Abstract: The bullwhip effect is the amplification of demand variability along a supply chain: a company bullwhips if it purchases from suppliers more variably than it sells to customers. Such bullwhips (amplifications of demand variability) can lead to mismatches between demand and production, and hence to lower supply chain efficiency. We investigate the bullwhip effect in a sample of 4,689 public U.S. companies over 1974-2008. Overall, about two thirds of firms bullwhip. The sample's mean and median bullwhips, both significantly positive, respectively measure 15.8% and 6.7% of total demand variability. Put another way, the mean quarterly standard deviation of upstream orders exceeds that of demand by $20 million. We decompose the bullwhip by information transmission lead time. Estimating the bullwhip's information-lead-time components with a two-stage estimator, we find that demand signals firms observe with more than three quarters' notice drive 30% of the bullwhip, and those firms observe with less than one quarter's notice drive 51%. From 1974-94 to 1995-2008, our sample's mean bullwhip dropped by a third.
An Empirical Signal Processing Model of the Supply Chain with Haim Mendelson.
Abstract: We present a structural econometric supply chain framework. The framework analizes supply chain black boxes by measuring how input demand signals transform into output order signals. Modeling this transformation as a linear, time invariant system, we characterize inventory policies with impulse response functions. We develop an algorithm to estimate these impulse response functions, and the structural parameters underlying them, with single-firm sales and production data. With our estimates, we (1) decompose the bullwhip effect by driver, and (2) conduct a forecast-accuracy counterfactual analysis.
