Quality management

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  • Points which might be emphasized include: - Statistical process control measures the performance of a process, it does not help to identify a particular specimen produced as being “good” or “bad,” in or out of tolerance. - Statistical process control requires the collection and analysis of data - therefore it is not helpful when total production consists of a small number of units - While statistical process control can not help identify a “good” or “bad” unit, it can enable one to decide whether or not to accept an entire production lot. If a sample of a production lot contains more than a specified number of defective items, statistical process control can give us a basis for rejecting the entire lot. The issue of rejecting a lot which was actually good can be raised here, but is probably better left to later.
  • Students should understand both the concepts of natural and assignable variation, and the nature of the efforts required to deal with them.
  • Once the categories are outlined, students may be asked to provide examples of items for which variable or attribute inspection might be appropriate. They might also be asked to provide examples of products for which both characteristics might be important at different stages of the production process.
  • Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
  • Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
  • Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
  • Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
  • Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
  • Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
  • Quality management

    1. 1. Quality ManagementQuality Management
    2. 2. Quality • PMI’s quality philosophy summarized by – Definition of quality – No gold-plating – Prevention over inspection
    3. 3. PMI Quality Definition • QUALITY IS CONFORMANCE TO REQUIREMENTS AND FITNESS OF USE
    4. 4. No Gold-plating • Don’t give the customer extras • Adds no-value to the project because – it is beyond the scope – Could cost more – May be based on impressions not requests
    5. 5. Prevention over inspection – Quality must be planned NOT inspected
    6. 6. Six SigmaSix Sigma  Originally developed by Motorola,Originally developed by Motorola, Six Sigma refers to an extremelySix Sigma refers to an extremely high measure of process capabilityhigh measure of process capability  A Six Sigma capable process willA Six Sigma capable process will return no more than 3.4 defects perreturn no more than 3.4 defects per million operations (DPMO)million operations (DPMO)  Highly structured approach toHighly structured approach to process improvementprocess improvement
    7. 7. Six sigma
    8. 8. Six SigmaSix Sigma 1.1. Define critical outputsDefine critical outputs and identify gaps forand identify gaps for improvementimprovement 2.2. Measure the work andMeasure the work and collect process datacollect process data 3.3. Analyze the dataAnalyze the data 4.4. Improve the processImprove the process 5.5. Control the new process toControl the new process to make sure new performancemake sure new performance is maintainedis maintained DMAIC ApproachDMAIC Approach
    9. 9.  Tools Of TQMTools Of TQM  Check SheetsCheck Sheets  Scatter DiagramsScatter Diagrams  Cause-and-Effect DiagramCause-and-Effect Diagram  Pareto ChartsPareto Charts  Flow ChartsFlow Charts  HistogramsHistograms  Statistical Process Control (SPC)Statistical Process Control (SPC)
    10. 10. / / / / /// / // /// // //// /// // / Hour Defect 1 2 3 4 5 6 7 8 A B C / / // Seven Tools for TQMSeven Tools for TQM (a)(a) Check Sheet: An organized method ofCheck Sheet: An organized method of recording datarecording data Figure 6.5Figure 6.5
    11. 11. Seven Tools for TQMSeven Tools for TQM (b)(b) Scatter Diagram: A graph of the valueScatter Diagram: A graph of the value of one variable vs. another variableof one variable vs. another variable AbsenteeismAbsenteeism ProductivityProductivity Figure 6.5Figure 6.5
    12. 12. Seven Tools for TQMSeven Tools for TQM (c)(c) Cause and Effect Diagram: A tool thatCause and Effect Diagram: A tool that identifies process elements (causes) thatidentifies process elements (causes) that might effect an outcomemight effect an outcome Figure 6.5Figure 6.5 CauseCause MaterialsMaterials MethodsMethods ManpowerManpower MachineryMachinery EffectEffect
    13. 13. Seven Tools for TQMSeven Tools for TQM (d)(d) Pareto Charts: A graph to identify and plotPareto Charts: A graph to identify and plot problems or defects in descending order ofproblems or defects in descending order of frequencyfrequency Figure 6.5Figure 6.5 FrequencyFrequency PercentPercent AA BB CC DD EE
    14. 14. Pareto Chart
    15. 15. Seven Tools for TQMSeven Tools for TQM (e)(e) Flow Charts (Process Diagrams): A chartFlow Charts (Process Diagrams): A chart that describes the steps in a processthat describes the steps in a process Figure 6.5Figure 6.5
    16. 16. Operator takes phone order. Orders wait to be picked up. Supervisor inspects orders. Order is fulfilled. Order waits for sales rep. Is order complete? Yes No Orders are moved to supervisor’s in-box. Orders wait for supervisor. Flow ChartsFlow Charts
    17. 17. Seven Tools for TQMSeven Tools for TQM (f)(f) Histogram: A distribution showing theHistogram: A distribution showing the frequency of occurrence of a variablefrequency of occurrence of a variable Figure 6.5Figure 6.5 DistributionDistribution Repair time (minutes)Repair time (minutes) FrequencyFrequency
    18. 18. Seven Tools for TQMSeven Tools for TQM (g)(g) Statistical Process Control Chart: A chart withStatistical Process Control Chart: A chart with time on the horizontal axis to plot values of atime on the horizontal axis to plot values of a statisticstatistic Figure 6.5Figure 6.5 Upper control limitUpper control limit Target valueTarget value Lower control limitLower control limit TimeTime
    19. 19.  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) Statistical Process Control (SPC)
    20. 20. Natural VariationsNatural Variations  Also called common causesAlso called common causes  Affect virtually all production processesAffect virtually all production processes  Expected amount of variationExpected amount of variation  Output measures follow a probability distributionOutput measures follow a probability distribution  For any distribution there is a measure of centralFor any distribution there is a measure of central tendency and dispersiontendency and dispersion  If the distribution of outputs falls within acceptableIf the distribution of outputs falls within acceptable limits, the process is said to be “in control”limits, the process is said to be “in control”
    21. 21. Assignable VariationsAssignable Variations  Also called special causes of variationAlso called special causes of variation  Generally there is some change in the processGenerally there is some change in the process  Variations that can be traced to a specific reasonVariations that can be traced to a specific reason  The objective is to discover when assignable causesThe objective is to discover when assignable causes are presentare present  Eliminate the bad causesEliminate the bad causes  Incorporate the good causesIncorporate the good causes
    22. 22. SamplesSamples To measure the process, we takeTo measure the process, we take samples and analyze the samplesamples and analyze the sample statistics following these stepsstatistics following these steps (a)(a) Samples of theSamples of the product, say fiveproduct, say five boxes of cerealboxes of cereal taken off the fillingtaken off the filling machine line, varymachine line, vary from each other infrom each other in weightweight FrequencyFrequency WeightWeight ## #### ## #### #### ## ## ## #### ## #### ## ## #### ## #### ## #### Each of theseEach of these represents onerepresents one sample of fivesample of five boxes of cerealboxes of cereal Figure S6.1Figure S6.1
    23. 23. SamplesSamples (b)(b) After enoughAfter enough samples aresamples are taken from ataken from a stable process,stable process, they form athey form a pattern called apattern called a distributiondistribution The solid lineThe solid line represents therepresents the distributiondistribution FrequencyFrequency WeightWeightFigure S6.1Figure S6.1
    24. 24. SamplesSamples (c)(c) There are many types of distributions, includingThere are many types of distributions, including the normal (bell-shaped) distribution, butthe normal (bell-shaped) distribution, but distributions do differ in terms of centraldistributions do differ in terms of central tendency (mean), standard deviation ortendency (mean), standard deviation or variance, and shapevariance, and shape WeightWeight Central tendencyCentral tendency WeightWeight VariationVariation WeightWeight ShapeShape FrequencyFrequency Figure S6.1Figure S6.1
    25. 25. SamplesSamples (d)(d) If only naturalIf only natural causes ofcauses of variation arevariation are present, thepresent, the output of aoutput of a process forms aprocess forms a distribution thatdistribution that is stable overis stable over time and istime and is predictablepredictable WeightWeight Time Time FrequencyFrequency PredictionPrediction Figure S6.1Figure S6.1
    26. 26. SamplesSamples (e)(e) If assignableIf assignable causes arecauses are present, thepresent, the process output isprocess output is not stable overnot stable over time and is nottime and is not predicablepredicable WeightWeight Time Time FrequencyFrequency PredictionPrediction ???? ?? ?? ?? ?? ?? ?????? ?? ?? ?? ?? ?? ?? ?????? Figure S6.1Figure S6.1
    27. 27. Control ChartsControl Charts Constructed from historical data, theConstructed from historical data, the purpose of control charts is to helppurpose of control charts is to help distinguish between natural variations anddistinguish between natural variations and variations due to assignable causesvariations due to assignable causes
    28. 28. Types of DataTypes of Data  Characteristics that can take any real value  May be in whole or in fractional numbers  Continuous random variables VariablesVariables AttributesAttributes  Defect-relatedDefect-related characteristicscharacteristics  Classify products asClassify products as either good or bad oreither good or bad or count defectscount defects  Categorical or discreteCategorical or discrete random variablesrandom variables
    29. 29. Control Charts for VariablesControl Charts for Variables  For variables that have continuousFor variables that have continuous dimensionsdimensions  Weight, speed, length, strength, etc.Weight, speed, length, strength, etc.  x-charts are to control the centralx-charts are to control the central tendency of the processtendency of the process  R-charts are to control the dispersion ofR-charts are to control the dispersion of the processthe process  These two charts must be used togetherThese two charts must be used together
    30. 30. Control ChartControl Chart
    31. 31. Patterns in Control ChartsPatterns in Control Charts Normal behavior.Normal behavior. Process is “in control.”Process is “in control.” Upper control limitUpper control limit TargetTarget Lower control limitLower control limit Figure S6.7Figure S6.7
    32. 32. Upper control limitUpper control limit TargetTarget Lower control limitLower control limit Patterns in Control ChartsPatterns in Control Charts One plot out above (orOne plot out above (or below). Investigate forbelow). Investigate for cause. Process is “outcause. Process is “out of control.”of control.” Figure S6.7Figure S6.7
    33. 33. Upper control limitUpper control limit TargetTarget Lower control limitLower control limit Patterns in Control ChartsPatterns in Control Charts Trends in eitherTrends in either direction, 5 plots.direction, 5 plots. Investigate for cause ofInvestigate for cause of progressive change.progressive change. Figure S6.7Figure S6.7
    34. 34. Upper control limitUpper control limit TargetTarget Lower control limitLower control limit Patterns in Control ChartsPatterns in Control Charts Two plots very nearTwo plots very near lower (or upper)lower (or upper) control. Investigate forcontrol. Investigate for cause.cause. Figure S6.7Figure S6.7
    35. 35. Upper control limitUpper control limit TargetTarget Lower control limitLower control limit Patterns in Control ChartsPatterns in Control Charts Run of 5 above (orRun of 5 above (or below) central line.below) central line. Investigate for cause.Investigate for cause.Figure S6.7Figure S6.7
    36. 36. Upper control limitUpper control limit TargetTarget Lower control limitLower control limit Patterns in Control ChartsPatterns in Control Charts Erratic behavior.Erratic behavior. Investigate.Investigate. Figure S6.7Figure S6.7

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