Production Smoothing and the Bullwhip Effect

Abstract

T he bullwhip effect and production smoothing appear antithetical because their empirical tests oppose one another: production variability exceeding sales variability for bullwhip, and vice versa for smoothing. But this is a false dichotomy. We distinguish between the phenomena with a new production smoothing measure, which estimates how much more variable production would be absent production volatility costs. We apply our metric to an automotive manufacturing sample comprising 162 car models and find 75% smooth production by at least 5%, despite the fact that 99% exhibit the bullwhip effect. Indeed, we estimate both a strong bullwhip (on average, production is 220% as variable as sales) and robust smoothing (on average, production would be 22% more variable without deliberate stabilization). We find firms smooth both production variability and production uncertainty. We measure production smoothing with a structural econometric production scheduling model, based on the generalized order-up-to policy.Keywords: production smoothing; bullwhip effect; demand signal processing; generalized order-up-to policy; martingale model of forecast evolution History:

Behavioral causes of the bullwhip effect: An analysis using linear control theory

“…Empirical research, in fact, demonstrates that order smoothing and the bullwhip effect are concurrent in industry (Bray and Mendelson, 2015). We show that order smoothing is beneficial for the system's performance when demand is stationary.…”

Why Empirical Research Is Good for Operations Management, and What Is Good Empirical Operations Management?

“…The central contribution of the paper comes from the data. Finally, Bray and Mendelson (2015) use detailed data from a single industry (automobile), using structural estimation to uncover multiple mechanisms that enhance or mitigate the bullwhip effect. Here the contribution arises from both the data and the estimation methodology.…”

“…However, the authors did find some bullwhip effect in their deseasonalized data and were left to ponder whether variability in demand or uncertainty in demand was driving this effect. To understand this question, Bray and Mendelson (2015) build and estimate a structural model to show that once one incorporates firm-level data, it is clear that the bullwhip effect does exist and that it is driven by both variability and uncertainty in demand. Subsequent work has continued to unpack assumptions within the bullwhip models to test normative theory and in so doing continues to shape subsequent theory (Bray and Mendelson 2015, Mackelprang and Malhotra 2015, Baron et al 2018, Bray et al 2019.…”

Bullwhip and anti-bullwhip effects in a supply chain

“…Fair 1989;Fitzgerald 1997), but on why the two effects co-exist and how they are related to each other. Bray and Mendelson (2015) proposed that the two effects may coexist if we change the bullwhip effect measurements from order variability to order uncertainty. Chen and Lee (2012) built a theoretical framework to investigate the impacts of various factors on the bullwhip effect, including the system capacity, batch-ordering policy, demand seasonality, and cost and aggregation levels.…”