Probabilistic Sales Forecasting for Small and Medium-Size Business Operations
One of the most important aspects of operating a business is the forecasting of sales and allocation of resources to fulfill sales. Sales assessments are usually based on mental models that are not well defined, may be biased, and are difficult to refine
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1 Introduction One of the most important aspects of operating a business is the forecasting of sales and allocation of resources to fulfill sales. While customer relationship management and enterprise resource management systems have helped provide larger organizations with methodologies for forecasting and measuring sales, many small- and medium-size business operations rely on the assessment abilities of one or more sales managers to estimate and manage the sales pipeline. In turn, the outputs of these forecasts as provided to operational mangers are often overly simplistic and do not provide sufficient depth to support resource planning adequately. The fundamental problem is that sales assessments are usually based on mental models that are not well defined, may be biased, and are difficult to refine and improve over time. Furthermore, defining sales forecasting models for small- and medium-size business operations is often difficult when the number of sales events is small but the revenue per sales event is large. Little or no data may be available to support estimations, and the effect of assumptions and uncertainties is magnified when only a few sales make up the entire revenue stream. Hence, it is not practical to use traditional budgeting processes that set a fixed annual budget when there is a high degree of variability in the expected revenue. Soft computing approaches, such as Monte Carlo techniques, have been used to determine the general range of possible outcomes where information that is critical to planning is either unknown or unreliable. Savage (2002) focused attention on the problems of using single point estimates in corporate accounting and how Monte Carlo techniques can be used to reflect a more realistic state of corporations’ finances. Monte Carlo techniques have also been championed for use in a wide range of B. Prasad (Ed.): Soft Computing Applications in Business, STUDFUZZ 230, pp. 129–146, 2008. springerlink.com © Springer-Verlag Berlin Heidelberg 2008
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R.E. Duran
applications related to risk modeling and risk analysis (Koller 2000). There has also been research into using adaptive processes and rolling forecasts for budget planning (Hope and Fraser 2003). The objective of the research described in this chapter is to develop an approach that combines computer-based Monte Carlo analysis with adaptive business planning to help provide more accurate business forecast information and support operational resource planning. This chapter reviews the challenges of sales forecasting in this environment and describes how incomplete and potentially suspect information can be used to produce more coherent and adaptable sales forecasts. It outlines an approach for developing sales forecasts based on estimated probability distributions of sales closures. These distributions are then used with Monte Carlo methods to produce sales forecasts. Distribution estimates are adjusted over time, based on new developments in the sales opportunities. Furthermore, revenue from several types of sources can b
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