2026-09-20T23:48:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/452302026-06-06T01:03:30Zcom_1721.1_7582com_1721.1_7581com_1721.1_101402col_1721.1_131023col_1721.1_101610
Chris Caplice.
Lee, Gil Su
Massachusetts Institute of Technology. Engineering Systems Division.
Massachusetts Institute of Technology. Engineering Systems Division
2009-04-29T17:11:59Z
2009-04-29T17:11:59Z
2008
2008
http://hdl.handle.net/1721.1/45230
304398256
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.
Includes bibliographical references (leaves 64-65).
Managing a big supply chain for one of the largest quick service restaurant companies, especially when the company has a policy called "Never Run Out," is very challenging. A traditional way of managing inventory requires high level of safety stock if high level of uncertainty is involved. Sources of uncertainty include variability in demand from frequent promotions or seasonal effect, variability in order lead time from using low-cost mode of transportation, or lack of information sharing. This project developed an expediting policy with dual reorder points with demand threshold and tested the policy with a Monte Carlo simulation. Previous research on two reorder points provide great foundation for this study but they lack consideration on demand variability and approaches to set up reorder points. We propose an algorithm with a demand threshold to trigger an expedited order and heuristic approaches to develop reorder points where the total cost can be minimized while service requirements are met.
by Gil Su Lee.
M.Eng. in Logistics
65 leaves
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
The impact of "Never Run Out" policy assured supply chain with dual reorder point expediting
Thesis
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<Abstract>Managing a big supply chain for one of the largest quick service restaurant companies, especially when the company has a policy called "Never Run Out," is very challenging. A traditional way of managing inventory requires high level of safety stock if high level of uncertainty is involved. Sources of uncertainty include variability in demand from frequent promotions or seasonal effect, variability in order lead time from using low-cost mode of transportation, or lack of information sharing. This project developed an expediting policy with dual reorder points with demand threshold and tested the policy with a Monte Carlo simulation. Previous research on two reorder points provide great foundation for this study but they lack consideration on demand variability and approaches to set up reorder points. We propose an algorithm with a demand threshold to trigger an expedited order and heuristic approaches to develop reorder points where the total cost can be minimized while service requirements are met.</Abstract>
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