Supply chain cash-flow bullwhip effect: A simulation approach

This paper investigates the cash-flow bullwhip (CFB) phenomenon, which refers to the amplification of financial volatility across supply chains, using simulation modeling across multiple network topologies. While prior studies confirmed CFB empirically and analytically, most focused on simple serial chains. Here, we extend the analysis to serial, convergent, divergent, con-div, and general network structures, incorporating both the traditional inventory bullwhip effect (BWE) and financial dynamics. Designed experiments test how stochastic procurement and payment lead times and information sharing influence CFB under both steady-state and demand-shock conditions. Results show that lead-time variability, demand forecasting, and information sharing significantly shape CFB propagation, with effects most pronounced upstream. Convergent networks dampen CFB the most, up to 78–86% lower variance than other topologies, while divergent networks amplify it fastest. Information sharing reduces mean CFB and BWE by roughly 70 and 60%, respectively, with benefits evident even at the first upstream tier. Exponential smoothing produces lower CFB and forecast errors than stochastic financial analytics. Under demand shocks, the rate of CFB amplification increases by 2–3.4 times, highlighting how operational and financial dynamics interact to shape supply chain resilience.