{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/117977"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/117977","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Long range planning of manufacturing footprint","abstract":"Firms developing an Operations Strategy need to make decisions across a wide spectrum. Within the field of operations strategy, common practice defines the stratification of these decisions into structural and infrastructural elements. Structural decisions relating to the amount of capacity and facilities a firm deploys can impact a firm's cost competitiveness if implemented incorrectly because of the large capital expenditures and time horizons involved. Boston Scientific, a medical device manufacturer, recognizes the importance of operations strategy in achieving competitive success and continually seeks tools that assist in the creation of strategy as it pursues growth. This thesis discusses the development of a scenario planning tool that is focused on estimation of manufacturing footprint requirements for the company's internal manufacturing network. The tool we develop takes a demand forecast as an input and converts it to a physical space requirement in square feet. Additionally, the tool exhibits significant flexibility in being able to develop multiple scenarios, especially given the ability to modify parameters ranging from growth rates to improvement factors within facilities. The tool also offers a deeper level of detail than previously available, with the critical decision unit being the value stream, rather than an aggregation of data to only present factory or network level results. Whilst this work is applied to the context of a medical device manufacturer, the methodology is easily transferable to a range of industries. The work can be applied to any manufacturing setting where investment decisions for new facilities take significant time and capital. Our research of the literature on this topic identified a gap, and the development of the tool is a positive addition to the field of estimation of manufacturing footprint.","abstract_html":"Firms developing an Operations Strategy need to make decisions across a wide spectrum. Within the field of operations strategy, common practice defines the stratification of these decisions into structural and infrastructural elements. Structural decisions relating to the amount of capacity and facilities a firm deploys can impact a firm's cost competitiveness if implemented incorrectly because of the large capital expenditures and time horizons involved. Boston Scientific, a medical device manufacturer, recognizes the importance of operations strategy in achieving competitive success and continually seeks tools that assist in the creation of strategy as it pursues growth. This thesis discusses the development of a scenario planning tool that is focused on estimation of manufacturing footprint requirements for the company's internal manufacturing network. The tool we develop takes a demand forecast as an input and converts it to a physical space requirement in square feet. Additionally, the tool exhibits significant flexibility in being able to develop multiple scenarios, especially given the ability to modify parameters ranging from growth rates to improvement factors within facilities. The tool also offers a deeper level of detail than previously available, with the critical decision unit being the value stream, rather than an aggregation of data to only present factory or network level results. Whilst this work is applied to the context of a medical device manufacturer, the methodology is easily transferable to a range of industries. The work can be applied to any manufacturing setting where investment decisions for new facilities take significant time and capital. Our research of the literature on this topic identified a gap, and the development of the tool is a positive addition to the field of estimation of manufacturing footprint.","abstract_has_math":false,"creators":["Awuondo, Benjamin Martin Onyango"],"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":["Jonas Jonasson and Stanley Gershwin."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:21:48Z","subjects":["Sloan School of Management.","Mechanical Engineering.","Leaders for Global Operations Program."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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Additionally, the tool exhibits significant flexibility in being able to develop multiple scenarios, especially given the ability to modify parameters ranging from growth rates to improvement factors within facilities. The tool also offers a deeper level of detail than previously available, with the critical decision unit being the value stream, rather than an aggregation of data to only present factory or network level results. Whilst this work is applied to the context of a medical device manufacturer, the methodology is easily transferable to a range of industries. The work can be applied to any manufacturing setting where investment decisions for new facilities take significant time and capital. 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