Business Definition for: Forecasting
The prediction of outcomes, trends, or expected future behavior of a business, industry sector, or the economy through the use of statistics. Forecasting is an operational research technique used as a basis for management planning and decision making. Common types of forecasting include trend analysis, regression analysis, Delphi technique, time series analysis, correlation, exponential smoothing, and input-output analysis.
METHODS:
DELPHI METHOD:
The Delphi method is a systematic, interactive forecasting method which relies on a panel of independent experts. The carefully selected experts answer questionnaires in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts’ forecasts from the previous round as well as the reasons they provided for their judgments. Thus, experts are encouraged to revise their earlier answers in light of the replies of other members of their panel. It is believed that during this process the range of the answers will decrease and the group will converge towards the "correct" answer. Finally, the process is stopped after a pre-defined stop criterion (e.g. number of rounds, achievement of consensus, stability of results) and the mean or median scores of the final rounds determine the results.
Delphi [pron: delfI] is based on the principle that forecasts from a structured group of experts are more accurate than those from unstructured groups or individuals. The technique can be adapted for use in face-to-face meetings, and is then called mini-Delphi or Estimate-Talk-Estimate (ETE). Delphi has been widely used for business forecasting and has certain advantages over another structured forecasting approach, prediction markets.
Categories of forecasting methods:
Time series methods:
Time series methods use historical data as the basis of estimating future outcomes.
Rolling forecast is a projection into the future based on past performances, routinely updated on a regular schedule to incorporate data.
*Moving average
*Exponential smoothing
*Extrapolation
*Linear prediction
*Trend estimation
*Growth curve
Causal / econometric methods:
Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. For example, sales of umbrellas might be associated with weather conditions. If the causes are understood, projections of the influencing variables can be made and used in the forecast.
*Regression analysis using linear regression or non-linear regression
*Autoregressive moving average (ARMA)
*Autoregressive integrated moving average (ARIMA)
e.g. Box-Jenkins
Econometrics
Judgmental methods:
Judgmental forecasting methods incorporate intuitive judgements, opinions and subjective probability estimates.
*Composite forecasts
*Surveys
*Delphi method
*Scenario building
*Technology forecasting
*Forecast by analogy
Other methods:
*Simulation
*Prediction market
*Probabilistic forecasting and Ensemble forecasting
*Reference class forecasting
Necessity for forecasting demand:
Often forecasting demand is confused with forecasting sales. But, failing to forecast demand ignores two important phenomena. There is a lot of debate in the demand planning literature as how to measure and represent historical demand, since the historical demand forms the basis of forecasting. Should we use the history of outbound shipments or customer orders or a combination of the two to proxy for demand.
Stock effects:
The effects that inventory levels have on sales. In the extreme case of stock-outs, demand coming into your store is not converted to sales due to a lack of availability. Demand is also untapped when sales for an item are decreased due to a poor display location, or because the desired sizes are no longer available. For example, when a consumer electronics retailer does not display a particular flat-screen TV, sales for that model are typically lower than the sales for models on display. And in fashion retailing, once the stock level of a particular sweater falls to the point where standard sizes are no longer available, sales of that item are diminished.
Market response effects:
The effect of market events that are within and beyond a retailer’s control. Demand for an item will likely rise if a competitor increases the price or if you promote the item in your weekly circular. The resulting sales increase reflects a change in demand as a result of consumers responding to stimuli that potentially drive additional sales. Regardless of the stimuli, these forces need to be factored into planning and managed within the demand forecast.
In this case demand forecasting uses techniques in causal modeling. Demand forecast modeling considers the size of the market and the dynamics of market share versus competitors and its effect on firm demand over a period of time. In the manufacturer to retailer model, promotional events are an important causal factor in influencing demand. These promotions can be modeled with intervention models or use a consensus process to aggregate intelligence using internal collaboration with the Sales and Marketing functions.
