7 - 2 McGraw-Hill/Irwin Operations Now, 2/e © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. Operations Management Framework Insert New Resource/Profit

7 - 2

McGraw-Hill/IrwinOperations Now, 2/e

© 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.

Operations Management Framework

Insert New Resource/Profit Model

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C H A P T E R 7

Quality Tools

L E A R N I N G O B J E C T I V E S

▬ Explain the function of the general-purpose quality analysis tools.▬ Explain how each quality tool aids in the QI story and DMAIC processes.▬ Describe and make computations for process capability using Cp and Cpk

capability indices.▬ Describe how statistical process control can be used to prevent defects

from occurring.▬ Describe how acceptance sampling works and the role of the operating

characteristics curve.▬ Understand the Kano model.▬ Explain how the Six Sigma quality relates to process capability.▬ Describe service quality applications, including service blueprinting and

moment-of-truth analysis.▬ Describe how “recovery” applies to quality failures.

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Quality Analysis

• Six Sigma’s DMAIC and TQM’s QI Story provide structure, but neither defines how activities are to be accomplished. That can be determined through the use of a broad set of analysis tools.

Insert exhibit 7.1 DMAIC and QI

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General-Purpose Quality Analysis Tools

- Flow Charts- Run Charts- Cause & Effect Diagram - Pareto Charts- Histograms- Check Sheets- Scatter Diagrams- Control Charts

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General-Purpose Quality Analysis Tools: Flow Chart

• Flow Chart: A diagram of the steps in a process

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General-Purpose Quality Analysis Tools: Run Charts

Run Charts: Plotting a variable against time.

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General-Purpose Quality Analysis Tools: Cause & Effect Diagram

Effect

ManMachine

MaterialMethod

Environment

Possible causes: The results

or effect

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

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General-Purpose Quality Analysis Tools: Cause & Effect Diagram

Also known as:Ishikawa DiagramsFishbone DiagramsRoot Cause Analysis

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General-Purpose Quality Analysis Tools: Checksheet

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

Billing Errors

Wrong Account

Wrong Amount

A/R Errors

Wrong Account

Wrong Amount

Monday

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Data Analysis Example

Exhibit 7.6: SleepCheap Hotel Survey Data

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General-Purpose Quality Analysis Tools: Histogram

Hotel Complaints

0

10

20

30

40

50

60

70

Show er Toilet Vanity Desk Bed Dresser Floor TV

Area

# o

f C

om

pla

ints

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

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General-Purpose Quality Analysis Tools: Pareto Analysis

• Variant of histogram that helps rank order quality problems so that most important can be identified

50.5% of complaints are that something is dirty

63.5% of complaints are about the bathroom

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General-Purpose Quality Analysis Tools: Scatter Plots

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General-Purpose Quality Analysis Tools: Control Charts

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

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Statistical Process Control (SPC)

• Takes advantage of our knowledge about the standardized distribution of these measures

• Process Capability- Uses sampling to determine if the process can produce consistently within

acceptable customer limits

- Cp and Cpk

• Process Control- Identifies potential problems before defects are created by watching the

process unfold- X-bar & R Charts

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• Measure a sample of the process output- Four to five units of output for most applications- Many (>25) samples

• Calculate sample means ( X ), grand mean (X), & ranges (R)

• Calculate “process capability”- Can you deliver within tolerances defined by the customer

• Traditional standard is “correct 99.74% of the time”

• Monitor “process control”- Is anything changing about the process?

• In terms of mean or variation

SPC Steps

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Process Capability

• Capability Index: quantifying the relationship between control limits and customer specifications

- Cp -- Used to determine “capability” when the process is “mean-centered”

Exhibit 7.14: Process Control Chart for Soft Drink Can

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Process Capability

• Capability Index: quantifying the relationship between control limits and customer specifications

- Cpk -- Used to determine “capability” when the process is “mean-shifted”Exhibit 7.15 Process Shifted Downward From Center

• Difference between Cp and Cpk is minimal

- Cpk approach works fine to calculate capability of mean-centered

process (but not vice versa!!!)

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Cpk Calculation

• LCS - Lower control specification• UCS - Upper control specification• X - “Grand” mean of process performance - Standard deviation of process performance

,

3σ

XUCS,

3σ

LCSXminC pk

• If Cpk is > 1.000 then the process is “Capable”- Translation, we will produce good parts at least 99.74% of the time

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Example 7.3: Cpk Calculation

• Customer specification- Mean of .375 inches- + or - .002 inches

- Therefore, customer specification limits at .373 and .377

• Process performance- Actual mean is .376- Standard deviation is 0.0003

Cpk = min[ 0.376 – 0.373 , 0.377 – 0.376 ] 0.0009 0.0009

= min [3.333, 1.111]= 1.111

The process is capable.

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Cp Calculation is Simpler Version of Cpk

• LCS - Lower control specification• UCS - Upper control specification - Standard deviation of process performance

• Mean is assumed to sit exactly between UCS and LCS!!

6σ

LCSUCSC p

• If Cp is > 1.000 then the process is “Capable”- Translation, we will produce good parts at least 99.74% of the time

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Example 7.2 Cp Calculation

• Solution:

- Cp = 0.377 – 0.373 = 0.27778

6(0.0024)

• Customer specification- Mean of .375 inches- + or - .002 inches

- Therefore, customer specification limits at .373 and .377

• Process performance- Actual mean is .375- Standard deviation is 0.0024

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Another Cp Calculation: Metal Fabrication

A metal fabricator produces connecting rods with an outer diameter that has a 1 ± .01 inch specification. A machine operator takes several sample measurements over time and determines the sample mean outer diameter to be 1.002 inches with a standard deviation of .003 inch.

Calculate the process capability for this example.

What does this solution tell you about the process?

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Another Cp Calculation: Metal Fabrication

• Solution:Cpk = min[ 1.002 –.99 , 1.01 – 1.002 ]

3(.003) 3(.003)

= min [1.333, 0.889]= 0.889

Process, as configured, is not capable.

How can it be made capable?

• Cp or Cpk?- Cpk – it is not a mean centered process

• Customer specification- Mean

• 1 inch- LCS

• .99 inch = (1 inch – .01 inch)- UCS

• 1.01 inches = (1 inch + .01 inch)

• Fabrication process performance- Actual mean

• 1.002 inches- Standard deviation

• .003 inch

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Process Control

• Cp and Cpk tell us whether the process will produce defective output as part of its normal operation.

- i.e., is it “capable”?

• Control charts are maintained on an ongoing basis so that operators can ensure that a process is not changing

- i.e., drifting to a different level of performance- i.e., is it “in control”

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• Measure a sample of the process output- Four to five units of output for most applications- Many (>25) samples

• Calculate sample means ( X ), grand mean (X), & ranges (R)

• Calculate “process capability”- Can you deliver within tolerances defined by the customer

• Traditional standard is “correct 99.74% of the time”

• Monitor “process control”- Is anything changing about the process?

• In terms of mean or variation

SPC Steps

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X-Bar and R-Chart Construction

Insert Exhibit 7.17

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Control Charts: X-bar

Exhibit 7.18 X-bar Chart for Example 7.4

• Distinguishing between random fluctuation and fluctuation due to an assignable cause.

- X-bar chart tracks the trend in sample means to see if any disturbing patterns emerge.

• Steps-Calculate Upper & Lower Control Limits (UCL & LCL).

•Use special charts based on sample size

-Plot X-bar value for each sample-Investigate “Nonrandom” patterns

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Control Charts: R

• Provide monitoring of variation within each sample.- i.e., within each subgroup that you measure when calculating process capability

• Always paired with X-bar charts.Exhibit 7.19 R-Chart for Example 7.4

• Steps• Calculate Upper & Lower

Control Limits (UCL & LCL).• Use special charts based on

sample size• Different from those used in

X-bar chart

• Plot R value for each sample

• Investigate “Nonrandom” patterns

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Nonrandom Patterns on Control Charts• Investigate the process if X-bar or R chart illustrates:

- One data point above +3 or below -3 - 2 out of 3 data points between +2 and +3 or between -2 and -3 - 4 out of 5 data points between +1 and +3 or between -1 and -3 - 8 successive points above the grand mean or 8 successive points below the grand mean.

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Acceptance Sampling

• Purposes- Sampling to accept or reject the immediate lot of product at hand- Ensure quality is within predetermined level

• Advantages- Economy- Less handling damage- Fewer inspectors- Upgrading of the inspection job- Applicability to destructive testing- Entire lot rejection (motivation for improvement)

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Acceptance Sampling (Continued)

• Disadvantages- Risks of accepting “bad” lots and rejecting “good” lots- Added planning and documentation- Sample provides less information than 100-percent inspection

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Acceptance Sampling

• Acceptable Quality Level (AQL)- Max. acceptable percentage of defectives

that defines a “good” lot- Producer’s risk is the probability of

rejecting a good lot

• Lot tolerance percent defective (LTPD)- Percentage of defectives that defines

consumer’s rejection point- Consumer’s risk is the probability of

accepting a bad lot

• Plan developed based on risk tolerance to determine size of sample and number in sample that can be defective

Exhibit 7.21 Operating Characteristics Curve

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Six Sigma Quality – Role of interdependencies

• At 3, the probability that an assembly of interdependent parts works, given “n” parts:- 1 part = .99741 = 99.74%- 10 parts = .997410 = 97.43%- 50 parts = .997450 = 87.79%- 100 parts = .9974100 = 77.08%- 267 parts = .9974267 = 49.90% - 1000 parts = .99741000 = 7.40%

Simulation

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Six Sigma Quality

Exhibit 7.23 Process Capability for Six Sigma Quality

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

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Six Sigma and Failure Rates

"Sigma" Level

Percent Error Free Output

Error Free/Million

Defects/Million (DPMO)

1 31% 310,000 690,000 2 69% 690,000 310,000 3 93.30% 933,000 67,000 4 99.40% 994,000 6,000 5 99.98% 999,800 200 6 99.9997% 999,997 3

• Odds of random fluctuation creating a result that is 6 from the mean are 2 in 1 billion- 99.9999998% confident of a good outcome

• In practice, process mean is allowed to shift ±1.5

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0%

25%

50%

75%

100%

1 250 500 750 1000 1250 1500

# of interdependent parts

Pro

ba

bili

ty o

f n

on

-fa

ilure

(%

)

Six Sigma and Failure Rates

3 line

6 (1.5 mean-shift) line

Failure Rates in the presence of component interdependencies

6 (mean-centered) line

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Moment-of-Truth Analysis

• Moment-of-Truth Analysis: The identification of the critical

instances when a customer judges service quality and

determines the experience enhancers, standard expectations, and experience detractors.

• Experience enhancers: Experiences that make the customer feel good about the interaction and make the interaction better.

• Standard expectations: Experiences that are expected and taken for granted.

• Experience detractors: Experiences viewed by the customer as reducing the quality of service.

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Customer Relationship Management (CRM)

• Customer loyalty increases profitability: Advances in technologies and techniques have enhanced companies’ ability to manage relationships with customers.

• CRM: Systems designed to improve relationships with customers and improve the business’ ability to identify valuable customers.

- Includes call center management software, sales tracking, and customer service.

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Recovery

• There will always be times when customers do not get what they want.

• Failure to meet customers’ expectations does not have to mean lost customers.

• Recovery plans: Policies for how employees are to deal with quality failures so that customers will return.

• Example: A recovery for a customer who has had a bad meal at a restaurant might include eliminating the charges for the meal, apologizing, and offering gift certificates for future meals.

Documents

7 - 2 McGraw-Hill/Irwin Operations Now, 2/e © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. Operations Management Framework Insert New Resource/Profit