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E-raamat: Introduction to Distribution Logistics

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(Politecnico di Torino, Torino, Italy), (Politecnico di Torino, Torino, Italy)
  • Formaat: PDF+DRM
  • Sari: Statistics in Practice
  • Ilmumisaeg: 13-Aug-2007
  • Kirjastus: Wiley-Interscience
  • Keel: eng
  • ISBN-13: 9780470170045
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Introduction to Distribution LogisticsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Statistics in Practice
  • Ilmumisaeg: 13-Aug-2007
  • Kirjastus: Wiley-Interscience
  • Keel: eng
  • ISBN-13: 9780470170045
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Using real-life examples, Brandimarte and Zotteri (quantitative methods for finance and logistics, and industrial engineering, respectively, Politecnico di Torino) focus on supply chain management for students of mathematics, business, economics, statistics and optimization by emphasizing quantification and showing how to build models that effectively support logistic decisions. They cover network design and transportation, forecasting, inventory management with determinant demand, the stochastic case for inventory control and managing inventories in multi-echelon supply chains, incentives in the supply chain and vehicle routing. For those needing extra help they provide extended appendices on probability, statistics and mathematical programming. The result requires a degree of mathematical sophistication but gives students a good grounding in both theory and intermediate to advanced practice. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

unique introduction to distribution logistics that focuses on both quantitative modeling and practical business issues

Introduction to Distribution Logistics presents a complete and balanced treatment of distribution logistics by covering both applications and the required theoretical background, therefore extending its reach to practitioners and students in a range of disciplines such as management, engineering, mathematics, and statistics. The authors emphasize the variety and complexity of issues and sub-problems surrounding distribution logistics as well as the limitations and scope of applicability of the proposed quantitative tools. Throughout the book, readers are provided with the quantitative approaches needed to handle real-life management problems, and areas of study include:

  • Supply chain management
  • Network design and transportation
  • Demand forecasting
  • Inventory control in single- and multi-echelon systems
  • Incentives in the supply chain
  • Vehicle routing

Complete with extensive appendices on probability and statistics as well as mathematical programming, Introduction to Distribution Logistics is a valuable text for distribution logistics courses at both the advanced undergraduate and beginning graduate levels in a variety of disciplines, and prior knowledge of production planning is not assumed. The book also serves as a useful reference for practitioners in the fields of applied mathematics and statistics, manufacturing engineering, business management, and operations research. The book's related Web site includes additional sections and numerical illustrations.

Arvustused

"An excellent introduction to logistics of distribution process .A good text for university students in the area of logistics and associated branches." (Zentralblatt Math, 2008/16) "An excellent introduction to logistics of distribution process .A good text for university students in the area of logistics and associated branches." (Zentralblatt Math, 2008)

"Very extensive (approximately 146 pages) and useful appendixes contain material on statistics, probability, and mathematical programming." (CHOICE, February 2008)

"Valuable text for distribution logistics course at both the advanced undergraduate and beginning graduate levels in a variety of disciplines." (Mathematical Reviews 2008)

Preface xiii
1 Supply Chain Management 1
1.1 What do we mean by logistics?
1
1.1.1 Plan of the chapter
4
1.2 Structure of production/distribution networks
6
1.3 Competition factors, cost drivers, and strategy
9
1.3.1 Competition factors
9
1.3.2 Cost drivers
12
1.3.3 Strategy
16
1.4 The role of inventories
18
1.4.1 A classical model: Economic order quantity
19
1.4.2 Capacity-induced stock
25
1.5 Dealing with uncertainty
26
1.5.1 Setting safety stocks
27
1.5.2 A two-stage decision process: Production planning in an assemble-to-order environment
30
1.5.3 Inventory deployment
39
1.6 Physical flows and transportation
40
1.7 Information flows and decision rights
41
1.8 Time horizons and hierarchical levels
42
1.9 Decision approaches
44
1.10 Quantitative models and methods
48
1.11 For further reading
50
References
51
2 Network Design and Transportation 53
2.1 The role of intermediate nodes in a distribution network
55
2.1.1 The risk pooling effect: reducing the uncertainty level
56
2.1.2 The role of distribution centers and transit points in transportation optimization
59
2.2 Location and flow optimization models
71
2.2.1 The transportation problem
72
2.2.2 The minimum cost flow problem
74
2.2.3 The plant location problem
76
2.2.4 Putting it all together
80
2.3 Models involving nonlinear costs
83
W.2.4 Continuous-space location models
88
W.2.5 Retail-store location models
88
2.6 For Further Reading
89
References
89
3 Forecasting 91
3.1 Introduction
91
3.2 The variable to be predicted
93
3.2.1 The forecasting process
98
3.3 Metrics for forecast errors
103
3.3.1 The Mean Error
104
3.3.2 Mean Absolute Deviation
104
3.3.3 Root Mean Square Error
106
3.3.4 Mean Percentage Error and Mean Absolute Percentage Error
107
3.3.5 ME%, MAD%, RMSE%
110
3.3.6 Theil's U statistic
112
3.3.7 Using metrics of forecasting accuracy
113
3.4 A classification of forecasting methods
116
3.5 Moving Average
120
3.5.1 The demand model
120
3.5.2 The algorithm
121
3.5.3 Setting the parameter
121
3.5.4 Drawbacks and limitations
125
3.6 Simple exponential smoothing
127
3.6.1 The demand model
127
3.6.2 The algorithm
128
3.6.3 Setting the parameter
132
3.6.4 Initialization
134
3.6.5 Drawbacks and limitations
138
3.7 Exponential Smoothing with Trend
138
3.7.1 The demand model
138
3.7.2 The algorithm
138
3.7.3 Setting the parameters
139
3.7.4 Initialization
140
3.7.5 Drawbacks and limitations
142
3.8 Exponential smoothing with seasonality
144
3.8.1 The demand model
144
3.8.2 The algorithm
145
3.8.3 Setting the parameters
147
3.8.4 Initialization
147
3.8.5 Drawbacks and limitations
153
3.9 Smoothing with seasonality and trend
154
3.9.1 The demand model
154
3.9.2 The algorithm
154
3.9.3 Initialization
155
3.10 Simple linear regression
158
3.10.1 Setting up data for regression
165
3.11 Forecasting models based on multiple regression
165
3.12 Forecasting demand for new products
166
3.12.1 The Delphi method and the committee process
166
3.12.2 Lancaster model: forecasting new products through product features
171
3.12.3 The early sales model
172
3.13 The Bass model
177
3.13.1 Limitations and drawbacks
185
References
185
4 Inventory Management with Deterministic Demand 187
4.1 Introduction
187
4.2 Economic Order Quantity
195
4.3 Robustness of EOQ model
208
4.4 Case of LT > 0: the (Q, R) model
210
4.5 Case of finite replenishment rate
212
4.6 Multi-item EOQ
214
4.6.1 The case of shared ordering costs
215
4.6.2 The multi-item case with a constraint on ordering capacity
217
4.7 Case of nonlinear costs
220
4.8 The case of variable demand with known variability
225
References
230
5 Inventory Control: The Stochastic Case 233
5.1 Introduction
233
5.2 The newsvendor problem
245
5.2.1 Extensions of the newsvendor problem
259
5.3 Multi-period problems
269
5.4 Fixed quantity: the (Q, R) model
270
5.4.1 Optimization of the (Q,R) model in case the stockout cost depends on the size of the stockout
278
5.4.2 (Q,R) system: case of constraint on the type II service level
284
5.4.3 (Q, R) system: case of constraint on type I service level
287
5.5 Periodic review: S and (s,S) policies
288
5.6 The S policy
290
5.7 The (3,S) policy
296
5.5.8 Optimization of the (Q,R) model when the cost of a stockout depends on the occurrence of a stockout
299
References
301
6 Managing Inventories in Multiechelon Supply Chains 303
6.1 Introduction
303
6.2 Managing multiechelon chains: Installation vs. Echelon Stock
309
6.2.1 Features of Installation and Echelon Stock logics
312
6.3 Coordination in the supply chain: the Bullwhip effect
324
6.4 A linear distribution chain with two echelons and certain demand
335
6.5 Arborescent chain: transit point with uncertain demand
342
6.6 A two-echelon supply chain in case of stochastic demand
351
References
357
7 Incentives in the Supply Chain 359
7.1 Introduction
359
7.2 Decisions on price: double marginalization
361
7.2.1 The first best solution: the vertically integrated firm
362
7.2.2 The vertically disintegrated case: independent manufacturer and retailer
363
7.2.3 A way out: designing incentive schemes
369
7.3 Decision on price in a competitive environment
372
7.3.1 The vertically disintegrated supply chain: independent manufacturer and retailer.
373
7.4 Decision on inventories: the newsvendor problem
375
7.4.1 The first best solution: the vertically integrated firm
375
7.4.2 The vertically disintegrated case: independent manufacturer and retailer
376
7.4.3 A way out: designing incentives and reallocating decision rights
378
7.5 Decision on effort to produce and sell the product
384
7.5.1 The first best solution: the vertically integrated firm
385
7.5.2 The vertically disintegrated case: independent retailer and manufacturer
386
7.5.3 A way out: designing incentive schemes.
389
7.5.4 The case of efforts both at the upstream and downstream stage
390
7.6 Concluding remarks
393
References
394
8 Vehicle Routing 397
8.1 Network routing problems: The TSP
398
8.1.1 Other network routing problems
402
8.2 Solution methods for symmetric TSP
403
8.2.1 Nearest-neighbor heuristic
404
8.2.2 Insertion-based heuristics
405
8.2.3 Local search methods
407
8.3 Solution methods for basic VRP
412
8.3.1 Constructive methods for VRP
414
8.3.2 Decomposition methods for VRP: cluster first, route second
421
8.4 Additional features of real-life VRP
425
8.4.1 Constructive methods for the VRP with time windows
427
8.5 Final remarks
430
8.6 For further reading
430
References
431
Appendix A A Quick Tour of Probability and Statistics 433
A.1 Sample space, events, and probability
434
A.2 Conditional probability and independence
438
A.3 Discrete random variables
442
A.3.1 A few examples of discrete distributions
446
A.4 Continuous random variables
452
A.4.1 Some continuous distributions
457
A.5 Jointly distributed random, variables
461
A.6 Independence, covariance, and conditional expectation
463
A.6.1 Independent random variables
463
A.6.2 Covariance and correlation
465
A.6.3 Distributions obtained from the normal and the central limit theorem
467
A.6.4 Conditional expectation
471
A.7 Stochastic processes
475
A.8 Parameter estimation
481
A.8.1 Sample covariance and correlation
485
A.8.2 Confidence intervals
490
A.9 Hypothesis testing
494
A.9.1 An example of a nonparametric test: the chi-square test
498
A.9.2 Testing hypotheses about the difference in the mean of two populations
499
A.10 Simple linear regression
501
A.10.1 Best fitting by least squares
503
A.10.2 Analyzing properties of regression estimators
506
A.10.3 Confidence intervals and hypothesis testing for regression estimators
519
A.10.4 Performance measures for linear regression
521
A.10.5 Verification of the underlying assumptions
524
A.10.6 Using linear regression to estimate nonlinear relationships
528
A.11 Multiple linear regression
533
A.12 For further reading
533
References
534
Appendix B An Even Quicker Tour in Mathematical Programming 535
B.1 Role and limitations of optimization models
537
B.2 Optimization models
544
B.3 Convex sets and functions
548
B.4 Nonlinear programming
553
B.4.1 The case of inequality constraints
556
B.4.2 An economic interpretation of Lagrange multipliers: shadow prices
559
B.5 Linear programming
562
B.6 Integer linear programming
564
B.6.1 Branch and bound methods
566
B.6.2 Model building in integer programming
571
B.7 Elements of multiobjective optimization
575
B.8 For further reading
579
References
579
Index 581
Paolo Brandimarte is Full Professor of Quantitative Methods for Finance and Logistics at Politecnico di Torino in Italy. He is the author of several publications, including six books, on the application of optimization and simulation to diverse areas such as production management, telecommunications, and finance. Dr. Brandimarte has extensive teaching experience in engineering and economics faculties, including master's- and PhD-level courses.

Giulio Zotteri, PhD, is Associate Professor in the School of Industrial Engineering at Politecnico di Torino in Italy. Dr. Zotteri's research interests include demand management and forecasting, inventory control, and operations management.
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