Integral Logistics Management — Operations Management and Supply Chain Management Within and Across Companies

10.5 Using Forecasts in Planning

Intended learning outcomes: Produce an overview on the choice of suitable forecasting technique. Describe consumption distributions and their limits, continuous and discontinuous demand. Explain demand forecasting of variants of a product family. Present safety demand calculation for various planning periods. Disclose the translation of forecast into quasi-deterministic demand.



Course section 10.5: Subsections and their intended learning outcomes

  • 10.5 Using Forecasts in Planning

    Intended learning outcomes: Produce an overview on the choice of suitable forecasting technique. Describe consumption distributions and their limits, continuous and discontinuous demand. Explain demand forecasting of variants of a product family. Present safety demand calculation for various planning periods. Disclose the translation of forecast into quasi-deterministic demand.

  • 10.5.1 Comparison of Techniques and Choice of Suitable Forecasting Technique

    Intended learning outcomes: Differentiate between various areas of applicability of forecasting techniques.

  • 10.5.2 Consumption Distributions and Their Limits

    Intended learning outcomes: Identify variables for a consumption distribution. Present expected value and variance of the consumption distribution. Explain distribution function, expected value, and variance of the consumption distribution under the assumption of a Poisson distribution for the frequency of events.

  • 10.5.2b Managing Discontinuous Demand, or Lumpy Demand

    Intended learning outcomes: Describe the demand filter to handle a discontinuous demand due to infrequent large issues. Disclose effects of length of statistical period on demand fluctuations.



Course 5: Sections and their intended learning outcomes

  • Course 10 – Demand Planning and Demand Forecasting

    Intended learning outcomes: Produce an overview of forecasting techniques. Explain history-oriented techniques for constant demand in detail. Identify history-oriented techniques with trend-shaped behavior. Describe three future-oriented techniques. Disclose how to use forecasts in planning.

  • 10.1 Overview of Demand Planning and Forecasting Techniques

    Intended learning outcomes: Produce an overview on the problem of demand planning. Present the subdivision of forecasting techniques. Disclose principles of forecasting techniques with extrapolation of time series and the definition of variables.

  • 10.2 Historically Oriented Techniques for Constant Demand

    Intended learning outcomes: Describe the moving average forecast. Explain the first-order exponential smoothing forecast. Differentiate between the moving average forecast and the first-order exponential smoothing forecast.

  • 10.3 Historically Oriented Techniques with Trend-Shaped Behavior

    Intended learning outcomes: Explain the regression analysis forecast and the second-order exponential smoothing forecast. Describe the Trigg and Leach adaptive smoothing technique. Produce an overview on seasonality.

  • 10.4 Future-Oriented Techniques

    Intended learning outcomes: Explain the trend extrapolation forecast and the Delphi method. Describe scenario forecasts.

  • 10.5 Using Forecasts in Planning

    Intended learning outcomes: Produce an overview on the choice of suitable forecasting technique. Describe consumption distributions and their limits, continuous and discontinuous demand. Explain demand forecasting of variants of a product family. Present safety demand calculation for various planning periods. Disclose the translation of forecast into quasi-deterministic demand.

  • 10.6 Summary

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  • 10.7 Keywords

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  • 10.8 Scenarios and Exercises

    Intended learning outcomes: Choose an appropriate forecasting technique. Calculate an example for the moving average forecasting technique and for the first-order exponential smoothing technique. Differentiate between the moving average forecast and the first-order exponential smoothing forecast.