart: -Chart: Step 5Step 5

5. Plot the individual sample proportions, the average of the proportions, and the control limits

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Observation

p

UCL

LCL

Sample n Defectives p1 100 4 0.042 100 2 0.023 100 5 0.054 100 3 0.035 100 6 0.066 100 4 0.047 100 3 0.038 100 7 0.079 100 1 0.01

10 100 2 0.0211 100 3 0.0312 100 2 0.0213 100 2 0.0214 100 8 0.0815 100 3 0.03

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Process CapabilityProcess CapabilityProcess CapabilityProcess Capability

Process Stability and CapabilityProcess Stability and Capability– Once a process is stable, the next Once a process is stable, the next

emphasis is to ensure that the process is emphasis is to ensure that the process is capable.capable.

– Process capability refers to the ability of a Process capability refers to the ability of a process to produce a product that meets process to produce a product that meets specifications.specifications.

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Process CapabilityProcess Capability

Process limitsProcess limits

Tolerance limitsTolerance limits

How do the limits relate to one another?How do the limits relate to one another?

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If the process capability of a normally If the process capability of a normally distributed process is .084, the process is distributed process is .084, the process is in control, and is centered at .550. What in control, and is centered at .550. What are the upper and lower control limits for are the upper and lower control limits for

this process? this process?

Process Capability = 6

6 = .084 = .014

UCL = .550 + 3(.014)= .592

LCL = .550 - 3(.014) = .508

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X4.90 4.95 5.00 5.05 5.10 5.15

cm

Tolerance band

Process capability (6 )LTL UTL

Outputout of spec

Outputout of spec

Process outputdistribution

5.010

s

Process Capability Chart

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This process is CAPABLE of producing all good output.

ä Control the process.

This process is NOT CAPABLE.

ä INSPECT - Sort out the defectives

××

LowerTolerance

Limit

UpperTolerance

Limit

Process Capability

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Process Capability Index, CProcess Capability Index, Cpkpk

3

X-UTLor

3

LTLXmin=C pk

Shifts in Process Mean

Capability Index shows how well parts being produced fit into design limit specifications.

As a production process produces items small shifts in equipment or systems can cause differences in production performance from differing samples.

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Given:Given:

process mean = 1.0015process mean = 1.0015

= .001= .001

LTL = .994LTL = .994

UTL = 1.006UTL = 1.006

Process Capability Index- ExampleProcess Capability Index- Example

3Upper Tol Limit - X

3

X - Lower Tol Limit–OR{

Cpk

=

Smaller of:

Cpk= min

1.0015 -.994 1.006 - 1.0015

3(.001) 3 (.001)or

Cpk= min [2.5 or 1.5] = 1.5

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LTL UTL

Cpk = 1.0

LTL UTL

Cpk = 0.60

LTL UTL

Cpk = 1.33

LTL UTL

Cpk = 0.80

(f)

LTL UTL

Cpk = 1.0

(d)

LTL UTL

Cpk = 3.0

Process Capability: Cpk Varieties

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Acceptance SamplingAcceptance SamplingAcceptance SamplingAcceptance Sampling

Acceptance SamplingAcceptance Sampling– A statistical quality control technique used A statistical quality control technique used

in deciding to accept or reject a shipment in deciding to accept or reject a shipment of input or output.of input or output.

– Acceptance sampling inspection can range Acceptance sampling inspection can range from 100% of the Lot to a relatively few from 100% of the Lot to a relatively few items from the Lot items from the Lot (N=2)(N=2) from which the from which the receiving firm draws inferences about the receiving firm draws inferences about the whole shipment.whole shipment.

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Acceptance SamplingAcceptance SamplingPurposesPurposes– Determine quality levelDetermine quality level– Ensure quality is within predetermined levelEnsure quality is within predetermined level

AdvantagesAdvantages– EconomyEconomy– Less handling damageLess handling damage– Fewer inspectorsFewer inspectors– Upgrading of the inspection jobUpgrading of the inspection job– Applicability to destructive testingApplicability to destructive testing– Entire lot rejection (motivation for improvement) Entire lot rejection (motivation for improvement)

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Acceptance SamplingAcceptance Sampling

DisadvantagesDisadvantages– Risks of accepting “bad” lots and rejecting Risks of accepting “bad” lots and rejecting

“good” lots“good” lots– Added planning and documentationAdded planning and documentation– Sample provides less information than 100-Sample provides less information than 100-

percent inspection percent inspection

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Statistical Sampling Statistical Sampling TechniquesTechniques

Statistical Sampling Statistical Sampling TechniquesTechniques

nn and and c c – The bottom line in acceptance sampling is The bottom line in acceptance sampling is

that acceptance sampling plans are that acceptance sampling plans are designed to give us two things: designed to give us two things: nn and and cc, , where where nn = the sample size of a particular sampling plan = the sample size of a particular sampling plan

cc = the maximum number of defective pieces for = the maximum number of defective pieces for a a sample to be rejected