Using K-means clustering to create cost and demand functions that decrease excess inventory and better manage inventory in defense
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1051223334-MIT.pdf
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Author(s)
Porter, Danaka M. (Danaka Michele)
Advisor(s)
Sergio Caballero.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Excess inventory is prevalent in both the armed forces and defense companies; it takes up space and resources that could be used elsewhere. This thesis proposes a method to reduce the excess inventory and associated costs, while maintaining instant part availability, despite design changes which alter the number of parts required. A single period model extension was created based on K-means clustering of the parts according to lead-time and cost. These groupings provided the backbone of the cost functions created in the thesis. A predictive demand function was also created so that the design change's alterations to demand would be captured. The cost function was optimized using the predicted demand, to find an optimal order quantity that met the demand requirements and was the lowest cost option. Together these single period model function extensions allowed for a 31 percent decrease in excess inventory and 34 percent decrease in total cost. Due to the nature of this report the companies' names have been removed, and the data naming conventions were altered so as to protect the nature of the parts.
Description
Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged student-submitted from PDF version of thesis.
Includes bibliographical references (pages 59-63).
Subjects
Supply Chain Management Program.
MIT Department
Massachusetts Institute of Technology. Supply Chain Management Program
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