Targeted Automation of Order Decisions Using Machine Learning

35 Pages Posted: 9 Apr 2021

See all articles by Christina Imdahl

Christina Imdahl

Kuehne Logistics University

Kai Hoberg

Kuehne Logistics University

William Schmidt

Cornell University - Samuel Curtis Johnson Graduate School of Management

Date Written: March 5, 2021

Abstract

In many practical settings, human decision-makers can review the recommendations that a decision support model generates and either approve or override the recommendation. Overrides can be rare when the decision support model is well-tuned to its task. However, the human decision-maker is still burdened with reviewing a potentially large number of recommendations. In this paper, we examine whether a machine learning (ML) prediction model can address this inefficiency.
We develop a set of ML models in conjunction with our research partner, a large materials handling equipment manufacturer, and employ the models using our partner's procurement ordering process. This process covers 535,168 orders at suppliers over four years for 36 decision-makers (planners).
Using only features that are available at the time a system makes a recommendation, our proposed set of ML models predict (1) whether or not the decision-maker will modify the recommendation and (2) whether such a modification will improve or impair the performance of the system. Our analysis is complicated by the fact that some of the planners have few observations in our training data, leading to poor predictions for those planner-level models. We address this by augmenting our planner-level models with cluster-level models. The latter draws inferences from the predicted behavior of clusters of planners with similar behavior.
Using our ML models, we identify a material portion of the order recommendations that can be automated with little, or even a positive, impact on performance.
Using our results to automate orders, the firm can (1) free the decision-makers' time for other value-added activities and (2) improve the system's performance.

Keywords: automation, machine learning, inventory management, clustering

Suggested Citation

Imdahl, Christina and Hoberg, Kai and Schmidt, William, Targeted Automation of Order Decisions Using Machine Learning (March 5, 2021). Available at SSRN: https://ssrn.com/abstract=3822131 or http://dx.doi.org/10.2139/ssrn.3822131

Christina Imdahl (Contact Author)

Kuehne Logistics University ( email )

Grosser Grasbrook 17
Hamburg, 20457
Germany

Kai Hoberg

Kuehne Logistics University ( email )

Grosser Grasbrook 17
Hamburg, 20457
Germany

William Schmidt

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

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