Product visibility in the medical device supply chain is a challenge for suppliers, distributors, and hospitals. The lack of visibility makes managing inventory complex, and it is made more difficult when businesses have a segregated distribution model. In this model, a surplus of systems collects inventory data at the supply chain nodes, but the data is not integrated due to system barriers. This aspect of ‘big data’ is a current problem multiple supply chains are facing as they look towards future data analytic capabilities. In this capstone, we evaluated the potential of integrating the sponsoring company’s data sets from fragmented planning systems in enabling advanced data analytics and visualization that can improve inventory management. We successfully created a data aggregation tool after cleaning and transforming the data sets and performed data analysis on the aggregated data. SKU segmentation was completed, and their inventory distribution analyzed. Results support using aggregated data sets for data analytics in medical device supply chains. We recommend that the sponsoring company integrate the tool into their business processes and use customer centric data to enhance their inventory management. The medical device industry struggles with product visibility and the lack of connectivity is a barrier, but as companies continue to strive towards aggregated systems for data analytics, these capabilities would lay the framework for better inventory management in their distribution networks.