Learning to shortcut and shortlist order fulfillment deciding
Brian Quanz, Ajay Deshpande, Dahai Xing, Xuan Liu

TL;DR
This paper proposes a data-driven approach to simplify complex order fulfillment decisions by predicting likely assignments, thereby reducing computational costs through shortcutting and shortlisting techniques.
Contribution
It introduces a novel method that leverages data mining to identify patterns in past decisions, enabling efficient prediction and reduction of decision complexity in fulfillment systems.
Findings
High-confidence predictions can shortcut decision processes
Shortlisting reduces the number of candidates for fulfillment assignment
The approach improves decision efficiency during peak times
Abstract
With the increase of order fulfillment options and business objectives taken into consideration in the deciding process, order fulfillment deciding is becoming more and more complex. For example, with the advent of ship from store retailers now have many more fulfillment nodes to consider, and it is now common to take into account many and varied business goals in making fulfillment decisions. With increasing complexity, efficiency of the deciding process can become a real concern. Finding the optimal fulfillment assignments among all possible ones may be too costly to do for every order especially during peak times. In this work, we explore the possibility of exploiting regularity in the fulfillment decision process to reduce the burden on the deciding system. By using data mining we aim to find patterns in past fulfillment decisions that can be used to efficiently predict most likely…
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Taxonomy
TopicsMaritime Ports and Logistics · Outsourcing and Supply Chain Management · Optimization and Packing Problems
