Chapter 15 Quality Management To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved.

2 Definition: Total Quality Management Total Quality Management (TQ, QM or TQM) and Six Sigma (6  ) are sweeping “culture change” efforts to position a company for greater customer satisfaction, profitability and competitiveness. TQ may be defined as managing the entire organization so that it excels on all dimensions of products and services that are important to the customer. We often think of features when we think of the quality of a product or service; TQ is about conformance quality, not features.

Meeting Our Customer’s Requirements Doing Things Right the First Time; Freedom from Failure (Defects) Consistency (Reduction in Variation) Continuous Improvement Quality in Everything We Do Total Quality Is… 3

A Quality Management System Is… A belief in the employee’s ability to solve problems A belief that people doing the work are best able to improve it A belief that everyone is responsible for quality 4

Elements for Success Management Support Mission Statement Proper Planning Customer and Bottom Line Focus Measurement Empowerment Teamwork/Effective Meetings Continuous Process Improvement Dedicated Resources 5

Measurement Empowerment/ Shared Leadership Process Improvement/ Problem Solving Team Management Customer Satisfaction Business Results The Continuous Improvement Process... 6

Modern History of Quality Management Frederick W. Taylor wrote Principles of Scientific Management in Walter A. Shewhart used statistics in quality control and inspection, and showed that productivity improves when variation is reduced (1924); wrote Economic Control of Manufactured Product in W. Edwards Deming and Joseph M. Juran, students of Shewhart, went to Japan in 1950; began transformation from “shoddy” to “world class” goods. In 1960, Dr. K. Ishikawa formalized “quality circles” - the use of small groups to eliminate variation and improve processes. In the late ‘70’s and early ‘80’s: –Deming returned from Japan to write Out of the Crisis, and began his famous 4-day seminars in the United States –Phil Crosby wrote Quality is Free –NBC ran “If Japan can do it, why can’t we?” –Motorola began 6 Sigma 7

Deming’s 14 Points 1. Create constancy of purpose for improvement 2. Adopt a new philosophy 3. Cease dependence on mass inspection 4. Do not award business on price alone 5. Work continually on the system of production and service 6. Institute modern methods of training 7. Institute modern methods of supervision of workers 8. Drive out fear 9. Break down barriers between departments 10. Eliminate slogans, exhortations, and targets for the work force 11. Eliminate numerical quotas 12. Remove barriers preventing pride of workmanship 13. Institute a vigorous program of education and retraining 14. Take action to accomplish the transformation History of Quality Management 8

Deming’s Concept of “Profound Knowledge”  Understanding (and appreciation) of Systems - optimizing sub-systems sub-optimizes the total system - the majority of defects come from systems, the responsibility of management (e.g., machines not in good order, defective material, etc.  Knowledge of Statistics (variation, capability, uncertainty in data, etc.) - to identify where problems are, and point managers and workers toward solutions  Knowledge of Psychology (Motivation) - people are afraid of failing and not being recognized, so they fear how data will be used against them  Theory of Knowledge - understanding that management in any form is a prediction, and is based on assumptions 9

According to Dr. Joseph M. Juran (1991): “On the assembly line at the Ford Motor Company in 1923, most of the workers producing Model T’s were immigrants and could not speak English. Many were also illiterate. Workers learned their trade by modeling the actions of other workers. They were unable to plan, problem-solve, and make decisions. As a result, the Taylor scientific school of management flourished, and MBAs and industrial engineers were invented to do this work. Today, however, the workforce is educated. Workers know what is needed to improve their jobs, and companies that do not tap into this significant source of knowledge will truly be at a competitive disadvantage.” History of Total Quality10

According to Phil Crosby, Quality is... An attitude: - Zero Defects - Continuous Improvement A measurement: - Price of Conformance, plus - Price of Nonconformance (defects) History of Total Quality11

TQ: Transforming an Organization12

13 Statistical Process Control Take periodic samples from processTake periodic samples from process Plot sample points on control chartPlot sample points on control chart Determine if process is within limitsDetermine if process is within limits Prevent quality problemsPrevent quality problems UCL LCL 13

14 Variation Common Causes Common Causes Variation inherent in a process Variation inherent in a process Can be eliminated only through improvements in the system Can be eliminated only through improvements in the system Special Causes Special Causes Variation due to identifiable factors Variation due to identifiable factors Can be modified through operator or management action Can be modified through operator or management action 14

15 Types of Data Attribute data Attribute data Product characteristic evaluated with a discrete choice Product characteristic evaluated with a discrete choice Good/bad, yes/no Good/bad, yes/no Variable data Variable data Product characteristic that can be measured Product characteristic that can be measured Length, size, weight, height, time, velocity Length, size, weight, height, time, velocity 15

16 SPC Applied to Services Nature of defect is different in services Nature of defect is different in services Service defect is a failure to meet customer requirements Service defect is a failure to meet customer requirements Monitor times, customer satisfaction Monitor times, customer satisfaction 16

17 Service Quality Examples Hospitals Hospitals Timeliness, responsiveness, accuracy of lab tests Timeliness, responsiveness, accuracy of lab tests Grocery Stores Grocery Stores Check-out time, stocking, cleanliness Check-out time, stocking, cleanliness Airlines Airlines Luggage handling, waiting times, courtesy Luggage handling, waiting times, courtesy Fast food restaurants Fast food restaurants Waiting times, food quality, cleanliness, employee courtesy Waiting times, food quality, cleanliness, employee courtesy 17

18 Service Quality Examples Catalog-order companies Catalog-order companies Order accuracy, operator knowledge and courtesy, packaging, delivery time, phone order waiting time Order accuracy, operator knowledge and courtesy, packaging, delivery time, phone order waiting time Insurance companies Insurance companies Billing accuracy, timeliness of claims processing, agent availability and response time Billing accuracy, timeliness of claims processing, agent availability and response time 18

19 Control Charts Graph establishing process control limits Graph establishing process control limits Charts for variables Charts for variables Mean (x-bar), Range (R) Mean (x-bar), Range (R) Chart for attributes Chart for attributes P Chart P Chart 19

20 Process Control Chart Sample number Uppercontrollimit Processaverage Lowercontrollimit Out of control Figure

21 A Process is In Control if 1.No sample points outside limits 2.Most points near process average 3.About equal number of points above & below centerline 4.Points appear randomly distributed 21

22 Development of Control Chart Based on in-control data Based on in-control data If non-random causes present, find the special cause and discard data If non-random causes present, find the special cause and discard data Correct control chart limits Correct control chart limits 22

23 Control Chart for Attributes p Charts p Charts Calculate percent defectives in sample Calculate percent defectives in sample 23

24 p-Chart UCL = p + z  p LCL = p - z  p where z=the number of standard deviations from the process average p=the sample proportion defective; an estimate of the process average  p =the standard deviation of the sample proportion p =p =p =p = p(1 - p) n24

25 The Normal Distribution  =0 1111 2222 3333 -1  -2  -3  95% 99.74% 25

26 Control Chart Z Values Smaller Z values make more sensitive charts Smaller Z values make more sensitive charts Z = 3.00 is standard Z = 3.00 is standard Compromise between sensitivity and errors Compromise between sensitivity and errors 26

27 p-Chart Example 20 samples of 100 pairs of jeans NUMBER OFPROPORTION SAMPLEDEFECTIVESDEFECTIVE ::: Example

28 p-Chart Example 20 samples of 100 pairs of jeans NUMBER OFPROPORTION SAMPLEDEFECTIVESDEFECTIVE ::: Example 15.1 p= = 200 / 20(100) = 0.10 total defectives total sample observations 28

29 p-Chart Example 20 samples of 100 pairs of jeans Example 15.1 UCL = p + z = p(1 - p) n 0.10( ) 100 UCL = LCL = LCL = p - z = p(1 - p) n 0.10( )

Proportion defective Sample number UCL = LCL = p = 0.10 p-Chart 30

31 Control Charts for Variables Mean chart ( x -Chart ) Mean chart ( x -Chart ) Uses average of a sample Uses average of a sample Range chart ( R-Chart ) Range chart ( R-Chart ) Uses amount of dispersion in a sample Uses amount of dispersion in a sample 31

32 Range ( R ) Chart UCL = D 4 RLCL = D 3 R R =R =R =R = RRkkRRkkk where R= range of each sample k= number of samples 32

33 Range ( R- ) Chart nA2D3D4nA2D3D4 SAMPLE SIZEFACTOR FOR x-CHARTFACTORS FOR R-CHART Table

34 R-Chart Example OBSERVATIONS (SLIP-RING DIAMETER, CM) SAMPLE k 12345xR Example

35 R-Chart Example Example 15.3 RkRk R = = = UCL = D 4 R = 2.11(0.115) = LCL = D 3 R = 0(0.115) = 0 UCL = LCL = 0 Range Sample number R = |1|1 |2|2 |3|3 |4|4 |5|5 |6|6 |7|7 |8|8 |9|9 | – 0.24 – 0.20 – 0.16 – 0.12 – 0.08 – 0.04 – 0 – 35

36 x-Chart Calculations x =x =x =x = x 1 + x x k k = UCL = x + A 2 RLCL = x - A 2 R ==where x= the average of the sample means = 36

37 x-Chart Example UCL = x + A 2 R = (0.58)(0.115) = 5.08 LCL = x - A 2 R = (0.58)(0.115) = 4.94 = = x = = = 5.01 cm = xkxk

38 x-Chart Example UCL = 5.08 LCL = 4.94 Mean Sample number |1|1 |2|2 |3|3 |4|4 |5|5 |6|6 |7|7 |8|8 |9|9 | – 5.08 – 5.06 – 5.04 – 5.02 – 5.00 – 4.98 – 4.96 – 4.94 – 4.92 – x = 5.01 =38

39 Using x- and R-Charts Together Each measures the process differently Each measures the process differently Both process average and variability must be in control Both process average and variability must be in control 39

40 Sample Size Determination Attribute control charts Attribute control charts 50 to 100 parts in a sample 50 to 100 parts in a sample Variable control charts Variable control charts 2 to 10 parts in a sample 2 to 10 parts in a sample 40

Process Capability Process limits (The “Voice of the Process” or The “Voice of the Data”) - based on natural (common cause) variation Tolerance limits (The “Voice of the Customer”) – customer requirements Process Capability – A measure of how “capable” the process is to meet customer requirements; compares process limits to tolerance limits 41

42 Process Capability Range of natural variability in process Range of natural variability in process Measured with control charts. Measured with control charts. Process cannot meet specifications if natural variability exceeds tolerances Process cannot meet specifications if natural variability exceeds tolerances 3-sigma quality 3-sigma quality Specifications equal the process control limits. Specifications equal the process control limits. 6-sigma quality 6-sigma quality Specifications twice as large as control limits Specifications twice as large as control limits 42

43 Process Capability (b) Design specifications and natural variation the same; process is capable of meeting specifications most the time. Design Specifications Process (a) Natural variation exceeds design specifications; process is not capable of meeting specifications all the time. Design Specifications Process Figure

44 Process Capability Figure 15.5 (c) Design specifications greater than natural variation; process is capable of always conforming to specifications. Design Specifications Process (d) Specifications greater than natural variation, but process off center; capable but some output will not meet upper specification. Design Specifications Process 44

45 Process Capability Measures Process Capability Index C pk = minimum x - lower specification limit 3  = upper specification limit - x 3  =, 45

46 Computing C pk Net weight specification = 9.0 oz  0.5 oz Process mean = 8.80 oz Process standard deviation = 0.12 oz C pk = minimum = minimum, = 0.83 x - lower specification limit 3  = upper specification limit - x 3  =, (0.12) (0.12) Example

Interpreting the Process Capability Index C pk < 1Not Capable C pk > 1Capable at 3  C pk > 1.33Capable at 4  C pk > 1.67Capable at 5  C pk > 2Capable at 6  47

What is Six Sigma? A goal of near perfection in meeting customer requirements A sweeping culture change effort to position a company for greater customer satisfaction, profitability and competitiveness A comprehensive and flexible system for achieving, sustaining and maximizing business success; uniquely driven by close understanding of customer needs, disciplined use of facts, data, and statistical analysis, and diligent attention to managing, improving and reinventing business processes (Source:The Six Sigma Way by Pande, Neuman and Cavanagh) 48

49 Is 99% Quality Good Enough? 22,000 checks will be deducted from the wrong bank accounts in the next 60 minutes. 20,000 incorrect drug prescriptions will be written in the next 12 months. 12 babies will be given to the wrong parents each day. 49

50 Six Sigma Quality The objective of Six Sigma quality is 3.4 defects per million opportunities! 50

51 But is Six Sigma Realistic? · K 10K 100K (66810 ppm) · IRS – Tax Advice (phone-in) Best in Class (3.4 ppm) Domestic Airline Flight Fatality Rate (0.43 ppm) · (233 ppm) Average Company Purchased Material Lot Reject Rate Air Line Baggage Handling Wire Transfers Journal Vouchers Order Write-up Payroll Processing Doctor Prescription Writing Restaurant Bills · · · · · · · Defects Per Million Opportunities (DPMO) SIGMA 51

Six Sigma Improvement Methods DMAIC vs. DMADV Define Measure Analyze Design Validate Improve Control Continuous ImprovementReengineering 52

Six Sigma DMAIC Process Measure Control Define Analyze Improve Define: Define who your customers are, and what their requirements are for your products and services – Their expectations. Define your team goals, project boundaries, what you will focus on and what you won’t. Define the process you are striving to improve by mapping the process. 53

Six Sigma DMAIC Process Measure Control Define Analyze Improve Measure: Eliminate guesswork and assumptions about what customers need and expect and how well processes are working. Collect data from many sources to determine speed in responding to customer requests, defect types and how frequently they occur, client feedback on how processes fit their needs, how clients rate us over time, etc. The data collection may suggest Charter revision. 54

Six Sigma DMAIC Process Measure Control Define Analyze Improve Analyze: Grounded in the context of the customer and competitive environment, analyze is used to organize data and look for process problems and opportunities. This step helps to identify gaps between current and goal performance, prioritize opportunities to improve, identify sources of variation and root causes of problems in the process. 55

Six Sigma DMAIC Process Measure Control Define Analyze Improve Improve: Generate both obvious and creative solutions to fix and prevent problems. Finding creative solutions by correcting root causes requires innovation, technology and discipline. 56

Six Sigma DMAIC Process Measure Control Define Analyze Improve Control: Insure that the process improvements, once implemented, will “hold the gains” rather than revert to the same problems again. Various control tools such as statistical process control can be used. Other tools such as procedure documentation helps institutionalize the improvement. 57

Six Sigma DMADV Process Measure Validate Define Analyze Design Design: Develop detailed design for new process. Determine and evaluate enabling elements. Create control and testing plan for new design. Use tools such as simulation, benchmarking, DOE, Quality Function Deployment (QFD), FMECA analysis, and cost/benefit analysis. 58

Six Sigma DMADV Process Measure Validate Define Analyze Design Validate: Test detailed design with a pilot implementation. If successful, develop and execute a full-scale implementation. Tools in this step include: planning tools, flowcharts/other process management techniques, and work documentation. 59

60 Key Learning's History of quality management TQM Six sigma Control charts Process capability 9/12/2015