Chapter 17: Process Improvement Using Control Charts

Process Improvement Using Control Charts 17.1 Quality: Meaning and Historical Perspective 17.2 Statistical Process Control and Causes of Variation 17.3 Sampling a Process, Rational Subgrouping and Control Charts 17.4 x and R Charts

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Chapter 17Process Improvement Using Control ChartsCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/IrwinProcess Improvement Using Control Charts17.1 Quality: Meaning and Historical Perspective17.2 Statistical Process Control and Causes of Variation17.3 Sampling a Process, Rational Subgrouping and Control Charts17.4 x and R Charts17-*Process Improvement Using Control Charts Continued17.5 Comparison of a Process with Specifications: Capability Studies17.6 Charts for Fraction Nonconforming17.7 Cause and Effect, Defect Concentration Diagrams (Optional)17-*17.1Quality: Meaning and Historical PerspectiveQualityFitness for useExtent to which customer expectations are metTypes of qualityQuality of designQuality of conformanceQuality of performanceLO17-1: Discuss the principles and importance of quality improvement.17-*History of the Quality Movement1924 Statistical Quality Control/Control Charts, Shewart/Bell Telephone1920’s Statistical Acceptance Sampling, Bell Telephone1946 American Society for Quality Control created1950 W. Edwards Deming introduces statistical quality control in Japan1951 Deming Prize established in Japan1980’s Total Quality Management (TQM)1988 Malcolm Baldrige National Quality Awards established1990’s ISO 9000, international quality standards adoptedLO17-117-*17.2 Statistical Process Control and Causes of Process VariationHistorical inspection approachInspection of outputAction on outputScrap, rework, downgrade (expensive!)Statistical process controlMonitor and study process variationGoal: Continuous process improvementPreventing by quality through process improvementLO17-2: Distinguish between common causes and assignable causes of process variation.17-*Causes of Process VariationCommon causesTypical (random) variation inherent in process designProcess in statistical controlAssignable causesUnusual process variationIntermittent or permanent process changesNot common to all process observationsProcess not in statistical controlLO17-217-*17.3 Sampling a Process and Rational Subgrouping and Control ChartsMust decide which process variables to studyBest to study a quantitative variableThis means we are employing measurement dataWe will take a series of samples over timeUsually called subgroupsUsually of size two to sixUsually observed over a short period of timeWant to observe often enough to detect important process changesLO17-3: Sample a process by using rational subgrouping.17-*Control ChartsA control chart employs a center line, upper control limit and lower control limitThe center line represents average performanceThe upper and lower control limits are established so that when in control almost all plot points will be between the limitsLO17-317-*17.4 x and R Chartsx and R charts are the most commonly used control charts for measurement datax chart plots subgroup means versus timeR chart plots subgroup range versus timex chart monitors the process meanR chart monitors the amount of variabilityThese two charts must be used togetherLO17-4: Use x and R charts to establish process control.17-*Pattern AnalysisAn observation beyond the control limits indicates the presence of an assignable causeOther types of patterns also indicate the presence of an assignable causeThese patterns are more easily described in terms of control chart zonesA, B, CLO17-5: Detect the presence of assignable causes through pattern analysis.17-*17.5 Comparison of a Process with Specifications: Capability StudiesNatural tolerance limits for a normally distributed process in statistical control will contain about 99.73 percent of the process observations and is given by If the natural tolerance limits are inside the process specification limits, we say that the process is capable of meeting specificationsLO17-6: Decide whether aprocess is capable of meeting specifications.17-*17.6 Charts for Fraction NonconformingSometimes we inspect items and simply decide if they conform to standards or notNonconforming: does not meet standardsDefectiveConforming: meets standardsUse a p chartObserve subgroups of n units over timeDetermine the number nonconformingLO17-7: Use p charts to monitor process quality.17-*17.7 Cause-and-Effect Concentration Diagrams (Optional)A cause-and-effect diagram for “why tables are not cleared quickly in a restaurant”Also known as Ishikawa diagrams or fishbone chartsLO17-8: Use diagramsTo discern the causes of quality problems (Optional).Figure 17.2617-*