Business forecasting - Essay Example

Business forecasting College: Business forecasting Business forecasting refers to the process used in estimating and predicting future patterns in business using business data, forecasting is essential in business, it helps in informing decisions concerning activities like scheduling of transportation, production, and workforce, and offers a guide to management’s long-term strategic planning (Hoshmand 2009, p. 17). Forecasting is an integral part in decision making process at management level and it plays an essential role in the various business areas. Business forecasting is done on short-term, long-term, and medium depending on the particular application. Short-term forecast may include decisions on scheduling of production, personnel and transportation, and sometimes demand forecasts are also done on short-term. Medium-term forecasts involve decisions on future resource requirements and they guide the management in knowing how much to purchase, how many to hire or the machinery to buy. Long-term forecasts mainly involve strategic planning and take into account the internal resources, environmental factors and market opportunities (Malehorn & Jain 2005, p. 269). Business areas where forecasting is commonly used include estimation of product demand for existing and newly introduced products, estimating sales, supply chain and inventory re-order timing, website traffic, prediction of exposure to risk and fraud, customer lifetime value, and workforce attrition. Business areas like budgeting, scheduling of transport, personnel and transportation, strategic planning by management, procurement, and hiring of personnel largely depend on business forecasting. Budget forecasting Budgets do not simply indicate the upcoming period’s expenses and revenues, as competition in the market place increase, companies have recognized the need to accurately predict their future operations’ outcomes and the related resource requirements. Budgeting involves use of forecasting techniques, it is an organization-wide process and is central in strategic planning. Effectiveness of the budgeting process depend on the accuracy of forecasts, reliability, simplicity, and cost effectiveness, this accuracy can be enhanced by use of various techniques, using more than one method of forecasting, and obtaining enough and more accurate data (Rasmussen 2003, p. 113). Where budget forecasts fall far from actual outcomes for the period, it is likely that poor decisions on future projections are made and management may end up lacking trust in budgets made while the whole organization may suffer poor planning. Poor budgeting forecasts results from poor budgeting tools lack of staff training on forecasting techniques, too little time allocated in making correct projections. Correct budget forecasts makes financial planning and execution of operating activities like staffing, inventory management, marketing plans, and performance easier. Budget forecasting is used in making different types of budgets including purchase of raw materials, inventory levels, adjusting incentive plans and staffing levels. Budget forecasting is central in project planning and costing, financial outlays and expected return on the investment made are all based ob forecasting (Hamper & Baugh 1990, p. 159). Sales forecasting Sales forecasting involves prediction of amount of purchases given the conditions of sales and product features (Harvardar 2010, p. 120). Sales forecasts are important in decision making concerning new ventures to invest and in existing businesses. They aid managers in determining the price to charge, salaries to pay the sales workforce, inventories to hold, number of workers, and amount to put in advertising budget (Dalrymple 1987, p. 381). Marketers use sales forecasts to draw their marketing plans, given the impact of revenue to firm’s profitability, sales forecasts require adequate focus. Sales forecasts helps management to decide on what products to offer and which ones to remove from the market based on how forecast figures match with actual sales volume (Berry 2010, p. 2). Forecasting techniques Forecasting techniques are categorized into quantitative and qualitative, qualitative techniques include Delphi method, which is normally used where there is no need for statistical details (Rowe & Wright 2001, p. 42). Quantitative techniques include statistical survey and time series analysis (Makridakis et al 1998, p. 67). Businesses usually use simple forecasting techniques most of which are not based on statistical modeling (Tayton 2003, p. 256). However statistical modeling has gained popularity in businesses due to its ability to offer more accurate estimates and more systematic and stable ways of making future projections (Mentzer & Kahn 1995, p. 466). In budgeting, managers mostly make use of time series techniques like linear methods where they evaluate the budgets over period and how consistent that has been with the actual outcomes, these figures are used to predict future outcomes. Some businesses make use of data mining methods where no formal statistical models are developed. Data mining methods are effective especially where huge amounts of data are used. Predictions methods used in data mining include support vector machines and neural networks (Zhang 2004, p. 6). In sales forecasting, techniques used include time series techniques like exponential smoothing and statistical methods. Most business used direct extrapolation technique in sales forecasting (Moon & Mentzer 2005, p. 18). Direct extrapolation technique can either make use of statistical data or can simply be judgmental. Judgmental extrapolations are more preferred than quantitative extrapolations where the market has registered several changes that are contrary to the trend built from the historical series (Hanssens et al 2001, p. 117). Direct extrapolation of the sales data for short intervals like less than a year use seasonal adjustments to enhance accuracy of forecast (Wallace & Stahl 2002, p. 38). Use of time series requires adequate data, where such data is not available, firms use judgmental forecasts. Common time series technique used in industry sales forecast is naive forecasting where future sales values in the time series are set equal to the values last observed (Chatfield et al 2001, p. 149). Naive forecasting is also commonly used in stock index and stock price forecasting. Conclusion This study has explored business areas that use forecasting discussing the various types of forecasts including short term, long-term, and medium term (Evans 2002, p. 18). The study has selected two areas sales forecasting and budget forecasting and discussed how forecasting helps in the mentioned areas and the various forecasting techniques used including quantities and qualitative techniques like time series and other statistical models. References Berry, T (2010), Sales and Market Forecasting for Entrepreneurs, Business Expert Press, New York. Chatfield, C., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2001), A new look at models for exponential smoothing, Journal of the Royal Statistical Society, Series D: The Statistician, Vol. 50, No.2, pp. 147–159. Dalrymple, D. J. (1987), Sales forecasting practices: Results from a U.S. survey, International Journal of Forecasting, Vol. 3, No. 2, pp. 379-91. Evans, M (2002), Practical business forecasting, Wiley-Blackwell, Boston. Hamper, R & Baugh, S (1990), Strategic Market Planning, McGraw-Hill Professional, London. Hanssens, D. M., Parsons, L. J. & Schultz, R. L. (2001), Market Response Models: Econometric and Time Series Analysis, second edn, Kluwer Academic Publishers, Boston. Harvardar (2010), Business Marketing: Text & Cases, 3E, Tata McGraw-Hill Education, India. Hoshmand, R (2009), Business Forecasting: A Practical Approach, Taylor & Francis, Oxford. Makridakis, S., Wheelwright, S. C. & Hyndman, R. J. (1998), Forecasting: methods and applications, 3rd edn, John Wiley & Sons, New York. Malehorn, J & Jain, C (2005), Practical guide to business forecasting, Institute of Business Forecasting, New York. Mentzer, J.T. & Kahn, K.B. (1995), Forecasting technique familiarity, satisfaction, usage, and application, Journal of Forecasting, Vol. 14, No. 4, pp. 65-76. Moon, M & Mentzer, J (2005), Sales forecasting management: a demand management approach, SAGE, London. Rasmussen, N (2003), Process improvement for effective budgeting and financial reporting, John Wiley and Sons, New Jersey. Rowe, G. & Wright, G. (2001), The Delphi technique as a forecasting tool, in J. S. Armstrong (ed.) Principles of Forecasting: Handbook .for Researchers and Practitioners, Kluwer Academic Publishers, Norwell, MA. Tayton, W (2003), Business Forecasting and Its Practical Application, Kessinger Publishing, Montana. Wallace, T & Stahl, R (2002), Sales forecasting: a new approach : why and how to emphasize teamwork, not ..., T. F. Wallace & CO, Ohio. Zhang, P (2004), Neural networks in business forecasting, Idea Group Inc (IGI), New York. Read More