Predictive capacity planning modeling with tactical and strategic applications

The focus of my internship was the development of a predictive capacity planning model to characterize the storage requirements and space utilization for Amazon's Campbellsville (SDF) Fulfillment Center (FC). Amazon currently has a functional model that serves the purpose of capacity planning, but they were looking for something that would provide more insight into their storage requirements against various demand forecasts and time horizons. With a more comprehensive model in place, Amazon would then have the ability to initiate studies, with the intent of optimizing the key parameters of their capacity flow Amazon utilizes a fairly robust and complex software solution for allocating items to storage locations as it receives shipments from its network of manufacturers and distributors. Amazon designates its capacity storage areas as being Prime, Reserve or Random Stow. Prime storage locations are bins from which workers pick items to fulfill orders. Reserve storage consists of pallet locations from which workers replenish Prime bins. Random Stow is a special case form of storage not taken into consideration for the purposes of my internship. The algorithm that determines the capacity allocation for a particular item is driven by two key parameters. The first parameter Amazon refers to as Days of Cover, which serves as a cycle and safety stock control variable. The maximum Days of Cover setting dictates the quantity of a particular item allocated to Prime locations, with any remaining items in the shipment being sent to Reserve. The minimum Days of Cover serves as the trigger resulting in a replenishment move from Reserve to Prime.

Department dc:contributor.department

Leaders for Manufacturing Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Zeppieri, Michael A. (Michael Anthony), 1975-

Advisor dc:contributor.advisor

• Stephen C. Graves and David Simchi-Levi.

• Civil and Environmental Engineering.
• Sloan School of Management.
• Leaders for Manufacturing 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/1721.1/34742

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/34742