Foundations & Principles of Distributed Manufacturing (eBook)

Hermann Kühnle joined the Otto-von-Guericke-University of Magdeburg as a Full University Professor for “Factory Operations and Production Systems” and Executive Director of the Institute for „Ergonomics, Manufacturing Systems and Automation in 1994. From 1994 to 2001 he also was Foundation- and Executive Director of the Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg.Since 1995, Prof. Dr. Kühnle is the speaker of the research field "Advanced Production systems in Saxony-Anhalt" and board member of several companies and venture capital groups. From 1980 to 1994 he worked for Fraunhofer Institute for Production Engineering and Automation IPA in Stuttgart, on Material Flow Planning, Enterprise Planning and Organisation, Computer Integrated Manufacturing, since 1991 as Research Director and Head of the division Enterprise Planning and Control“ where he was in charge of the Management of a number of international, national and regional research programmes and projects as well as research implementation projects with leading national and international companies. He is author of numerous editions, publications, lectures notes and congress contributions.Günter Bitsch is the founder and CEO of the software provider and consultancy unit becos GmbH in Stuttgart, Germany, which successfully promotes and implements cutting-edge production planning and control software solutions in specialised sectors of sheet metal and metal processing fabrication. His expertise includes all organisational backgrounds tightly linked to software implementation solutions and all related qualification and coaching subjects as well. Since more than two decades he is known as a leading expert for MES and scheduling solutions in world-class companies. Moreover, he is engaged in academic work, especially with theoretical studies in the coaching field. Important milestones have been published in a number of books and research papers. In 2010 he was promoted as a Ph.D. at the University of Siegen. Since 2013 he teaches at Reutlingen University as a honorary professor on the field of Manufacturing Execution Systems and Production Planning and Control.

Foreword I 6
Foreword II 8
Foreword III 9
Preface 11
Contents 13
Acronyms 15
1 Introduction 18
1.1 Intentions of the Book 19
1.2 Intended Audience 21
2 Description of the Working Field 22
2.1 Relevant ICT Thrusts 23
2.2 Revisiting Manufacturing Fundamentals 27
References 31
3 Theory Building Approach 34
References 41
4 Core Models, Principles, and Networks’ Structuring 43
4.1 Generic Models in the Core 44
4.2 Levels of Detail 50
4.3 Laws and Concurrency Principles 54
4.4 Information Models in Manufacturing 63
4.4.1 Product Models 65
4.4.2 Process Models 66
4.4.3 Decision Support Models 67
References 68
5 Smart Manufacturing Units 70
5.1 Properties 70
5.1.1 Acceptance of Existing Boundaries and Network Participation 71
5.1.2 Modularity 72
5.1.3 Heterogeneity 73
5.1.4 Scalability 74
5.1.5 Context Awareness 74
5.1.6 Autonomy 77
5.1.7 Interoperability 79
5.1.8 Networkability 80
5.2 Distributed Manufacturing Maturity Model D3M 81
References 85
6 Networking Tools and Information Models—Selected Manufacturing Examples Applying Concurrency Principles and Novel Units’ Properties 86
6.1 Distributed Manufacturing Case Studies 88
6.1.1 Case 1: Smart Reactive Scheduling 88
6.1.2 Decision-Cycle 91
6.1.2.1 Strategy and Objectives 91
6.1.2.2 Network (Re)Design 91
6.1.2.3 Monitoring and Analysis 91
6.1.2.4 Level 1: Improvement 92
6.1.2.5 Level 2: Adaptation 92
6.1.2.6 Decision 92
6.1.3 Distributed Manufacturing Maturity 93
6.1.3.1 Networkability—Mapping of Running Processes (3) 93
6.1.3.2 Acceptance of Boundaries and Network Participation—Fulltime Network Unit (4) 93
6.1.3.3 Context Awareness—Proactive with Predefined Pattern (4) 93
6.1.3.4 Heterogeneity—Homogenous Communication Heterogeneous Logic (2)
6.1.3.5 Interoperability—Interconnectable (2) 94
6.1.3.6 Autonomy—Decision Autonomy with Self-defined Pattern (4) 94
6.1.3.7 Modularity—Homogeneous Modules, Loose Couplings (3) 94
6.1.3.8 Scalability—Capacity + Time (3) 95
6.1.4 Application Experiences 95
6.1.4.1 Oven Planer 95
6.1.4.2 Simulation 95
6.1.5 Case 2: Cloud Computing Supported Tracing of Supply, Manufacturing and Delivery Items 98
6.1.6 Smartness of Units, Granularity and Engaged Models 99
6.1.7 Application Experiences 100
6.1.8 Case 3: Smart Adaptive Distributed Scheduling 101
6.1.9 Case 4: Hybrid Decision-Making—A Crucial Element in Industry 4.0 105
6.1.10 Application Experiences 111
6.2 Lessons Learned and Managerial Implications 112
References 117
Conclusions and Outlook 119
Annex 126
Bibliography 129
Index 131