Intended learning outcomes: Explain one possible breakdown of forecasting techniques. Disclose another possible subdivision of forecasting techniques.
Figure 10.1.2.1 shows one possible subdivision of forecasting techniques.
Fig. 10.1.2.1 Breakdown of forecasting techniques.
- Historically oriented forecasting techniques predict future demand based on historical data, for example, on consumption statistics. If a forecast can be made only for an item family or a rough-cut item, then the predicted quantity must subsequently be applied to the detailed items with the use of an allocation key. Historically oriented forecasting techniques can be further subdivided into:
- Mathematical forecasting techniques, predominant among which is the extrapolation of a time series. Future demand is calculated by extrapolating a series of demands in the past. Such procedures are used widely.
- Graphical forecasting techniques, where a time series is represented graphically; a mean course and width of deviation are judged by “eyeballing” and are projected into the future based on past experience.
- Future-oriented forecasting techniques take information already at hand about future demand into account, such as bids, firm orders, orders in the concluding phases, or surveys of consumer behavior. Such techniques are further subdivided into:
- Mathematical forecasting techniques; for example, extrapolation. Beginning with confirmed orders, future order volume is calculated empirically.
- Intuitive forecasting techniques attempt to estimate the future behavior of target customers in an intuitive way. These techniques are particularly useful when new or significantly enhanced products are introduced to the market, or if surrounding systems significantly change. Surveys, juries of executive opinion, or estimations are simple intuitive techniques. Relevant information can be provided by the sales department, the sellers, or market research institutes that use surveys to assess customer behavior, or by customers themselves (direct contact). Rather technical methods are expert systems, neural networks, decision support systems (DSS), or other statistics and operations research techniques . As typically intuitive techniques, the Delphi method and the Scenario-based forecasts will be presented .
A combination of these techniques is also thinkable. For example, forecasts produced using a mathematical technique may be “eyeballed” for accuracy using a graphical representation.
Another possible subdivision of forecasting techniques is the following (see [ASCM22]):
- Qualitative forecasting techniques based on intuitive expert opinion and judgment (e.g., manual forecast, Delphi method)
- Quantitative forecasting techniques using historical demand data to project future demand; these techniques are further subdivided as follows:
- Intrinsic forecasting techniques are based on internal factors, such as an average of past sales, and are useful for individual product sales.
- Extrinsic forecasting techniques are based on a correlated leading indicator (a business activity index that indicates future trends), such as estimating sales of disposable diapers based on birth rates or estimating furniture sales based on housing starts ([ASCM22]). Extrinsic forecasts tend to be more useful for large aggregations, such as total company sales.
Course section 10.1: Subsections and their intended learning outcomes
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.
10.1.1 The Problem of Demand Planning
Intended learning outcomes: Disclose the difference between human forecasting and IT-supported forecasting techniques with regard to a forecast’s precision. Present a possible demand planning procedure.
10.1.2 Subdivision of Forecasting Techniques
Intended learning outcomes: Explain one possible breakdown of forecasting techniques. Disclose another possible subdivision of forecasting techniques.
10.1.3 Principles of Forecasting Techniques with Extrapolation of Time Series
Intended learning outcomes: Present an example of a time series. Explain possible and common demand models.
10.1.3b Statistical Methods to Determine Mean and Dispersion and Definition of Variables
Intended learning outcomes: Produce an overview on statistical methods to determine mean and dispersion. Identify definitions of variables, each calculated at the end of a statistical period.
