Evaluation of automated storage and retrieval in a distribution center

In the face of e-commerce growth and rising customer delivery expectations, companies must adapt to meet shorter contract shipping requirements with existing infrastructures. The "Amazon effect", an evolution resulting in increased online shopping and direct-to-consumer order fulfillment, is reverberating through the retail industry and requiring manufacturers to evaluate their existing supply chain networks to meet two- and one-day shipping from their distribution centers (DC). This thesis evaluates speed and execution improvements using automated storage and retrieval systems (ASRS) in DCs. Adopting ASRS can provide the essential capability upgrades to reduce material processing time and meet direct-to-consumer deliveries. The ASRS analysis reviews current DC metrics, future throughput and inventory requirements, comparisons of ASRS technologies utilized today, expected impact of ASRS inside of an existing DC, and sizing and selection of an ASRS. Analysis of the DC evaluated showed that the primary cause of delays in shipping time was due to high variability in task completion time, rather than a high average completion time, causing extended wait times to propagate throughout the DC. While methods to reduce task time variation can be implemented, a warehouse logic upgrade would allow for real-time sequencing to get products shipped in the correct order based on factors like shipping method, customer, or priority. Implementation of ASRS in the DC evaluated could decrease processing time in storage and retrieval by 67% and total processing time through the DC by 37% due to ideal sequencing, diminished downstream variability, and reduced work in progress. The payback period for ASRS is projected to be 4 to 5 years..

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Turner, Adriane(Adriane A.)

Advisor dc:contributor.advisor

• Stephen Graves and Maria Yang.

• Sloan School of Management.
• Mechanical Engineering.
• Leaders for Global Operations Program.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/126987

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/126987