Inventory and Supply Chain Management with Forecast Updates (eBook)

Contents 6
List of Figures 11
List of Tables 12
Preface 14
Notation 18
Chapter 1 INVENTORY AND SUPPLY CHAIN MODELS WITH FORECAST UPDATES 20
1.1. Introduction 20
1.2. Aims of the Book 21
1.3. Information Dynamics in Supply Chains 24
1.4. Inventory and Supply Chains with Multiple Delivery Modes 29
1.5. Supply Contracts 31
1.6. Competitive Supply Chains 33
References 36
Chapter 2 EXAMPLES FROM INDUSTRY 42
2.1. Introduction 42
2.2. Industry Observations 43
2.3. Multistage Forecasts 50
2.4. Operational Factors Affecting Forecasting Process 52
2.5. Concluding Remarks 60
2.6. Notes 61
Chapter 3 INVENTORY MODELS WITH TWO CONSECUTIVE DELIVERY MODES 64
3. I. Introduction 64
3.2. Notation and Model Formulation 65
3.3. Dynamic Programming and Optimal Nonanticipative Policy 70
3.4. Optimality of Base- Stock Policies 78
3.5. The Nonstationary Infinite- Horizon Problem 88
3.6. An Example 95
3.7. Concluding Remarks 103
3.8. Notes 103
Chapter 4 INVENTORY MODELS WITH TWO CONSECUTIVE DELIVERY MODES AND FIXED COST 108
4.1. Introduction 108
4.2. Notation and Model Formulation 109
4.3. Dynamic Programming and Optimal Nonanticipative Policy 111
4.4. Optimality of (s, S) Ordering Policies 113
4.5. Monotonicity Properties 134
4.6. The Nonstationary Infinite- Horizon Problem 140
4.7. Concluding Remarks 143
4.8. Notes 144
References 145
Chapter 5 INVENTORY MODELS WITH THREE CONSECUTIVE DELIVERY MODES 148
5.1. Introduction 148
5.2. Notation and Model Formulation 149
5.3. Dynamic Programming and Optimal Nonanticipative Policies 155
5.4. Optimality of Base- Stock lype Policies 163
5.5. The Nonstationary Infinite-Horizon Problem 177
5.6. Concluding Remarks 180
5.7. Notes 181
References 182
Chapter 6 MULTIPERIOD QUANTITY- FLEXIBILITY CONTRACTS 184
6.1. Introduction 184
6.2. Model and Problem Formulation 185
6.3. Contingent Order Quantity at Stage 2 189
6.4. Optimal Purchase Quantity at Stage 1 194
6.6. Multiperiod Problems 221
6.7. Numerical Example 225
6.8. Concluding Remarks 235
6.9. Notes 237
Chapter 7 PURCHASE CONTRACT MANAGEMENT: FIXED EXERCISE COST 242
7.1. Introduction 242
7.2. Problem Formulation 243
7.3. Optimal Solution for Stage 2 245
7.4. Optimal Solution for a Class of Demand Distributions 251
7.5. Analysis for Uniformly Distributed Demand 255
7.6. Concluding Remarks 272
7.7. Notes 273
Chapter 8 PURCHASE CONTRACT MANAGEMENT: TWO- PLAYER GAMES 276
8.1. Introduction 276
8.2. Problem Formulation 277
8.3. Reaction Strategies Under Uniformly Distributed Demand 281
8.4. A Static Noncooperative Game 286
8.5. A Dynamic Noncooperative Game 295
8.6. Concluding Remarks 300
8.7. Notes 302
Copyright Permissions 304
Index 306

2.2. Industry Observations (p. 24-25)
A major security-system manufacturing company produces and distributes security systems for military, residential, commercial, and industrial applications. It has a design center in California, a manufacturing center in Asia, and three regional distribution centers in San Francisco, Amsterdam, and Singapore. The company sources components and subassemblies around the world. The management objectives are to improve the response time to meet market demand, to reduce inventory, and to shorten lead time (including the time for manufacturing and distribution). In the security-system market, customers expect to have the required device or system within one month. Therefore, given long lead times in procurement and production, the manufacturing operation relies largely on forecasts.

From a practical point of view, forecasts are never accurate, and the company updates its demand forecasts until the real demand is realized. When too little raw material is ordered, the company has to pay a higher price to secure them or use air shipment to expedite them (if these options are feasible). When too many raw materials and subassemblies are ordered, the company has to keep them in inventory. These materials often become obsolete. These updates in forecasting also make it difficult for the company to allocate its production capacity efficiently.

A key component in security systems is the microcontroller, which makes up 30% to 40% of the total materials cost. A microcontroller is a central processing unit (CPU) chip with a built-in memory and interface circuits. The read-only memory (ROM) contains permanent data (program code). See Spasov [5] for a discussion of related concepts about microcontrollers and their technology. The company can order microcontrollers with user-supplied data requirements. If user-supplied data is provided, the semiconductor manufacturing includes a process known as custom photo masking in the wafer-fabrication process. Alternatively, the company can purchase microcontrollers with a programmable ROM such as one-time-programmable (OTP) read-only memory or erasable programmable read-only memory (EPROM). The company inputs the data into these programmable microcontrollers after the chips are received. To order custom-masked chips, the users are required to provide the data (program code) prior to manufacturing, and a significant lead time is required. On the other hand, since programmable ROMs are generic, these microcontrollers can be produced with a considerably shorter lead time. However, the OTP chips are about twice as expensive as custom-masked chips and EPROM chips are even more expensive. The company must decide how to order both custom-masked and OTP chips.

The company uses a half-year rolling window for demand forecasting. These forecasts are made and updated monthly by the regional offices. The headquarter coordinates the forecasts and passes them to its logistics and manufacturing functions. Procurement decisions are made based on the demand forecast and the lead time required by its vendors. The company divides the raw materials into two classes: critical and regular. The components that have fewer sources, and have a higher value content, and require a longer lead time are classified into their critical materials. Microcontrollers are a typical example.