Many business forecasting problems are characterized by infrequent occurrences, a large number of variables, presence of error, and great complexity. Because no forecasting models and tools are effective in handing these problems, managers often use the outcomes of past analogous cases to predict the outcome of the current one. They (1) observe significant attributes in describing a case, (2) identify the past cases similar in these attributes to the current case, and (3) predict the outcome of the current case based on those of the analogous cases identified through some mental simulation and adjustment. This process of forecasting can be termed forecasting-by-analogy. In spite of fairly frequent use of this forecasting process in practice, however, it has not been recognized as a primary forecasting tool, nor applied on a regular basis. In this paper, by automatizing this process using computer models, we develop a case-based forecasting system (CBFS), which identifies relevant cases and applies their coutcomes to generate a forecast. We demonstrate the effectiveness of the CBFS in terms of its accuracy in predicting the outcome of the current problem based on the similar cases identified. We compare the forecasting accuracy of the CBFS with that of regression models developed by stepwise procedure under varied simulated problem conditions. The CBFS outperforms regression models in most comparisons. The CBFS could be used as an effective forecasting tool.