Industrial Objectives and Industrial Performance: Concepts and Fuzzy Handling

This book aims to provide a synthesis of work and ideas done by our team over the last fifteen years in the field of information processing for expression of industrial performance. The statement of objectives on the one hand and the calculation of the other performances are discussed, with the search for the explanation of the link between these two basic steps of an industrial improvement. Beyond the synthetic and typological character of this study, the originality of this work lies in the consideration of the temporal dimension of the objectives, and spread on performance expressions. A fuzzy processing and multi-criteria aggregations time information that can be quantitative, qualitative or symbolic are proposed, in line with industrial practice and literature in the field of performance management.

Table of Contents:
Foreword ix Chapter 1. The Industrial System 1 1.1. Introduction 1 1.2. The RB company’s “Hydraulic Cylinder Production” line 2 1.2.1. The Overall Equipment Effectiveness – OEE 4 1.2.2. The Non-compliance rate 5 1.2.3. The Throughput time 5 1.3. Characterization of the industrial system 6 1.3.1. General comments about systems theory 7 1.3.2. The role of the observer 12 1.3.3. Abstraction levels 13 1.3.4. Structure of the industrial system 14 1.3.5. Behavior of the industrial system 17 1.3.6. To summarize these system characteristics 23 1.4. A few words about information handling for the “Hydraulic Cylinder Production” line of the RB company 24 1.5. Objectives and systems theory 26 1.6. Summary 29 Chapter 2. Industrial Objectives: The Variable 31 2.1. Introduction 31 2.2. The objective and the variable: re-reading the tale of the chicken and the egg 34 2.3. Definition of the notion of a variable 37 2.4. When a variable becomes a criterion 42 2.5. Industrial typology 47 2.5.1. Key success factors and key performance factors 49 2.5.2. Strategic, tactical and operational variables 50 2.5.3. Action variables and state variables 51 2.5.4. Customer satisfaction, productivity and context 53 2.6. Relationships between variables: industrial practice 54 2.6.1. Hierarchical approaches 54 2.6.2. Cognitive approaches 60 2.7. Semantic and choice of a variable: the power of an intention 62 2.8. Summary 68 Chapter 3. Industrial Objectives: The Value 71 3.1. Introduction 71 3.2. A value to define the objective 73 3.3. The value and the intention 78 3.3.1. The desire-objective 78 3.3.2. The requirement-objective 80 3.3.3. Inadequacy, improvement and desire 84 3.3.4. The value, the desire-objectives and the requirement-objectives 87 3.4. The value and the time 89 3.4.1. Achieving the objective, a question of time 89 3.4.2. Some characteristics of the temporal horizon 91 3.4.3. Summary 94 3.5. The observer’s intention and the temporal horizon: converging perspectives 95 3.6. What is said about objectives 97 3.7. Summary 105 Chapter 4. Industrial Objectives: A Fuzzy Formalization to Move from Natural Language to Numbers 107 4.1. Introduction 107 4.2. The interest of using the theory of fuzzy subsets 109 4.3. When Mr. C.C. expresses himself about the Throughput time of the “Hydraulic Cylinder Production” line 113 4.4. Numbers and words 114 4.5. Graduality and fuzzy subsets 121 4.5.1. Membership function 121 4.5.2. Fuzzy meaning and description 124 4.6. Operations between fuzzy subsets 126 4.6.1. Fuzzy union, intersection and complement 126 4.6.2. Example of use of the operator of fuzzy union. 127 4.6.3. Example of use of the fuzzy intersection operator 129 4.6.4. Triangular norms 132 4.6.5. Triangular conorms 133 4.7. Imprecision of measurements and theory of possibilities 134 4.7.1. Generalities about measurement uncertainties 136 4.7.2. Confidence intervals and possibility distribution 138 4.7.3. Fuzzy descriptions of an imprecise measurement 141 4.8. Summary 144 Chapter 5. Industrial Objectives: Outlining Performance Expression 147 5.1. Introduction 147 5.2. The notion of performance 148 5.2.1. General comments 148 5.2.2. Industrial performance 151 5.3. From performance to performance expression 155 5.3.1. General comments 155 5.3.2. Semantics of performance expression 157 5.4. The process of precisiation of the finality into objectives: model and notations 159 5.4.1. Principle 160 5.4.2. From the finality to the goal variables 162 5.4.3. From goal variables to objective variables 163 5.4.4. The process of precisiation 163 5.4.5. Objective attributes 163 5.5. Computation of performance expression: our assumptions 169 5.6. Summary 171 Chapter 6. Industrial Objectives: Computation of Performance Expression of the Desire-Objective 173 6.1. Introduction 173 6.2. Returning to the notion of the desire-objective 174 6.3. “Computation” of the performance expression of a desire-objective 176 6.4. The observer expresses their “feeling” directly 178 6.5. The observer has a measurement value associated with the considered variable 179 6.6. The observer has a set of measurement values or of information associated with the considered variable 182 6.7. Looking back over computation 187 6.8. Summary 189 Chapter 7. Industrial Objectives: Computation of the Performance Expression of the Requirement-Objective 191 7.1. Introduction 191 7.2. Returning to the notion of a requirement-objective 192 7.3. A few points about the notion of scale 194 7.4. Computation of the performance expression for the improvement-objective 196 7.4.1. The observer computes a numerical performance expression 197 7.4.2. The observer computes a linguistic performance expression 204 7.4.3. Looking back over the computation 212 7.5. Computation of the performance expression of the inadequacy-objective 214 7.5.1. The observer computes a performance expression 215 7.5.2. The observer computes a performance expression and represents it visually 220 7.5.3. Looking over the computation 227 7.6. Summary 227 Conclusion 229 Bibliography 233 Index 249

About the Author :
Lamia Berrah, Laboratory of Computer Science, Systems, Information Processing and Knowledge (LISTIC) - University of Savoy - Annecy, France. Vincent Clivillé, Laboratory of Computer Science, Systems, Information Processing and Knowledge (LISTIC) - University of Savoy - Annecy, France. Laurent Foulloy, Laboratory of Computer Science, Systems, Information Processing and Knowledge (LISTIC) - University of Savoy - Annecy, France.