A 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
Inventory control in single– and multi–echelon systems
Incentives in the supply chain
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.
1. Supply chain management.
1.1 What do we mean by logistics?
1.1.1 Plan of the chapter.
1.2 Structure of production/distribution networks.
1.3 Competition factors, cost drivers, and strategy.
1.3.1 Competition factors.
1.3.2 Cost drivers.
1.4 The role of inventories.
1.4.1 A classical model: Economic Order Quantity.
1.4.2 Cycle vs. capacity–induced stock.
1.5 Dealing with uncertainty.
1.5.1 Setting safety stocks.
1.5.2 A two–stage decision process: Production planning in an assemble–to–order environment.
1.5.3 Inventory deployment.
1.6 Physical flows and transportation.
1.7 Time horizons and hierarchical levels.
1.8 Decision approaches.
1.9 Information flows and decision rights.
1.10 Quantitative models and methods.
1.11 For further reading.
2. Network Design and Transportation.
2.1 The role of intermediate nodes in a distribution network.
2.1.1 The risk pooling effect: reducing the uncertainty level.
2.1.2 The role of transit points in transportation optimization.
2.2 Location and flow optimization models.
2.2.1 The transportation problem.
2.2.2 The minimum cost flow problem.
2.2.3 The plant location problem.
2.2.4 Putting it all together.
2.3 Models involving nonlinear costs.
2.4 For Further Reading.
3.2 The variable to be predicted.
3.2.1 The forecasting process.
3.3 Metrics for forecast errors.
3.3.1 The Mean Error.
3.3.2 Mean Absolute Deviation.
3.3.3 Root Mean Square Error.
3.3.4 Mean Percentage Error and Mean Absolute Percentage Error.
3.3.5 ME%, MAD%, RMSE%.
3.3.6 U Theil s statistic.
3.3.7 Using metrics of forecasting accuracy.
3.4 A classification of forecasting methods.
3.5 Moving Average.
3.5.1 The demand model.
3.5.2 The algorithm.
3.5.3 Setting the parameters.
3.5.4 Drawbacks and limitations.
3.6 Simple exponential smoothing.
3.6.1 The demand model.
3.6.2 The algorithm.
3.6.3 Setting the parameter.
3.6.5 Drawbacks and limitations.
3.7 Exponential Smoothing with Trend.
3.7.1 The demand model.
3.7.2 The algorithm.
3.7.3 Setting the parameters.
3.7.5 Drawbacks and limitations 125.
3.8 Exponential smoothing with seasonality.
3.8.1 The demand model.
3.8.2 The algorithm.
3.8.3 Setting the parameters.
3.8.5 Drawbacks and limitations.
3.9 Smoothing with seasonality and trend.
3.9.1 The demand model.
3.9.2 The algorithm.
3.10 Simple linear regression.
3.10.1 Setting up data for regression.
3.11 Forecasting new products.
3.11.1 The Delphi method and the committee process.
3.11.2 Lancaster model: forecasting new products through products features.
3.11.3 The early sales model.
3.12 The Bass model.
3.12.1 Limitations and drawbacks.
4. Inventory management with Deterministic Demand.
4.2 Economic Order Quantity.
4.3 Robustness of EOQ model.
4.4 Case of LT > 0: the (Q,R) model.
4.5 Case of finite replenishment rate.
4.6 Multi–item EOQ.
4.6.1 The case of shared ordering costs.
4.6.2 The multi–item case with a constraint on ordering capacity.
4.7 Case of nonlinear costs.
4.8 The case of variable demand with known variability.
5. Inventory control: the stochastic case.
5.2 The newsvendor problem.
5.2.1 Extensions of the Newsvendor problem.
5.3 Multi–period problems.
5.4 Fixed quantity: the (Q,R) model.
5.4.1 Optimization of the (Q,R) model in case the stockout cost depends on the size of the stockout.
5.4.2 (Q,R) system: case of constraint on the type II service level.
5.4.3 Optimization of the (Q,R) model in case the cost of a stock–out depends on the occurrence of a stockout.
5.4.4 (Q,R) system: case of constraint on type I service level.
5.5 Periodic review: S and (s, S) policies.
5.6 The S policy.
5.7 The (s, S) policy.
6. Managing inventories in multiechelon supply chains.
6.2 Managing multi–echelon chains: Installation vs. Echelon Stock.
6.2.1 Features of Installation and Echelon Stock logics.
6.3 Coordination in the supply chain: the Bullwhip effect.
6.4 A linear distribution chain with two echelons and certain demand.
6.5 Arborescent chain with two echelons: transit point with uncertain demand.
6.6 A two echelon supply chain in case of stochastic demand.
7. Incentives in the supply chain.
7.2 Decisions on price: double marginalization.
7.2.1 the first best solution: the vertically integrated firm.
7.2.2 The vertically disintegrated case: independent manufacturer and retailer.
7.2.3 A way out: designing incentive schemes.
7.3 Decision on price in a competitive environment.
7.3.1 the vertically disintegrated supply chain: independent manufacturer and retailer.
7.4 Decision on inventories: the Newsvendor problem.
7.4.1 The first best solution: the vertically integrated firm.
7.4.2 The vertically disintegrated case: independent manufacturer and retailer.
7.4.3 A way out: designing incentives and re–allocating decision rights.
7.5 Decision on effort to produce and sell the product.
7.5.1 The first best solution: the vertically integrated firm.
7.5.2 the vertically disintegrated case: independent retailer and manufacturers.
7.5.3 The case of efforts both at the upstream and downstream stage.
7.6 Concluding remarks.
8. Vehicle Routing.
8.1 Network routing problems.
8.2 Solution methods for symmetric TSP.
8.2.1 Nearest–neighbor heuristic.
8.2.2 Insertion–based heuristics.
8.2.3 Local search methods.
8.3 Solution methods for basic VRP.
8.3.1 Constructive methods for VRP.
8.3.2 Decomposition methods for VRP: cluster first, route second.
8.4 Additional features of real–life VRP.
8.4.1 Constructive methods for the VRP with time windows.
8.5 Final remarks.
8.6 For further reading.
Appendix A: A Quick Tour of Probability and Statistics.
A.1 Sample space, events, and probability.
A.2 Conditional probability and independence.
A.3 Discrete random variables.
A.3.1 A few examples of discrete distributions.
A.4 Continuous random variables.
A.4.1 Some continuous distributions.
A.5 Jointly distributed random variables.
A.6 Independence, covariance, and conditional expectation.
A.6.1 Independent random variables.
A.6.2 Covariance and correlation.
A.6.3 Distributions obtained from the normal and the central limit theorem.
A.6.4 Conditional expectation.
A.7 Stochastic processes.
A.8 Parameter estimation.
A.8.1 Sample covariance and correlation.
A.8.2 Confidence intervals.
A.9 Hypothesis testing.
A.9.1 An example of non–parametric test: The chi–square test.
A.9.2 Testing hypotheses about the difference in.
the mean of two populations.
A.10 Simple linear regression.
A.10.1 Best fitting by least squares.
A.10.2 Analyzing properties of regression estimators.
A.10.3 Confidence intervals and hypothesis testing for regression estimators.
A.10.4 Performance measures for linear regression.
A.10.5 Verification of the underlying assumptions.
A.10.6 Using linear regression to model nonlinear relationships.
A.11 For further reading.
Appendix B: An even Quicker Tour in Mathematical Programming.
B.1 Role and limitations of optimization models.
B.2 Optimization models.
B.3 Convex sets and functions.
B.4 Nonlinear programming.
B.4.1 The case of inequality constraints.
B.4.2 An economic interpretation of Lagrange multipliers: shadow prices.
B.5 Linear programming.
B.6 Integer linear programming.
B.6.1 Branch and bound methods.
B.6.2 Model building in integer programming.
B.7 Elements of multi–objective optimization.
B.8 For further reading.