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1 QUALITY CONTROL Chapter 10 MIS 373: Basic Operations Management Additional content from Jeff Heyl.

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1 1 QUALITY CONTROL Chapter 10 MIS 373: Basic Operations Management Additional content from Jeff Heyl

2 LEARNING OBJECTIVES After this lecture, students will be able to 1.Explain the need for quality control. 2.List and briefly explain the elements of the control process. 3.Explain Type I and Type II errors 4.Explain how control charts are used to monitor a process and the concepts that underlie their use. MIS 373: Basic Operations Management2

3 BACKGROUND KNOWLEDGE How many of you have had at least one statistics course? Normal distribution? Standard deviation? Z score?

4 MOTIVATIONS Making Beer Better With Quality and Statistics Quality for Life: Psychic Pizza

5 WHAT IS QUALITY CONTROL? Quality Control A process that evaluates output relative to a standard and takes corrective action when output doesn’t meet standards If results are acceptable no further action is required Unacceptable results call for correction action Phases of Quality Assurance MIS 373: Basic Operations Management5

6 INSPECTION Inspection An appraisal activity that compares goods or services to a standard Inspection issues: 1.What to inspect Count number of times defect occurs Measure the value of a characteristic 2.How much to inspect and how often 3.At what points in the process to inspect Raw materials and purchased parts Finished products Before a costly operation Before an irreversible process Costly, possibly destructive, and disruptive – non value-adding Full inspection vs. Sampling MIS 373: Basic Operations Management6

7 HOW MUCH TO INSPECT MIS 373: Basic Operations Management7

8 HOW MUCH TO INSPECT MIS 373: Basic Operations Management8 1 defect in 1 thousand unites 1 defect in 1 million unites 1 defect in 1 billion unites Trying to catch:

9 CENTRALIZED VS. ON-SITE INSPECTION Effects on cost and level of disruption are a major issue in selecting centralized vs. on-site inspection Centralized Specialized tests that may best be completed in a lab More specialized testing equipment More favorable testing environment On-Site Quicker decisions are rendered Avoid introduction of extraneous factors Quality at the source MIS 373: Basic Operations Management9

10 STATISTICAL PROCESS CONTROL (SPC) Quality control seeks Quality of Conformance A product or service conforms to specifications A tool used to help in this process: SPC Statistical evaluation of the output of a process Helps us to decide if a process is “in control” or if corrective action is needed “In control” means that the variation in the provided products/services is tolerable MIS 373: Basic Operations Management10

11 PROCESS VARIABILITY Two basic questions: concerning variability: 1.Issue of Process Control Are the variations random? If nonrandom variation is present, the process is said to be unstable.  Variations randomly distributed within control limits 2.Issue of Process Capability Given a stable process, is the inherent variability of the process within a range that conforms to performance criteria?  The control limits satisfy the design specification MIS 373: Basic Operations Management11

12 VARIATION Variation Random (common cause) variation: Natural variation in the output of a process, created by countless minor factors Assignable (special cause) variation: A variation whose cause can be identified. A nonrandom variation Illustration: M&M’s Size Color MIS 373: Basic Operations Management12

13 VARIATION Common cause Inappropriate procedures Poor design Poor maintenance of machines Lack of clearly defined standard operating procedures standard operating procedures Poor working conditions, e.g. lighting, noise, dirt, temperature, ventilation Substandard raw materials Measurement errorMeasurement Quality control errorQuality control Vibration in industrial processes Ambient temperature and humidity Normal wear and tearwear and tear Variability in settings Special cause Poor adjustment of equipment Operator falls asleepfalls asleep Faulty controllers Machine malfunction Fall of ground Computer crash Poor batch of raw material Power surges High healthcare demand from elderly people Broken part Abnormal traffic (click fraud) on web adsclick fraud Extremely long lab testing turnover time due to switching to a new computer system Operator absent MIS 373: Basic Operations Management13

14 SAMPLING AND SAMPLE DISTRIBUTION SPC involves periodically taking samples of process output and computing sample statistics: Sample means The number of occurrences of some outcome Sample statistics are used to judge the randomness of process variation MIS 373: Basic Operations Management14

15 SAMPLING AND SAMPLE DISTRIBUTION Sampling Distribution A theoretical distribution that describes the random variability of sample statistics The normal distribution is commonly used for this purpose Central Limit Theorem The distribution of sample averages tends to be normal regardless of the shape of the underlying process distribution MIS 373: Basic Operations Management15

16 DEMO Use simulation to test the Central Limit Theorem

17 THE NORMAL DISTRIBUTION MIS 373: Basic Operations Management17

18 CONTROL PROCESS Sampling and corrective action are only a part of the control process Steps required for effective control: Define: What is to be controlled? Measure: How will measurement be accomplished? Compare: There must be a standard of comparison Evaluate: Establish a definition of out of control Correct: Uncover the cause of nonrandom variability and fix it Monitor results: Verify that the problem has been eliminated MIS 373: Basic Operations Management18

19 CONTROL CHARTS: THE VOICE OF THE PROCESS Control Chart A time ordered plot of representative sample statistics obtained from an ongoing process (e.g. sample means), used to distinguish between random and nonrandom variability Control limits The dividing lines between random and nonrandom deviations from the mean of the distribution Upper and lower control limits define the range of acceptable variation Upper control limit = UCL = mean + zσ Lower control limit = LCL = mean + zσ MIS 373: Basic Operations Management19

20 UCL LCL Mean CONTROL CHART EXAMPLE Each point on the control chart represents a sample of n observations MIS 373: Basic Operations Management Sample number |||||||||||| Variation due to assignable causes Variation due to natural causes Out of control 20

21 ERRORS Type I error Narrow control limits Concluding a process is not in control when it actually is. Manufacturer’s Risk Type II error Wide control limits Concluding a process is in control when it is not. Consumer’s Risk MIS 373: Basic Operations Management Alarm No Alarm Process In-Control Process Out-of-Control Type I Type II no-error 21

22 ERRORS ILLUSTRATION Q: I always get confused about Type I and II errors. Can you show me something to help me remember the difference? Source: Effect Size FAQs by Paul EllisEffect Size FAQs

23 CONTROL CHARTS MIS 373: Basic Operations Management Every process displays variation in performance: normal or abnormal Control charts monitor process to identify abnormal variation Do not tamper with a process that is “in control” with normal variation Correct an “out of control” process with abnormal variation Control charts may cause false alarms – too narrow - (or missed signals – too wide) by mistaking normal (abnormal) variation for abnormal (normal) variation Out of Control In ControlImproved LCL  UCL 23

24 CONTROL CHARTS Data that are measured “x-bar” charts (Mean) Used to monitor the central tendency of a process. R charts (Range) Used to monitor the process dispersion MIS 373: Basic Operations Management24

25 X-BAR (SAMPLE AVERAGE) CHART CONTROL LIMITS MIS 373: Basic Operations Management25

26 X-BAR CHART MIS 373: Basic Operations Management

27 R-CHART: CONTROL LIMITS Range charts or R-charts are used to monitor process dispersion MIS 373: Basic Operations Management27

28 MEAN AND RANGE CHARTS MIS 373: Basic Operations Management (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 x -chart ( x -chart detects shift in central tendency) UCL LCL 28

29 MEAN AND RANGE CHARTS MIS 373: Basic Operations Management 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 does not detect the increase in dispersion) UCL LCL 29

30 RUN TESTS Even if a process appears to be in control, the data may still not reflect a random process Analysts often supplement control charts with a run test Run test A test for patterns in a sequence Run Sequence of observations with a certain characteristic MIS 373: Basic Operations Management30

31 RUN TESTS MIS 373: Basic Operations Management31 A: Above B: Below U: Upward D: Downward

32 PATTERNS IN CONTROL CHARTS MIS 373: Basic Operations Management UCL Target LCL Erratic behavior. UCL Target LCL Run of 5 above (or below) central line. UCL Target LCL Two plots very near lower (or upper) control. 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. 32

33 DEMO ASQ Control chart template tools/overview/asq-control-chart.xlshttp://asq.org/learn-about-quality/data-collection-analysis- tools/overview/asq-control-chart.xls

34 KEY POINTS All processes exhibit random variation. Quality control's purpose is to identify a process that also exhibits nonrandom (correctable) variation on the basis of sample statistics (e.g., sample means) obtained from the process. Control charts and run tests can be used to detect nonrandom variation in sample statistics. It is also advisable to plot the data to visually check for patterns. MIS 373: Basic Operations Management34


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