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Mitigating forecast errors from product variety through information sharing
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DOI：10.1080/00207543.2018.1449974.png)
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Abstract
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We examine the impact of increasing product variety on two measures of a firm's forecast performance - forecast accuracy and forecast bias - and test whether shared information mitigates this impact. With companies under pressure to expand product variety yet maintain good forecast accuracy understanding this relationship is critical. We use data gathered pre and post a vertical integration event, where some information forecasted prior to the merger was now available. We show that increasing product variety, and thus the number of forecasts, indeed deteriorates both forecast accuracy and bias. The vertical integration event, providing information sharing, results in improved forecast performance. Further, different product variety attributes (e.g. brand variety and pack variety) are found to have differing impacts. Increasing brand variety is found to have a significantly greater impact on forecast accuracy than pack variety. Using the vertical integration event as a natural experiment we document that expanding product variety negatively impacts forecasts and that information can help mitigated the impact. This is an important contribution as it tests the value of 'truthful' information given the elimination of the firm boundary post merger. Further, we show that a firm's decision to expand product variety should include product variety attributes given their differential impact.
Keywords:
forecasting
product variety
information sharing
cross-functional interface
archival research
regression analysis
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