The What, Why and How of Business Forecasting
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By John Alexander Adam
The use of business forecasting models is now regarded as a basic necessity in business management and planning. Setting up the plan for the months and years ahead always involves prediction of trends in demand based on factors such as seasonality, competition and wider economic factors. Management use these forecasts, which are essentially informed guesses, in their budget planning and things like HR strategy, capital investment and marketing spend are all shaped based on business forecasting.
There are two main categories of business forecasting methodology. Judgement forecasting, most commonly termed ‘qualitative forecasting’, is based on human judgement while ‘quantitative forecasting’ is data driven. Both have their common applications, strengths and weaknesses.
Qualitative ‘Judgement’ Forecasting
Qualitative Forecasting is essentially based upon ‘informed consensus’, either that of consumers themselves and harvested via market research, or ‘experts’. Qualitative business forecasting is most commonly used for shorter term decision making and in situations where there the scope of the forecast is more limited. It is generally accepted that its reliance on opinion over data means that judgement forecasting is not a reliable over the longer term.
Qualitative forecasting is generally most relied upon when calculating likely demand before a product or service is launched. Market research and focus groups are examples of qualitative ‘judgement’ forecasting based on a consensus of consumer opinion. The compilation of the opinion of a cross-section of relevant experts is referred to as the ‘Delphi’ method of qualitative business forecasting.
Quantitative business forecasting methodology is founded on the principle that human opinion is both fickle and unreliable, at least over the longer term and when the scope of the forecast is not very limited. There are three main methodologies that quantitative forecasting relies upon:
- The Indicator Approach
This approach looks at the various ‘indicators’ or influences that can impact upon demand. It isolates particularly important indicators which cause changes in demand even when all other influences retain their status quo. The influences are termed ‘lead indicators’ and tend to be big macro-economic factors such as unemployment rates and GDP, though different kinds of products and services will use different lead indicators depending upon their historical influence on demand. Historical data will show how changes to these indicators impact demand and will base sales forecasting on forecasted fluctuations in these lead indicators. The indicator approach will almost certainly not be used in isolation for business forecasting and is usually combined with the other main quantitative methodologies.
- The Econometric Approach
Econometric methodology is essentially a more complex version of the indicator approach and incorporates all of the indicators that are considered to have been shown to impact upon demand. This methodology uses complex statistical models to forecast not only how changes to different indicators in isolation are likely to impact demand but how they work together. Indicator 1 may be shown to have historically impacted demand for a company’s category of product by x percent when it has changed by y percent. The same might be said of indicator 2. However, when indicators 1 and 2 have changed by these same percentages together, the impact on demand may have been different. Add in another several indicators and the model becomes a sophisticated algorithm based on frequency, probability and statistical inference involving large groupings of statistical data interacting with each other to produce different forecasted outcomes.
- The Time Series Approach
The Time Series Methodology is more of an ‘editor’ to the other methodologies covered than an independent methodology in its own right. It takes the historical data used in the other methodologies and attaches greater weighting to more current data and can discount older data or less influential data as ‘outliers’ than can corrupt the forecast.
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