Distribution Logistics Best Practice

Day 1

Supply chain management

• What do we mean by logistics?
• Plan of the chapter.
• Structure of production/distribution networks.
• Competition factors, cost drivers, and strategy.
• Competition factors.
• Cost drivers.
• Strategy.
• The role of inventories.
• A classical model: Economic Order Quantity.
• Cycle vs. capacity-induced stock.
• Dealing with uncertainty.
• Setting safety stocks.
• A two-stage decision process: Production planning in an assemble-to-order environment.
• Inventory deployment.
• Physical flows and transportation.
• Time horizons and hierarchical levels.
• Decision approaches.
• Information flows and decision rights.
• Quantitative models and methods.
• For further reading.

Network Design and Transportation

• The role of intermediate nodes in a distribution network.
• The risk pooling effect: reducing the uncertainty level.
• The role of transit points in transportation optimization.
• Location and flow optimization models.
• The transportation problem
• The minimum cost flow problem.
• The plant location problem
• Putting it all together
• Models involving nonlinear costs.
• For Further Reading.

Day 2

Forecasting

• Overview of forecasting.
• The variable to be predicted.
• The forecasting processes.
• Metrics for forecast errors.
• The Mean Error.
• Mean Absolute Deviation.
• Root Mean Square Error.
• Mean Percentage Error and Mean Absolute Percentage Error.
• ME%, MAD%, RMSE%.
• U Theil’s statistic.
• Using metrics of forecasting accuracy.
• A classification of forecasting methods
• Moving Average
• The algorithm.
• Setting the parameters.
• Drawbacks and limitations.
• Simple exponential smoothing.
• The algorithm.
• Setting the parameter.
• Initialization.
• Drawbacks and limitations.
• Exponential Smoothing with Trend.
• The demand model.
• The algorithm.
• Setting the parameters.
• Initialization.
• Drawbacks and limitations.
• Exponential smoothing with seasonality.
• The demand model.
• The algorithm.
• Setting the parameters.
• Initialization.
• Drawbacks and limitations.
• Smoothing with seasonality and trend.
• The demand model.
• The algorithm.
• Initialization.
• Simple linear regression.
• Setting up data for regression.
• Forecasting new products.
• The Delphi method and the committee process.
• Lancaster model: forecasting new products through products features.
• The early sales model.
• The Bass model.
• Limitations and drawbacks.

Inventory management with Deterministic Demand

• Economic Order Quantity.
• Robustness of EOQ model.
• Case of LT > 0: the (Q,R) model.
• Case of finite replenishment rate.
• Multi-item EOQ.
• The case of shared ordering costs.
• The multi-item case with a constraint on ordering capacity.
• Case of nonlinear costs.
• The case of variable demand with known variability.

Day 3

Inventory control: the stochastic case.

• The newsvendor problem.
• Extensions of the Newsvendor problem.
• Multi-period problems.
• Fixed quantity: the (Q,R) model.
• Optimization of the (Q,R) model in case the stock out cost depends on the size of the stock out.
• (Q,R) system: case of constraint on the type II service level.
• Optimization of the (Q,R) model in case the cost of a stock-out depends on the occurrence of a stock out.(Q,R) system: case of constraint on type I service level.
• Periodic review: S and (s, S) policies.
• The S policy.
• The (s, S) policy.

Managing inventories in multiechelon supply chains

• Managing multi-echelon chains: Installation vs. Echelon Stock.
• Features of Installation and Echelon Stock logics.
• Coordination in the supply chain: The Bullwhip effect.
• A linear distribution chain with two echelons and certain demand.
• Arbores cent chain with two echelons: transit point with uncertain demand.
• A two-echelon supply chain in case of stochastic demand.

Day 4

Incentives in the supply chain

• Decisions on price: double marginalization.
• The first best solution: the vertically integrated firm.
• The vertically disintegrated case: independent manufacturer and retailer.
• A way out: designing incentive schemes.
• Decision on price in a competitive environment.
• The vertically disintegrated supply chain: independent manufacturer and retailer.
• Decision on inventories: The Newsvendor problem.
• The first best solution: the vertically integrated firm.
• The vertically disintegrated case: independent manufacturer and retailer.
• A way out: designing incentives and re-allocating decision rights.
• Decision on effort to produce and sell the product.
• The first best solution: the vertically integrated firm.
• The vertically disintegrated case: independent retailer and manufacturers.
• The case of efforts both at the upstream and downstream stage.

Day 5

Vehicle Routing

• Network routing problems.
• Solution methods for symmetric TSP.
• Nearest-neighbour heuristic.
• Insertion-based heuristics.
• Local search methods.
• Solution methods for basic VRP.
• Constructive methods for VRP.
• Decomposition methods for VRP: cluster first, route second.
• Additional features of real-life VRP.
• Constructive methods for the VRP with time windows.