Modeling and Analyzing the Inventory Level for Demand Uncertainty in the VUCA World: Evidence From Biomedical Manufacturer
针对VUCA环境下的需求不确定性和供应复杂性，通过库存定位和机器学习预测优化生物医学设备（如膝关节植入物）的库存水平，利用离散事件仿真和OptQuest最小化缺货和总成本。
As the world is witnessing unprecedented events such as the COVID-19 pandemic, we live in a volatile, uncertain, complex, ambiguity (VUCA) world. Where volatility in supplies, Uncertainty in demand, Complexity in getting the products, and Ambiguity in understanding the issues. Such a scenario constitutes a VUCA world, and inventory positioning is no exception. Inventory positioning manages the safety stock across echelons to maintain customer service levels undersupply or demand uncertainties. Therefore, this article focuses on optimizing the inventory levels in demand uncertainty and supply complexity through inventory positioning and making reliable forecasts using machine learning for biomedical equipment, especially knee implants. The product flow is mapped through a discrete event simulation model by considering a biechelon supply chain. The parameters like reorder point, order quantity, supply lead time, and inventory costs are considered, and Arena modelled and simulated inventory replenishment. They are optimized with in-built OptQuest to minimize back orders and total costs. The model determines the safety stock inventories positioned at both echelons to achieve service level constraints. The uncertainty in demand is the root cause of the abovementioned issues and may be reduced through more reliable forecasts.