Floor entry task prioritization for highly automated fulfillment centers

As automation continues to gain prevalence within the retail industry, informed decision-making by users of robotic systems is critical for management of throughput and operating expenditures. On robotic fulfillment floors, obstructions such as fallen product and deactivated robots can degrade robotic floor throughput by blocking access to product, forcing robots to re-route, and increasing worker idle time. Workers can walk onto the floor to address obstructions during operation, but such entry affects robot movement and can undermine the original intention of restoring throughput. This project aims to provide insight into the cost-benefit tradeoff of resolving obstructions to enable task prioritization and reduce unnecessary floor entry during operation, thereby improving system performance and reducing operating costs. We introduce a novel framework for modeling floor entry to determine the "value" of resolving an obstruction and apply an agile approach to rapidly develop and pilot a software tool for delivery of model recommendations in the field. During the treatment shifts, z-scores of measured pick work unavailability (our chosen performance metric, for which a reduction is indicative of improved throughput), were -0.72, -1.04, and -0.16 as compared with a control sample of similar shifts. The approximate fraction of obstructions resolved during non-operation increased by a factor of three, with recommendation adherence measurements indicating that the increase was driven by elimination of unnecessary (as determined by the model) floor entries during operation. While the sample size was not large enough to achieve a statistically significant outcome, these results offer useful insights regarding future analytical work, testing, and associated organizational changes.

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

• Amlani, Ankur.

Advisor dc:contributor.advisor

• Yanchong (Karen) Zheng and Kamal Youcef-Toumi.

• 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/1721.1/126943

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/126943