Forecasting techniques are quite different from each other. But four features and assumptions underlie the business of forecasting. They are:
* Forecasting techniques generally assume that the same underlying causal relationship that existed in the past will continue to prevail in the future. In other words, most of the techniques are based on historical data. * Forecasts are rarely perfect. Therefore, for planning purposes, allowances should be made for inaccuracies. For example, the company should always maintain a safety stock in anticipation of a sudden depletion of inventory. * Forecast accuracy decreases as the time period covered by the forecast increases. Generally speaking, a long-term forecast tends to be more inaccurate than a short-term forecast because of the greater uncertainty. * Forecasts for groups of items tend to be more accurate than forecasts for individual items, because forecasting errors among items in a group tend to cancel each other out. For example, industry forecasting is more accurate than individual firm forecasting
Managers use forecasts to inform and support their decisions. A small business owner can use sales forecasts to determine if he should hire new employees, while the chief executive of a large company can use customer research surveys to plan marketing campaigns. Unlike quantitative forecasting, numbers are not at the core of qualitative forecasting, which relies on judgment, experience and opinions. Predictive Ability
The main advantage of qualitative forecasting is its ability to predict changes in sales patterns and customer behavior based on the experience and judgment of senior executives and outside experts. Management can use the qualitative inputs in conjunction with quantitative forecasts and economic data to forecast sales trends. Quantitative forecasting uses past results to predict future trends, while economic data includes short- and long-term interest rates and unemployment levels. For example, if the economy is expected to decline in the short term, a small business owner may rely on his own experience and that of his senior sales staff to estimate a small decline in sales next year. Flexibility
Qualitative forecasting gives management the flexibility necessary to use non-numerical data sources, such as the intuition and judgment of experienced managers, sales professionals and industry experts. This can improve the quality of a forecast because quantitative data cannot capture nuances that years of experience can detect. For example, if a small business is planning to open a new store, the quantitative data may show strong historical sales trends for the area. However, due diligence may indicate that recently approved zoning changes for a new shopping mall could have a significant impact on sales going forward, which would make the new location unacceptable. Management could then use its collective judgment to go ahead with the expansion, delay it or scale it back. Ambiguity
Qualitative forecasting is useful when there is ambiguous or inadequate data. For example, a start-up technology company developing a new software application will not have historical data for any kind of quantitative analysis. It can use results from comparable companies and estimates of market size to predict future sales, but it is the judgment and intuition of the founders that will guide most of the key decisions. Large companies may have the resources to conduct focus groups and field tests to design and fine-tune their new products, but their sales forecasts are still going to need qualitative inputs. Considerations
The qualitative method of forecasting has certain disadvantages, such as anchoring events and selective perception. Anchoring events mean that forecasters allow recent events to influence perceptions about future events. For example, a retailer may have received an unusually large order this year,...