{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/73398"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/73398","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A network planning process and inventory strategy for high-mix low-volume markets","abstract":"From June to December 2011, the impact of rapid international expansion on the Global Supply Chain organization at Amgen - one of the world's leading human therapeutics companies in the biotechnology industry - was investigated and a strategy was developed to mitigate the major challenges associated with globalization. Amgen is transitioning from predominantly high-volume low-mix manufacturing and a \"one size fits all\" Operations mentality; the company is challenged to not only now effectively and efficiently accommodate high-mix low-volume markets, but to also increase its overall market responsiveness and flexibility. To mitigate the challenges, an end-to-end depiction of a specific product's supply chain was developed and an inventory supermarket strategy supported by an Excel macro was developed. We believe the strategy mitigates the challenges, specifically by decreasing manufacturing lead time, buffering against supply chain variability, improving demand signaling and sustaining on-time deliveries. The Excel macro described in this thesis serves as a stepping-stone for the development of a future, more sustainable solution for Amgen's enterprise-wide systems.","abstract_html":"From June to December 2011, the impact of rapid international expansion on the Global Supply Chain organization at Amgen - one of the world's leading human therapeutics companies in the biotechnology industry - was investigated and a strategy was developed to mitigate the major challenges associated with globalization. Amgen is transitioning from predominantly high-volume low-mix manufacturing and a "one size fits all" Operations mentality; the company is challenged to not only now effectively and efficiently accommodate high-mix low-volume markets, but to also increase its overall market responsiveness and flexibility. To mitigate the challenges, an end-to-end depiction of a specific product's supply chain was developed and an inventory supermarket strategy supported by an Excel macro was developed. We believe the strategy mitigates the challenges, specifically by decreasing manufacturing lead time, buffering against supply chain variability, improving demand signaling and sustaining on-time deliveries. The Excel macro described in this thesis serves as a stepping-stone for the development of a future, more sustainable solution for Amgen's enterprise-wide systems.","abstract_has_math":false,"creators":["Smith, Sally A. (Sally Amanda)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Leaders for Global Operations Program at MIT","school":null,"contributors":[],"advisors":["Deborah Nightingale and J. Bradley Morrison."],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-22T22:21:24Z","subjects":["Sloan School of Management.","Engineering Systems Division.","Leaders for Global Operations Program."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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Amgen is transitioning from predominantly high-volume low-mix manufacturing and a \"one size fits all\" Operations mentality; the company is challenged to not only now effectively and efficiently accommodate high-mix low-volume markets, but to also increase its overall market responsiveness and flexibility. To mitigate the challenges, an end-to-end depiction of a specific product's supply chain was developed and an inventory supermarket strategy supported by an Excel macro was developed. We believe the strategy mitigates the challenges, specifically by decreasing manufacturing lead time, buffering against supply chain variability, improving demand signaling and sustaining on-time deliveries. 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