Amazon UK transshipment stow process analysis through discrete event simulation

This thesis investigated Amazon's implementation of transshipment, the act of moving inventory between fulfillment centers to facilitate order fulfillment, inventory rebalancing, and operational cost reduction. The role of transshipment in Amazon's fulfillment center network was explored and a proposed transshipment stow process is modelled to examine potential reductions in transshipment cycle time. As Amazon's fulfillment network has grown in size, the function of individual fulfillment center has evolved from shipping customer orders to providing a network-level, low-cost fulfillment option through transshipment. The complexity of this process, from the source fulfillment center to transportation and finally to the destination fulfillment center is examined in-depth to provide insight into transshipment processes that are typically not considered or modelled in research. One particular process-transshipment stow-was determined to have a large impact on transshipment cycle time. A proposed transshipment tote stow process was envisioned and modeled using discrete event simulation in order to determine cycle time savings versus volume efficiency tradeoffs. While the proposed transshipment tote stow process simulation was able to reduce transshipment cycle time by more than 80%, the tote stow process required an additional 7.5x amount of space compared to the current stow process. Based on initial simulations, the benefits of transshipment tote stow cycle time reduction does not outweigh the costs in space utilization. Transshipment-specific item stowing areas with periodic inventory consolidation may be the best tradeoff between cycle time, additional processes, and space.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Young, Eric, S.M. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Luca Daniel and Stephen C. Graves.

• Electrical Engineering and Computer Science.
• Sloan School of Management.
• Leaders for Global Operations Program.

Statement dc: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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/106075

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/106075