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for helpful comments and suggestions. We gratefully acknowledge the participating firm for data accessibility. Does learning by disaggregating accelerate learning by doing? The effect of forecast disaggregation on the rate of improvement in demand forecast accuracy ABSTRACT Demand forecast accuracy is critical to organizational planning and coordination, and forecast accuracy is an important source of competitive advantage. Indeed, surveys indicate that CFOs name forecast error as their top internal concern and identify demand forecasting as one of their top organizational priorities. In this study, we examine whether the disaggregation of a demand forecast into separate forecasts for each source of demand accelerates the rate of improvement over time in the accuracy of managers' demand forecast judgments. We first predict that forecast accuracy improves through learning by doing: The rate of decline in forecast error with shortening forecast horizon (e.g., from 9-month to 1-month) increases with experience. We then hypothesize that disaggregation of the forecast accelerates learning by doing. Exploiting our unique access to proprietary data from a multinational manufacturing organization we find evidence that is consistent with our predictions. Our study provides evidence of how a change in the way in which managers formulate and communicate forecaststhat is, forecasting different sources of demand separately-can help address the vexing problem of demand forecast error.

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