{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/126946"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/126946","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Inventory modeling for active pharmaceutical ingredient supply chains","abstract":"Pharmaceutical companies traditionally manufactured drugs in-house, and have only recently been increasingly outsourcing production to contract manufacturing organizations (CMOs). In this work, we use inventory modeling to explore the trade-off between the attractive purchase price and the hidden costs of outsourcing for two active pharmaceutical ingredient (API) supply chains at AstraZeneca (AZ). We assess the inventory levels recommended by a base stock policy with deterministic purchase order lead times at each contracted stage of the supply chains. The single-echelon calculations reveal that safety stock levels are not systematically inflated at individual stages. The current inventory costs and service levels vary widely across products studied, as performance is hard to track over long periods of time and different inventory types. However, we demonstrate with a multi-echelon inventory optimization that a fully integrated API supply chain would yield savings compared to a purely external chain. Today, AZ's organizational processes allow it to partially coordinate with CMOs and capture up to 60 % of the value left on the table by not being able to optimize the full chain due to outsourcing. We propose using cost premium frontiers to prioritize further improvements at strategic outsourced nodes and align incentives. Partnering with CMOs to shorten lead times and increase flexibility is set to become a key advantage in a changing pharmaceutical environment with exacerbated volatility.","abstract_html":"Pharmaceutical companies traditionally manufactured drugs in-house, and have only recently been increasingly outsourcing production to contract manufacturing organizations (CMOs). In this work, we use inventory modeling to explore the trade-off between the attractive purchase price and the hidden costs of outsourcing for two active pharmaceutical ingredient (API) supply chains at AstraZeneca (AZ). We assess the inventory levels recommended by a base stock policy with deterministic purchase order lead times at each contracted stage of the supply chains. The single-echelon calculations reveal that safety stock levels are not systematically inflated at individual stages. The current inventory costs and service levels vary widely across products studied, as performance is hard to track over long periods of time and different inventory types. However, we demonstrate with a multi-echelon inventory optimization that a fully integrated API supply chain would yield savings compared to a purely external chain. Today, AZ's organizational processes allow it to partially coordinate with CMOs and capture up to 60 % of the value left on the table by not being able to optimize the full chain due to outsourcing. We propose using cost premium frontiers to prioritize further improvements at strategic outsourced nodes and align incentives. 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Today, AZ's organizational processes allow it to partially coordinate with CMOs and capture up to 60 % of the value left on the table by not being able to optimize the full chain due to outsourcing. We propose using cost premium frontiers to prioritize further improvements at strategic outsourced nodes and align incentives. Partnering with CMOs to shorten lead times and increase flexibility is set to become a key advantage in a changing pharmaceutical environment with exacerbated volatility."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.B.A.","S.M."]},{"key":"dc:title","label":"Title","values":["Inventory modeling for active pharmaceutical ingredient supply chains"]}]}],"canonical_facts":{"dc:contributor.advisor":["Thomas Roemer and David Simchi-Levi."],"dc:contributor.department":["Sloan School of Management","Sloan","Massachusetts Institute of Technology. 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