{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/81029"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/81029","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Developing a strategic roadmap for supply chain process improvement in a regulated utility","abstract":"This thesis covers work done at Tracks Energy, a regulated utility, to develop a strategic roadmap for supply chain process improvement. The focus of Tracks Energy has always been on keeping the lights on and the gas flowing for its customers, and the organizational structure of the company has been aligned by functional expertise to accomplish this goal. Existing supply chain operations span across the areas of responsibility for four senior executives and ten different operational groups. The cost and responsiveness of the supply chain has been negatively impacted by groups working to improve performance directly associated with their tasks, at the expense of the supply chain as a complete system. We propose a methodology for developing a strategic supply chain process improvement roadmap based on process map development, benchmarking, and data analysis to outline projected performance. We also present two different inventory models for developing inventory policies based on minimizing total material cost. The first inventory policy model applies a common framework based on stochastic optimization using normal distribution assumptions for demand and lead time. The objective of this model is to minimize costs over an infinite horizon given desired service levels. The second model is a multi-period model adapted from a robust framework. The objective of the second model is to minimize costs given unfavorable demand bounded by potential values unrestricted by a specific probability distribution function. The strategic roadmap for supply chain process improvements presented in this thesis is currently being pursued through the development of a newly developed supply chain management team. The opportunities presented as a strategic roadmap represent the potential for significant capital and operational savings by focusing on the end to end supply chain over individual department functions.","abstract_html":"This thesis covers work done at Tracks Energy, a regulated utility, to develop a strategic roadmap for supply chain process improvement. The focus of Tracks Energy has always been on keeping the lights on and the gas flowing for its customers, and the organizational structure of the company has been aligned by functional expertise to accomplish this goal. Existing supply chain operations span across the areas of responsibility for four senior executives and ten different operational groups. The cost and responsiveness of the supply chain has been negatively impacted by groups working to improve performance directly associated with their tasks, at the expense of the supply chain as a complete system. We propose a methodology for developing a strategic supply chain process improvement roadmap based on process map development, benchmarking, and data analysis to outline projected performance. We also present two different inventory models for developing inventory policies based on minimizing total material cost. The first inventory policy model applies a common framework based on stochastic optimization using normal distribution assumptions for demand and lead time. The objective of this model is to minimize costs over an infinite horizon given desired service levels. The second model is a multi-period model adapted from a robust framework. The objective of the second model is to minimize costs given unfavorable demand bounded by potential values unrestricted by a specific probability distribution function. The strategic roadmap for supply chain process improvements presented in this thesis is currently being pursued through the development of a newly developed supply chain management team. The opportunities presented as a strategic roadmap represent the potential for significant capital and operational savings by focusing on the end to end supply chain over individual department functions.","abstract_has_math":false,"creators":["Yoder, Brent E. (Brent Edward)"],"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":["Georgia Perakis and Mort Webster."],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013","date_published":"2013","updated_at":"2026-07-22T22:22:26Z","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. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/81029","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Georgia Perakis and Mort Webster."]},{"key":"dc:contributor.department","label":"Department","values":["Leaders for Global Operations Program at MIT","Massachusetts Institute of Technology. Engineering Systems Division","Sloan School of Management"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Leaders for Global Operations Program."]},{"key":"dc:creator","label":"Author","values":["Yoder, Brent E. 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The focus of Tracks Energy has always been on keeping the lights on and the gas flowing for its customers, and the organizational structure of the company has been aligned by functional expertise to accomplish this goal. Existing supply chain operations span across the areas of responsibility for four senior executives and ten different operational groups. The cost and responsiveness of the supply chain has been negatively impacted by groups working to improve performance directly associated with their tasks, at the expense of the supply chain as a complete system. We propose a methodology for developing a strategic supply chain process improvement roadmap based on process map development, benchmarking, and data analysis to outline projected performance. We also present two different inventory models for developing inventory policies based on minimizing total material cost. The first inventory policy model applies a common framework based on stochastic optimization using normal distribution assumptions for demand and lead time. The objective of this model is to minimize costs over an infinite horizon given desired service levels. The second model is a multi-period model adapted from a robust framework. The objective of the second model is to minimize costs given unfavorable demand bounded by potential values unrestricted by a specific probability distribution function. The strategic roadmap for supply chain process improvements presented in this thesis is currently being pursued through the development of a newly developed supply chain management team. 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The second model is a multi-period model adapted from a robust framework. The objective of the second model is to minimize costs given unfavorable demand bounded by potential values unrestricted by a specific probability distribution function. The strategic roadmap for supply chain process improvements presented in this thesis is currently being pursued through the development of a newly developed supply chain management team. The opportunities presented as a strategic roadmap represent the potential for significant capital and operational savings by focusing on the end to end supply chain over individual department functions."],"dc:description.degree":["S.M.","M.B.A."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/81029"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. 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