Bullwhip Effect Measurement and Its Implications

The bullwhip effect, or demand information distortion, has been a subject of both theoretical and empirical studies in the operations management literature. In this paper, we develop a simple set of formulas that describe the traditional bullwhip measure as a combined outcome of several important drivers, such as finite capacity, batch-ordering, and seasonality. Our modeling framework is descriptive in nature as it features certain plausible approximations that are commonly employed in practical inventory systems. The results are nonetheless compelling and can be used to explain various conflicting observations in previous empirical studies. Building on the theoretical framework, we discuss the managerial implications of the bullwhip measurement. We show that the measurement can be completely noninformative about the underlying supply chain cost performance if it is not linked to the operational details (such as decision intervals and leadtimes). Specifically, we show that an aggregated measurement over relatively long time periods can mask the operational-level bullwhip. In addition, we show that masking also exists under product or location aggregation in some illustrative cases.