BookPDF Available

Global Supply Chain and Operations Management

Authors:
Dmitry Ivanov at Berlin School of Economics and Law
Dmitry Ivanov
Jörn Schönberger at TU Dresden
Jörn Schönberger
Alexander Tsipoulanidis
Alexander Tsipoulanidis
Alexander Tsipoulanidis
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Download full-text PDF
Read full-text
Download citation
Content uploaded by Dmitry Ivanov
Author content
All content in this area was uploaded by Dmitry Ivanov on Oct 23, 2017
Content may be subject to copyright.
Contents
1 Basics of Supply Chain and Operations Management .......... 1
1.1 Introductory Case Study: The Magic Supply Chain and the Best
Operations Manager . . . . . . . ........................ 2
1.2 Basic Definitions and Decisions . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Transformation Process, Value Creation and Operations
Function ................................. 3
1.2.2 Supply Chain Management . . . . . . . . . .......... 5
1.2.3 Decisions in Supply Chain and Operations
Management .............................. 6
1.3 Careers and Future Challenges in Supply Chain and Operations
Management . . .................................. 9
1.4 Key Points . . .................................... 13
Bibliography .......................................... 14
2 Examples from Different Industries, Services and Continents .... 15
2.1 Examples of Operations and Supply Chains in
Manufacturing ................................... 15
2.1.1 Nike: Sourcing Strategy in the Integrated Supply
Chain................................... 15
2.1.2 Dangote Cement: Establishing Sophisticated Supply
Chain Management in Africa .................. 17
2.1.3 Toyota: Supply Chain Disruption Management . .... 20
2.1.4 Adidas “Speedfactory”: 3D Printing and Industry
4.0 in Supply Chain and Operations Management . . . 21
2.2 Examples of Operations and Supply Chains in Services ..... 22
2.2.1 SCOM in Restaurants: Case Study Starbucks
Corporation ............................... 22
2.2.2 Operations Management at Airport Madrid/Barajas . . . 23
2.2.3 Time-Critical Supply Chains: Disaster Management
and Humanitarian Logistics . . . . . . . . . . . . . . . . . . . 25
2.2.4 Operations Issues in Car Sharing . . . . . . ......... 28
2.2.5 REWE: Expanding the Logistics Network . . . . . . . . 29
xv
2.3 Examples of e-Operations and Supply Chains . . . . . . . . . . . . 30
2.3.1 Fab.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.3.2 Homeplus: The Store Comes to Your Home ....... 34
Bibliography .......................................... 35
3 Processes, Systems, and Models ........................... 37
3.1 Introductory Case-Study: AirSupply . .................. 37
3.1.1 E-procurement . ........................... 38
3.1.2 Vendor-Managed Inventory . . . ................ 39
3.1.3 Implementation ............................ 40
3.2 Business Process Management ....................... 41
3.2.1 Process Optimization and Re-engineering . . ....... 41
3.2.2 Business Process Modelling . . . ................ 43
3.3 Management Information Systems . . . . . . . . . . . . . . . . . . . . 45
3.3.1 Role of Information Technology in Supply Chain
and Operations Management .................. 45
3.3.2 Types of Management Information Systems . . . .... 45
3.3.3 Management Information Systems and
Organization .............................. 46
3.3.4 ERP Systems . . . ........................... 47
3.3.5 APS Systems .............................. 48
3.3.6 SCEM and RFID . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.3.7 Business Analytics and E-Business . . . . . . . . . . . . . . 52
3.4 Problem Solving Methods and Research Methodologies ..... 54
3.4.1 Problems, Systems, and Decision-Making . . . . . . . . . 54
3.4.2 Models and Modeling . . . .................... 58
3.4.3 Model-Based Decision-Making ................ 59
3.4.4 Quantitative Models and Operations Research ...... 61
3.4.5 Integrated Decision-Making Support ............ 62
3.4.6 Research Methodologies . . . . . . . . . . . . . . . . . . . . . 63
3.5 Key Points . . .................................... 65
Bibliography .......................................... 66
4 Operations and Supply Chain Strategy ..................... 69
4.1 Introductory Case-Study “Quick and Affordable”: Zara,
UNIQLO & Primark . . ............................. 69
4.2 Operations and Supply Chain Strategies . . . . . . . . . . . . . . . . 73
4.2.1 Value Added and Costs ...................... 73
4.2.2 Operations Strategies . . . . . . . . . . . . . . . . . . . . . . . . 74
4.2.3 Supply Chain Strategies and “Strategic Fit” . . . . . . . 74
4.3 Supply Chain Coordination . . . ....................... 79
4.3.1 Bullwhip Effect . . . . . . ...................... 79
4.3.2 Vendor-Managed Inventory . . . ................ 82
4.3.3 Collaborative Planning, Forecasting and
Replenishment . ........................... 85
4.3.4 Supply Chain Contracting . . . . . . . . . . . . . . . . . . . . 86
xvi Contents
4.4 Supply Chain Resilience and Sustainability .............. 87
4.4.1 Supply Chain Sustainability: Examples of
Coca-Cola and Mercadona .................... 88
4.4.2 Supply Chain Resilience and Ripple Effect . ....... 91
4.5 Key Points . . .................................... 93
Bibliography .......................................... 94
5 Sourcing Strategy ...................................... 97
5.1 Introductory Case Study “New Logistics Concept
(NLK: Das Neue Logistik Konzept) at Volkswagen” . . . . . . . 97
5.2 Sourcing Process and Principles . . .................... 100
5.2.1 Procurement, Purchasing and Sourcing . . . . . . . . . . . 100
5.2.2 Sourcing Process ........................... 101
5.2.3 Make-or-Buy and Outsourcing . . . .............. 102
5.2.4 Organization of Sourcing Processes . . . .......... 105
5.3 Sourcing Strategies ............................... 106
5.3.1 Single vs. Dual and Multiple Sourcing . . . . . . . . . . . 106
5.3.2 Local vs. Global Sourcing .................... 107
5.3.3 Just-in-Time . . . . .......................... 110
5.4 Supplier Relationship Management . . . . . . . . . . . . . . . . . . . . 111
5.4.1 Strategic Supplier Analysis . . . . . . . . . .......... 112
5.4.2 Supplier Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.4.3 Supplier Integration and Development . . . ........ 116
5.5 Key Points . . .................................... 117
Bibliography .......................................... 118
6 Production Strategy .................................... 121
6.1 Introductory Case-Study DELL vs. Lenovo .............. 121
6.2 Postponement and Modularization . .................... 126
6.2.1 Problem: Mass Production or Product
Customization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
6.2.2 Principles: Postponement and Modularization . . . . . . 126
6.2.3 Examples of Postponement Strategies ............ 127
6.3 Push-Pull Views and Order Penetration Point . . . . . . . . . . . . 130
6.4 Selection of a Production Strategy . . ................... 132
6.4.1 Types of Production Strategies ................. 132
6.4.2 Method: Lost-Sales Analysis . . . . . . . . . . . . . . . . . . 137
6.5 Key Points . . .................................... 139
Bibliography .......................................... 139
7 Facility Location Planning and Network Design .............. 141
7.1 Introductory Case Study Power Pong Sports, China . . . . . . . . 142
7.2 Supply Chain Design Framework ..................... 144
7.3 Global Supply Chain Design . . . ...................... 146
7.3.1 Warehouse Location Problem and Its
Formalization . ............................ 146
7.3.2 A Spreadsheet Approach to the WLP . . . . . . . . . . . . 149
Contents xvii
7.3.3 Branch-&-Bound: How the Solver Add-In
Works ................................... 155
7.3.4 Capacitated WLP . . . . . ..................... 160
7.4 Regional Facility Location .......................... 166
7.4.1 Management Problem Description . ............. 167
7.4.2 A Mathematical Model of the Decision Situation . . . 167
7.4.3 Solving the Mathematical Model: Centre-of-Gravity
Approach ................................ 168
7.5 Factor-Ranking Analysis . . . . ....................... 175
7.5.1 Case-Study OTLG Germany . . . . . . . . . . . . . . . . . . 175
7.5.2 Factor-Rating Method . . . . . . . . . . . . . . . . . . . . . . . 175
7.5.3 Utility Value Analysis . . . . . . . . . . . . . . . . . . . . . . . 180
7.6 Key Points . . .................................... 184
Bibliography .......................................... 186
8 Distribution and Transportation Network Design ............. 189
8.1 Introductory Case Study: Bavarian Wood ............... 190
8.2 Generic Transport Network Structures . . . . . . . . . . . . . . . . . . 192
8.3 Realizing Economies of Scale in Transportation . . . ....... 194
8.3.1 Consolidation of Shipments . .................. 194
8.3.2 Postponement . . ........................... 196
8.3.3 Milk-Runs ................................ 197
8.3.4 Transshipment ............................. 202
8.4 Trade-Off-Based Transportation Network Design .......... 206
8.5 Capacity Allocation in a Many-to-Many Network . . ....... 209
8.5.1 The Transportation Problem . . . . . . . . . . . . . . . . . . . 210
8.5.2 Decision Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
8.5.3 Finding the First Feasible Model Solution ......... 212
8.5.4 Optimality Check .......................... 216
8.5.5 Solution Improvement . . . . . . . . . . . . . . . . . . . . . . . 217
8.6 Distribution Network Design . . . . . . . . . . . . . . . . . . . . . . . . 221
8.6.1 Case Study: ALDI vs. Homeplus . . . . . . . . . . . . . . . 221
8.6.2 Types of Distribution Networks ................ 224
8.6.3 Case Study: Seven-Eleven Japan . . ............. 225
8.6.4 Transportation Modes ....................... 228
8.7 Key Points . . .................................... 231
Bibliography .......................................... 231
9 Factory Planning and Process Design ....................... 233
9.1 Introductory Case-Study “Factory Planning at Tesla” . . ..... 233
9.2 Factory Planning ................................. 235
9.2.1 Role of Factory Planning in SCOM ............. 235
9.2.2 Processes of Factory Planning . . ............... 236
9.3 Capacity Planning ................................ 240
9.3.1 Little’s Law . . . . . . . . . . . . . ................. 242
9.3.2 Bottleneck Analysis/Theory of Constraints . . . . . . . . 244
xviii Contents
9.3.3 Drum, Buffer, Rope . . . . . . . . ................. 245
9.3.4 Break-Even Analysis . . ...................... 246
9.3.5 Decision Trees ............................ 248
9.3.6 Queuing Theory ........................... 250
9.3.7 Simulation: Case Study AnyLogic .............. 254
9.4 Process Flow Structures . ........................... 256
9.4.1 Job Shop ................................. 256
9.4.2 Batch Shop . . ............................. 257
9.4.3 Assembly Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257
9.4.4 Continuous Flow ........................... 262
9.4.5 Product-Process Matrix . . . . . . . . . . . . . . . . . . . . . . 262
9.5 Lean Production Systems ........................... 263
9.5.1 Lean Thinking ............................. 263
9.5.2 Lean Production Principles . . ................. 265
9.5.3 Lean Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . 269
9.6 Modern Trends: Industry 4.0 ......................... 271
9.7 Key Points and Discussion Questions .................. 273
Bibliography .......................................... 275
10 Layout Planning ....................................... 279
10.1 Introductory Case-Study “OTLG Ludwigsfelde” . . . . . . . . . . 279
10.2 Layout Planning in Manufacturing . . . ................. 280
10.2.1 Fixed Position Layout . . . . . . . . . . . . . . . . . . . . . . . 281
10.2.2 Process Flow Layout . . . . . . . . . . . . . . . . . . . . . . . . 282
10.2.3 Product Flow Layout . . ...................... 284
10.2.4 Cell-Based Layout . ......................... 288
10.3 Layout Planning in Warehouses . . . . .................. 290
10.3.1 Incoming Area . ........................... 290
10.3.2 Storage Area . . . . . ......................... 291
10.3.3 Put-Away and Order Pick-Up . . ................ 291
10.3.4 Layout Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
10.4 Methods of Layout Planning . . . . . . . . . . . . . . . . . . . . . . . . . 293
10.4.1 REL-Charts . . ............................. 293
10.4.2 Quadratic Assignment Problem ................ 295
10.4.3 Simulation: Modeling Operations at Pharmaceutical
Distribution Warehouses with AnyLogic .......... 297
10.5 Key Points . . .................................... 299
Bibliography .......................................... 300
11 Demand Forecasting .................................... 301
11.1 Introductory Case Study ............................ 301
11.2 Forecasting Process and Methods ..................... 304
11.2.1 Forecasting Process and Time Horizons . . . . . . . . . . 304
11.2.2 Forecasting Methods . ....................... 305
11.2.3 Forecasting Quality . . . . . . . . . . . . . . . . . . . . . . . . . 307
11.3 Statistical Methods . . . ............................. 308
Contents xix
11.3.1 Linear Regression .......................... 308
11.3.2 Moving Average . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
11.3.3 Simple Exponential Smoothing . . . . . . . . . . . . . . . . 312
11.3.4 Double Exponential Smoothing ................ 313
11.4 Key Points and Outlook ............................ 314
Bibliography .......................................... 315
12 Production and Material Requirements Planning ............. 317
12.1 Introductory Case-Study SIBUR: Integrated Operations and
Supply Chain Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318
12.2 Planning Horizons/MRP-II . . . . . . . . . . . . . . . . . . . . . . . . . . 321
12.3 Sales and Operations Planning . ...................... 322
12.3.1 Role of Sales and Operations Planning ........... 322
12.3.2 Options for Aggregate Planning . . . . . . . . . . . . . . . . 324
12.3.3 Methods for Aggregate Planning . . . . . . . . . . . . . . . 325
12.4 Sales and Production Planning with Linear Programming . . . . 328
12.4.1 Problem Description . . . ..................... 328
12.4.2 Method: Linear Programming . ................ 329
12.4.3 Graphical Solution . ......................... 331
12.5 Master Production Schedule and Rolling Planning . . . . . . . . . 333
12.5.1 Master Production Schedule . . . . . . . . . . . . . . . . . . . 333
12.5.2 Rolling Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . 334
12.6 Material Requirements Planning . . . ................... 335
12.6.1 Bill-of-Materials . . . ........................ 336
12.6.2 MRP Calculation . .......................... 338
12.7 Key Points . . .................................... 342
Bibliography .......................................... 342
13 Inventory Management ................................. 345
13.1 Introductory Case-Study: Amazon, Volkswagen
and DELL ...................................... 345
13.2 Role, Functions and Types of Inventory ................ 346
13.3 Material Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348
13.3.1 ABC Analysis ............................. 348
13.3.2 XYZ Analysis ............................. 351
13.4 Deterministic Models . . . ........................... 354
13.4.1 EOQ Model .............................. 355
13.4.2 EOQ Model with Discounts . . ................. 358
13.4.3 EPQ Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
13.4.4 Re-order Point ............................. 361
13.5 Stochastic Models . . .............................. 362
13.5.1 Service Level and Safety Stock . ............... 362
13.5.2 Single Period Systems (“Newsvendor Problem”) . . . . 366
13.5.3 Safety Stock and Transportation Strategy: Case
DailyMaersk .............................. 368
xx Contents
13.6 Inventory Control Policies .......................... 370
13.6.1 Fixed Parameters . .......................... 371
13.6.2 Dynamic View . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
13.7 Dynamic Lot-Sizing Models ......................... 375
13.7.1 Least Unit Cost Heuristic . . . . . . . . . . . . . . . . . . . . . 376
13.7.2 Silver-Meal Heuristic ....................... 377
13.7.3 Wagner-Whitin Model . . . . . . . . . . . . . . . . . . . . . . . 379
13.8 Aggregating Inventory . . . . ......................... 381
13.9 ATP/CTP . ...................................... 383
13.10 Key Points and Outlook ............................ 385
Bibliography .......................................... 387
14 Routing and Scheduling ................................. 389
14.1 Introductory Case Study RED SEA BUS TRAVEL ........ 390
14.2 Shortest Paths in a Network . . . . . . . . . . . . . . ........... 391
14.2.1 Outline of the Shortest Path Problem (SPP) in a
Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
14.2.2 Mathematical Graphs ........................ 393
14.2.3 The SPP as Graph-Based Optimization Model . . . . . 393
14.2.4 Dijkstra’s Algorithm for the Identification of a
Shortest s-t-Path . . . . . . . . . . . . . . . . . . . . . . . . . . . 394
14.3 Round Trip Planning/Travelling Salesman Problem ........ 397
14.3.1 Travelling Salesman Problem . . . . . ............. 398
14.3.2 A Mixed-Integer Linear Program for
TSP-Modelling ............................ 401
14.3.3 Heuristic Search for High Quality Round Trips ..... 402
14.4 Vehicle Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409
14.4.1 Case Study ORION: Vehicle Routing at UPS . . . . . . 410
14.4.2 Decision Situation Outline . . . . . . . . . . . . . . . . . . . . 411
14.4.3 Current Approach for the Route Compilation . . . . . . 412
14.4.4 Capacitated Vehicle Routing Problem . . . . . . . . . . . 414
14.4.5 The Sweep Algorithm . . . . . . . . . . . . . . . . . . . . . . . 417
14.5 Machine Scheduling . . . . . . . . . . . . . . . ................ 421
14.5.1 The Problem of Scheduling a Machine ........... 421
14.5.2 Priority Rule-Based Scheduling . . . . . . . . . . . . . . . . 424
14.5.3 Scheduling Algorithm of Moore ................ 426
14.5.4 Scheduling Two Machines in a Flow Shop ........ 427
14.5.5 Further Challenges in Machine Scheduling ........ 430
14.6 Key Points . . .................................... 430
Bibliography .......................................... 432
Appendix Case-Study “Re-designing the Material Flow in a Global
Manufacturing Network” .................................. 435
Index ................................................. 441
Contents xxi
http://www.springer.com/978-3-319-24215-6

Chapters (15)

Fig. 1.4. Supply chain
Fig. 1.6. Chocolate supply chain
Fig. 1.8. House of SCOM
Basics of Supply Chain and Operations Management
Full-text available
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.1-14
This chapter introduces global supply chain and operations management. Underlying issues related to the observed transformation process(es) and value creation are analysed. The definitions of operations, supply chains, operations management and supply chain management are provided. Subsequently, typical decisions in the scope of supply chain and operations management are systematically rolled out. Practically relevant objectives to measure supply chain and operations performance are discussed. The question of which qualifications should obtain a future supply chain and operations manager is addressed and, finally, possible career paths for supply chain and operations managers are discussed.
Fig. 2.2. Dangote Cement's supply chain
Fig. 2.4. Global supply emergency network for humanitarian logistics (source: World Food Program)
Examples from Different Industries, Services and Continents
Full-text available
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.15-36
This chapter provides up-to-date examples of supply chain and operations management in manufacturing, services, and e-operations. The case-studies include examples of operations and supply chains from different industries, services and continents. Particular focus is directed to e-operations and e-supply chains. With the help of the case-studies the readers obtain an overview of typical decisions and trade-offs in supply chain and operations management that will be addressed in detail in further chapters of the textbook. An E-Supplement containing additional case studies and video streams provides additional insights related to the content of this chapter.
Processes, Systems, and Models
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.37-67
This chapter starts with an introductory case-study from aerospace industry. In the first part, the interrelations between business processes, quantitative models, and information systems are discussed. Next, the role of business process management in operations and supply chains is considered. Subsequently, the effects of management information systems in supply chain and operations management are analysed. In particular, ERP, APS, WMS as well as RFID technologies are considered. In the second part, the terms “planning”, “problem”, and “decision” are clarified. The role of models and modelling in decision-making is discussed. Furthermore, this chapter analyses the crucial role of uncertainty, resilience, and risk management in decision-making. Finally, quantitative methods of decision-making are presented and discussed with regard to their applicability to supply chain and operations management. An E-Supplement provides additional case studies and video streams.
Figure 2: 2 Phases in the supply chain optimization project
Figure 3: 3 Transition from push to agile SC
Figure 4: 4 Agile supply chain. EPEI: Every Part Every Interval, BIB: Batch-In-Batch
Fig. 4.5 Bullwhip-effect in the SC Many retailers, each with little variability in their orders, can cause great variability for a smaller number of wholesalers, and even greater variability for a single manufacturer. The main reasons for the bullwhip effect can be divided into behavioral and operational areas (Lee et al. 1997; Sterman 2000): Behavioural Causes • misuse of base-stock policies • misperceptions of feedback and time delays • panic ordering reactions after unmet demand • perceived risk of other players' bounded rationality.
Fig. 4.14 Supply chain resilience (Ivanov et al. 2014) Basic areas of resilience include system, process, and product resilience. To the basic elements of increasing SC resilience belong the following (Blackhurst et al. 2011, Pettit et al. 2013; Chopra and Sodhi 2014): • Back-up suppliers • Excessive inventory • Excessive capacity • Information coordination and data transparency • Process flexibility • Product flexibility.
+12
Operations and Supply Chain Strategy
Full-text available
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.69-96
This chapter discusses basic supply chain and operations strategies. It starts with an introductory case-study considering supply chain strategies in the apparel industry. In the first part, operations strategies as well as the “strategic fit” are discussed. Subsequently, efficient and responsive supply chain strategies are distinguished. In the second part, the bullwhip-effect in the supply chain and mitigating coordination strategies such as Vendor-managed Inventory (VMI) and Collaborative Planning, Forecasting, and Replenishment (CPFR) are presented. Finally, the issues of supply chain resilience and sustainability are discussed. Additional case studies, Excel templates, tasks and video streams as part of an E-supplement enrich this chapter.
Fig. 4.1. Value-added and costs
Fig. 4.3. Transition from push to agile SC
Fig. 4.4. Agile supply chain
Fig. 4.5. Bullwhip-effect in the SC Many retailers each with little variability in their orders can lead to greater variability for a smaller number of wholesalers, and can lead to even greater variability for a single manufacturer. Main reasons for the bullwhip effect can be divided into behavioural and operational areas (Lee et al. 1997;Sterman 2000): Behavioural causes  misuse of base-stock policies  misperceptions of feedback and time delays
Fig. 4.7. Vendor-managed inventory strategy
+6
Sourcing Strategy
Full-text available
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.97-119
This chapter discusses sourcing strategies. It starts with an introductory case-study considering the logistics coordination concept at a global car manufacturer. In the first part, the roles of purchasing, procurement, and sourcing in supply chain and operations management are elaborated. The basic elements of a sourcing process are defined. Next, the issues of make-or buy vs outsourcing as well as organization issues in sourcing are discussed. Subsequently, sourcing strategies are classified according to the number of suppliers, geographical supplier distribution as well as sourcing principles. The methods of spend analysis and supplier selection are presented. Basic elements of the supplier relationship management (SRM) are classified. All proposed concepts are further analyzed by examples such as the sourcing strategy of a global electronics company and the just-in-time strategy in the automotive industry. Additional case studies, Excel templates, tasks and video streams as part of an E-supplement enrich this chapter.
Figure 3: 3 Order penetration point
Figure 4: 4 Production strategies in the supply chain
Figure 5: 5 Modular production strategy (based on SupplyOn video)
Figure 6: 6 Intense collaboration with suppliers throughout the whole production process of an aircraft
Figure 7: 7 Start of supplier collaboration
+1
Production Strategy
Full-text available
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.121-140
In this chapter, production strategies are discussed. It starts with an introductory case-study considering production strategies at global electronics companies. At the beginning, push and pull views of the supply chain are discussed. Next, the concepts of mass customization and modularization as well as the order penetration point and postponement approaches are introduced. Subsequently, basic production strategies in the supply chain are depicted. Finally, a method for the analysis of the order penetration point location in the supply chain is presented. The concepts are assessed within numerous examples such as production strategies in furniture, automotive, and aerospace industries. Additional case studies, Excel templates, tasks and video streams as part of an E-supplement enrich this chapter.
Facility Location Planning and Network Design
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.141-187
This chapter starts with an outline of a recent real-world location planning problem. It provides the analysis of different decision tasks in a typical location planning scenario and reveals the interactions between individual decision situations of the tasks. The generic decision task regarding which regions should be incorporated into a supply network is addressed. A further part is dedicated to the identification of explicit location proposals for those regions that contribute to supply chain cost efficiency. The multiple factor search for the right location for a facility in a region is addressed in the final section. The chapter is completed by the respective E-Supplement providing additional case studies, Excel templates, tasks and video streams.
Figure 2: 2 Transportation link
Figure 6: 6 Piece-wise constant function of the total transportation costs for given payload quantity
Figure 7: 7 Costs per ton of payload (the cost-per-ton-values for fewer than 10 tons of payload are not shown in order to preserve the readability of the graphs)
Figure 8.10 Relation between desired customer response time (x-axis) and the number of facilities required to ensure this response time Fig. 8.10 clearly shows that there is a strong relationship between the number of facilities for storing supply materials and the customer service level. In the Bavarian Wood case each trans-shipment point will hold some quantities of different types of wood which is in transit from the import ports in Germany and (prospectively) in the Netherlands to the manufacturing sites. Mike emphasizes that storing even small quantities in a trans-shipment yard can contribute to ensuring a quick re-supply. The major reason is the reduced distance from a transshipment yard to the factories compared to the average distance from the seaport to the manufacturing sites. However, Mike also explains that the number of facilities play a higher role in the distribution of goods with late customer orders.
Figure 8.21 Completely filled transportation matrix after assignment of the residual quantity of 15 tons to the transportation link Hamburg in regard to the dummy demand node (6 th step)
+18
Distribution and Transportation Network Design
Full-text available
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.189-232
This chapter addresses basic strategies for the configuration of physical distribution and transportation networks so that given supplying locations are physically connected with demanding locations. At the beginning, it introduces a case study to discuss the challenges related to the installation of transportation links. The next section suggests generic types of transportation network set-ups as a starting point for the setup of more sophisticated network layouts. Subsequently, the concept of shipment consolidation as the key approach to the realization of economies of scale in transportation is considered. Additionally, different transport network configuration objectives and their balancing are analysed, so that efficient and profitable network layouts can be set up. The incorporation of transport service providers (freight carriers) is discussed. Finally different case studies regarding the distribution network design are presented and the concepts, models and principles are discussed with the backup of numerous examples such as distribution and transportation strategies at global retailers. The chapter is accompanied by an E-Supplement providing additional case studies, Excel templates, tasks and video streams.
Factory Planning and Process Design
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.233-277
In this chapter, factory planning and process design principles and models are discussed. It starts with an introductory case-study considering factory planning issues at an e-car manufacturer. At the beginning, the role of factory planning in supply chain and operations management is discussed. Next, factory planning processes are presented. Subsequently, the role and methods of capacity planning are considered. Furthermore, options for process flow design are elaborated. Finally, lean production systems are presented and modern trends, e.g., Industry 4.0, are discussed. The chapter is accompanied by an E-Supplement providing additional case studies, Excel templates, tasks and video streams.
Layout Planning
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.279-300
In this chapter, layout planning concepts are discussed. It starts with an introductory case-study considering layout planning at a distribution center for spare parts. At the beginning, the role of layout planning in supply chain and operations management is discussed. Next, layout planning concepts in manufacturing and logistics are presented. Finally, quantitative and qualitative methods of layout planning are discussed. The concepts are reinforced by examples for manufacturing and warehouse layouts.
Fig.11.2. Forecasting process
Fig. 11.5. Time series analysis
Demand Forecasting
Full-text available
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.301-315
In this chapter, demand forecasting methods are considered. At the beginning, the role of demand forecasting in supply chain and operations management is discussed. Next, the role of expert methods in forecasting is analysed and it is demonstrated how to apply statistical methods for forecasting. Subsequently, it is shown how to calculate the forecasts based on statistical methods, understand and apply the measures for forecast quality assessments. The chapter is enriched by an E-Supplement providing additional Excel templates, tasks and video streams.
Fig. 12.1. SIBUR's SC structure
Fig. 12.2. Planning process
Fig. 12.4. Interrelations of strategic, tactical and operation planning decisions
Fig. 12.5 MRP II process
Figure 12.6. Graphical solution to LP problem with the help of corner-point method
+6
Production and Material Requirements Planning
Full-text available
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.317-343
In this chapter, the sales and operation planning (S&OP) concept is presented. It starts with an introductory case-study considering sales and operation planning at a petrochemistry company. At the beginning, different planning horizons and the role of aggregate planning are discussed. Afterwards, different options of matching demand and supply at an aggregated level are explained. Next, the rolling planning concept is introduced. Subsequently, the concept of the master production schedule (MPS) is presented. We learn how to apply linear programming methods to production planning. Finally, the principles of exploiting the bill-of-materials are discussed in order to learn how to compute dependent material requirements taking into account the lead time. This chapter is accompanied by an E-Supplement providing additional case studies, Excel templates, tasks and video streams.
Figure 5: 3.5 Graphical representation of EOQ model
Figure 9: 3.9 Inventory control policies
Figure 10: 3.10 (t,q)-inventory control policy
Fig. 13.8 ROP with safety stock and backlogs
Table 13 .3 Initial data for ABC/XYZ analysis
+4
Inventory Management
Full-text available
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.345-388
In this chapter, inventory management principles are discussed. It starts with an introductory case-study considering different inventory management principles in automotive, electronics, and e-commerce branches. At the beginning, the trade-off among “service levels” and “costs” in inventory management is highlighted. Next, the role of inventory in the supply chain is analysed. Therefore, the ABC and XYZ analysis is introduced and the use of the EOQ/EPQ models for independent inventory demand is explained. Subsequently, it is shown how to compute a reorder point and how to calculate service levels and probabilistic inventory models. In the next part, the applicability of dynamic lot-sizing models is elaborated. Finally, the discussion and computation of the effects of inventory aggregation takes place leading to the explanation of the ATP/CTP concept. The chapter is accompanied by an E-Supplement providing additional case studies, Excel templates, tasks and video streams.
Routing and Scheduling
Chapter
  • Jan 2017
  • Global Supply Chain and Operations Management
  • pp.389-434
In this chapter, scheduling and routing principles are discussed. At the beginning, a typical case for operative decision making and mathematical graphs for the representation of decision situations in a network structure are introduced. Additionally, the first insights into the algorithmic processing of graph-data as the basic ingredient for decision making in network structures are provided. The consideration of complex restrictions during the deployment of a resource is discussed by means of the traveling salesman problem (TSP) in which the sequencing of operations to build a schedule for a resource is in the focus of the decision making. The integrated consideration of assignment and scheduling/sequencing decision problems under limited resource availability is addressed in the context of the capacitated vehicle routing problem (CVRP). Finally, a short introduction to the scheduling of the production machines is given. The chapter is completed by an E-Supplement providing additional case studies, Excel templates, tasks and video streams.
Erratum to: Global Supply Chain and Operations Management
Full-text available
Chapter
  • Nov 2017
  • Global Supply Chain and Operations Management
  • pp.E1-E1
... This situation forces these companies to develop complex processes that depend on supply plans based on demand forecasts. Any forecast error can be detrimental to this sector, so it must be as accurate as possible [17]. Forecasts considerably higher than actual demand will impact higher capital flow and inventory management costs, while lower forecasts will lead to lower service levels [17]. ...
... Any forecast error can be detrimental to this sector, so it must be as accurate as possible [17]. Forecasts considerably higher than actual demand will impact higher capital flow and inventory management costs, while lower forecasts will lead to lower service levels [17]. On the other hand, the articles/ products show very different supply times and consumption behaviors. ...
... In general, there is a large number of articles that have used different machine learning (ML) approaches to forecast demand [21] but a few studies have been focused on evaluating the model quality, considering clusters of products according to the demand features evidenced in the TS [30], or using ensemble learning techniques in this context [7,20,33,36]. There are initial works that analyze forecasting methods that are better adapted to some types of data, such as the work [9,[15][16][17], which analyzed several forecasting methods (and combinations of them), with seven features of TS (seasonality, trend, cycle, randomness, number of observations, interval between demands and coefficient of variation). Also, there are initial works that propose an automated method to obtain weighted forecast combinations using TS features, such as [3,8,13,22,27,28,32,34,35,37] approaches. ...
A demand forecasting system of product categories defined by their time series using a hybrid approach of ensemble learning with feature engineering
Article
Full-text available
  • Sep 2024
  • COMPUTING
Retail companies face major problems in the estimation of their product’s future demand due to the high diversity of sales behavior that each good presents. Different forecasting models are implemented to meet the demand requirements for efficient inventory management. However, in most of the proposed works, a single model approach is applied to forecast all products, ignoring that some methods are better adapted for certain features of the demand time series of each product. The proposed forecasting system addresses this problem, by implementing a two-phase methodology that initially clusters the products with the application of an unsupervised learning approach using the extracted demand features of each good, and then, implements a second phase where, after a feature engineering process, a set of different forecasting methods are evaluated to identify those with best performs for each cluster. Finally, ensemble machine learning models are implemented using the top-performing models of each cluster to carry out the demand estimation. The results indicate that the proposed forecasting system improves the demand estimation over the single forecasting approaches when evaluating the R², MSE, and MASE quality measures.
View
... Operational management is the total and optimal arrangement and management of labor, machines, equipment, raw materials, or other product factors to produce products of goods and services for trade (Ivanov et al., 2021). In other words, operational management is the management of resources related to products and services thus that operational activities run efficiently. ...
Exploration of the plastic packaging product industry towards green manufacturing, efforts to consolidate environmental impact versus productivity issues
Article
Full-text available
  • Nov 2024
Plastic products, including plastic packaging, were products whose increasing demand continued because the community still needed plastic as packaging. On the other hand, plastic waste, which was increasingly high and difficult to decompose, was a problem that needed to be solved together. This study aims to understand how plastic company packaging implements TQM, its environmental impact, and how plastic packaging companies are taking steps towards green manufacturing. This research used a qualitative phenomenological method to understand the problem based on the actor’s perspective. The data collection method was in-depth interviews with informants from 3 plastic companies in East Java, Indonesia, followed by observation and FGD. We carried out Triangulation, member checking, and professional involvement to determine the data’s validity, reliability, and trustworthiness. The results of this study indicated a management system that promotes quality as a business strategy and is oriented towards customer satisfaction by involving all members of the organization. TQM emphasized continuous improvement, customer satisfaction, and employee involvement. By implementing aspects of TQM, plastic packaging companies could improve their production processes and reduce waste, increasing efficiency and profitability. In addition, TQM could also contribute to the company’s green performance by promoting environmentally friendly practices, including using electric machines to replace hydraulic machines, thereby reducing the use of electrical energy and CO2 emissions. The use of solar panels was a step towards green manufacturing. Companies that adopt TQM principles are more likely to implement environmentally friendly initiatives such as reducing energy consumption and using recyclable materials and can demonstrate a commitment to corporate social responsibility. The company’s membership in EcoVadis and SMETA further strengthens the company’s direction towards Green Manufacturing and competitive advantage.
View
... Through the use of ML, natural language processing (NLP), computer vision, and robotic process automation (RPA). ML algorithms analyze large volumes of historical data to uncover patterns and trends, which helps improve the accuracy of inventory predictions and adjustments (Ivanov et al., 2021). NLP interprets unstructured data from sources like social media and customer reviews, offering deeper insights into market trends and customer preferences (Cambria and White, 2014). ...
View
... While the long-term relationship with wholesalers and transportation companies in the customer-driven SC rests upon the customer experience, the resourceefficient SC aims at coordinating resources with distribution partners and requires trust from both parties. In general, the resource-efficient SC is characterised by continuously redesigning its global logistics network and processes to maintain efficiency, especially in a global environment (Ivanov, Tsipoulanidis, and Schönberger 2021). This includes optimisation and reduction of stocks through buffering strategies and optimisation of transportation (e.g. ...
Supply chain strategies in a global context: a customer value-based perspective
Article
  • Oct 2024
Customer value is influenced by several factors, which impose major challenges to global Supply Chains (SCs) and their management. This study aims to understand how companies tackle these challenges by focusing their global SC management on major strategies and supporting practices. Based on customer value theory, and recognising major trends affecting what end consumers value, we identify four global SC strategies: customer-driven, service-driven, resource-efficient, and closed-loop. A multiple case study carried out in eleven companies in the consumer goods industry explores the practices adopted per each SC strategy in managing global sourcing, production, and distribution networks. Results show the key requirement of selecting tailored practices for SC management that align with the context and the value expected by customers. Operational SC practices entail managing collaborative actions both up and downstream and competing with other SCs and can benefit from the implementation of appropriate digital technologies for customer value creation and delivery, as well as for continuous learning about customer needs.
View
... There are various delivery methods. To transport cargo (physical goods), the main transportation modes are roads, railroads, shipping lanes, and airline networks (Ivanov et al., 2021). IT support division or company: IT support suppliers include those offering information architecture services such as library online public access catalogues and discovery platforms, which provide one simple search interface for a wide range of library resources (Kaffashan Kakhki et al., 2022;Patra, 2017;Sonawane, 2018). ...
An integrated academic library supply chain (IALSC) model
Article
  • Jul 2024
  • J Model Manag
Purpose This exploratory study illustrated an integrated academic library supply chain (IALSC) model to design the strategic planning management tool of the academic library. The supply chain (SC) model has been widely used in manufacturing industries and has also been applied in many service industries with the same objectives. However, very few studies for academic libraries, particularly the implementation of the integrated SC model, are being executed, although it has been proven that SC management in practice can enhance stakeholder satisfaction, increase revenues and decrease total costs. The academic library also needs to be successful in providing quality products, services and information to fulfil the library users’ needs within the library budget. This research aims to develop a verified model of the integrated SC for the academic library. Design/methodology/approach This research used both qualitative and quantitative approaches to achieve its objectives. The proposed conceptual SC model, named as IALSC, for the academic library has been developed using the system thinking method; eventually, it has been validated through the fuzzy Delphi method, an expert judgement technique. Findings The research findings could contribute to academic library management in planning and formulating a roadmap for the library to increase its quality services for all stakeholders. Originality/value The conceptual model would have a high potential to be proposed as the strategic decision-making tool for an academic library, i.e. the flow of funds through the operations of the library, the library stakeholders’ satisfaction measurement, the decision process currently made by the library management team on the purchase of new library resources, the library resource suppliers, etc.
View
... The objective of the Steiner Traveling Salesman Problem (STSP) [2,7,8,10,12,24,25] is, given a list of locations to be visited and the mutual distances between them, to find the shortest possible path. This path should encompass all locations, ultimately returning to the initial position [21,26,27]. ...
DETERMINING OPTIMAL ORDER PICKING ROUTE IN WAREHOUSES
Article
  • Jun 2024
The objective of this research paper is to find the optimal order picking route thus improving the operational efficiency of logistics processes. Computing and selecting the best route are crucial for minimizing order completing time and operational costs. To achieve this, an algorithm is developed by use of several warehouse logistics methods. This algorithm is applied to a single warehouse and the paths are computed using the s-method, the return method, the middle point method, and the composite method. The verification of the algorithm is conducted through thirty cases, each with different order picking locations. This research shows that when larger number of parts need to be picked from various racks, the paths created by the composite method and the s-method provide the shortest route for the designated warehouse.
View
أثر القيادة الرقمية على الريادة الاستراتيجية في شركة مصر للطيران: الدور الوسيط للمرونة التنظيمية The Impact of Digital Leadership on Strategic Entrepreneurship at EgyptAir: The Mediating Role of Organizational Flexibility
Article
Full-text available
  • Dec 2024
تعد القيادة الرقمية إحدى أساليب القيادة الفعالة التي تتبناها شركات الطيران للتكيف مع متطلبات العصر الرقمي وتحقيق ميزة تنافسية، عن طريق تحسين جودة المنتجات والخدمات وتعزيز الريادة الإستراتيجية من خلال الاستفادة من التطور التكنولوجي والتقنيات الرقمية. وفي هذا السياق، تهدف الدراسة الحالية إلي تقييم تأثير القيادة الرقمية علي تعزيز الريادة الإستراتيجية في شركة مصر للطيران، مع التركيز بشكل خاص علي الدور الوسيط للمرونة التنظيمية. لتحقيق أهداف الدراسة، تم تصميم استمارة استقصاء وتوزيعها علي عينة من العاملين في شركة مصر للطيران بلغ عددهم 362، وكانت الاستمارات الصالحة للتحليل 316 استمارة بنسبة استجابة بلغت 87.3%. واعتمدت الدراسة علي تحليل البيانات باستخدام برنامجي SPSS v. 28 و AMOS v.24. توصلت الدراسة إلي عدة نتائج أبرزها، وجود تأثير إيجابي ومعنوي للقيادة الرقمية وأبعادها الأربعة (القيادة الحكيمة ذات الرؤية، الثقافة الرقمية، المواطنة الرقمية والتميز في الممارسات الرقمية) علي الريادة الإستراتيجية والمرونة التنظيمية لشركة مصر للطيران، كما توصلت نتائج الدراسة إلي وجود تأثير معنوي وإيجابي للمرونة التنظيمية علي الريادة الإستراتيجية لشركة مصر للطيران. بالإضافة إلي ذلك، أبرزت النتائج أن المرونة التنظيمية تلعب دوراً وسيطاً جزئياً في العلاقة بين القيادة الرقمية والريادة الإستراتيجية لشركة مصر للطيران. وتوصي الدراسة بضرورة توفير برامج تدريبية للقادة والعاملين لإكسابهم المهارات الرقمية المطلوبة حول كيفية الاستفادة من التقنيات الرقمية في مهام عملهم داخل الشركة، وذلك من أجل تعزيز الريادة الإستراتيجية وزيادة مستويات الإبداع والابتكار.
View
Theory of AI-Driven Scheduling (TAIS): A Service-Oriented Scheduling Framework by Integrating Theory of Constraints and AI
Article
This paper introduces the Theory of AI-driven scheduling (TAIS), an innovative framework designed to revolutionize service-oriented scheduling by integrating the theory of constraints (TOC) (see APPENDIX I) with cutting-edge Artificial Intelligence (AI) technologies. TAIS extends the traditional five steps of TOC by introducing three additional layers to enhance adaptability, efficiency, and applicability in service-oriented environments. TAIS incorporates continuous monitoring and adjustment mechanisms across all five TOC steps to improve adaptability, ensuring real-time responsiveness to dynamic scheduling challenges. To enhance efficiency, TAIS integrates lifecycle management and governance into the core framework, which helps in the efficient allocation of resources throughout the scheduling process. Lastly, TAIS includes robust security and compliance measures to bolster applicability. By leveraging AI, TAIS offers a predictive, flexible, and scalable solution capable of addressing the complexities and rapid changes inherent in dynamic scheduling tasks and providing a significant leap forward in scheduling and operations management.
View
Enhancing supply chain resilience through industry-specific approaches to mitigating disruptions
Article
Full-text available
  • Oct 2024
Supply chain resilience has become increasingly important in modern industrial conversations, primarily due to the growing interconnection and shocks in the global economy. This study provides a thorough and evaluative analysis of supply chain resilience in many industries, encompassing the automobile, airline, oil and gas, electronics, liner shipping, pharmaceutical, and food sectors, to provide the best strategies and solutions. This study comprehensively examines scholarly literature and empirical data to ascertain various industries’ primary obstacles and susceptibilities. These encompass a broad spectrum of issues, including but not limited to natural calamities, geopolitical conflicts, and worldwide pandemics. Moreover, this study investigates the adaptive methods and resilience mechanisms utilized by firms operating in various industries to manage risks and maintain uninterrupted operations. This review uses a range of theoretical frameworks and case study examples to understand supply chain resilience comprehensively. It highlights the complex nature of this concept and emphasizes the significance of proactive risk management techniques and agile reaction mechanisms. Moreover, this study offers valuable perspectives on potential avenues for future practices and practical approaches to improve the resilience of supply chains in various industrial settings.
View
Multi-Objective Location-Routing Network Optimization for Pre-Disaster Planning: A Case Study in the Philippines
Conference Paper
  • Aug 2024
A significant issue influencing the humanitarian supply chain (HSC) is facility location. The logistics of moving relief supplies from the main distribution center (DC) to distribution hubs (DHs) to evacuation centers (ECs), which need to be strategically placed and easily accessible from the affected areas, serve as the inspiration for this paper's location model. This enables the HSC to be prepared for any calamity and respond to it as quickly as possible. This work presents a multi-objective location-routing network optimization model designed for the pre-disaster phase. It focuses on optimizing locations of distribution hubs and evacuation centers to manage mandatory evacuations within the allowable response time. The model aims to minimize infrastructure costs while maximizing the coverage of evacuation centers to nearby residential areas, ensuring that relocations are carried out promptly during emergencies. A case study showcasing the model's effectiveness was conducted using AnyLogistix (ALX) software. Our findings identify the optimal placement of evacuation centers and distribution hubs, achieving cost reduction and improved response times.
View
87 4.4.1 Supply Chain Sustainability: Examples of Coca-Cola and
  • Sustainability............. Mercadona.................. Supply Chain Resilience
Supply Chain Resilience and Sustainability.............. 87 4.4.1 Supply Chain Sustainability: Examples of Coca-Cola and Mercadona.................... 88 4.4.2
Integrated Operations and Supply Chain Planning
  • 318-330
  • Introductory Case-Study
Introductory Case-Study SIBUR: Integrated Operations and Supply Chain Planning............................. 318 12.2
217 8.6 Distribution Network Design
  • Solution Improvement
Solution Improvement....................... 217 8.6 Distribution Network Design........................ 221 8.6.1
126 6.2.3 Examples of Postponement Strategies
  • Modularization............... Postponement
Principles: Postponement and Modularization...... 126 6.2.3 Examples of Postponement Strategies............ 127 6.3
280 10.2.1 Fixed Position Layout
  • 10
  • Layout Planning In Manufacturing
Layout Planning in Manufacturing.................... 280 10.2.1 Fixed Position Layout....................... 281 10.2.2 Process Flow Layout........................ 282 10.2.3 Product Flow Layout........................ 284 10.2.4 Cell-Based Layout.......................... 288 10.3
375 13.7.1 Least Unit Cost
  • 13
  • Dynamic Lot-Sizing Models
Dynamic Lot-Sizing Models......................... 375 13.7.1 Least Unit Cost Heuristic..................... 376 13.7.2 Silver-Meal Heuristic....................... 377 13.7.3 Wagner-Whitin Model....................... 379 13.8
421 14.5.1 The Problem of Scheduling a Machine
  • 430
  • Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . Scheduling
Machine Scheduling............................... 421 14.5.1 The Problem of Scheduling a Machine........... 421 14.5.2 Priority Rule-Based Scheduling................ 424 14.5.3 Scheduling Algorithm of Moore................ 426 14.5.4 Scheduling Two Machines in a Flow Shop........ 427 14.5.5 Further Challenges in Machine Scheduling........ 430
370 13.6.1 Fixed Parameters
  • 13-20
  • ............................................................................. Inventory Control Policies
Inventory Control Policies.......................... 370 13.6.1 Fixed Parameters........................... 371 13.6.2 Dynamic View............................ 375 13.7
130 6.4 Selection of a Production Strategy
  • Push-Pull
  • Views
  • ............................. Order Penetration Point
Push-Pull Views and Order Penetration Point............ 130 6.4 Selection of a Production Strategy..................... 132 6.4.1
321 12.3 Sales and Operations Planning
  • 12
  • Planning Horizons
Planning Horizons/MRP-II.......................... 321 12.3 Sales and Operations Planning....................... 322 12.3.1 Role of Sales and Operations Planning........... 322 12.3.2 Options for Aggregate Planning................ 324 12.3.3 Methods for Aggregate Planning............... 325 12.4 Sales and Production Planning with Linear Programming.... 328 12.4.1 Problem Description........................ 328 12.4.2 Method: Linear Programming................. 329 12.4.3 Graphical Solution.......................... 331 12.5