2026-09-19T16:59:15Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1466502022-12-01T03:32:52Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Chun, Jung-Hoon
Zheng, Y. Karen
Quintella Correia, Felipe
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
2022-11-30T19:39:02Z
2022-11-30T19:39:02Z
2022-05
2022-08-25T19:15:42.431Z
https://hdl.handle.net/1721.1/146650
0000-0001-5339-286X
The automotive industry is suffering from large supply-chain disruptions and restrictions, and sourcing contracts are signed during concept and design phases, fixing capacity commitments years ahead of time based on highly inaccurate demand fore- casts. Companies struggle with demand variation and the inability of the supply-chain to react, with suppliers having limited ability to respond to updated demand signals and varying market conditions. This thesis proposes an algorithmic solution to optimize allocation of demand order volumes into different product types when supply capacity is constrained, while trying to maintain the originally requested mix shares and volumes as much as possible.
The model allows the company to approach supply-chain flexibility under a different lens and the results demonstrate the volume and financial improvements to current processes, with analyzed used cases showing an increased volume output of 10% and profit increases of up to 11% over a manual re-allocation process, or 7% increases when constraining the model to a lower volume output. Finally, a few additional model improvements are suggested as grounds for future work.
M.B.A.
S.M.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Optimizing Demand Re-Allocation under Fixed Capacity Commitments
Thesis
application/pdf
Master
Master of Business Administration
Master of Science in Mechanical Engineering
Publication
unknown
unknown
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<Abstract>The automotive industry is suffering from large supply-chain disruptions and restrictions, and sourcing contracts are signed during concept and design phases, fixing capacity commitments years ahead of time based on highly inaccurate demand fore- casts. Companies struggle with demand variation and the inability of the supply-chain to react, with suppliers having limited ability to respond to updated demand signals and varying market conditions. This thesis proposes an algorithmic solution to optimize allocation of demand order volumes into different product types when supply capacity is constrained, while trying to maintain the originally requested mix shares and volumes as much as possible.
The model allows the company to approach supply-chain flexibility under a different lens and the results demonstrate the volume and financial improvements to current processes, with analyzed used cases showing an increased volume output of 10% and profit increases of up to 11% over a manual re-allocation process, or 7% increases when constraining the model to a lower volume output. Finally, a few additional model improvements are suggested as grounds for future work.</Abstract>
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