Decision Making Using Technical Forecasting

The process of trying to forecast the future of any business in relation to the economy that surrounds it is considered to be forecasting. Generally, forecasting is a common procedure for all businesses, investment analysts, economists and governmental groups. Business forecasting is a process in which data is acquired, put into mathematical models to derive answers to predict future events in order to assist that business with its efforts in achieving maximum profitability (Walonick, 1993).

For all businesses, what happens today is linked to what happens in the future. Businesses need accurate predictions to better plan for the future. There is a certain level of unpredictability for future data relating to ordinary business operating procedures, manufacturing, inventory systems, sales events and other business actions that happen in the course of operation of a business. Predicting the future (by using forecasting) attempts to reduce the unknowns.

Business forecasting must also take into consideration the macro environment. For example, if a business's sales are directly related to consumer purchasing and there is a period of high unemployment the pool of potential customers may be reduced. If there is a period of inflation consumers tend to be more conservative with their money as they see their dollar reducing value for what it can buy.

Business forecasting has changed. Hand written notes and calculations have given way to computer aided processes. The software available for business forecasting has become highly automated and available commonly (Hyndman. 2009). Generally, the forecasting process has certain decision-making steps to follow. First - determination of what will be forecasted is necessary, second - identification and evaluation of past forecasting and the variables to be considered where applicable should be made, third - collection of accurate and relevant data should be made, fourth - relationship and identification must be clarified, fifth - the correct model must be used and sixth - analysis of the results can be computed then compared to prior data then evaluated for acceptability in making a forecast (Business Forecasting 2009).

By using quantitative statistical data for forecasting, businesses can increase the quality of their decision-making. Generally, the widespread benefits of forecasting include better planning for business resources, cost savings and more predictive data for use in decision-making. Difficulties in forecasting can occasionally arise. Using the formula models may provide ease of complex calculation but the results may not be as accurate as hoped for. Since these forecasting models are based on historical data they often make no allowance for unusual events that happen. These models are rigid and do not allow for a degree of flexibility. In addition forecasting models may give the illusion that the derived data is 100% accurate for predicting the probable future.

Businesses have evolved complicated business relationships with their supply chains. To facilitate efficiency in the entire web that connects all of these forecasting models using historical data to predict future factors. Using forecasting models, any business can streamline their logistics structures and utilize efficient, low levels of inventory and just in time processes as methods to increase efficiency.

Economic forecasting is also used by big organizational structures like our government. Business cycles of expansion and contraction can be modeled with forecasting and this sometimes providing valid data that allows an organization like the Federal Reserve to then make adjustments by increasing or lowering interest rates, by changing the total supply volume of the money in circulation and through enforcement of rules systems throughout the federal banking system to then create stabilization of the United States economy.

References

Business Forecasting. (2009). Forecasting Business Enterprise. Retrieved from http://ecommerce.hostip.info/pages/457/Forecasting-Business.html

Hyndman. J. ( 2009). Business forecasting methods. Retrieved from Http://www.robjhyndman.com/papers/businessforecasting.pd

Walonick, S. (1993) An Overview of Forecasting Methodology. Retrieved from http://www.statpac.com/research-papers/forecasting.htm