Introduction:
This fully in-depth course will cover both applications and the important theoretical background, therefore extending its reach to practitioners and delegates in a range of disciplines such as management, engineering, mathematics, and statistics.
It serves as an important reference for practitioners in the fields of applied mathematics and statistics, manufacturing engineering, business management, and operations research.
Objectives
Upon completion of this course, you should be able to:
• Handle the quantitative approaches needed to handle real-life management problems.
• Recognise the limitations and scope of applicability of the proposed quantitative methods.
• Handle many issues on probability and statistics as well as mathematical programming
• Gain a broad comprehending on Network design and transportation, and Demand forecasting
• Assess inventory control in single- and multi-echelon systems, and Incentives in the supply chain
• Recognise Network routing problems and develop solution methods for symmetric TSP.
Course Outline
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 on 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.
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