Developing a Data-Driven Approach to Reducing Excess Inventory in a Multi-Echelon Supply Chain

Abstract

The medical technology company MedTechCo, specifically its Spine division, has deployed millions of implants in hospitals to meet demand. When inventory deployment and allocation are not managed appropriately to ensure that products are in the right place at the right time, excess inventory arises. Currently, MedTechCo Spine holds large amounts of excess inventory that are not utilized effectively.

The objective of this research is to leverage a data-driven approach to define and reduce implant excess inventory at scale for MedTechCo’s Spine business unit in the United States. The research strategy used in this thesis begins with a root cause analysis to understand the causes of excess inventory. A robust data model was then developed to determine appropriate inventory levels by SKU, map all excess field inventory, and prioritize the most valuable excess SKUs. This data model was used to automate the company’s ERP system to repurpose excess inventory, limit unnecessary inventory deployments to the field, and eliminate redundant backorders. Finally, an impact analysis was performed to measure the potential excess inventory reduction in both dollar value and units.

Time constraints limited the implementation of the recommendations during the research period. However, MedTechCo Spine agreed to incorporate the proposed recommendations into its ERP system and operational processes in mid-2025. These recommendations will help reduce implant excess field inventory, unlocking tied-up capital, creating flexibility in the supply chain to meet demand changes, and enabling additional investment in innovation.