DEVELOPING STRUCTURED PROCEDURAL AND METHODOLOGICAL ENGINEERING DESIGNS : applied... industrial engineering tools

This book is designed to assist industrial engineers and production managers in developing procedural and methodological engineering tools to meet industrial standards and mitigate engineering and production challenges. It offers practitioners expert guidance on how to implement adequate statistical process control (SPC), which takes account of the capability to ensure a stable process and then regulate if variations take place due to variables other than a random variation. Powerful engineering models of new product introduction (NPI), continuous improvement (CI), and the eight disciplines (8D) model of problem solving techniques are explained. The final three chapters introduce new methodological models in operations research (OR) and their applications in engineering, including the hyper-hybrid coordination for process effectiveness and production efficiency, and the Kraljic-Tesfay portfolio matrix of industrial buying. Foreword Foreword Foreword Book Summary Preface Introduction Overview of the Subject Matter Briefing the Topics of the Book Why This Book? Description of the Target Audience Sections of the Book Description of the Chapters in the Book What Are the Impacts of This Book? Acknowledgments Editorial Advisory Board Contents About the Author Part I: The Engineering Definition of Quality Chapter 1: Quality in the Context of Engineering 1.1 Introduction 1.2 Some Interesting Definitions of Quality 1.2.1 Definition of Quality Based on Quality Attribute Dimensions 1.2.2 The Three Crucial Elements of Quality 1.3 Quality Engineering Terminology 1.3.1 Specification Limits and Tolerance 1.4 Over-the-Wall Model: The Old Design 1.5 Concurrent (Simultaneous) Engineering Model 1.6 Process Planning 1.6.1 Process Requirement Analysis (PRA) 1.6.2 Team Building 1.6.3 Planning Implementation 1.6.4 Process Auditing 1.6.5 Project Closure 1.7 Total Quality Control (TQC) 1.7.1 Quality Leadership (QL) 1.7.2 Quality Technology (QT) 1.7.3 Organizational Commitment (OC) 1.8 Quality Assurance (QA) 1.8.1 Quality Policy 1.8.2 Procedures 1.8.3 Work Instructions 1.8.4 Records 1.9 Quality Control (QC) 1.10 Total Quality Management (TQM) 1.11 Statistical Process Control (SPC) 1.12 Quality Systems and Standards 1.12.1 ISO 9001 1.12.2 Examples of Standards 1.12.3 The Seven Quality Management Principles 1.13 ISO 9001-Quality Management Systems of Requirements (Source: Adapted from the ISO 9001:2008 Standard, International Stand... 1.13.1 ISO 9001-Quality Management Systems of Requirements (Source: Adapted from the ISO 9001:2015 Standard, International Sta... 1.14 Quality Management Systems: Requirements 1.14.1 Scope 1.14.2 Normative References 1.14.3 Terms and Definitions 1.14.4 Context of the Organization 1.14.4.1 Understanding the Organization and Its Context 1.14.4.2 Understanding the Needs and Expectations of Interested Parties 1.14.4.3 Determining the Scope of the Quality Management System 1.14.4.4 Quality Management System and Its Processes 1.14.5 Leadership 1.14.5.1 Leadership and Commitment General Customer Focus 1.14.6 Policy 1.14.6.1 Establishing the Quality Policy 1.14.6.2 Communicating the Quality Policy 1.14.6.3 Organizational Roles, Responsibilities, and Authorities 1.14.7 Planning 1.14.7.1 Actions to Address Risks and Opportunities 1.14.7.2 Quality Objectives and Planning to Achieve Them 1.14.7.3 Planning of Changes 1.14.8 Support 1.14.8.1 Resources General People Infrastructure Environment for the Operation of Processes Monitoring and Measuring Resources General Measurement Traceability Organizational Knowledge 1.14.8.2 Competence 1.14.8.3 Awareness 1.14.8.4 Communication 1.14.8.5 Documented Information General Creating and Updating Control of Documented Information 1.14.9 Operation 1.14.9.1 Operational Planning and Control 1.14.9.2 Requirements for Products and Services Customer Communication Determining the Requirements for Products and Services Review of the Requirements for Products and Services Changes to Requirements for Products and Services 1.14.9.3 Design and Development of Products and Services General Design and Development Planning Design and Development Inputs Design and Development Controls Design and Development Outputs Design and Development Changes 1.14.9.4 Control of Externally Provided Processes, Products, and Services General Type and Extent of Control Information for External Providers 1.14.9.5 Production and Service Provision Control of Production and Service Provision Identification and Traceability Property Belonging to Customers or External Providers Preservation Postdelivery Activities Control of Changes 1.14.9.6 Release of Products and Services Control of Nonconforming Outputs 1.14.10 Performance Evaluation 1.14.10.1 Monitoring, Measurement, Analysis, and Evaluation General Customer Satisfaction Analysis and Evaluation 1.14.10.2 Internal Audit 1.14.10.3 Management Review General Management Review Inputs Management Review Outputs 1.14.11 Improvement 1.14.11.1 General 1.14.11.2 Nonconformity and Corrective Action 1.14.11.3 Continual Improvement Bibliography Part II: Acceptance Sampling and Statistical Process Control (SPC) Chapter 2: Engineering Designs of Acceptance Sampling 2.1 Introduction 2.2 Foundations of Acceptance Sampling 2.3 Sample Size Calculation 2.4 Some Examples of Sample Size Calculations 2.5 Essential Concepts of Sampling 2.5.1 First Sampling Concept 2.5.2 Operating Characteristics Curve (OC Curve) 2.5.2.1 Operating Characteristics Curve (OC Curve) for a Perfect Sampling Plan 2.5.2.2 Generating an OC Curve 2.5.2.3 The Poisson Approximation of a Binomial Distribution 2.5.3 Critical Parameters of Acceptance Sampling 2.5.3.1 OC Curve with Variable Acceptance Number (c) 2.5.3.2 The OC Curve and Varying Sample Size 2.5.4 Risks of Acceptance Sampling 2.5.4.1 Sampling risk 2.5.4.2 Risks Related to Acceptance Sampling 2.5.4.3 Nonsampling Risk 2.5.5 Essential Terms in Acceptance Sampling 2.5.5.1 Acceptance Quality Limit (AQL) 2.5.5.2 Lot Tolerance Percent Defective (LTPD) 2.5.5.3 Average Outgoing Quality (AOQ) 2.5.5.4 Average Outgoing Quality Limit (AOQL) Annex Incoming Inspection Flow Chart Disposition of Nonconforming Material Flow Chart Scrap Ticket Flow Chart Material Review Board (MRB) Flow Chart Normal probability Table Bibliography Chapter 3: Engineering Sampling Standards and Plans 3.1 Introduction 3.2 Terms and Definitions 3.3 Sampling Plan 3.3.1 Single Sampling Plans (SSP) 3.3.1.1 General Inspection Levels (GIL) 3.3.1.2 Special Inspection Level (SIL) 3.3.1.3 Acceptance Number 3.3.1.4 Limitations of Single Sampling Plan 3.3.2 Double Sampling Plan (DSP) 3.3.2.1 Decision Rule of the DSP 3.3.3 Multiple Sampling Plan (MSP) 3.3.4 Sequential Sampling Plan (SqSP) 3.3.4.1 Decision Measures Calculation 3.4 General Inspection Levels of Sampling and Sample Size 3.5 Sampling Scheme 3.5.1 Sampling Inspection Categories: Reduced, Normal, and Heightened 3.6 Cost of Rejection 3.7 Switching Rules 3.8 ANSI/ASQ Z1.9: The Variable Sampling Plan 3.9 Dodge-Romig Sampling Tables (DRST) 3.10 Sample Integrity Annex Cumulative Binomial Probabilities Cumulative Probability of Poisson Distribution Bibliography Chapter 4: Statistical Process Control (SPC) 4.1 Introduction 4.2 Benefits of Using Control Charts 4.3 Types of Within Process Variations 4.3.1 Common Cause Variation 4.3.1.1 Illustrations of Common Causes of Variance 4.3.2 Special Cause Variation 4.3.2.1 Instances of Special Causes of Variance 4.3.3 Process States 4.4 The Three Key Elements of Control Charts 4.4.1 Selection of Variables 4.4.1.1 Discrete Data and Continuous 4.4.1.2 Defect and Defective 4.5 Rational Subgrouping 4.6 The Eight Rules for Statistical Process Control (SPC) Bibliography Chapter 5: Process Control Charts 5.1 Control Charts for Variable Data 5.1.1 (X-Bar) and R Chart 5.1.1.1 Steps of Building the and R Charts Chart Calculation of the Critical Values of the Chart Calculation of the Critical Values of the R Chart 5.1.2 Chart and S Chart 5.1.2.1 Steps of Building the and S Charts Chart Calculation of the Critical Values of the S Chart Interpretation of the and S Chart 5.1.2.2 Individual and Moving Range (I-MR or X-MR) Charts 5.1.3 Statistical information for constructing the individual and moving range (I-MR or X-MR) chart 5.1.4 How to Select the Appropriate Variable Control Chart 5.2 Attribute Statistical Control Charts 5.2.1 How to Select the Appropriate Attribute Control Chart 5.2.2 p-Chart and np-Chart 5.2.3 Proportion Chart (p-chart) 5.2.4 np Chart 5.2.5 c-Chart and u-Chart 5.2.6 Count Chart (c-Chart) 5.2.7 u-Chart 5.3 Systematic Flow Chart to Select the Best Control Chart for the Given Process Dataset 5.4 Analyzing and Interpreting Control Charts 5.4.1 Controlled Process 5.4.2 Unhealthy Process and Special Cause Variations 5.5 Common Control Chart Analysis Errors Annex: X-Bar and R Chart and X-Bar and S Chart constants Bibliography Chapter 6: Process Capability Analysis 6.1 Introduction 6.2 Pre-control Charts 6.3 Terms and Definitions 6.4 Short-Run Statistical Process Control (SR-SPC) 6.5 The Fundamental Concept Process Capability 6.5.1 Calculation of Short-Term Standard Deviation 6.5.2 Calculation of Long-Term Standard Deviation 6.5.3 Process Capability Index 6.5.4 Calculation of Cp and Cpk 6.5.5 Difference Between Cp, Cpk and Pp, Ppk 6.5.5.1 Potential Process Capability Analysis (Cp, Cpk) 6.5.5.2 Actual Process Performance Analysis (Pp, Ppk) 6.5.6 Notes on Cpk Bibliography Part III: Engineering Problem-Solving Tools and Continuous Improvement Techniques Chapter 7: Concurrent Problem-Solving Models for Industrial Applications 7.1 Introduction 7.2 Steps of the 8D is a Problem-Solving Model 7.2.1 Step 1: Planning and Formation of Team 7.2.2 Step 2: Define and Describe the Problem 7.2.3 Step 3: Interim Containment Action (ICA) 7.2.4 Step 4: Determine, Identify, and Verify Root Causes and Escape Points 7.2.4.1 The Fundamental Principles of Root Cause Analysis (RCA) 7.2.4.2 Types of Root Causes 7.2.4.3 Techniques of Root Cause Analysis (RCA) 7.2.4.4 Comparative Analysis 7.2.4.5 Pareto Charts 7.2.4.6 The 5Whys Model 7.2.4.7 The Fishbone Diagram: Cause and Effect (CAE) Diagram 7.2.4.8 Control Charts 7.2.4.9 Design of Experiments (DoE): Analysis of Variance (ANOVA) 7.2.5 Step 5: Select and Implement Permanent Corrective and Preventive Action (CAPA) 7.2.5.1 Plan-Do-Check-Act (PDCA) 7.2.5.2 Failure Mode and Effects Analysis (FMEA) 7.2.6 Step 6: Implement and Validate Corrective Actions 7.2.7 Step 7: Take Preventive Actions 7.2.8 Step 8: Closure and Team Celebration 7.3 The 8D Report Form Bibliography Chapter 8: Models of Continuous Improvement 8.1 Introduction 8.2 Systematic Continuous Improvement Categories 8.2.1 Customer Focus 8.2.2 Employee Focus 8.2.3 Process Focus 8.2.4 Leadership Support 8.3 Tools of Continuous Improvement 8.3.1 Total Quality Management (TQM) 8.3.1.1 Total 8.3.1.2 Quality 8.3.1.3 Management 8.3.2 Kaizen 8.3.3 Plan-Do-Check-Act (PDCA) 8.3.3.1 The Steps of the PDCA Cycle Step 1: Plan Step 2: Do Step 3: Check (Study) Step 4: Act (or Adjust) 8.3.4 Six Sigma Methodology 8.3.4.1 Foundations and Focus Areas of Six Sigma 8.3.4.2 Essential Metrics of Six-Sigma 8.3.4.3 Define-Measure-Analyze-Improve-Control (DMAIC) 8.3.4.4 Tollgate Review Process 8.3.4.5 DMAIC Cyclic Structure 8.3.4.6 The Five-Steps of DMAIC Step 1: Define Step 2: Measure Step 3: Analyze Step 4: Improve Step 5: Control 8.3.4.7 Summary of Tools Uses in Each Step of DMAIC Process 8.3.4.8 Substructure and Belts Within the Six Sigma 8.3.4.9 Guiding Principle on the Six Sigma Implementation 8.3.5 Theory of Constraint 8.3.5.1 Four-Steps of Operational ToC Step 1: Identify the Constraint Step 2: Exploit the Constraint Step 3: Subordinate the Entire Process to the Constraint Step 4: Elevate the Constraint Bibliography Part IV: Quality Design for New Product Introduction Chapter 9: Quality Design for New Product Introduction 9.1 Introduction 9.2 Design Inputs and Outputs 9.2.1 Sources of Design Inputs 9.2.2 Models to Capture Customer Needs 9.2.3 What Are Design Inputs? 9.3 Translation of Design Input (DI) into Design Output (DO) 9.3.1 Quality Function Deployment (QFD) 9.3.2 Robust Design 9.3.2.1 Uncontrollable Variations 9.3.2.2 Controllable Variations 9.3.3 Design for Excellence 9.3.4 Design for Manufacturing and Assembly 9.3.5 Design for Reliability (DFR) 9.3.6 Design for Maintainability and Serviceability (DMS) 9.3.7 Design for Six-Sigma (DFSS) 9.3.7.1 Why DFSS? 9.3.8 Quality by Design 9.3.8.1 The Difference Between Quality by Design and QbD 9.4 Design Review Process Bibliography Part V: Some Innovative Engineering Quantitative Models and Their Applications Chapter 10: Analyzing the Impact of the Bullwhip Effect 10.1 Introduction 10.2 Literature Review 10.2.1 The Bullwhip Effect 10.2.2 Empirical Studies on the Bullwhip Effect 10.3 Hypotheses and Their Theoretical Foundations 10.4 Design of Data and Methodology 10.4.1 The Data 10.4.1.1 Exogenous Variables of the Study 10.4.1.2 Endogenous Variables of the Study 10.4.2 Methodology 10.4.2.1 One-Way Analysis of Variance (ANOVA) 10.4.2.2 Signal Processing 10.4.2.3 Recursive Autoregression: Cochrane-Orcutt Autoregression 10.5 Results and Discussions 10.5.1 Implications to Theory of Organizational Coordination 10.5.2 Critics Toward Transaction Cost Analysis (TCA) 10.5.3 The Hyper-Hybrid Coordination 10.5.4 The Kraljic-Tesfay Portfolio Matrix 10.5.4.1 The Best Coordination with Supplier Having Adequate-Intraorganizational Coordination 10.5.4.2 The Best Coordination with Supplier Having Inadequate-Intraorganizational Coordination 10.6 Conclusions and Recommendations 10.6.1 Conclusions 10.6.2 Recommendations Annex: Data from the Beer Distribution Game Bibliography Chapter 11: Industrial Applications of Tesfay Process and Tesfay Coordination 11.1 Introduction 11.2 The Hypotheses 11.3 Literature Review 11.4 Design of Dataset and Methodology 11.4.1 The Design of Dataset 11.4.2 The Methodology 11.4.2.1 Panel Data Regression Models 11.4.2.2 Structural Equations of a Seemingly Unrelated Regression Model 11.4.2.3 Recursive Autoregressive-SUR Model 11.4.2.4 Adjusting Cross-sectional Variability: Bartlett ́s Test of Heteroscedasticity 11.5 Results and Discussions 11.5.1 Examination of Hypothesis 1: Causes of the Bullwhip Effects 11.5.2 Examination of Hypothesis 2: Implications of the Causality Analysis on Transaction Cost Analysis (TCA) 11.5.3 Tesfay Process 11.5.4 The New Discovered Organizational Coordination: Tesfay Coordination 11.5.5 Implications of Examination of Hypothesis 3 11.5.5.1 The Best Coordination with the Supplier Having Good Intraorganizational Coordination 11.5.5.2 The Best Coordination with the Supplier Having Inadequate Intraorganizational Coordination 11.6 Applications of Tesfay Coordination in the Airline Industry 11.7 Conclusions and Recommendations 11.7.1 Conclusions 11.7.2 Recommendations Appendix: Experimental Data from the Beer Distribution Game Bibliography Chapter 12: Industrial Applications of Tesfay Dynamic Regression Model 12.1 Introduction 12.2 The Problem 12.3 Literature Review 12.3.1 Load Factor Measures the Performance of Airlines 12.4 Data and Methodology 12.4.1 The Data 12.4.2 Methodology 12.4.2.1 Signal Processing 12.4.2.2 Ljung-Box Test 12.4.2.3 Tesfay Dynamic Regression 12.4.3 Model Adequacy and Diagnostics 12.5 Results and Discussion 12.5.1 Evaluation of the Load Factor ́s Regional Characteristics 12.5.2 Structure of the Load Factor ́s Autocorrelation 12.5.2.1 The Load Factor ́s Autocorrelation Configuration of North Atlantic-Europe Flights 12.5.2.2 The Load Factor ́s Autocorrelation Configuration of Mid Atlantic-Europe Flights 12.5.3 Fitting the Load Factor ́s Panel Data Regression Model 12.6 Conclusions and Recommendations 12.6.1 Conclusions 12.6.2 Recommendations and Policy Implications 12.6.3 Further Studies Bibliography Index