1. Operations and Supply Chain Management
MGMT 3306
Lecture 04
Instructor: Dr. Yan Qin

2. Outline – Managing Quality
What is Quality
Cost of Quality (COQ)
International Quality Standards
7 Concepts of Total Quality Management
Statistical Process Control
Variations in processes
Process capability
Process control charts

3. What is Quality?
David Garvin, in his book Managing Quality, summarized five principal approaches to defining quality:
Transcendental view: “I can’t define it, but I know when I see it”;
Product-based view: Quality is viewed as quantifiable and measurable characteristics or attributes; (Design quality)
User-based view: Quality is an individual matter, and products that best satisfy their preferences are those with the highest quality;

4. What is Quality? Five principal approaches to defining quality (Cont.)
Manufacturing-based view: “conformance to requirements” (Conformance quality)
Value-based view: Quality is defined in terms of costs and prices as well as a number of other attributes.

5. 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

6. Quality Program Fundamental to any quality program is
the determination of quality specifications, and
the costs of achieving (or not achieving) those specifications.

7. Quality Specification
Design Quality measures how well a product meets customer expectation.
Specifications of Design Quality
Functions/features intended to deliver
Reliability/durability
Serviceability
Aesthetics
Conformance Quality measures how well design specifications are met in production.

8. Cost of Quality
Cost of Quality refers to all of the costs attributable to the production of quality that is not 100% perfect.
It is estimated that the cost of quality is between 15% and 20% of every sales dollar. (Philip Crosby: <2.5%)
Three basic assumptions:
Failures are caused;
Prevention is cheaper;
Performance can be measured.
Cost of poor quality

9. Four types of costs Total Cost Total Cost External Failure
Quality Improvement
Total Cost
External Failure
Internal Failure
Prevention
Appraisal

10. More Quality Definitions
Fitness for use: Design quality + Conformance quality
Quality Management is the totality of functions involved in the determination and achievement of quality.
Quality Control focuses on the process of producing the product or service with the intent of eliminating problems that might result in defects.
Quality Assurance is a complete system to assure the quality of a product.

11. International Quality Standards
Series of standards agreed upon by the International Organization for Standardization (ISO)
Adopted in 1987
More than 160 countries
A prerequisite for global competition?
ISO 9000 an international reference for quality, ISO is primarily concerned with environmental management

12. International Quality Standards
ISO 9000 series
Common quality standards for products sold in Europe (even if made in U.S.)
2008 update places greater emphasis on leadership and customer requirements and satisfaction
ISO series (Europe/EC)
Core elements include Environmental management; Auditing; Performance evaluation; Labeling; Life cycle assessment

13. Total Quality Management is the management of an entire organization so that it excels in all aspects of goods and services that are important to the customer.

14. Deming’s 14 Points
Quality expert Edwards Deming used 14 points to indicate how he implemented TQM:
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

15. Deming’s 14 Points – Cont. Start training Emphasize leadership
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

16. Seven Concepts of TQM
The authors of the textbook developed Deming’s 14 points into seven concepts for an effective TQM program:
Continuous improvement
Six Sigma
Employee empowerment
Benchmarking
Just-in-time (JIT)
Taguchi concepts
Knowledge of TQM tools

17. 1. Continuous Improvement
Represents continual improvement of all processes .
Walter Shewhart developed a circular model know as Plan-Do- Check-Act as his version of continuous improvement.
PDCA Model

18. 2. Six Sigma
Originally developed by Motorola, adopted and enhanced by Honeywell and GE.
Six-sigma is a philosophy and methods used to eliminate defects by reducing the variation in the processes.
Statistically, sigma is a measure of variation. In business processes, it is a measure of how many defects or failures are likely to occur per million opportunities.
4 sigma = 6,000 defects per million
6 sigma = 3.4 defects per million

19. Metric in 6-sigma
The calculation of Defects per million opportunities (DPMO) requires three pieces of information:
Unit: the item produced or being serviced;
Defect: Any item or event that does not meet the customer’s specification limits;
Opportunities: an chance for a defect to occur

20. Examples: DPMO
Example 1: The customers of a mortgage bank expect to have their mortgage applications processed within 10 days of filing. Suppose all defects are counted and it is determined that there are 150 loans in the 1,000 applications processed last month that don’t meet this customer requirement. Then what is the DPMO in this case?
Example 2: Now suppose the production of a product consists of two operations. Defects can occur at each operation. There were 100 defective units out of the total 2,000 units produced. What was the DPMO in this case?

21. Examples: DPMO – Calculations
𝐷𝑃𝑀𝑂= 150 1×1,000 ×1,000,000=150,000
Example 2:
𝐷𝑃𝑀𝑂= 100 2×2,000 ×1,000,000=25,000

22. 6-sigma Methodology
Uses many of the same statistical tools as other quality control programs.
The difference is that the tools are used in a systematic project-oriented fashion through define, measure, analyze, improve, and control (DMAIC) cycle.
The focus of DMAIC is understanding and achieving what the customer wants.

23. DMAIC
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

24. 6-sigma Implementation
Emphasize defects per million opportunities as a standard metric
Provide extensive training
Focus on corporate sponsor support
Create qualified process improvement experts (Black Belts, Green Belts, etc.)
Set stretch objectives
This cannot be accomplished without a major commitment from top level management

25. 3. Employee Empowerment
Getting employees involved in product and process improvements
85% of quality problems are due to process and material
Techniques include:
Build communication networks that include employees
Develop open, supportive supervisors
Move responsibility to employees
Build a high-morale organization
Create formal team structures

26. Use internal benchmarking if you’re big enough
Selecting best practices to use as a standard for performance.
Steps include:
Determine what to benchmark
Form a benchmark team
Identify benchmarking partners
Collect and analyze benchmarking information
Take action to match or exceed the benchmark
Use internal benchmarking if you’re big enough

27. Best Practices for Resolving Customer Complaints
Justification
Make it easy for clients to complain
It is free market research
Respond quickly to complaints
It adds customers and loyalty
Resolve complaints on first contact
It reduces cost
Use computers to manage complaints
Discover trends, share them, and align your services
Recruit the best for customer service jobs
It should be part of formal training and career advancement

28. 5. Just-in-Time (JIT)
JIT (Just-in-Time) is an approach of continuous and forced problem solving via a focus on throughput and reduced inventory.
Throughput is the time required to move orders through the production process, from receipt to delivery.
Basically it means making only what is needed at the time when it is need!

29. 6. Taguchi Concepts
Engineering and experimental design methods to improve product and process design
Identify key component and process variables affecting product variation
Taguchi Concepts
Quality robustness: Ability to produce products uniformly in adverse manufacturing and environmental conditions
Quality loss function
Target-oriented quality: a Philosophy of continuous improvement to bring the product exactly on target

30. Target-oriented quality
Quality Loss Function
Shows that costs increase as the product moves away from what the customer wants
Costs include customer dissatisfaction, warranty and service, internal scrap and repair, and costs to society
Traditional conformance specifications are too simplistic
Target-oriented quality

31. 7. Tools of TQM Tools for Generating Ideas
Check sheets
Scatter diagrams
Cause-and-effect diagrams
Tools to Organize the Data
Pareto charts
Flowcharts
Tools for Identifying Problems
Histogram
Statistical process control chart

32. 1. Check Sheet An organized method of recording data / Hour
Defect A B C / //
/// // /
/ / /// /
// ///
// //// /
According to the check sheet, there were 3 occurrences of defect A, 2 occurrences of defect B, and 1 occurrence of defect C in the first hour of operation.

33. 2. Scatter Diagram
A graph of the value of one variable vs. another variable
Productivity decreases as Absenteeism increases.

34. 3. Cause-and-Effect Diagram
A tool that identifies process elements (causes) that might effect an outcome
Causes are classified into 4 categories in this diagram.

35. Example: Cause-and-Effect

36. 4. Pareto Chart
A graph to identify and plot problems or defects in descending order of frequency
Bars represent frequencies and the dashed curve represents cumulative probability.

37. Example: Pareto Chart

38. 5. Flowchart (Process Diagram)
A chart that describes the steps in a process

39. Example: MRI Flow Chart
Physician schedules MRI
Patient taken to MRI
Patient signs in
Patient is prepped
Technician carries out MRI
Technician inspects film
If unsatisfactory, repeat
Patient taken back to room
MRI read by radiologist
MRI report transferred to physician
Patient and physician discuss

40. 6. Histogram
A distribution showing the frequency of occurrences of a variable

41. 7. Statistical Process Control Chart
A chart with time on the horizontal axis to plot values of a statistic
Upper control limit
Target value
Lower control limit
Time

42. Statistical Process Control (SPC)
Statistical Process Control is a number of statistical techniques designed to evaluate quality from a conformance view.
Situations where SPC can be applied:
How many paint defects are there in the finish of a car?
How long does it take to execute market orders?
How long do customers wait to be served from our drive- through window?

43. Variations around us
All processes have inherent statistical variability which can be evaluated by statistical methods.
Basic forms of Variations:
Assignable variation: caused by factors that can be clearly identified and possibly managed, such as workers inadequately trained.
Common variation: also called random variation, variation that is inherent in the production process. It is usually a result of the type of equipment used in the process.

44. USL and LSL It is impossible to have zero variation!
Engineers therefore assign acceptable limits for variation.
The limits are known as the upper and lower specification limits (USL/LSL).
Also known as upper and lower tolerance limits (UTL/LTS);
The range defined by USL and LSL is called the specification range.

45. Example: USL and LSL
Consider the production of boxes of cereal. Suppose the target value of the weight of each cereal box is 12 oz. The design specifications might be 12 +/- 2 oz.
This tells the production department that the production should aim for the weight of 12 oz. However, any weight between 10 and 14 oz. is also acceptable.
In this case, USL = 14, LSL = 10.

46. Process capability
Process capability is the long-term performance level of the process after it has been brought under statistical control, that is, having only common variation.
There are two popular measures for determine process capability:
Process Capability Ratio (Cp)
Process Capability Index (Cpk)
Both of them are measured based on samples taken from production or test runs.

47. Process Capability Ratio (Cp)
The process capability ratio is calculated as:
𝑪 𝒑 = 𝑼𝒑𝒑𝒆𝒓 𝑺𝒑𝒆𝒄𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝑳𝒊𝒎𝒊𝒕 −𝑳𝒐𝒘𝒆𝒓 𝑺𝒑𝒆𝒄𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝑳𝒊𝒎𝒊𝒕 𝟔𝝈
𝝈 is the population standard deviation of the quality measure of interest.
When only samples are available, we use s instead of 𝝈 in calculation.
A capable process must have a Cp of at least 1.0
Six Sigma quality requires a Cp = 2.0

48. Example: Mean and Sample Std. Deviation
Suppose a sample from the production of a product consists of 5 data points summarized as follows:
What is the mean and standard deviation of the sample?
Data points 9 10
8.8
10.2 5

49. Example: Solution
Notes: In the calculation above, 𝑥 𝑖 represents the ith data point in the sample and n refers to the sample size, that is, the number of data points in the sample.

50. Example: Cp Suppose for an insurance claims process,
Process mean 𝑥 = minutes
Process standard deviation s = minutes
Design specification range = 210 ± 3 minutes
What is the process capability ratio in this case?

51. Upper Specification - Lower Specification
Example: Solution
Cp =
Upper Specification - Lower Specification 6s
= = 1.938
6(.516)

52. Process Capability Index (Cpk)
To determine process capability, two sets of parameters required:
Mean and standard deviation of the quality measure of interest in real production (or in the test run);
Target (Aim) value and Design specification limits (USL and LSL)

53. How to calculate Capability Index ( 𝐶 𝑝𝑘 )σ
Cpk = Minimum of ,
𝒙 −Lower specification 3σ
Upper specification – 𝒙

54. How to interpret 𝐶 𝑝𝑘 ?
Question 1: What does it tell you if the 𝐶 𝑝𝑘 is determined by LSL or USL?
Answer: If the value is determined by LSL (USL), it means that the mean of the sample shifts to the left (right) of the center of the specification range.
Question 2: How to interpret a negative/positive 𝐶 𝑝𝑘 value?
Answer: A positive value means that the sample mean is within the specification range. A negative value indicates the opposite.

55. How to interpret 𝐶 𝑝𝑘 ?
Question 3: What does it mean when 𝐶 𝑝𝑘 <1 or >1?
Answer: When 𝐶 𝑝𝑘 >1, it means the specification limits are larger than the three sigma allowed in the process. All units in the sample are within design limits.
Question 4: Is 𝐶 𝑝𝑘 the higher the better?
Answer: Yes. “You must have a 𝐶 𝑝𝑘 of 1.33 [4 sigma] or higher to satisfy most customers.” Joe Perito

56. Example: 𝐶 𝑝𝑘
The quality assurance manager is assessing the capability of a process that puts pressurized grease in an aerosol can.
The design specifications call for an average of 60 pounds per square inch of pressure in each can with an upper tolerance limit of 65 psi and a lower tolerance limit of 55 psi.
A sample is taken from production and it is found that the cans average 61 psi with a standard deviation of 2 psi.
Question: What is the process capability index of the process?

57. Example: Solution 𝐶 𝑝𝑘 = min 𝑥 −𝐿𝑆𝐿 3𝜎 , 𝑈𝑆𝐿− 𝑥 3𝜎
6 sigma = Cpk = 2
=0.6667

58. Process Control Charts
Sampling by Attributes (Go or no-go information)
Defectives refers to the acceptability of product across a range of characteristics.
Defects refers to the number of defects per unit which may be higher than the number of defectives.
Tools: p-chart (1 defect for each unit), c-chart (>1 defect each unit)
Sampling by Variable (Continuous)
Amount of deviation from a set standard for a single variable.
Tools: X-bar chart and R chart

59. p-charts
In p-charts, we create the control limits for the proportion of defects. We call the limits Upper Control Limit (UCL) and Lower Control Limit (LCL).
Plot the sample points and see if they fall within the control limits. If a sample point falls within the control limits, it means that the sample is under statistical control.
We usually use 3-sigma control limits.

60. Using p-charts
Let z be the number of standard deviations. For 99.7% confidence z =3. For 99% confidence, z = 2.58.
We usually just set z = 3.
Fraction defective

61. Example: p-chart
Hometown Bank is concerned about the number of wrong customer account numbers recorded. Each week a random sample of 2,500 deposits is taken and the number of incorrect account numbers is recorded
The results for the past 12 weeks are shown in the table on the next slide.(We therefore have 12 samples in this case.)
Is the booking process out of statistical control? Use three-sigma control limits.

62. Example: p-chart (Cont.)
Sample Number
Wrong Account Numbers 1 15 7 24 2 12 8 3 19 9 10 4 17 5 11 6
Total
147

63. Example: Solution – Step 1
Step 1: Using this sample data to compute parameters
p =
Total defectives
Total number of observations
147
12(2,500)
= =
σp = p (1 – p)/n
= 0.0049(1 – )/2,500 =
UCL = p + zσp
= (0.0014) =
LCL= p – zσp
= – 3(0.0014) =

64. Solution – Step 2 & 3
Step 2: Calculate the sample proportion defective.
Step 3: Plot each sample proportion defective on the chart,
Sample proportion defective = # 𝒐𝒇 𝒅𝒆𝒇𝒆𝒄𝒕𝒊𝒗𝒆𝒔 𝒊𝒏 𝒂 𝒔𝒂𝒎𝒑𝒍𝒆 𝒔𝒂𝒎𝒑𝒍𝒆 𝒔𝒊𝒛𝒆
For sample 1, the proportion of defectives is 15/2,500 =

65. Solution – Step 3
Fraction Defective
Sample
Mean
UCL
LCL
.0091
.0049
.0007
| | | | | | | | | | | | X
The p-Chart Showing sample defective 7 is Out of Control

66. Using c-chart With p charts, each item can only have one defect.
With a c chart, each item can have multiple defects.

67. Example: Lumber yard
Lumber yard expects 4 knotholes per eight-foot board.
Then,

68. 𝑥 and R charts
𝑥 and R charts are a type of control charts that monitors a variable’s data using multiple samples collected.
𝑥 chart is a plot of the means of the samples.
R chart is a plot of the range within each sample, while range is the difference between the highest and the lowest values in each sample.
Preferably using 25 or so samples of size 4 or 5.

69. How to construct 𝑥 and R charts
For x-bar chart,
Upper Control Limit = 𝑋 + 𝐴 2 𝑅
Lower Control Limit = 𝑋 − 𝐴 2 𝑅
where,
𝑋 is the mean of a sample, 𝑋 = 𝑖=1 𝑛 𝑋 𝑖 𝑛
𝑋 is the mean of all the sample means, 𝑋 = 𝑗=1 𝑚 𝑋 𝑗 𝑚
n is the sample size and m is the number of samples in total
𝑅 𝑗 is the range of sample j. 𝑅 is the mean of all ranges.
𝑅 = 𝑗=1 𝑚 𝑅 𝑗 𝑚

70. How to construct 𝑋 and R charts
For R chart,
Upper Control Limit = 𝐷 4 𝑅
Lower Control Limit = 𝐷 3 𝑅
where, 𝑅 is the mean of all ranges of the samples.
The values of A2, D3, and D4 are determined by sample size n.

71. Control chart factors Sample Size Mean Factor Upper Range Lower Range
n A2 D4 D3

72. Example: X-bar and R charts
The Watson Electric Company produces incandescent light bulbs. The following data on the number of lumens for 40-watt light bulbs were collected when the process was in control.
a. Calculate control limits for an R-chart and an X-chart.
b. Since these data were collected, some new employees were hired. A new sample obtained the following readings: 570, 603, 623, and 583. Is the process still in control?
Observation
Sample 1 2 3 4
604
612
588
600
597
601
607
603
581
570
585
592
620
605
595 5
590
614
608

73. Example: Solution Sample 𝑿 R 1 601 24 2 602 10 3 582 22 4 32 5 604
Total
2,991
112
Average
𝑿 =𝟓𝟗𝟖.𝟐
𝑹 =𝟐𝟐.𝟒

74. Example: Solution (Cont.)
The R-chart
UCL= 𝐷 4 𝑅 =
LCL= 𝐷 3 𝑅 =
2.282*22.4 = 51.12
0*22.4 = 0
The 𝑿 -chart
UCL= 𝑋 + 𝐴 2 𝑅 =
LCL= 𝑋 − 𝐴 2 𝑅 =
*22.4 =
598.2 – 0.729*22.4 =
b. The range is 53 (or 623 – 570), which is outside the UCL for the R-chart. A search for assignable causes inducing excessive variability must be conducted.

75. Sample Control charts Normal – No action UCL Variations Nominal LCL
Sample number
Normal – No action

76. Sample control chart Run – Take action UCL Variations Nominal LCL
Sample number
Run – Take action

77. Sample control chart Sudden change – Monitor UCL Variations Nominal
LCL
Variations
Sample number
Sudden change – Monitor

78. Sample control charts Exceeds control limits – Take action UCL
Nominal
UCL
LCL
Variations
Sample number
Exceeds control limits – Take action