1. Chapter 6 Quality Management

2. Types of Quality to Consider User based qualityUser based quality –Eyes of the customer, sometimes not measurable Product based qualityProduct based quality –Measurable Quality characteristics Manufacturing based QualityManufacturing based Quality –Conforms to standards / designs

3. Dimensions of Quality (Garvin) 1.Performance Basic operating characteristics 2.Features “Extra” items added to basic features 3.Reliability Probability product will operate over time

4. Dimensions of Quality (Garvin) 4.Conformance Meeting pre-established standards 5.Durability Life span before replacement 6.Serviceability Ease of getting repairs, speed & competence of repairs

5. Dimensions of Quality (Garvin) 7.Aesthetics Look, feel, sound, smell or taste 8.Safety Freedom from injury or harm 9.Other perceptions Subjective perceptions based on brand name, advertising, etc

6. Total Quality Management 1.Customer defined quality 2.Top management leadership 3.Quality as a strategic issue 4.All employees responsible for quality 5.Continuous improvement / Kaizen 6.Shared problem solving 7.Statistical quality control 8.Training & education for all employees

7. Cost of Quality Cost of achieving good quality Prevention Planning, Product design, Process, Training, Information Appraisal Inspection and testing, Test equipment, Operator

8. Cost of Quality Cost of poor quality Internal failure costs Scrap, Rework, Process failure, Process downtime, Price- downgrading External failure costs Customer complaints, Product return, Warranty, Product liability, Lost sales

9. Quality–Cost Relationship Increased prevention costs lead to decreased failure costs Improved quality leads to increased sales and market share Quality improvement at the design stage Higher quality products can command higher prices

10. S6 - 10© 2014 Pearson Education, Inc. Statistical Process Control The objective of a process control system is to provide a statistical signal when assignable causes of variation are present

11. S6 - 11© 2014 Pearson Education, Inc. ► 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 (SPC)

12. S6 - 12© 2014 Pearson Education, Inc. 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”

13. S6 - 13© 2014 Pearson Education, Inc. 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

14. S6 - 14© 2014 Pearson Education, Inc. Samples To measure the process, we take samples and analyze the sample statistics following these steps (a)Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weight Frequency Weight # ## # ## ## # ### #### ######### # Each of these represents one sample of five boxes of cereal Figure S6.1

15. S6 - 15© 2014 Pearson Education, Inc. Samples To measure the process, we take samples and analyze the sample statistics following these steps (b)After enough samples are taken from a stable process, they form a pattern called a distribution The solid line represents the distribution Frequency Weight Figure S6.1

16. S6 - 16© 2014 Pearson Education, Inc. Samples (c)There are many types of distributions, including the normal (bell- shaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape Weight Central tendency Weight Variation Weight Shape Frequency Figure S6.1 To measure the process, we take samples and analyze the sample statistics following these steps

17. S6 - 17© 2014 Pearson Education, Inc. Samples To measure the process, we take samples and analyze the sample statistics following these steps (d)If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable Weight Time Frequency Prediction Figure S6.1

18. S6 - 18© 2014 Pearson Education, Inc. Samples To measure the process, we take samples and analyze the sample statistics following these steps (e)If assignable causes are present, the process output is not stable over time and is not predicable Weight Time Frequency Prediction ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? Figure S6.1

19. S6 - 19© 2014 Pearson Education, Inc. Control Charts Constructed from historical data, the purpose of control charts is to help distinguish between natural variations and variations due to assignable causes

20. S6 - 20© 2014 Pearson Education, Inc. Process Control Figure S6.2 Frequency (weight, length, speed, etc.) Size Lower control limit Upper control limit (a)In statistical control and capable of producing within control limits (b) In statistical control but not capable of producing within control limits (c) Out of control

21. S6 - 21© 2014 Pearson Education, Inc. Control Charts for Variables ► Characteristics that can take any real value ► May be in whole or in fractional numbers ► Continuous random variables x -chart tracks changes in the central tendency R-chart indicates a gain or loss of dispersion These two charts must be used together

22. S6 - 22© 2014 Pearson Education, Inc. Setting Chart Limits For x-Charts when we know  Where=mean of the sample means or a target value set for the process z =number of normal standard deviations  x =standard deviation of the sample means  =population (process) standard deviation n =sample size

23. S6 - 23© 2014 Pearson Education, Inc. Setting Control Limits ▶ Randomly select and weigh nine ( n = 9) boxes each hour WEIGHT OF SAMPLE HOUR (AVG. OF 9 BOXES)HOUR (AVG. OF 9 BOXES)HOUR (AVG. OF 9 BOXES) 116.1516.5916.3 216.8616.41014.8 315.5715.21114.2 416.5816.41217.3 Average weight in the first sample

24. S6 - 24© 2014 Pearson Education, Inc. Setting Control Limits Average mean of 12 samples

25. S6 - 25© 2014 Pearson Education, Inc. Setting Control Limits Average mean of 12 samples

26. S6 - 26© 2014 Pearson Education, Inc. 17 = UCL 15 = LCL 16 = Mean Sample number |||||||||||| 123456789101112 Setting Control Limits Control Chart for samples of 9 boxes Variation due to assignable causes Variation due to natural causes Out of control

27. S6 - 27© 2014 Pearson Education, Inc. Restaurant Control Limits For salmon filets at Darden Restaurants Sample Mean x Bar Chart UCL = 11.524 = – 10.959 LCL = – 10.394 ||||||||| 1357911131517 11.5 – 11.0 – 10.5 – Sample Range Range Chart UCL = 0.6943 = 0.2125 LCL = 0 ||||||||| 1357911131517 0.8 – 0.4 – 0.0 –

28. S6 - 28© 2014 Pearson Education, Inc. Mean and Range Charts (a) These sampling distributions result in the charts below (Sampling mean is shifting upward, but range is consistent) R-chart (R-chart does not detect change in mean) UCL LCL Figure S6.5 x-chart (x-chart detects shift in central tendency) UCL LCL

29. S6 - 29© 2014 Pearson Education, Inc. Mean and Range Charts R-chart (R-chart detects increase in dispersion) UCL LCL (b) These sampling distributions result in the charts below (Sampling mean is constant, but dispersion is increasing) x-chart (x-chart indicates no change in central tendency) UCL LCL Figure S6.5

30. S6 - 30© 2014 Pearson Education, Inc. Control Charts for Attributes ► For variables that are categorical ► Defective/nondefective, good/bad, yes/no, acceptable/unacceptable ► Measurement is typically counting defectives ► Charts may measure 1.Percent defective ( p -chart) 2.Number of defects ( c -chart)

31. Six Sigma Developed by Motorola, a disciplined approach calling for at most 3 defects for every million units of production / customers served Seven Tools will not be covered in depth

32. Benchmarking Comparing product / service against best-in-classComparing product / service against best-in-class In computingIn computing –Standard test cases Auto safetyAuto safety –Crash tests In a large company, internal benchmarking:In a large company, internal benchmarking: –Compare different divisions or departments In General: Who does it the best and how do we compare?In General: Who does it the best and how do we compare?

33. Producing only what is needed, when it is needed A philosophy An integrated management system JIT’s mandate: Eliminate all waste “Poka-yokes” used to eliminate errors and wasteful repetition What is JIT ?

34. Kaizen Continuous improvement Requires total employee involvement Essence of JIT is willingness of workers to: Spot quality problems Halt production when necessary Generate ideas for improvement Analyze problems Perform different functions

35. Visual Control