Real Optimization with SAP APO

By: Josef Kallrath, Thomas I. Maindl

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Hardcover | 24 April 2006

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348 Pages

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Optimization is a serious issue, touching many aspects of our life and activity. But it has not yet been completely absorbed in our culture. In this book the authors point out how relatively young even the word "model" is. On top of that, the concept is rather elusive. How to deal with a technology that ?nds applicationsinthingsasdi?erentaslogistics,robotics,circuitlayout,?nancial deals and tra?c control? Although, during the last decades, we made signi?cant progress, the broad public remained largely unaware of that. The days of John von Neumann, with his vast halls full of people frantically working mechanical calculators are long gone. Things that looked completely impossible in my youth, like solving mixed integer problems are routine by now. All that was not just achieved by ever faster and cheaper computers, but also by serious progress in mathematics. But even in a world that more and more understands that it cannot a?ord to waste resources, optimization remains to a large extent unknown. R It is quite logical and also fortunate that SAP , the leading supplier of enterprise management systems has embedded an optimizer in his software. The authors have very carefully investigated the capabilities and the limits of APO. Remember that optimization is still a work in progress. We do not have the tool that does everything for everybody.

Industry Reviews

From the reviews: "'SAP APO' stands for 'SAP Advanced Planning and Optimizer.' ... I recommend this book for personnel in corporations and governmental entities contemplating improving supply chain operations with SAP software. The book may also be of more general interest to operations researchers and mathematical optimization professionals, especially those interested in mixed integer linear programs, those wanting to keep their academic activities relevant to real-world concerns, and those wishing to gain experience in commercial applications." (R. Baker Kearfott, SIAM Review, Vol. 49 (2), 2007)

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Non-Fiction Business & Management Management & Management Techniques Management of Specific Areas Production of Quality Control Management Industry & Industrial Studies Business Mathematics & Systems Computing & I.T.Computer Science Mathematics Applied Mathematics Optimisation

Linear Programming Engineering & Technology Technology in General Engineering in General

Foreword	p. VII
Preface	p. IX
Conventions and Abbreviations	p. XXV
Introduction
Supply Chain Management and Advanced Planning Systems	p. 3
Supply Chain Planning - a Brief Introduction	p. 3
The Supply-Chain Operations Reference Model	p. 4
Supply Chain Planning and Advanced Planning Systems	p. 6
Advanced Planning Systems and Optimization	p. 8
SAP APO as an Advanced Planning System	p. 9
Components of SAP APO	p. 9
Optimization in SAP APO	p. 10
Fundamentals of Supply Network Planning in SAP APO	p. 12
Planning Methods in SAP APO Supply Network Planning	p. 15
SNP Heuristics	p. 15
SNP Capable-to-Match	p. 16
SNP Optimization	p. 18
Introduction: Models, Model Building and Optimization	p. 21
An Important Warning on Modeling and Optimization	p. 21
Mathematical Optimization	p. 23
The Main Ingredients of Optimization Models	p. 26
Indices and Index Sets	p. 27
Data	p. 28
Variables	p. 29
Constraints	p. 30
The Objective Function	p. 31
Classes of Problems in Mathematical Optimization	p. 32
A Deterministic Standard MINLP Problem	p. 32
Comments on Solution Algorithms	p. 33
Optimization Versus Simulation	p. 35
Multi-objective Optimization	p. 35
Optimization Under Uncertainty	p. 36
Implementing Models and Solving Optimization Problems	p. 38
Implementing Optimization Models	p. 38
Solving Optimization Problems	p. 39
Optimization and SAP APO	p. 39
Model Building in SAP APO Supply Network Planning	p. 41
The Example Supply Chain Model	p. 42
The Supply Chain Structure	p. 42
Constraints and Costs in the Example Model	p. 43
A Mathematical Formulation of the Example Model	p. 45
Supply Chain Model Master Data	p. 51
Models and Planning Versions in SAP APO	p. 51
Locations	p. 53
Products and Location Products	p. 55
Resources	p. 59
General Resource Data	p. 59
Resource Capacity Variants	p. 61
Production Process Models	p. 61
General PPM Data	p. 62
Components	p. 64
Modes	p. 65
Product Plan Assignment	p. 67
PPM Activation	p. 67
PPM Data in the Example Model	p. 68
Assembling the Parts with the Supply Chain Engineer	p. 69
Transportation Lanes	p. 72
Optimization in SAP APO	p. 79
Recap of the Supply Chain Model	p. 79
Optimizer Setup	p. 80
The Optimization Profile	p. 81
Scaling the Costs in the Master Data - the SNP Cost Profile and SNP Cost Maintenance	p. 86
Working Towards Steady Results - the SNP Optimization Bound Profile	p. 87
Lot Sizes for Shipments - the SNP Lot Size Profile	p. 88
Decomposition Methods and the SNP Priority Profile	p. 89
Settings in SAP APO Customizing - the SNP Planning and Parallel Processing Profiles	p. 89
The Time Bucket Profile	p. 90
The Planning Run	p. 91
SNP Planning Books	p. 91
Continuous Variant of the Model	p. 93
Discrete Variant of the Model	p. 98
Some Observations	p. 100
A Mathematician's View	p. 100
The Standard Business Software View	p. 101
Detailed Case-Studies
Planning in Semiconductor Manufacturing	p. 105
Semiconductor Manufacturing	p. 105
The Manufacturing Process	p. 106
Semiconductor Business Challenges	p. 107
Supply Chain Business Practices	p. 108
Semiconductor Capacity and Master Planning	p. 109
Semiconductor Supply Chain Modeling	p. 110
Semiconductor Supply Chain Planning and SAP APO	p. 111
Capable-to-Match for Semiconductor	p. 111
Optimization for Semiconductor	p. 113
The Semiconductor Case Study	p. 115
The Business Objectives and Project Scope	p. 115
The Supply Chain Structure	p. 116
SNP Implementation in SAP APO	p. 117
Consumer Products	p. 119
Supply Chain Challenges Characterizing the Consumer Products Industry	p. 119
The Carlsberg Case	p. 120
The Carlsberg Business Objectives and Project Scope	p. 121
The Supply Chain Structure in the Carlsberg Case	p. 121
SNP Optimization Implementation in SAP APO	p. 123
Customized Optimization Solutions for the Automotive and Chemical Industries	p. 125
Automotive Case Study	p. 125
The Complete Planning System	p. 126
Strategic Planning	p. 126
Mid-Range (Budget and Master Production) Planning	p. 128
Goals	p. 129
Constraints	p. 129
Decomposition	p. 130
Checker	p. 130
User Interface	p. 131
Order-driven Planning	p. 131
Decomposition	p. 132
Goals	p. 132
Constraints	p. 133
Checker	p. 133
User Interface	p. 134
Conclusion	p. 134
Chemical Case Study	p. 134
The Architecture of the Complete Planning System	p. 135
Production Planning and Detailed Scheduling	p. 136
Approximation Methods in SAP APO PP/DS	p. 138
Goals	p. 138
Constraints	p. 138
Cartridge	p. 138
Cartridge Planning Scenario	p. 139
Optimization Problem (Overview)	p. 139
Decomposition	p. 139
Smelter/Simple Extruder Model	p. 140
Extruder Model	p. 141
Checker	p. 141
User Interface	p. 141
Conclusion	p. 142
The Future	p. 142
Operative Planning in the Process Industry	p. 143
Problem Description	p. 143
A Tailored MILP Model	p. 146
Basic Assumptions and Limitations of the Model	p. 147
General Framework of the Mathematical Model	p. 147
Indices	p. 147
Index Sets and Indicator Tables	p. 148
The Problem Data	p. 149
Time Discretization	p. 151
The Concept of Modes	p. 153
The Variables	p. 154
The Mathematical Model - the System of Constraints	p. 156
Modeling the Production	p. 156
Modeling Mode-changing Reactors	p. 157
Multi-stage Production	p. 162
Minimum Production Requirements	p. 164
Batch and Campaign Production	p. 167
Modeling Stock Balances and Inventories	p. 169
Dedicated Inventories at Sites (Free Origin)	p. 169
Modeling Transport	p. 176
Keeping Track of the Origin of Products	p. 179
Including Demands and Demand Constraints	p. 180
Defining the Objective Functions	p. 181
Maximizing Contribution Margin	p. 182
Maximizing Margin - Satisfying Demand	p. 185
Minimizing Cost While Satisfying Full Demand	p. 185
Maximizing Total Sales	p. 185
Maximizing Net Profit	p. 187
Multi-criteria Objectives	p. 187
Maximizing Total Production	p. 188
Maximizing Production of Requested Products	p. 188
Implementation of the Model	p. 188
Estimating the Quality of the Solution	p. 189
Comparing Solutions of Different Scenarios	p. 190
Description of the Output	p. 191
Real Life Issues	p. 193
Diagnosing Infeasibilities	p. 193
Seemingly Implausible Results	p. 195
Relaxation of Constraints	p. 195
The SAP APO View on this Problem	p. 196
(Non)linear Costs	p. 197
Objective Functions	p. 197
Demand Satisfaction	p. 198
Detailed Comments on the Tailored MILP Model	p. 199
Basic Assumptions and Limitations	p. 200
General Framework of the Model	p. 200
The Mathematical Model - the Constraints	p. 201
Description of the Outputs	p. 203
Diagnosing Infeasibilities	p. 203
Concluding Statement	p. 203
Case Studies - Interfacing Tailored Models to SAP APO	p. 205
Developing Tailored Models	p. 205
The ILOG Cartridge Concept	p. 206
ILOG Background	p. 207
The Optimization Development Framework and Cartridges	p. 208
The Bulk Distribution Case	p. 208
Business Context	p. 208
SAP APO Project	p. 209
Cartridge Motivations	p. 209
Solution Outline	p. 211
Integration	p. 215
Project Information	p. 215
The Load Builder Case	p. 216
Business Context	p. 216
SAP APO Project	p. 218
Cartridge Motivations	p. 218
Solution Outline	p. 219
Integration	p. 222
Project Information	p. 222
The Cartridge Architecture	p. 223
External Architecture	p. 224
Data Integration	p. 227
Internal Architecture	p. 227
About Cartridge Projects	p. 228
The Team	p. 228
The Inception Phase	p. 230
Elaboration	p. 233
Construction	p. 233
Transition	p. 234
Risks	p. 234
Production and Sales Planning in the Chemical Industry	p. 237
Situation	p. 237
Task and Objectives	p. 237
Solution	p. 238
Data Download from SAP R/3 and SAP APO	p. 239
Checks on Completeness and Consistency	p. 240
User Input	p. 240
Optimization	p. 241
Iterations and Adjustments	p. 241
Reporting	p. 242
Discussion	p. 242
The "Human Factor"	p. 243
"Plug-in" Solution Versus SNP	p. 243
Future Enhancements	p. 245
Situation	p. 245
Task and Objectives	p. 246
Solution	p. 246
Concluding Considerations - The Future
Summary, Visions and Perspective	p. 253
What Can Be Learned from this Book?	p. 253
A Summary of Experience in Optimization Projects	p. 255
When Is Optimization Useful at All?	p. 255
Data and Optimization Model	p. 261
Rules in Planning and Scheduling Problems	p. 262
Implementation	p. 263
Interfacing Tailored Models	p. 265
Further Developments in Real World Optimization	p. 265
Simultaneous Operative and Strategic Optimization	p. 266
Rigorous Approaches to Scheduling	p. 267
Planning and Scheduling Under Uncertainty	p. 268
A Mathematician's Dream	p. 268
SAP APO as a Modeling Tool	p. 269
The Future of Optimization with SAP APO	p. 269
Appendix
The Hitchhiker's Guide to SAP APO	p. 273
SAP APO Components	p. 273
Hierarchical Planning	p. 274
Mathematical Foundations of Optimization	p. 277
Linear Programming	p. 277
A Primal Simplex Algorithm	p. 279
Computing Initial Feasible LP Solutions	p. 283
LP Problems with Upper Bounds	p. 284
Dual Simplex Algorithm	p. 286
Interior-point Methods	p. 287
Mixed Integer Linear Programming	p. 290
Multicriteria Optimization and Goal Programming	p. 294
Glossary	p. 297
List of Figures	p. 303
List of Tables	p. 305
References	p. 307
About the Authors	p. 313
Index	p. 315
Table of Contents provided by Ingram. All Rights Reserved.

Real Optimization with SAP APO

At a Glance

Hardcover

Industry Reviews

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How to return your order

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