/  Distribution Logistics Training

Distribution Logistics Training

BMC Training provides a training course in Distribution Logistics in Warehouse , Logistics , Supply Chain and Inventory

Course Title
Venue
Start Date
End Date
  • Kuala Lumpur
    21 - 10 - 2018
    1 - 11 - 2018
  • Istanbul
    28 - 10 - 2018
    8 - 11 - 2018
  • Muscat
    21 - 10 - 2018
    1 - 11 - 2018
  • Dubai
    28 - 10 - 2018
    8 - 11 - 2018
  • London
    18 - 11 - 2018
    29 - 11 - 2018
  • Dubai
    4 - 11 - 2018
    15 - 11 - 2018
  • Paris
    18 - 11 - 2018
    29 - 11 - 2018
  • Kuala Lumpur
    9 - 12 - 2018
    20 - 12 - 2018
  • Istanbul
    16 - 12 - 2018
    27 - 12 - 2018
  • Muscat
    9 - 12 - 2018
    20 - 12 - 2018
  • London
    20 - 1 - 2019
    31 - 1 - 2019
  • Dubai
    13 - 1 - 2019
    24 - 1 - 2019
  • Paris
    20 - 1 - 2019
    31 - 1 - 2019
  • Kuala Lumpur
    3 - 2 - 2019
    14 - 2 - 2019
  • Istanbul
    24 - 2 - 2019
    7 - 3 - 2019
  • Muscat
    3 - 2 - 2019
    14 - 2 - 2019
  • London
    10 - 3 - 2019
    21 - 3 - 2019
  • Dubai
    3 - 3 - 2019
    14 - 3 - 2019
  • Paris
    10 - 3 - 2019
    21 - 3 - 2019
  • Kuala Lumpur
    28 - 4 - 2019
    9 - 5 - 2019
  • Istanbul
    21 - 4 - 2019
    2 - 5 - 2019
  • Muscat
    28 - 4 - 2019
    9 - 5 - 2019
  • London
    5 - 5 - 2019
    16 - 5 - 2019
  • Dubai
    26 - 5 - 2019
    6 - 6 - 2019
  • Paris
    5 - 5 - 2019
    16 - 5 - 2019
  • Kuala Lumpur
    16 - 6 - 2019
    27 - 6 - 2019
  • Istanbul
    9 - 6 - 2019
    20 - 6 - 2019
  • Muscat
    16 - 6 - 2019
    27 - 6 - 2019
  • London
    14 - 7 - 2019
    25 - 7 - 2019
  • Dubai
    21 - 7 - 2019
    1 - 8 - 2019
  • Paris
    14 - 7 - 2019
    25 - 7 - 2019
  • Kuala Lumpur
    25 - 8 - 2019
    5 - 9 - 2019
  • Istanbul
    4 - 8 - 2019
    15 - 8 - 2019
  • Muscat
    25 - 8 - 2019
    5 - 9 - 2019
  • London
    1 - 9 - 2019
    12 - 9 - 2019
  • Dubai
    8 - 9 - 2019
    19 - 9 - 2019
  • Paris
    1 - 9 - 2019
    12 - 9 - 2019

Introduction:

This comprehensive course 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. It serves as a useful reference for practitioners in the fields of applied mathematics and statistics, manufacturing engineering, business management, and operations research.

On completion of this course you should be able to: 

  • Deal with the quantitative approaches needed to handle real-life management problems.
  • Identify the limitations and scope of applicability of the proposed quantitative tools.
  • Discuss many issues on probability and statistics as well as mathematical programming
  • Gain a broad understanding on Network design and transportation, and Demand forecasting
  • Examines inventory control in single- and multi-echelon systems, and Incentives in the supply chain
  • Identify Network routing problems and develop solution methods for symmetric TSP.

Course Outline

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.

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.

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.

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.

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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