Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations
Jiankun Sun (j.sun@imperial.ac.uk),
Dennis J. Zhang (denniszhang@wustl.edu),
Haoyuan Hu (haoyuan.huhy@cainiao.com) and
Jan A. Van Mieghem (vanmieghem@kellogg.northwestern.edu)
Additional contact information
Jiankun Sun: Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom
Dennis J. Zhang: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130
Haoyuan Hu: Cainiao, Alibaba Group, Hangzhou 311101, China
Jan A. Van Mieghem: Kellogg School of Management, Northwestern University, Evanston, Illinois 60208
Management Science, 2022, vol. 68, issue 2, 846-865
Abstract:
Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box) focus on box volume utilization yet tend to overlook human behavioral deviations. We observe that packing workers at the warehouses of the Alibaba Group deviate from algorithmic prescriptions for 5.8% of packages, and these deviations increase packing time and reduce operational efficiency. We posit two mechanisms and demonstrate that they result in two types of deviations: (1) information deviations stem from workers having more information and in turn better solutions than the algorithm; and (2) complexity deviations result from workers’ aversion, inability, or discretion to precisely implement algorithmic prescriptions. We propose a new “human-centric bin packing algorithm” that anticipates and incorporates human deviations to reduce deviations and improve performance. It predicts when workers are more likely to switch to larger boxes using machine learning techniques and then proactively adjusts the algorithmic prescriptions of those “targeted packages.” We conducted a large-scale randomized field experiment with the Alibaba Group. Orders were randomly assigned to either the new algorithm (treatment group) or Alibaba’s original algorithm (control group). Our field experiment results show that our new algorithm lowers the rate of switching to larger boxes from 29.5% to 23.8% for targeted packages and reduces the average packing time of targeted packages by 4.5%. This idea of incorporating human deviations to improve optimization algorithms could also be generalized to other processes in logistics and operations.
Keywords: warehouse operations; behavioral operations; field experiment; online retailing (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:2:p:846-865
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