Abstract
In this paper, we investigate the potential of a supply chain (SC) digital twin framework to help decision makers in managing inventory and cash flows throughout the SCs. The proposed SC digital twin framework integrates machine learning (ML) and simulation to identify the inventory replenishment policies that minimize the cash conversion cycle of an SC, currently absent from the literature. The results show that the upstream member of an SC plays a pivotal role in mitigating the bullwhip effect and consequently minimizing the cash conversion cycle of the SC.
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