A demand forecast is an expression of the probable course of demand along the time axis. A demand must be forecast if the cumulative lead time is longer than the customer tolerance time. Such a situation occurs, for example, in trade in consumer goods, in components for services, or in single parts of investment goods. Forecasts are transformed into demand for resources later and then compared with the organization’s supply capacity. However, every forecast is associated with uncertainty. Therefore, forecasts must be compared to demand continually, e.g., in a rolling manner. A significant deviation in demand may require the selection of a different technique.
We distinguished two basic types of forecasting techniques: historically oriented and future-oriented. Both basic types are further subdivided into mathematical, graphical, or intuitive techniques. The selection of a technique is made according to a series of criteria intended to produce a reasonable alignment of the forecast to the demand, at reasonable expense.
Historically oriented techniques calculate demand based on consumption with the help of mathematical statistics (extrapolation of time series). There are simple techniques for continuous demand, such as moving average or first-order exponential smoothing. For linear trends, we may make use of linear regression or second-order exponential smoothing. In addition, the Trigg and Leach adaptive technique examines and adapts the parameters used in exponential smoothing. All the techniques may be expanded to account for the effect of seasonality. Trend extrapolation, the Delphi method, and scenario forecasts were discussed as future-oriented techniques, although these also contain historically oriented elements.
The more discontinuously consumption occurs, the more difficult it is to forecast reliably. The definition of consumption distributions as an overlay of the distribution of consumption events and the distribution of consumption quantities per event helps describe discontinuous conditions. A suitable length of the statistical period can lead to a smoothing of demands. Where there are few variants and repetitive production, forecast for variant demand of a product family may be calculated using option percentages. This is a stochastic variable with an expected value and standard deviation.
In all cases, larger fluctuations in demand lead to safety demand, which is calculated on the basis of standard deviation. The expected value and standard deviation are related to the statistical period, while independent demand is related to the planning period. The conversion of expected value is proportional to the ratio of the two time periods, whereas in the standard deviation the conversion is proportional to its square root. The expected value of the demand increased by safety demand is set as independent demand per planning period; the latter is then available as stochastic demand for further handling in the context of materials management. When dependent demand is calculated later, using a quasi-deterministic bill of materials explosion, it will contain the corresponding safety demand.
For each independent demand, the item ID, the forecast quantity, and the quantity of the forecast already “consumed” by orders are recorded, as well as the planning date. The total of all independent demands belongs to the production schedule, or, when referring to trade items, the purchase schedule. Independent demand can be recalculated or canceled by rolling planning, either manually or with automated techniques. In general, actual demand successively replaces or reduces independent demand.
Course sections and their intended learning outcomes
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.
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.
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.
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.
Intended learning outcomes: Explain the trend extrapolation forecast and the Delphi method. Describe scenario forecasts.
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.
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.