Business forecasting is a process used to estimate or predict future patterns. Executives, managers and analysts use the forecasted results to aid in making better-informed business decisions. For instance, business forecasts are used to estimate quarterly sales, inventory levels, supply chain re-orders, website traffic and risk exposure. While business forecasting is usually achieved by using statistical techniques, data mining has also proved to be a useful tool for businesses with much historical data.
Tools used for business forecasting depend on the needs of the business and the amount of data involved. These tools include spreadsheets, enterprise resource planning, advanced supply chain management systems and other network or web technologies. In general, the tools used should allow easy sharing of data between departments or business units, uploading of data from multiple sources, an assortment of analysis technique and graphical viewing of results.
Three methods of business forecasting are available for different types of data and analysis. The time-series model is the most common, where data is projected forward. Statistical calculations for this model include the moving average, exponential smoothing and Box-Jenkins methods. Time-series models are simple in that after the formula is determined, inserting historical data will output the forecasted results. It is only useful when the historical data shows a strong pattern, unaccounted for anomalies.
Explanatory models are another method of business forecasting. These models do not need as much historical data as the time-series analysis in order to receive useful business forecasts. Linear regressions, nonparametric additive and lag regressions are commonly used methods. For instance, a linear regression can be used to determine how much website traffic will bring in for the desired advertisement revenue.
Data mining is a third method of business forecasting, and it is gaining in popularity as businesses gather and save more of its data in digital format. This method relies on sifting through historical data for patterns. This data is typically retrieved and combined from different departments, emails and reports. Algorithms can be based on data-mining for making predictions automatically, such as Amazon.com’s system of offering its customers recommended books.
Errors in business forecasting are common due to software issues, mathematical errors, unnecessary tweaking and biases. Reducing or eliminating errors can be accomplished by recalculating, comparing the results when using a different formula or method, minimizing tweaks and removing opportunities for biases. Estimations should be clearly identified with an explanation of how the estimation was created. Initial forecasts may prove to be inaccurate when compared with actual results, so constant tweaking may be needed in order to produce stronger future predictions.