1. Quality Management Class 4: 2/9/11

2. Total Quality Management Defined Quality Specifications and Costs Six Sigma Quality and Tools External Benchmarking ISO 9000 Service Quality Measurement Process Variation Process Capability Process Control Procedures OBJECTIVES

3. T OTAL Q UALITY M ANAGEMENT (TQM) Total quality management is defined as managing the entire organization so that it excels on all dimensions of products and services that are important to the customer Careful design of product or service Ensuring that the organization’s systems can consistently produce the design TQM was a response to the Japanese superiority in quality

4. Q UALITY S PECIFICATIONS Design quality: Inherent value of the product in the marketplace Dimensions include: Performance, Features, Reliability/Durability, Serviceability, Aesthetics, and Perceived Quality. Conformance quality: Degree to which the product or service design specifications are met

5. Crosby : conformance to requirements Deming : A predictable degree of uniformity and dependability at low cost and suited to the market Juran : fitness for use (satisfies customer’s needs) Three Quality Gurus Define Quality

6. Create consistency of purpose Lead to promote change Build quality into the products Build long term relationships Continuously improve product, quality, and service Start training Emphasize leadership Deming’s 14 Points

7. Drive out fear Break down barriers between departments Stop haranguing workers Support, help, improve Remove barriers to pride in work Institute a vigorous program of education and self- improvement Put everybody in the company to work on the transformation Deming’s 14 Points

8. 4.Act1.Plan 3.Check 2.Do Identify the improvement and make a plan Test the planIs the plan working Implement the plan Shewhart’s PDCA Model

9. C OSTS OF Q UALITY External Failure Costs Appraisal Costs Prevention Costs Internal Failure Costs Costs of Quality

10. S IX S IGMA Q UALITY A philosophy and set of methods companies use to eliminate defects in their products and processes Seeks to reduce variation in the processes that lead to product defects

11. S IX S IGMA Q UALITY (C ONTINUED ) Six Sigma allows managers to readily describe process performance using a common metric: Defects Per Million Opportunities (DPMO)

12. S IX S IGMA Q UALITY (C ONTINUED ) Example of Defects Per Million Opportunities (DPMO) calculation. Suppose we observe 200 letters delivered incorrectly to the wrong addresses in a small city during a single day when a total of 200,000 letters were delivered. What is the DPMO in this situation? So, for every one million letters delivered this city’s postal managers can expect to have 1,000 letters incorrectly sent to the wrong address. Cost of Quality: What might that DPMO mean in terms of over-time employment to correct the errors?

13. S IX S IGMA Q UALITY : DMAIC C YCLE Define, Measure, Analyze, Improve, and Control (DMAIC) Developed by General Electric as a means of focusing effort on quality using a methodological approach Overall focus of the methodology is to understand and achieve what the customer wants A 6-sigma program seeks to reduce the variation in the processes that lead to these defects DMAIC consists of five steps….

14. S IX S IGMA Q UALITY : DMAIC C YCLE (C ONTINUED ) 1. Define (D) 2. Measure (M) 3. Analyze (A) 4. Improve (I) 5. Control (C) Customers and their priorities Process and its performance Causes of defects Remove causes of defects Maintain quality

15. E XAMPLE TO ILLUSTRATE THE PROCESS … We are the maker of this cereal. Consumer reports has just published an article that shows that we frequently have less than 16 ounces of cereal in a box. What should we do?

16. S TEP 1 - D EFINE What is the critical-to-quality characteristic? The CTQ (critical-to-quality) characteristic in this case is the weight of the cereal in the box.

17. 2 - M EASURE How would we measure to evaluate the extent of the problem? What are acceptable limits on this measure?

18. 2 – M EASURE ( CONTINUED ) Let’s assume that the government says that we must be within ± 5 percent of the weight advertised on the box. Upper Tolerance Limit = 16 +.05(16) = 16.8 ounces Lower Tolerance Limit = 16 –.05(16) = 15.2 ounces

19. 2 – M EASURE ( CONTINUED ) We go out and buy 1,000 boxes of cereal and find that they weight an average of 15.875 ounces with a standard deviation of.529 ounces. What percentage of boxes are outside the tolerance limits?

20. Upper Tolerance = 16.8 Lower Tolerance = 15.2 Process Mean = 15.875 Std. Dev. =.529 What percentage of boxes are defective (i.e. less than 15.2 oz)? Z = (x – Mean)/Std. Dev. = (15.2 – 15.875)/.529 = -1.276 NORMSDIST(Z) = NORMSDIST(-1.276) =.100978 Approximately, 10 percent of the boxes have less than 15.2 Ounces of cereal in them!

21. S TEP 3 - A NALYZE - H OW CAN WE IMPROVE THE CAPABILITY OF OUR CEREAL BOX FILLING PROCESS ? Decrease Variation Center Process Increase Specifications

22. S TEP 4 – I MPROVE – H OW GOOD IS GOOD ENOUGH ? M OTOROLA ’ S “S IX S IGMA ” 6  minimum from process center to nearest spec

23. M OTOROLA ’ S “S IX S IGMA ” Implies 2 ppB “bad” with no process shift. With 1.5  shift in either direction from center (process will move), implies 3.4 ppm “bad”.

24. S TEP 5 – C ONTROL Statistical Process Control (SPC) Use data from the actual process Estimate distributions Look at capability - is good quality possible Statistically monitor the process over time

25. A NALYTICAL T OOLS FOR S IX S IGMA AND C ONTINUOUS I MPROVEMENT : F LOW C HART No, Continue… Material Received from Supplier Inspect Material for Defects Defects found? Return to Supplier for Credit Yes Can be used to find quality problems

26. A NALYTICAL T OOLS FOR S IX S IGMA AND C ONTINUOUS I MPROVEMENT : R UN C HART Can be used to identify when equipment or processes are not behaving according to specifications 0.44 0.46 0.48 0.5 0.52 0.54 0.56 0.58 123456789101112 Time (Hours) Diameter

27. A NALYTICAL T OOLS FOR S IX S IGMA AND C ONTINUOUS I MPROVEMENT : P ARETO A NALYSIS Can be used to find when 80% of the problems may be attributed to 20% of the causes Can be used to find when 80% of the problems may be attributed to 20% of the causes Assy. Instruct. Frequency DesignPurch.Training 80%

28. A NALYTICAL T OOLS FOR S IX S IGMA AND C ONTINUOUS I MPROVEMENT : C HECKSHEET Billing Errors Wrong Account Wrong Amount A/R Errors Wrong Account Wrong Amount Monday Can be used to keep track of defects or used to make sure people collect data in a correct manner

29. A NALYTICAL T OOLS FOR S IX S IGMA AND C ONTINUOUS I MPROVEMENT : H ISTOGRAM Number of Lots Data Ranges Defects in lot 01234 Can be used to identify the frequency of quality defect occurrence and display quality performance

30. A NALYTICAL T OOLS FOR S IX S IGMA AND C ONTINUOUS I MPROVEMENT : C AUSE & E FFECT D IAGRAM 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)

31. A NALYTICAL T OOLS FOR S IX S IGMA AND C ONTINUOUS I MPROVEMENT : C ONTROL C HARTS Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality 970 980 990 1000 1010 1020 0123456789101112131415 LCL UCL

32. S IX S IGMA R OLES AND R ESPONSIBILITIES 1. Executive leaders must champion the process of improvement 2. Corporation-wide training in Six Sigma concepts and tools 3. Setting stretch objectives for improvement 4. Continuous reinforcement and rewards

33. T HE S HINGO S YSTEM : F AIL -S AFE D ESIGN Shingo’s argument: SQC methods do not prevent defects Defects arise when people make errors Defects can be prevented by providing workers with feedback on errors Poka-Yoke includes: Checklists Special tooling that prevents workers from making errors

34. ISO 9000 AND ISO 14000 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 14000 is primarily concerned with environmental management

35. T HREE F ORMS OF ISO C ERTIFICATION 1. First party: A firm audits itself against ISO 9000 standards 2. Second party: A customer audits its supplier 3. Third party: A "qualified" national or international standards or certifying agency serves as auditor

36. E XTERNAL B ENCHMARKING S TEPS 1. Identify those processes needing improvement 2. Identify a firm that is the world leader in performing the process 3. Contact the managers of that company and make a personal visit to interview managers and workers 4. Analyze data

37. Process Variation Process Capability Process Control Procedures Variable data Attribute data Process Control

38. B ASIC F ORMS OF V ARIATION Assignable variation is caused by factors that can be clearly identified and possibly managed Common variation is inherent in the production process Example: A poorly trained employee that creates variation in finished product output. Example: A molding process that always leaves “burrs” or flaws on a molded item.

39. T AGUCHI ’ S V IEW OF V ARIATION Incremental Cost of Variability High Zero Lower Spec Target Spec Upper Spec Traditional View Incremental Cost of Variability High Zero Lower Spec Target Spec Upper Spec Taguchi’s View Traditional view is that quality within the LS and US is good and that the cost of quality outside this range is constant, where Taguchi views costs as increasing as variability increases, so seek to achieve zero defects and that will truly minimize quality costs.

40. P ROCESS C APABILITY Process limits Specification limits How do the limits relate to one another?

41. P ROCESS C APABILITY I NDEX, C PK Shifts in Process Mean Capability Index shows how well parts being produced fit into design limit specifications. As a production process produces items small shifts in equipment or systems can cause differences in production performance from differing samples.

42. A simple ratio: Specification Width _________________________________________________________ Actual “Process Width” Generally, the bigger the better. P ROCESS C APABILITY – A S TANDARD M EASURE OF H OW G OOD A P ROCESS I S.

43. P ROCESS C APABILITY This is a “one-sided” Capability Index Concentration on the side which is closest to the specification - closest to being “bad”

44. T HE C EREAL B OX E XAMPLE We are the maker of this cereal. Consumer reports has just published an article that shows that we frequently have less than 16 ounces of cereal in a box. Let’s assume that the government says that we must be within ± 5 percent of the weight advertised on the box. Upper Tolerance Limit = 16 +.05(16) = 16.8 ounces Lower Tolerance Limit = 16 –.05(16) = 15.2 ounces We go out and buy 1,000 boxes of cereal and find that they weight an average of 15.875 ounces with a standard deviation of.529 ounces.

45. C EREAL B OX P ROCESS C APABILITY Specification or Tolerance Limits Upper Spec = 16.8 oz Lower Spec = 15.2 oz Observed Weight Mean = 15.875 oz Std Dev =.529 oz

46. W HAT DOES A C PK OF.4253 MEAN ? An index that shows how well the units being produced fit within the specification limits. This is a process that will produce a relatively high number of defects. Many companies look for a C pk of 1.3 or better… 6-Sigma company wants 2.0!

47. T YPES OF S TATISTICAL S AMPLING Attribute (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. p -chart application Variable (Continuous) Usually measured by the mean and the standard deviation. X-bar and R chart applications

48. Statistical Process Control (SPC) Charts UCL LCL Samples over time 1 2 3 4 5 6 UCL LCL Samples over time 1 2 3 4 5 6 UCL LCL Samples over time 1 2 3 4 5 6 Normal Behavior Possible problem, investigate

49. C ONTROL L IMITS ARE BASED ON THE N ORMAL C URVE x 0123-3-2 z  Standard deviation units or “z” units.

50. C ONTROL L IMITS We establish the Upper Control Limits (UCL) and the Lower Control Limits (LCL) with plus or minus 3 standard deviations from some x- bar or mean value. Based on this we can expect 99.7% of our sample observations to fall within these limits. x LCLUCL 99.7%

51. E XAMPLE OF C ONSTRUCTING A P -C HART : R EQUIRED D ATA Sample No. No. of Samples Number of defects found in each sample

52. S TATISTICAL P ROCESS C ONTROL F ORMULAS : A TTRIBUTE M EASUREMENTS ( P -C HART ) Given: Compute control limits:

53. 1. Calculate the sample proportions, p (these are what can be plotted on the p-chart) for each sample E XAMPLE OF C ONSTRUCTING A P - CHART : S TEP 1

54. 2. Calculate the average of the sample proportions 3. Calculate the standard deviation of the sample proportion E XAMPLE OF C ONSTRUCTING A P - CHART : S TEPS 2&3

55. 4. Calculate the control limits UCL = 0.0924 LCL = -0.0204 (or 0) UCL = 0.0924 LCL = -0.0204 (or 0) E XAMPLE OF C ONSTRUCTING A P - CHART : S TEP 4

56. E XAMPLE OF C ONSTRUCTING A P -C HART : S TEP 5 5. Plot the individual sample proportions, the average of the proportions, and the control limits 5. Plot the individual sample proportions, the average of the proportions, and the control limits 9A-56

57. E XAMPLE OF X - BAR AND R C HARTS : R EQUIRED D ATA

58. E XAMPLE OF X - BAR AND R CHARTS : S TEP 1. C ALCULATE SAMPLE MEANS, SAMPLE RANGES, MEAN OF MEANS, AND MEAN OF RANGES.

59. E XAMPLE OF X - BAR AND R CHARTS : S TEP 2. D ETERMINE C ONTROL L IMIT F ORMULAS AND N ECESSARY T ABLED V ALUES From Exhibit TN 8.7

61. E XAMPLE OF X - BAR AND R CHARTS : S TEPS 3&4. C ALCULATE X - BAR C HART AND P LOT V ALUES UCL LCL

62. E XAMPLE OF X - BAR AND R CHARTS : S TEPS 5&6. C ALCULATE R- CHART AND P LOT V ALUES UCL LCL