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 and improve over time. Defining sales forecasting models for small- and medium-size business operations is especially difficult when the number of sales events is small but the revenue per sales event is large. 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 combined 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 be combined in the forecast to cater for more complex business environments.