Reverse bullwhip effect: duality of a dynamic model of Supply Chain

Abstract

This study aims to investigate control strategies for the bullwhip effect based on a dynamic model of the linear supply chain, proposed by Helbing and Lammer (2005), which describes the inventory dynamics and production rates of productive units. We simulated the model for instability and stability conditions defined by mathematical analysis. Through these results, we verified both classical and reverse bullwhip effects associated with instability and stability conditions, respectively. The model revealed a duality once the control strategy proposed by Helbing and Lamer (2005) for the classical bullwhip effect ends up causing a reverse effect, which is equally troubling. In the reverse bullwhip effect, we observed amplification of the production rates in the network chains from the supplier to the customer in a way that the upstream chain was not able to meet the needs of the downstream chain. To withhold both effects, we suggest the dynamic control of the parameters that describe the network based on Helbing and Lammer (2005) model.

Robust Control of Bullwhip Effect for Supply Chain System With Time-Varying Delay on Basis of Discrete-Time Approach

“…Robust stability analysis was shown to be appealing, especially for multi-echelon chains, because operating strategies based on these results ensure supply chain stability [32]. Considering the uncertain disturbance and dynamic time-varying supply chain operation, the robust H∞ control method was proposed to inhibit bullwhip effect and make sure the stability of system [33], [34], [35], [36].…”

Reverse bullwhip effect in the automotive industry

“…The Forrester effect explains the reasons why fluctuations in demand increase within the upstream supply chain links in a higher proportion (as a consequence) than those generated in the downstream supply chain links (as a cause). The solution is integrated and real-time communication (Brito et al, 2020), but only in conjunction with realistic and harmonized internal performance metrics (forecast accuracy, production planning, productivity and output metrics, different stage inventory levels, perfect order deliveries) applied by each short loop supply chain (supplier -manufacturer -customer). Multi-tier supplier-manufacturer relationships and the use of EDI (either as partially or fully integrated software programs) as a means for faster (real-time), more reliable (user access and permissions) and longerterm planning (quarter, semester and/or annual forecasts) are not a guarantee against the bullwhip effect if the reliability of the data in the information exchange is not 100% accurate (Papanagnou, 2022).…”