Modeling and Analyzing the Inventory Level for Demand Uncertainty in the VUCA World: Evidence From Biomedical Manufacturer

Modeling and Analyzing the Inventory Level for Demand Uncertainty in the VUCA World: Evidence From Biomedical Manufacturer

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 consti...

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Bibliographic Details
Published in:IEEE transactions on engineering management Vol. 70; no. 8; pp. 1 - 11
Main Authors: Raghuram, P., S, Bhupesh, Manivannan, Raghul, Anand, P. Suya Prem, Sreedharan, V Raja
Format: Journal Article
Language:English
Published: New York IEEE 01-08-2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Ambiguity
Arena
Biological system modeling
biomedical equipment
Complexity
Costs
Customer services
Data models
Demand
demand forecasting
discrete event simulation
Discrete event systems
Flow mapping
inventory positioning
Lead time
Levels
long short-term memory
Machine learning
Optimization
OptQuest
Order quantity
Predictive models
random forests
Replenishment
Safety management
Safety stocks
Simulation models
Supply chains
Surgical implants
Uncertainty
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Summary: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.
ISSN:0018-9391
1558-0040
DOI:10.1109/TEM.2022.3201440