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Supply Chain Analytics Principles and Applications.pdf
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特别新，2026年刚上线的研究资料
内容扎实，400页，全部矢量文字，适合自动翻译或投喂给大模型形成知识库！
This reference text resolves problems of physical products, information, and financial transactions by using mathematical and statistical tools to provide
robust and validated solutions to real-world roblems of the supply chain.
It further discusses important concepts such as supply chain segmentation, constrained and unconstrained optimization, time series analysis, double exponential smoothing, and demand probability distributions.This resource:
• Discusses inventory replenishment models including economic order
quantity (EOQ) and economic production quantity (EPQ).
• Explains different mathematical and analytical models applied frequently, depending on the supply chain problem.
• Covers important topics such as linear, integer, and mixed integer
programming.
• Demonstrates how the variation in demand with time follows certain
patterns, which can be seasonal, cyclical, trend, and random.
• Highlights forecasting methods, namely, umulative, naive, moving averages, and exponential smoothing.This resource is primarily written for senior ndergraduate and graduate students, and academic researchers in the fields of manufacturing engineering, industrial engineering, supply chain management, mechanical engineering, and production engineering.
1 Supply chain management 1
1.1 Introduction 1
1.2 Different viewpoints on supply chain structure 4
1.3 Challenges in a supply chain structure 8
1.4 Illustration of the bullwhip effect 10
Formulas 12
1.5 Solutions for making a supply chain effective 12
1.6 Importance of analytical methods in the supply chain 14
1.7 Classification of models 16
1.8 Structure of models and analytical techniques 19
1.9 Summary 20
1.10 Glossary 21
1.11 Case study: Reaping the benefits of
modifications in supply chain 21
1.12 Model questions 23
2 Deterministic models 24
2.1 Introduction 24
2.2 Basic mathematical functions 25
2.2.1 Types of functions 26
2.3 Optimization 33
2.3.1 Unconstrained optimization 34
2.3.2 Constrained optimization 38
2.4 Network models and LPP 53
2.5 Summary 68
2.6 Glossary 68
vi Contents
2.7 Case study: Piston production scheduling 69
2.8 Model questions 70
3 Predictive models 75
3.1 Introduction 75
3.2 Basic probability laws 76
3.3 Probability distributions 81
3.3.1 Binomial distribution 84
3.3.2 Poisson distribution 86
3.3.3 Continuous probability distribution:
Normal distribution 89
3.3.4 Continuous probability distribution:
Uniform distribution 98
3.4 Descriptive statistics 102
3.4.1 Important application of descriptive statistics 109
3.5 Inferential statistics and predictive analysis 112
3.5.1 Central limit theorem 112
3.5.2 Confidence interval 120
3.5.3 Hypothesis testing 126
3.6 Building predictive models 134
3.7 Resampling 146
3.8 Summary 150
3.9 Glossary 151
3.10 Case study: DMAIC Six Sigma
project at Federal Mogul Ltd 152
3.11 Model questions 153
4 Forecasting 157
4.1 Introduction 157
4.2 Segmentation criterion of a supply chain 158
4.3 Demand planning and forecasting 163
4.4 Challenges in forecasting demand 164
4.5 Time series analysis 167
4.5.1 Sensitivity vs. stability 167
4.5.2 Forecast accuracy 168
4.5.3 Forecast errors 169
4.5.4 Time series components 172
4.6 Time series models 174
4.6.1 Level model: Cumulative average,
naive, and moving average 177
4.6.2 Exponential smoothing: Level only 181
Contents vii
4.6.3 Exponential smoothing: Level
and trend (Holt’s method) 184
4.6.4 Exponential smoothing: Level
and seasonal variation 187
4.6.5 Exponential smoothing: Level, trend, and
seasonal variation (Holt-Winter model) 192
4.7 Causal model of forecasting 196
4.8 Forecasting of intermittent demand: Croston’s method 202
4.9 Forecasting for new products 206
4.10 Summary 209
4.11 Glossary 210
4.12 Case study: Predicting sales of chicken burger combo 210
4.13 Model questions 212
5 Inventory management: Deterministic demand 216
5.1 Introduction 216
5.2 Inventory decisions 217
5.3 Classification of inventory 219
5.3.1 On the basis of process 219
5.3.2 On the basis of functional role 219
5.4 Inventory costs 221
5.5 Replenishment models 225
5.6 Model 1: Economic order quantity (EOQ) 226
5.6.1 EOQ sensitivity with change in batch size (Q) 229
5.6.2 EOQ sensitivity with change
in annual demand (D) 229
5.6.3 EOQ sensitivity with change in
time between orders (T) 231
5.7 Model 2: Non-instantaneous replenishment 234
5.8 Model 3: Quantity discount model 236
5.9 Model 4: Economic production quantity
(EPQ): Finite replenishment 244
5.10 Model 5: EOQ model with planned backorders 250
5.11 Model 6: EOQ model for multiple items with constraints 254
5.12 Summary 257
5.13 Glossary 257
5.14 Case study: Replenishing high-demand specialized honey 258
5.15 Model questions 260
viii Contents
6 Inventory management: Stochastic demand 263
6.1 Introduction 263
6.2 Estimation of stock-out, units sold, and total demand 265
6.2.1 Empirical estimation 265
6.2.2 Estimation when demand follows
uniform discrete distribution 267
6.2.3 Estimation when demand follows
uniform continuous distribution 269
6.2.4 Estimation when demand follows
normal distribution 273
6.2.5 Estimation when demand follows
triangle distribution 274
6.3 Single period inventory problem 279
6.3.1 Costs associated with single
period inventory problems 281
6.3.2 Estimation of optimal batch size (Q*) 284
6.3.3 Estimation of optimal batch size
(Q*) with salvage value 285
6.3.4 Estimation of profit without salvage cost 285
6.3.5 Profit estimation considering
salvage and penalty cost 287
6.4 Inventory control system under probabilistic demand 289
6.4.1 Continuous review (Q) system 291
6.4.2 Periodic review system 302
6.5 Application of periodic review system of
inventory control: Guaranteed service model 310
6.5.1 Model formulation 312
6.5.2 Multiple stage guaranteed service
model: Serial system 314
6.6 Inventory control system with stochastic lead time 315
6.6.1 Estimation of inventory position 318
6.7 Summary 319
6.8 Glossary 320
6.9 Case Study: Vanish stain remover 321
6.10 Model questions 323
7 Freight transportation 327
7.1 Introduction 327
7.2 Logistics cost 332
7.3 Impact of lead time variability 336
Contents ix
7.4 Mode selection 342
7.4.1 Case 1 343
7.5 Operations of a transportation model 347
7.6 One-to-one transportation model: Capacity constraint 351
7.7 One-to-many transportation model:
Vehicle routing problem (VRP) 354
7.8 Many-to-many transportation model:
Transportation and transhipment 360
7.8.1 Linear programming formulation of
many-to-many transportation model 361
7.8.2 Linear programming formulation of
many-to-many transhipment model 365
7.8.3 Application of transportation and transhipment
model in inventory management 369
7.9 Summary 373
7.10 Glossary 374
7.11 Case study: Optimizing shipping costs 375
7.12 Model questions 377
Index 383
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