Business forecasting uses statistical techniques and data mining to predict future patterns for better-informed business decisions. Tools used include spreadsheets, ERP, and advanced supply chain management systems. Three methods of forecasting are time series, explanatory models, and data mining. Errors can be reduced by recalculating, comparing results, minimizing changes, and removing bias.
Business forecasting is a process used to estimate or predict future patterns. Executives, managers and analysts use predicted results to help make better-informed business decisions. For example, business forecasts are used to estimate quarterly sales, inventory levels, supply chain reorders, website traffic, and risk exposure. While business forecasting is usually achieved using statistical techniques, data mining has also proven to be a useful tool for businesses with lots of historical data.
The 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 for easy sharing of data between departments or business units, loading data from multiple sources, an assortment of analysis techniques, and graphical visualization of results.
Three business forecasting methods are available for different types of data and analyses. The time series model is the most common, where the data is projected forward. Statistical calculations for this model include moving average, exponential smoothing, and Box-Jenkins methods. Time series models are simple in that once the formula is determined, entering historical data will generate the expected results. It is only useful when historical data shows a robust pattern, not accounted for outliers.
Explanatory models are another method of business forecasting. These models do not require all of the historical data from time series analysis to receive useful trading predictions. Linear regressions, nonparametric additive, and lag regressions are commonly used methods. For example, you can use linear regression to determine the amount of website traffic that will generate the desired advertising revenue.
Data mining is a third method of business forecasting and is gaining popularity as companies collect and save more data digitally. This method relies on sifting through historical data for models. This data is usually retrieved and combined from different departments, emails and reports. Algorithms can be based on data mining to make predictions automatically, such as Amazon.com’s system of offering its customers recommended books.
Errors in business forecasting are common due to software glitches, math errors, unnecessary changes and biases. Reducing or eliminating errors can be done by recalculating, comparing results when using a different formula or method, minimizing changes and removing opportunities for bias. Estimates should be clearly identified with an explanation of how the estimate was created. Initial forecasts may prove inaccurate with respect to actual results, so constant adjustments may be required to produce stronger future forecasts.
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