1. 6 Managing Quality PowerPoint presentation to accompany
Heizer and Render
Operations Management, 10e
Principles of Operations Management, 8e
PowerPoint slides by Jeff Heyl
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

2. Outline Defining Quality Total Quality Management
Implications of Quality
Ethics and Quality Management
Total Quality Management
Continuous Improvement
Six Sigma
Employee Empowerment
TQM in Services
Statistical Process Control (SPC)
Control Charts for Variables
Control Charts for Attributes
Process Capability
Process Capability Ratio (Cp)
Process Capability Index (Cpk )
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

3. Learning Objectives Define quality and TQM Explain Six Sigma
Explain the use of a control chart
Build 𝒙 -charts and R-charts
Build p-charts
Explain process capability and compute Cp and Cpk
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

4. Two Ways Quality Improves Profitability
Improved response
Flexible pricing
Improved reputation
Sales Gains via
Improved Quality
Increased Profits
Increased productivity
Lower rework and scrap costs
Lower warranty costs
Reduced Costs via
Figure 6.1
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

5. Defining Quality Different Views User-based Manufacturing-based
The totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs American Society for Quality
Different Views
User-based
Manufacturing-based
Product-based
User-based: better performance, more features
Manufacturing-based: conformance to standards, making it right the first time
Product-based: specific and measurable attributes of the product
Implications of Quality
Company reputation
Perception of new products
Employment practices
Supplier relations
Product liability
Reduce risk
Global implications
Improved ability to compete
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

6. Key Dimensions of Quality
Performance
Features
Reliability
Conformance
Durability
Serviceability
Aesthetics
Perceived quality
Value
07: Ch6S - Quality and SPC (MGMT3102:Fall13) 7

7. Ethics and Quality Management
Operations managers must deliver healthy, safe, quality products and services
Poor quality risks injuries, lawsuits, recalls, and regulation
Organizations are judged by how they respond to problems
All stakeholders much be considered
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

8. Deming’s Fourteen Points
Create consistency of purpose
Lead to promote change
Build quality into the product; stop depending on inspections
Build long-term relationships based on performance instead of awarding business on price
Continuously improve product, quality, and service
Start training
Emphasize leadership
Table 6.2
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

9. Deming’s Fourteen Points
Drive out fear
Break down barriers between departments
Stop haranguing workers
Support, help, and improve
Remove barriers to pride in work
Institute education and self-improvement
Put everyone to work on the transformation
Table 6.2
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

10. Continuous Improvement
Represents continual improvement of all processes
Involves all operations and work centers including suppliers and customers
People, Equipment, Materials, Procedures
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

11. 6 Six Sigma Program A highly structured program developed by Motorola
A discipline – DMAIC
Also,
Statistical definition of a process that is % capable, 3.4 defects per million opportunities (DPMO)
Mean
Lower limits
Upper limits
3.4 defects/million
±6
2,700 defects/million
±3
Figure 6.4
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

12. Six Sigma Define critical outputs and identify gaps for improvement
Measure the work and collect process data
Analyze the data
Improve the process
Control the new process to make sure new performance is maintained
DMAIC Approach
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

13. Employee Empowerment
Getting employees involved in product and process improvements
85% of quality problems are due to process and material
Techniques
Build communication networks that include employees
Develop open, supportive supervisors
Move responsibility to employees
Build a high-morale organization
Create formal team structures
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

14. TQM In Services
Service quality is more difficult to measure than the quality of goods
Service quality perceptions depend on
Intangible differences between products
Intangible expectations customers have of those products
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

15. Statistical Process Control (SPC)
Variability is inherent in every process
Natural or common causes
Special or assignable causes
Provides a statistical signal when assignable causes are present
Detect and eliminate assignable causes of variation
- Statistical process control measures the performance of a process, it does not help to identify a particular specimen produced as being “good” or “bad,” in or out of tolerance.
- Statistical process control requires the collection and analysis of data - therefore it is not helpful when total production consists of a small number of units
- While statistical process control can not help identify a “good” or “bad” unit, it can enable one to decide whether or not to accept an entire production lot. If a sample of a production lot contains more than a specified number of defective items, statistical process control can give us a basis for rejecting the entire lot. The issue of rejecting a lot which was actually good can be raised here, but is probably better left to later.
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

16. Natural Variations Also called common causes
Affect virtually all production processes
Expected amount of variation
Output measures follow a probability distribution
For any distribution there is a measure of central tendency and dispersion
If the distribution of outputs falls within acceptable limits, the process is said to be “in control”
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

17. Assignable Variations
Also called special causes of variation
Generally this is some change in the process
Variations that can be traced to a specific reason
The objective is to discover when assignable causes are present
Eliminate the bad causes
Incorporate the good causes
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

18. Types of Data Variables Attributes
Characteristics that can take any real value
May be in whole or in fractional numbers
Continuous random variables
Defect-related characteristics
Classify products as either good or bad or count defects
Categorical or discrete random variables
Once the categories are outlined, students may be asked to provide examples of items for which variable or attribute inspection might be appropriate. They might also be asked to provide examples of products for which both characteristics might be important at different stages of the production process.
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

19. Control Charts for Variables
For variables that have continuous dimensions
Weight, speed, length, strength, etc.
x-charts are to control the central tendency of the process
R-charts are to control the dispersion of the process
These two charts must be used together
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

20. Setting Chart Limits For x-Charts when we know s
Upper control limit (UCL) = x + zsx
Lower control limit (LCL) = x - zsx
where x = mean of the sample means or a target value set for the process
z = number of normal standard deviations
sx = standard deviation of the sample means
= s/ n
s = population standard deviation
n = sample size
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

21. Setting Control Limits
Hour 1
Sample Weight of Number Oat Flakes
1 17
2 13
3 16
4 18
5 17
6 16
7 15
8 17
9 16
Mean 16.1
s = 1
Hour Mean Hour Mean
n = 9
For 99.73% control limits, z = 3
UCLx = x + zsx = (1/3) = 17 ozs
LCLx = x - zsx = (1/3) = 15 ozs
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

22. Setting Control Limits
Control Chart for sample of 9 boxes
Variation due to assignable causes
Out of control
Sample number
| | | | | | | | | | | |
17 = UCL
15 = LCL
16 = Mean
Variation due to natural causes
Out of control
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

23. Setting Chart Limits For x-Charts when we don’t know s
Upper control limit (UCL) = x + A2R
Lower control limit (LCL) = x - A2R
where R = average range of the samples
A2 = control chart factor found in Table S6.1
x = mean of the sample means
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

24. Control Chart Factors Sample Size Mean Factor Upper Range Lower Range
n A2 D4 D3
Table S6.1
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

25. Setting Control Limits
Process average x = 12 ounces
Average range R = .25
Sample size n = 5
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

26. Setting Control Limits
Process average x = 12 ounces
Average range R = .25
Sample size n = 5
UCLx = x + A2R
= 12 + (.577)(.25) =
= ounces
From Table S6.1
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

27. Setting Control Limits
Process average x = 12 ounces
Average range R = .25
Sample size n = 5
UCL =
Mean = 12
LCL =
UCLx = x + A2R
= 12 + (.577)(.25) =
= ounces
LCLx = x - A2R =
= ounces
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

28. Restaurant Control Limits
For salmon filets at Darden Restaurants
Sample Mean
x Bar Chart
UCL =
x –
LCL –
| | | | | | | | |
11.5 –
11.0 –
10.5 –
Sample Range
Range Chart
UCL =
R =
LCL = 0
| | | | | | | | |
0.8 –
0.4 –
0.0 –
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

29. R – Chart Type of variables control chart
Shows sample ranges over time
Difference between smallest and largest values in sample
Monitors process variability
Independent from process mean
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

30. Setting Chart Limits For R-Charts Upper control limit (UCLR) = D4R
Lower control limit (LCLR) = D3R
where
R = average range of the samples
D3 and D4 = control chart factors from Table S6.1
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

31. Setting Control Limits
Average range R = 5.3 pounds
Sample size n = 5
From Table S6.1 D4 = 2.115, D3 = 0
UCL = 11.2
Mean = 5.3
LCL = 0
UCLR = D4R
= (2.115)(5.3)
= 11.2 pounds
LCLR = D3R
= (0)(5.3)
= 0 pounds
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

32. Mean and Range Charts (a)
These sampling distributions result in the charts below
(Sampling mean is shifting upward but range is consistent)
x-chart
(x-chart detects shift in central tendency)
UCL
LCL
R-chart
(R-chart does not detect change in mean)
UCL
LCL
Figure S6.5
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

33. Mean and Range Charts (b)
These sampling distributions result in the charts below
(Sampling mean is constant but dispersion is increasing)
x-chart
(x-chart does not detect the increase in dispersion)
UCL
LCL
R-chart
(R-chart detects increase in dispersion)
UCL
LCL
Figure S6.5
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

34. Control Charts for Attributes
For variables that are categorical
Good/bad, yes/no, acceptable/unacceptable
Measurement is typically counting defectives
Charts may measure
Percent defective (p-chart)
Number of defects (c-chart)
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

35. Control Limits for p-Charts
Population will be a binomial distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics
UCLp = p + zsp ^
p(1 - p) n
sp = ^
LCLp = p - zsp ^
Instructors may wish to point out the calculation of the standard deviation reflects the binomial distribution of the population
where p = mean fraction defective in the sample
z = number of standard deviations
sp = standard deviation of the sampling distribution
n = sample size ^
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

36. p-Chart for Data Entry p = = .04 sp = = .02 1 6 .06 11 6 .06
Sample Number Fraction Sample Number Fraction
Number of Errors Defective Number of Errors Defective
Total = 80
p = = .04 80
(100)(20)
(.04)( )
100
sp = = .02 ^
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

37. p-Chart for Data Entry UCLp = p + zsp = .04 + 3(.02) = .10^
LCLp = p - zsp = (.02) = 0 ^
.11 –
.10 –
.09 –
.08 –
.07 –
.06 –
.05 –
.04 –
.03 –
.02 –
.01 –
.00 –
Sample number
Fraction defective
| | | | | | | | | |
UCLp = 0.10
LCLp = 0.00
p = 0.04
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

38. Possible assignable causes present
p-Chart for Data Entry
UCLp = p + zsp = (.02) = .10 ^
Possible assignable causes present
LCLp = p - zsp = (.02) = 0 ^
.11 –
.10 –
.09 –
.08 –
.07 –
.06 –
.05 –
.04 –
.03 –
.02 –
.01 –
.00 –
Sample number
Fraction defective
| | | | | | | | | |
UCLp = 0.10
LCLp = 0.00
p = 0.04
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

39. Which Control Chart to Use
Variables Data
Using an x-Chart and R-Chart
Observations are variables
Collect samples of n = 4, or n = 5, or more, each from a stable process and compute the mean for the x-chart and range for the R-chart
Track samples of n observations each.
Table S6.3
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

40. Which Control Chart to Use
Attribute Data
Using the p-Chart
Observations are attributes that can be categorized as good or bad (or pass–fail, or functional–broken), that is, in two states.
We deal with fraction, proportion, or percent defectives.
There are several samples, with many observations in each. For example, 20 samples of n = 100 observations in each.
Table S6.3
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

41. Patterns in Control Charts
Normal behavior. Process is “in control.”
UCL
Target
LCL
UCL
Target
LCL
One plot out above (or below). Process is “out of control.”
UCL
Target
LCL
Trends in either direction, 5 plots. Progressive change.
Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
UCL
Target
LCL
Two plots very near lower (or upper) control.
UCL
Target
LCL
Run of 5 above (or below) central line.
UCL
Target
LCL
Erratic behavior.
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

42. Process Capability
The natural variation of a process should be small enough to produce products that meet the standards required
A process in statistical control does not necessarily meet the design specifications
Process capability is a measure of the relationship between the natural variation of the process and the design specifications
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

43. Process Capability Ratio
Cp =
Upper Specification - Lower Specification 6s
A capable process must have a Cp of at least 1.0
Does not look at how well the process is centered in the specification range
Often a target value of Cp = 1.33 is used to allow for off-center processes
Six Sigma quality requires a Cp = 2.0
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

44. Process Capability Ratio
Insurance claims process
Process mean x = minutes
Process standard deviation s = .516 minutes
Design specification = 210 ± 3 minutes
Cp =
Upper Specification - Lower Specification 6s
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

45. Process Capability Ratio
Insurance claims process
Process mean x = minutes
Process standard deviation s = .516 minutes
Design specification = 210 ± 3 minutes
Cp =
Upper Specification - Lower Specification 6s
= = 1.938
6(.516)
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

46. Process Capability Ratio
Insurance claims process
Process mean x = minutes
Process standard deviation s = .516 minutes
Design specification = 210 ± 3 minutes
Cp =
Upper Specification - Lower Specification 6s
= = 1.938
6(.516)
Process is capable
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

47. Process Capability Index
Cpk = minimum of ,
Upper Specification - x Limit 3s
Lower x - Specification Limit
A capable process must have a Cpk of at least 1.0
A capable process is not necessarily in the center of the specification, but it falls within the specification limit at both extremes
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

48. Process Capability Index
New Cutting Machine
New process mean x = .250 inches
Process standard deviation s = inches
Upper Specification Limit = .251 inches
Lower Specification Limit = .249 inches
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

49. Process Capability Index
New Cutting Machine
New process mean x = .250 inches
Process standard deviation s = inches
Upper Specification Limit = .251 inches
Lower Specification Limit = .249 inches
Cpk = minimum of ,
(.251)
(3).0005
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

50. Process Capability Index
New Cutting Machine
New process mean x = .250 inches
Process standard deviation s = inches
Upper Specification Limit = .251 inches
Lower Specification Limit = .249 inches
Cpk = minimum of ,
(.251)
(3).0005
(.249)
Both calculations result in
New machine is NOT capable
Cpk = = 0.67
.001
.0015
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

51. Interpreting Cpk Cpk = negative number Cpk = zero
Cpk = between 0 and 1
Cpk = 1
Cpk > 1
Figure S6.8
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

52. SPC and Process Variability
Lower specification limit
Upper specification limit
Process mean, m
(a) Acceptance sampling (Some bad units accepted)
(b) Statistical process control (Keep the process in control)
This may be a good time to stress that an overall goal of statistical process control is to “do it better,” i.e., improve over time.
(c) Cpk >1 (Design a process that is in control)
Figure S6.10
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

53. In-Class Problems from the Lecture Guide Practice Problems
Twenty-five engine mounts are sampled each day and found to have an average width of 2 inches, with a standard deviation of 0.1 inches. What are the control limits that include 99.73% of the sample means 𝑋 .
Problem 1 Solution:
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

54. In-Class Problems from the Lecture Guide Practice Problems
Several samples of size have been taken from today’s production of fence posts. The average post was 3 yards in length and the average sample range was yard. Find the 99.73% upper and lower control limits.
A2 = from Table S6.1
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

55. In-Class Problems from the Lecture Guide Practice Problems
The average range of a process is 10 lbs. The sample size is 10. Use Table S6.1 to develop upper and lower control limits on the range.
From Table S6.1, D4 = 1.78 and D3 = 0.22
𝑈𝐶𝐿 𝑅 = 𝐷 4 𝑅 = =17.8 𝑙𝑏𝑠
𝐿𝐶𝐿 𝑅 = 𝐷 3 𝑅 = =2.2 𝑙𝑏𝑠
07: Ch6S - Quality and SPC (MGMT3102:Fall13)

56. In-Class Problems from the Lecture Guide Practice Problems
Based on samples of 20 IRS auditors, each handling 100 files, we find that the total number of mistakes in handling files is 220. Find the 95.45% upper and lower control limits.
07: Ch6S - Quality and SPC (MGMT3102:Fall13)