Braulio Brunaud
Braulio Brunaud
Principal Data Scientist of the Supply Chain Digital & Data Science team at Johnson & Johnson
Dr. Braulio Brunaud is a Principal Data Scientist at the Supply Chain Digital & Data Science team of Johnson & Johnson. Over the last 3 years, he was developed operations research models and applications to make strategic and tactical operational decisions for all J&J Businesses. His work on strategic capacity expansion for medical manufacturing, and allocation of assets for orthopedic surgeries has provided more than 40MM in benefits. Dr. Brunaud obtained his PhD from the Chemical Engineering department at Carnegie Mellon University, working under Prof. Ignacio Grossmann. His thesis on multilevel supply chain optimization is a formidable contribution to the process systems engineering field.
Rami Musa
Rami Musa
Director, Supply Chain Modeling & Analytics at Johnson & Johnson
Dr. Rami Musa is a director of supply chain modeling & analytics at Johnson & Johnson leading a team of experts in the field to deliver scalable solutions and applications. He has been leading major programs utilizing optimization, simulation / digital twin, and Artificial Intelligence to solve complex supply chains problems, such as: network modeling, multi-echelon inventory optimization, capacity planning, scheduling & planning, investment rightsizing, portfolio optimization, supply chain segmentation, hedging, and freight optimization. Rami has a wide range of experience working for several Fortune 100 companies in healthcare, retail, logistics, and chemical industries. With his work, he delivered hundreds of millions of benefits for the companies with which he worked and collaborated. He holds a Ph.D. in industrial & systems engineering from Virginia Tech. He taught as an adjunct faculty member at Drexel University and Virginia Tech. He has been an active member at INFORMS and was one of the first to obtain his Certified Analytics Professional (CAP) certification. He wrote a chapter of the CAP study guide. In addition to that, Rami is a Certified Supply Chain Professional(CSCP) and Lead Six Sigma Master Black Belt (MBB).
Professional
Track: Life Sciences
End-to-End Inventory & Asset Optimization for Medical Devices
We are presenting a broad initiative that aims at optimizing inventory for a Medical Device (MD) franchise at J&J on end-to-end basis: (1) Multi-Echelon Inventory Optimization (MEIO) — representing upstream of MD supply chain handling global and regional hubs. (2) Asset and Inventory Management Right Sizing & Placement (AIM-Right) — representing downstream of MD supply chain inclusive of forward stocking locations / loan centers (FSLs) and hospitals.
(1) MEIO: As inventories represent cash stored in our warehouses, having too much inventory directly affects costs and our ability to provide customers and patients with reasonable prices. On the other hand, when the inventories are not enough, we are not prepared to face unexpected events such as demand spikes and supply issues. The second dimension is the placement of inventories. They can be either centralized in primary distribution centers or more atomized in downstream stocking locations, with the corresponding trade-off between total inventory and responsiveness. The optimal values for all our safety stocks are obtained using Multi-Echelon Inventory Optimization, a holistic approach that looks at the entire supply chain to understand the correct location and inventory for each material. During 2021, the Digital & Data Science, the business franchise, and the Planning Excellence organization teams have been collaborating to optimize the safety stocks settings for all main distribution centers. An internally developed optimization solution in collaboration with Carnegie Mellon University along with Coupa MEIO engine were used.
(2) AIM-Right Medical Device businesses are heavily dependent on investment in field assets and inventory to build the foundations and generate surgical capacity to fulfill surgery demand. To maximize demand fulfillment in the most cost-effective manner requires thousands of detailed and interdependent decisions regarding deployment of these resources. These decisions include hospital consignment levels, asset and inventory stocking levels at loaner sites, sharing of assets between loaner sites, redeployment of assets, and future investment plans. To meet this need, a cloud-based decision support system (AIM-Right) utilizing advanced mathematical optimization and a web-based user interface was developed in close collaboration with the business. At the heart of AIM-Right is a mixed-integer programming model to optimize loaner operations in minutes, as well as innovative algorithms to determine the right consignment levels for each SKU. Access to the most recent sales and inventory data from ERP systems is provided through integration with the J&J Common Data Layer (CDL) and a cloud-based architecture to manage the application. Complex data manipulations (ETL) are fully automated, and relevant reports and dashboards allow the user to have an efficient workflow. Outputs from the application are presented via interactive Tableau dashboards and downloadable reports to allow for easy comparison of different business scenarios.
