In many cases, models that work well for shorter periods of time become increasingly imprecise over longer time horizons, as the difference between the model and the underlying reality increases the farther it is predicted. Once you have the information you need, you can generate a forecast by applying one or more of the quantitative and qualitative forecasting techniques described in the next section. First of all, because if it's not on your shelf, you can't waste it, forecasting sales can help limit food waste. Demand forecasting is the process of using predictive analysis of historical data to estimate and predict future customer demand for a product or service.
If the forecast is for a specific product sold by a company, as is often the case, then the demand forecast produces the same practical result as the sales forecast for that product. Help your audiences better understand forecasting and become consumers of more sophisticated forecasts, by including contextual information and answers to questions that they may not know enough or don't feel comfortable enough to ask. Meanwhile, economic forecasters could operate under the tacit assumption that demand forecasts refer strictly to aggregate consumer demand, ignoring important issues for companies trying to predict customer demand. If you use a linear growth model, but demand growth is actually irregular, your forecast will be valid only to the extent that those packages turn out to approach a straight line when projected over time.
However, predicting based on historical quantitative data only works as long as the future changes little compared to the past, and when was the last time that happened? Employees, experts and customers have knowledge of events and plans that have not yet yielded figures, so despite major advances in data collection and analysis technologies, qualitative methods continue to play a major role in forecasting demand. For example, cleaning and validating data can be a fundamental step in some forecasting processes, while other forecasters can obtain high-quality data from other parts of the company that is ready for immediate use. When there isn't much data to work with, such as when a company is new or a product is launched to market, qualitative forecasting approaches are used. Demand forecasting methods are the specific techniques used to predict demand for a product or service, or for a category of product or service.
There are also other dimensions into which demand forecasts and forecasting processes can be classified; the usual one is qualitative versus quantitative, although the best forecasts usually include both types of methods. In some sectors, such as packaged consumer products, the challenge of forecasting is quite simple and the time horizon doesn't need to be long for a forecast to be extremely useful. Forecasting based on historical data can provide information about your two most important costs, food and labor, and help you make essential decisions about where and when to allocate your resources. The following six steps should apply to almost every demand forecasting team, whether it's creating something simple for the first time or running a complex set of continuous forecasts.
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