2026-09-19T18:50:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1560032024-08-13T03:45:54Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Quaadgras, Anne
Frey, Daniel
Kleinemolen, Ian
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
2024-08-12T14:14:35Z
2024-08-12T14:14:35Z
2024-05
2024-06-25T18:13:51.407Z
https://hdl.handle.net/1721.1/156003
Increasing volatility in the global supply chain following the Covid-19 pandemic has led to a challenge in reliably managing inventory, especially for high-complexity medical devices. An optimization and simulation-based inventory management model was developed to augment the decision making of supply planners in these networks. The model supports supply planners in safety stock allocation decisions by quantifying inventory cost and stockout probability risk for products with multi-stage, converging supply networks. Components of the model include iterative multi-echelon inventory optimization, monte carlo simulation of a custom base-stock inventory model and cycle service level modelling. An application of the model is explored in a case study of the J&J Ethicon surgical stapler supply chain. In addition, operational considerations for implementing inventory models are discussed, including data architecture, standardization, and centralization for complex supply chains.
M.B.A.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Inventory Optimization and Simulation Analysis for Supply Chain Disruption Events
Thesis
application/pdf
Master
Master of Business Administration
Master of Science in Mechanical Engineering
Publication
unknown
unknown
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<Abstract>Increasing volatility in the global supply chain following the Covid-19 pandemic has led to a challenge in reliably managing inventory, especially for high-complexity medical devices. An optimization and simulation-based inventory management model was developed to augment the decision making of supply planners in these networks. The model supports supply planners in safety stock allocation decisions by quantifying inventory cost and stockout probability risk for products with multi-stage, converging supply networks. Components of the model include iterative multi-echelon inventory optimization, monte carlo simulation of a custom base-stock inventory model and cycle service level modelling. An application of the model is explored in a case study of the J&J Ethicon surgical stapler supply chain. In addition, operational considerations for implementing inventory models are discussed, including data architecture, standardization, and centralization for complex supply chains.</Abstract>
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