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.
