Learning to shortcut and shortlist order fulfillment deciding

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 assignments for future decisions. Essentially, those assignments that can be predicted with high confidence can be used to shortcut, or bypass, the expensive deciding process, or else a set of most likely assignments can be used for shortlisting -- sending a much smaller set of candidates for consideration by the fulfillment deciding system.