Distribution Logistics

Course Code: PM 27

 

Course Objective

  • Improve and evaluate products and production processes in order to attain and maintain a competitive edge.
  • Pursue and achieve a great delivery capability and reliability with the lowest possible logistic and production costs.
  • Depict the extent to which the promised dates for the placed orders can be met.
  • Explain why the marketable production costs, delivery capability and delivery reliability are critical to a company's long-term market success.
  • Monitor the interactions between the performance and cost objectives constantly so as to ensure the production's economic efficiency.
  • Clearly demonstrate the mutual dependencies between the often contradictory logistic objectives.

 

Target Audience

  • This course will mainly benefit to purchasing managers, senior buyers, project managers, civil engineers, construction managers, contractors, sub-contractors, site engineers, senior management, and government agencies, architects, construction professionals, and anyone responsible for purchasing at a senior level who seeks to enhance their skills further.

 

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 process.
  • 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 demand model.
  • The algorithm.
  • Setting the parameters.
  • Drawbacks and limitations.
  • Simple exponential smoothing.
  • The demand model.
  • 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.

Day 3

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.
  • 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-neighbor 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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Date & Location

Date : 17 March 2019

Duration : 5 days

Place : Istanbul

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Date : 10 November 2019

Duration : 5 days

Place : Kuala Lumpur

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