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Introduction
Business organizations conventional goal is to realize good financial performance and sustain their solvency amid ever-increasing competition and various kinds of unforeseen risks of running a business (Barlow, 2005). This traditional business objective necessitates the use of informative business data in making strategies that can enable them to improve their financial performance by predicting or estimating future business patterns with reduced uncertainty (Hoshmand, 2009). This paper is a report on the uses of business forecasting.
Uses of Business Forecasting
Precise business forecasts are critical in the performance of business activities because of the dynamic characteristic of domestic and international markets. Business forecasting is used to provide the senior management with business decision alternatives that are derived from the use of a reliable statistical analysis (Hoshmand 2009; Barlow, 2005).
According to Hoshmand (2009), business forecasters or analysts develop authentic forecasts that form the foundation of production, marketing, and financial planning activities. In simple terms, business forecasting is used by business organizations and companies as an instrument in making sound and realistic business and economic decisions at a time when complexity arising partly from the globalization of business has increased risks related to business decisions (Barlow, 2005).
Quantitative Forecasting Models
Current business and economic forecasters have a variety of forecasting models that fall into three major categories including technological models, qualitative models and quantitative models (Hoshmand, 2009). Quantitative models which are also referred to as statistical models are the most dominant in the field of forecasting. They are essentially objective approaches to the process of estimation or prediction in business activities. Quantitative forecasting models comprise regression approaches and time series forecasting techniques. According to Hoshmand (2009), these approaches offer a logical sequence of steps that can be reproduced and used in various economic and business conditions.
Time series models anticipate about the future of a given variable entirely based on the variables history. Independent variable, in this case, is time (Hoshmand 2009). Time series models comprise moving averages, exponential smoothing, Box-Jenkins and time series decomposition. Moving average forecasting approach is a method where old observations are dropped and new observations are added to work out new averages (Hoshmand, 2009). Hoshmand (2009) contends that exponential smoothing is similar to moving average technique with the prime difference in that it needs less business data to move an average. It is used in estimating time series data with a linear trend.
Regression approaches or casual models are founded upon insight from economic assumptions (Hoshmand, 2009). In these approaches, hypothetical connections between variables are used as the signposts in forecasting. The assumption underlying regression approaches is that the value taken by a given independent variable has the potential to impact upon the value of another dependent variable (Hoshmand, 2009).
Qualitative Forecasting Models
Qualitative forecasting models are also known as judgmental or non-statistical approaches to economic and business forecasting. These models rely heavily upon experts understanding, opinion and intuitive judgment (Hoshmand, 2009). They are usually embraced when historical information is unavailable or scarce and unreliable. Qualitative forecasting models include sales force composite forecasts, focus group or panel consensus, the jury of executive opinion and the Delphi technique (Hoshmand, 2009).
Delphi decision-making approach is a process of the possibility that certain events will take place. It was developed in 1963 at the RAND Corporation. Hoshmand (2009) observes that it is founded upon the idea that well-informed stakeholders of a given organization understands their business better and can anticipate about the future of their business more reliably than theoretical models. Delphi decision-making approach uses a panel of experts who answers a sequence of questions that are not known to any one of them. The answers are then summed up before moving to another series of questions. Ultimately the group through a consensus process comes together towards the best response (Hoshmand, 2009).
Technological Forecasting Models
Technological Forecasting approaches are a combination of qualitative and quantitative forecasting models to facilitate long-term estimation. Technological Forecasting methodologies aim at responding to economic, political, social and technological changes in the markets to make a business prediction (Hoshmand 2009).
Reference List
Barlow, J. F. (2005). Excel models for business and operations management. New York, NY: John Wiley and Sons.
Hoshmand, A. R. (2009). Business Forecasting, Second Edition: A Practical Approach. New York, NY: Taylor & Francis.
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