2026-09-19T08:48:36Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/810042022-01-27T21:12:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Edgar Blanco and Stephen Graves.
Lee, Amy, M.B.A. Massachusetts Institute of Technology
Leaders for Global Operations Program.
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Engineering Systems Division
Sloan School of Management
2013-09-24T19:36:23Z
2013-09-24T19:36:23Z
2013
2013
http://hdl.handle.net/1721.1/81004
857789705
Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 58).
Amazon's phenomenal sales growth and desire to maintain "Earth's Biggest Selection" have led to an increase in the diversity of product offerings that has resulted in a corresponding increase in complexity of Amazon's warehouse storage management. There is currently limited insight into the trade offs between the capital, fixed and variable costs of Amazon's storage related operational decisions, leading to inefficient warehouse storage type allocations and higher operational costs. The focus of this six-month LGO internship was to develop a cost model that takes into account all relevant costs to develop recommendations on warehouse storage type allocations for both existing and new fulfillment centers in Amazon's North America Fulfillment Center network. This thesis begins with an overview of Amazon and a description of their fulfillment center network. The overview is followed by a literature review of current warehouse design frameworks and storage optimization research. The following chapter analyzes the current inbound warehouse processes to identify what the relevant storage decisions are, where they are being made, and the current decision making process. Finally, through the development and implementation of a cost model and an analysis of key findings, the thesis provides recommendations for cost-optimized warehouse storage type allocations. The major recommendations are to replace floor pallet storage within existing fulfillment centers, increasing Non-Sortable product mix in select existing Sortable fulfillment centers, and optimized storage type allocations for new fulfillment centers. The expected scaled annual cost savings associated with these cost optimized warehouse storage type allocations within the existing fulfillment centers is 34% across the entire network and 62% for the select Sortable fulfillment center. The expected scaled annual cost savings associated with the optimized storage type allocations for the new fulfillment centers is 24% per new Sortable building and 11% per new Non-Sortable building. The methodology utilized within the cost model to compare fixed, variable and capital costs can be applied more broadly to assess the cost impact of different storage types in any warehousing design framework.
by Amy Lee.
S.M.
M.B.A.
63 p.
eng
Massachusetts Institute of Technology
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
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http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
Engineering Systems Division.
Leaders for Global Operations Program.
Cost-optimized warehouse storage type allocations
Thesis
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Publication
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<Abstract>Amazon's phenomenal sales growth and desire to maintain "Earth's Biggest Selection" have led to an increase in the diversity of product offerings that has resulted in a corresponding increase in complexity of Amazon's warehouse storage management. There is currently limited insight into the trade offs between the capital, fixed and variable costs of Amazon's storage related operational decisions, leading to inefficient warehouse storage type allocations and higher operational costs. The focus of this six-month LGO internship was to develop a cost model that takes into account all relevant costs to develop recommendations on warehouse storage type allocations for both existing and new fulfillment centers in Amazon's North America Fulfillment Center network. This thesis begins with an overview of Amazon and a description of their fulfillment center network. The overview is followed by a literature review of current warehouse design frameworks and storage optimization research. The following chapter analyzes the current inbound warehouse processes to identify what the relevant storage decisions are, where they are being made, and the current decision making process. Finally, through the development and implementation of a cost model and an analysis of key findings, the thesis provides recommendations for cost-optimized warehouse storage type allocations. The major recommendations are to replace floor pallet storage within existing fulfillment centers, increasing Non-Sortable product mix in select existing Sortable fulfillment centers, and optimized storage type allocations for new fulfillment centers. The expected scaled annual cost savings associated with these cost optimized warehouse storage type allocations within the existing fulfillment centers is 34% across the entire network and 62% for the select Sortable fulfillment center. The expected scaled annual cost savings associated with the optimized storage type allocations for the new fulfillment centers is 24% per new Sortable building and 11% per new Non-Sortable building. The methodology utilized within the cost model to compare fixed, variable and capital costs can be applied more broadly to assess the cost impact of different storage types in any warehousing design framework.</Abstract>
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