Masking the bullwhip effect in retail: the influence of data aggregation

Abstract

Purpose – The accurate measurement of demand variability amplification across different nodes in the supply chain, or “bullwhip effect,” is critical for firms to achieve more efficient inventory, production, and ordering planning processes. Building on recent analytical research that suggests that data aggregation tends to mask the bullwhip effect in the retail industry, the purpose of this paper is to empirically investigate whether different patterns of data aggregation influence its measurement. Design/methodology/approach – Utilizing weekly, product-level order and sales data from three product categories of a consumer packaged goods manufacturer, the study uses hierarchical linear modeling to empirically test the effects of data aggregation on different measures of bullwhip. Findings – The authors findings lend strong support to the masking effect of aggregating sales and order data along product-location and temporal dimensions, as well as the dampening effect of seasonality on the measurement of the bullwhip effect. Research limitations/implications – These findings indicate that inconsistencies found in the literature may be due to measurement aggregation and statistical techniques, both of which should be applied with care by academics and practitioners in order to preserve the fidelity of their analyses. Originality/value – Using product-weekly level data that cover both seasonal and non-seasonal demand, this study is the first, to the author’s knowledge, to systematically aggregate data up to category and monthly levels to empirically examine the impact of data aggregation and seasonality on bullwhip measurement.

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