Tools And Methods For The Improvement Of Quality

Based on Dr. W. Edwards Deming's philosophy for the improvement of quality, productivity, and competitive position, this book is perfect for production, management science, statistics, and industrial engineering professionals. The book features enumerative and analytical statistical studies, showing the difference between fixed populations and processes; methods for improving a stable process with a known capability; techniques for analyzing and interpreting control chart patterns; and modern inspection policies, specifically Deming's kp rules, instead of traditional sampling plans. It also includes quality improvement stories, examples, and mini-case studies that convert complex topics into easy-to-understand material.

FOUNDATIONS OF QUALITY. FUNDAMENTALS OF QUALITY. Definition of Quality. Types of Quality: Quality of Design/Redesign. Quality of Conformance. Quality of Performance. The History of Quality. The Deming View of the Relationship Between Quality and Productivity: Benefits of Improving Quality. The Quality Environment: Fourteen Points for Managing Never-Ending Improvement of the Extended Process. Quality in Service. FUNDAMENTALS OF STATISTICAL STUDIES. Statistics Defined. Types of Statistical Studies: Enumerative Studies. Analytic Studies. Distinguishing Between Enumerative and Analytic Studies. Statistical Studies and Quality. DOCUMENTING AND DEFINING A PROCESS. Introduction. A Process Defined: The Feedback Loop. Examples of Processes. Documenting a Process: The Process Documentation Procedure. FOUNDATIONS FOR ANALYTIC STUDIES. BASIC PROBABILITY. Introduction. Probability Defined: The Classical Definition of Probability. The Relative Frequency Definition of Probability. Calculating Classical and Relative Frequency Probabilities. Rules of Classical Probability: Sets. Axioms of Classical Probability. Rules of Relative Frequency Probability: Axioms of Relative Frequency Probability. Marginal, Joint, and Conditional Probability: Marginal Probability. Joint Probability. Conditional Probability. Probability Theorems: The Addition Theorems. The Multiplication Theorems. BASIC STATISTICS. Introduction. Types of Data: Attribute Data. Variables Data. Characterizing Data: Enumerative Studies. Analytic Studies. Visually Describing Data: Tabular Displays. Graphical Displays. Numerically Describing Data: Measures of Central Tendency. Measures of Variability. Measures of Shape. PROBABILITY MODELS. The Need for Probability Models. Types of Random Variables and Probability Models: Discrete Probability Models. Continuous Probability Models. Fitting Probability Models to Frequency Distributions: Visual Inspection. Normal Probability Paper. Quantifying Variation: Variation in Normally Distributed Random Variables. Variation in Unimodal Probability Distributions. Variation in Unknown Probability Distributions. SAMPLING DISTRIBUTIONS. Introduction. Sampling Distributions Defined. Using Sampling Distributions in Analytic Studies. Attribute Sampling Distributions: The Sampling Distribution of Fraction Defective. The Sampling Distribution of Number of Defectives. The Sampling Distribution of Number of Defects per Unit. Variables Sampling Distributions: The Sampling Distribution of the Mean. The Sampling Distribution of the Range. The Sampling Distribution of the Standard Deviation. TOOLS AND METHODS FOR ANALYTIC STUDIES. STABILIZING AND IMPROVING A PROCESS WITH CONTROL CHARTS. Introduction. The Deming Cycle: Stage 1: Plan. Stage 2: Do. Stage 3: Check. Stage 4: Act. Using Control Charts to Stabilize and Improve a Process. Process Variation: Common Causes of Variation. Special Causes of Variation. Control Charts and Variation. The Need for the Continual Reduction of Variation. The Structure of Control Charts. Stabilizing a Process with Control Charts. Advantages of a Stable Process. Improving a Process with Control Charts. Quality Consciousness and Types of Control Charts: Defect Detection. Defect Prevention: Attribute Control Charts. Never-Ending Improvement: Variables Control Charts. An Example of an Attribute Control Chart. An Example of a Variables Control Chart. Two Possible Mistakes in Using Control Charts: Overadjustment. Underadjustment. Two Uses of Control Charts: Evaluating the Past. Evaluating the Present. Some Out-of-Control Evidence. Collecting Data: Rational Subgrouping: Arrangement One. Arrangement Two. Arrangement Three. ATTRIBUTE CONTROL CHARTS. Introduction. Types of Attribute Control Charts: Binomial Count Charts. Area of Opportunity Charts. Individuals Charts. Attribute Control Chart Paper. Binomial Count Charts: Conditions for Use. When Not to Use p Charts of np Charts. Constructing Binomial Count Charts. The p Chart for Constant Subgroup Sizes. The p Chart for Variable Subgroup Sizes. The np Chart. Area of Opportunity Charts: Conditions for Use. The Sampling Distributions for c Charts and u Charts. c Charts. u Charts. Individuals Charts: Conditions for Use. The Sampling Distribution. Constructing an Individuals Chart: An Example. Limitations of Attribute Control Charts. VARIABLES CONTROL CHARTS. Introduction. Variables Charts and the PDCA Cycle: Plan. Do. Check. Act. Subgroup Size and Frequency. x-Bar and R Charts: The Range Portion. The x-Bar Portion. x-Bar and R Charts: An Example. x-Bar and R Charts: Another Example. x-Bar and s Charts: The s Portion. The x-Bar Portion. x-Bar and s Charts: An Example. Median and Range Charts: Conditions for Use. Subgroup Data. Constructing a Median and Range Control Chart. Median and Range Charts: An Example. Individuals Charts: Constructing an Individuals Chart for Variables Data: An Example. Special Characteristics of Individuals Charts. Revising Control Limits for Variables Control Charts: Change in Process. Trial Control Limits. Removal of Out-of-Control Points. OUT-OF-CONTROL PATTERNS. Types of Variation That Create Control Chart Patterns: Between and Within Group Variation. Distinguishing Within Group Variation From Common Variation. Types of Control Chart Patterns: Natural Patterns. Shift in Level Patterns. Cycles. Wild Patterns. Multi-Universe Patterns. Instability Patterns. Relationship Patterns. Rules of Thumb for Detecting Out-of-Control Patterns: One-Sided Rules of Thumb. Two-Sided Rules of Thumb. Probability of Error. DIAGNOSING A PROCESS. Introduction. Brainstorming: Procedure. Rules. Aids to Better Brainstorming. An Example of Brainstorming. Cause-and-Effect Diagrams: Procedure. Constructing a C&E Diagram. Root Cause Analysis. Process Analysis. Summary. Check Sheets: Types of Check Sheets. Pareto Analysis: Constructing a Pareto Diagram. Cost Pareto Diagrams. Use of Pareto Diagrams. Use of Pareto Diagram for Root Cause Analysis. Use of a Pareto Diagram to Improve Chaotic Processes. Use of Pareto Diagrams to Gauge Improvement. Stratification. Dangers and Pitfalls of Poor Stratification. Single Case Boring. PROCESS PERFORMANCE IN ANALYTIC STUDIES. SPECIFICATIONS. Introduction. Types of Specifications: Performance Specifications. Technical Specifications. Performance Specifications and Technical Specifications. The Fallacy That Conformance to Technical Specifications Defines Quality. Created Dimensions: Law of the Addition of Component Dimension Averages. Law of the Differences of Component Dimensions of Averages. Law of the Sums and Differences of Component Dimension Averages. Law of the Addition of Component Dimension Standard Deviations. Law of the Average for Created Areas and Volumes. Law of the Standard Deviation for Created Areas and Volumes. PROCESS CAPABILITY AND IMPROVEMENT STUDIES. Introduction. Process Capability Studies: Attribute Process Capability Studies. Variables Process Capability Studies. Data Requirements for Process Capability Studies. Process Capability Studies on Unstable Processes. Process Capability Studies on Stable Processes. Estimating the Fraction Out of Specification. Process Capability Case Study. Process Capability Studies on Created Dimensions. The Relationships Between Control Limits, Natural Limits, and Specification Limits for Variables Control Charts. Process Capability Indices for Variables Data. Process Improvement Studies: Attribute Improvement Studies. Variables Improvement Studies. Quality Improvement Stories: Relationship Between QI Stories and the PDCA Cycle. Potential Difficulties. Pursuit of Objectives. Quality Improvement Story Case Study. Improvement of the QI Story Process. PROCESS/PRODUCT DESIGN. TAGUCHI METHODS-QUALITY IMPROVEMENT IN PRODUCT AND PROCESS DESIGN. Introduction. Point 1: The Quality of a Manufactured Product Is Measured by the Total Loss Created by That Product to Society. Point 2: Continuous Quality Improvement and Cost Reduction Are Necessary for an Organization's Health in a Competitive Economy. Point 3: Quality Improvement Requires the Never-Ending Reduction of Variation in Product and/or Process Performance around Nominal Values. Point 4: Society's Loss Due to Performance Is Frequently Proportional to the Square of the Deviation of the Performance Characteristic from Its Nominal Value. Point 5: Product and Process Design Can Have a Significant Impact on a Product's Quality and Cost. Point 6: Performance Variation Can Be Reduced by Exploiting the Nonlinear Effects Between a Product's and/or Process's Parameters and the Product's Desired Performance Characteristic: System Design. Parameter Design. Allowance Design. Point 7: Product and/or Process Parameter Settings That Reduce Performance Variation Can Be Identified with Statistically Designed Experiments: Design Parameter Variables. Noise Variables. INSPECTION POLICY. Inspecting Goods and Service. Acceptance Sampling: Lot-by-Lot Acceptance Sampling. Continuous Flow Acceptance Sampling. Special Sampling Plans. A Theoretical Invalidation of Acceptance Sampling: Stable Process. Chaotic Process. Plan for Minimum Average Total Cost for Test of Incoming Materials and Final Product for Stable Processes: The kp Rule. An Example of the kp Rule. Exceptions to the kp Rule. Component Costs of k1 and k2. Achieving Substantial Savings Over 100 Percent Inspection in the Cost of Incoming Material and Final Product for Chaotic Processes: Mild Chaos. Severe Chaos. Exceptions to Rules for Chaos. APPENDIX 16.1. Proof that the Number of Defectives in a Sample Are Independent of the Number of Defectives in the Remainder for Lots Drawn from a Stable Process. APPENDIX 16.2. Derivation of the kp Rule for Stable Processes. DEMING'S 14 POINTS AND THE REDUCTION OF VARIATION. The Purpose of Statistics. The Purpose of Management. An Aid to Understanding the Relationship Between Statistics and Management: The Funnel Experiment. Process Improvement. Deming's 14 Points and the Reduction of Variation: Point 1: Create constancy of purpose toward improvement of product and service with a plan to become competitive, stay in business, and provide jobs. Point 2: Adopt the new philosophy. Point 3: Cease dependence on mass inspection. Point 4: End the practice of awarding business on the basis of price tag. Point 5: Improve constantly and forever the system of production and service, to improve quality and productivity, and thus constantly decrease costs. Point 6: Institute modern methods of training. Point 7: Institute modern methods of supervision. Point 8: Drive out fear, so that everyone may work effectively for the company. Point 9: Break down organizational barriers-Everyone must work as a team to foresee and solve problems. Point 10: Eliminate arbitrary numerical goals, posters, and slogans for the work force that seek new levels of productivity without providing methods. Point 11: Eliminate work standards and numerical quotas. Point 12: Remove barriers that rob employees of their pride of workmanship. Point 13: Institute a vigorous program of education and training. Point 14: Create a structure that will push the prior 13 points every day. SOME CURRENT THINKING ABOUT STATISTICAL STUDIES AND PRACTICE. Introduction. Enumerative and Analytic Studies: Errors in Analytic Studies. Design of Analytic Studies. Analysis of Data from Analytic Studies. Principles of Professional Practice. STATISTICAL TABLES. REFERENCES. INDEX.