*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.*

*Demand forecasting* is, according to Section 1.1.6, estimating the future demand.

A *forecast error* is the difference between actual demand and demand forecast. It can be stated as an absolute value or as a percentage.

A *forecasting technique* is a systematic procedure for forecasting demand according to a particular model.

A certain degree of uncertainty and therefore forecast errors characterize every forecast, regardless of whether people or IT-supported techniques do the forecasting. The latter are a complement to human intuition and creativity. When planning the demand, we should make appropriate use of both according to the situation.

If there are only a few items and only a limited amount of information that can be stated explicitly, human forecasting tends to be more precise. This is because human intelligence can process fragmentary information as well as knowledge derived by analogy, thus taking many further factors necessary for forecasting into account. This can be important, e.g., in rough-cut planning, where we need only forecast few demands for item families.

On the other hand, when there are many items, or when we can use information on demand that is expressed explicitly, an IT-supported forecasting technique generally provides more precise forecasting. This is due to the capacity of computers to process large quantities of data accurately.

- Tendencies or trends, such as seasonality, can be calculated from consumption statistics. The length of the time frame to be observed makes this a difficult task for human beings.
- People tend to weigh unusual events too heavily. In this case, an IT-supported forecasting technique is more neutral in its “reactions.”
- People tend to focus overly on the recent past. If a forecast proves too high for the current period, they tend to forecast a demand that is too low for the next period, even though this is not justified from the medium-term perspective.

Demand planning is always based upon certain fundamental assumptions and constraints. Parameters are used to keep their selection as general or flexible as possible. If the demand situation changes, demand planning should reexamine the choice of both parameters and technique and change them if necessary. Figure 10.1.1.1 shows a possible procedure for choosing the forecasting technique and its Parameters.

**Fig. 10.1.1.1** A possible demand planning procedure.

- Choose a demand forecasting technique based on existing consumption or on partially known demand figures.
- Produce a forecast for future demand by applying the technique.
- When possible, make a visual check of the forecast and, if necessary, correct forecast values that vary too widely from intuitive assumptions. This check allows input of human knowledge of the behavior of the market into automated forecasting techniques.
- Break the demand forecast down into the needed resources — goods and capacity — according to temporal range and level of detail. This allows planners to estimate the consequences of implementing a forecast and to work out better variations if necessary.
- Adopt the optimal variant of the forecast as the production plan or procurement plan. These plans represent the independent demand; they are subsequently provided either to the next most detailed or shorter-term planning or to execution.
- At certain intervals in time, perform an analysis to see whether the course of demand or consumption agrees with the forecast. If the deviation analysis reveals too great a difference, repeat the cycle.

## 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.