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.
In a forecasting model for constant demand, planners obtain the forecast value for a future period using a mean from past consumption.
Figure 10.2.0.1 shows the forecast curve resulting from two techniques discussed in the following. The actual events — “damped” or “smoothed” [note 1004] — are projected into the future. However, smoothing always lags one statistical period behind, since it is a historically oriented forecast.
Fig. 10.2.0.1 Smoothing of consumption.
Despite the assumption of constant demand, we should always reckon that demand changes over the course of time. To take this into account, the mean is recalculated at the end of every statistical period, although the characteristic parameter of the mean calculation, that is, the number of the periods in the past included in the calculation or the smoothing constant, is usually kept constant.
Course section 10.2: Subsections and their intended learning outcomes
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.
10.2.1 Moving Average Forecast
Intended learning outcomes: Explain mean and standard deviation in the moving average forecasting technique. Disclose the average age of the observed values. Present an example of determining the forecast value using moving average.
10.2.2 First-Order Exponential Smoothing Forecast
Intended learning outcomes: Identify the weighted mean as well as exponential demand weighting. Explain first-order exponential smoothing: mean, MAD, and standard deviation. Disclose the average age of the observed values.
10.2.2b The Smoothing Constant α, or Alpha Factor
Intended learning outcomes: Describe how the smoothing constant α determines the weighting of the past. Present an example of first-order exponential smoothing.
10.2.3 Moving Average Forecast versus First-Order Exponential Smoothing Forecast
Intended learning outcomes: Disclose formulas for the relationship between α and n. Present the relationship between α and n in tabular form. Present an example of linear regression.
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.
