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

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.


Forecast values produced by techniques for a constant demand do not reflect actual demand in cases where the demand follows a trend. For this reason, a number of trend forecasting techniques have been developed.

A trend forecasting model takes into account stable trends in demand.[note 1006]

In Figure 10.3.0.1, all demand values fluctuate within the confidence limit around the calculated mean. Nevertheless, there is a systematic error (δv) in extrapolation of the mean. Regression analysis shows a rising demand trend. We can avoid the systematic error by extrapolating the regression lines.

Fig. 10.3.0.1      Demand with linear trend: comparison of extrapolation of the mean with that of regression.

To detect a trend in advance, we could, for example, tighten the control limits, (+/– 1 * standard deviation). As soon as the limits have been exceeded a particular number of times, a correction is made.


Course section 10.3: Subsections and their intended learning outcomes

  • 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.

  • 10.3.1 Regression Analysis Forecast

    Intended learning outcomes: Explain mean, standard deviation, and forecast error in linear regression.

  • 10.3.2 Second-Order Exponential Smoothing Forecast

    Intended learning outcomes: Disclose the determination of trend lines in second-order exponential smoothing. Explain the formulas for calculation of the trend line and forecast error in second-order exponential smoothing. Present an example of determination of forecast value using second-order exponential smoothing.

  • 10.3.3 Trigg and Leach Adaptive Smoothing Technique

    Intended learning outcomes: Identify forecast errors and their exponential weighting (mean deviation). Explain the tracking signal following Trigg and Leach. Describe the determination of the smoothing constant in first-order exponential smoothing.

  • 10.3.4 Seasonality Forecast

    Intended learning outcomes: Identify the seasonal index Sf. Explain forecasting that considers seasonality. Differentiate between “Additive seasonality” and “Multiplicative seasonality” formulation.



Course 10: 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.


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