2. Operations Management Framework
Insert New Resource/Profit Model

3. 7
C H A P T E R
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.

4. 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 3

5. General-Purpose Quality Analysis Tools
Flow Charts
Run Charts
Cause & Effect Diagram
Pareto Charts
Histograms
Check Sheets
Scatter Diagrams
Control Charts

6. General-Purpose Quality Analysis Tools: Flow Chart
Flow Chart: A diagram of the steps in a process

7. General-Purpose Quality Analysis Tools: Run Charts
Run Charts: Plotting a variable against time.

8. General-Purpose Quality Analysis Tools: Cause & Effect Diagram
Possible causes:
The results or effect
Man
Machine
Material
Method
Environment
Effect
Can be used to systematically track backwards to find a possible cause of a quality problem (or effect) 17

9. General-Purpose Quality Analysis Tools: Cause & Effect Diagram
Also known as:
Ishikawa Diagrams
Fishbone Diagrams
Root Cause Analysis

10. General-Purpose Quality Analysis Tools: Checksheet
Monday
Billing Errors
Wrong Account
Wrong Amount
A/R Errors
Can be used to keep track of defects or used to make sure people collect data in a correct manner 16

11. Data Analysis Example
Exhibit 7.6: SleepCheap Hotel Survey Data

12. General-Purpose Quality Analysis Tools: Histogram
Can be used to identify the frequency of quality defect occurrence and display quality performance 14

13. General-Purpose Quality Analysis Tools: Pareto Analysis
63.5% of complaints are
about the bathroom
50.5% of complaints are
that something is dirty
Variant of histogram that helps rank order quality problems so that most important can be identified

14. General-Purpose Quality Analysis Tools: Scatter Plots

15. General-Purpose Quality Analysis Tools: Control Charts
970
980
990
1000
1010
1020 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

16. 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 3

17. SPC Steps 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 3

18. 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

19. 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!!!)

20. Cpk Calculation LCS - Lower control specification
UCS - Upper control specification
X - “Grand” mean of process performance
 - Standard deviation of process performance
If Cpk is > then the process is “Capable”
Translation, we will produce good parts at least 99.74% of the time

21. Example 7.3: Cpk Calculation
Customer specification
Mean of .375 inches
+ or inches
Therefore, customer specification limits at .373 and .377
Process performance
Actual mean is .376
Standard deviation is
Cpk = min[ – , – ]
= min [3.333, 1.111]
= 1.111
The process is capable.

22. 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!!
If Cp is > then the process is “Capable”
Translation, we will produce good parts at least 99.74% of the time

23. Example 7.2 Cp Calculation
Customer specification
Mean of .375 inches
+ or inches
Therefore, customer specification limits at .373 and .377
Process performance
Actual mean is .375
Standard deviation is
Solution:
Cp = – =
6(0.0024)

24. 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 inches with a standard deviation of .003 inch.
Calculate the process capability for this example.
What does this solution tell you about the process?

25. Another Cp Calculation: Metal Fabrication
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 inch)
Fabrication process performance
Actual mean
1.002 inches
Standard deviation
.003 inch
Solution:
Cpk = min[ –.99 , 1.01 – ]
3(.003) 3(.003)
= min [1.333, 0.889]
= 0.889
Process, as configured, is not capable.
How can it be made capable?

26. 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” 3

27. SPC Steps 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 3

28. X-Bar and R-Chart Construction
Insert Exhibit 7.17 3

29. Control Charts: X-bar Steps
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.
Exhibit 7.18 X-bar Chart for Example 7.4
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

30. Control Charts: R Steps
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

31. 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.

32. Acceptance Sampling Purposes Advantages
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) 4

33. 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 5

34. 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

35. Six Sigma Quality – Role of interdependencies
At 3, the probability that an assembly of interdependent parts works, given “n” parts:
1 part = = 99.74%
10 parts = = 97.43%
50 parts = = 87.79%
100 parts = = 77.08%
267 parts = = 49.90%
1000 parts = = 7.40%
Simulation

36. Six Sigma Quality
“Six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs
Exhibit 7.23 Process Capability for Six Sigma Quality

37. Six Sigma and Failure Rates
Odds of random fluctuation creating a result that is 6 from the mean are 2 in 1 billion
% confident of a good outcome
In practice, process mean is allowed to shift ±1.5 

38. Six Sigma and Failure Rates
Failure Rates in the presence of component interdependencies
6 (mean-centered) line
6 (1.5 mean-shift) line
3 line

39. 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. 5

40. 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. 5

41. 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. 5