A network analysis of decision strategies of human experts in steel manufacturing

TL;DR

This paper introduces a network analysis method to understand human decision strategies in steel manufacturing, revealing key factors influencing order selection and aiding decision-support systems.

Contribution

The study presents a novel shuffling-aided network approach to formalize and analyze tacit knowledge in human expert decision-making within steel campaign planning.

Findings

Order choice is mainly influenced by carbon content.

Trace elements like manganese, silicon, and titanium have less impact than previously thought.

The method helps formalize implicit decision criteria for decision-support systems.

Abstract

Steel production scheduling is typically accomplished by human expert planners. Hence, instead of fully automated scheduling systems steel manufacturers prefer auxiliary recommendation algorithms. Through the suggestion of suitable orders, these algorithms assist human expert planners who are tasked with the selection and scheduling of production orders. However, it is hard to estimate, what degree of complexity these algorithms should have as steel campaign planning lacks precise rule-based procedures; in fact, it requires extensive domain knowledge as well as intuition that can only be acquired by years of business experience. Here, instead of developing new algorithms or improving older ones, we introduce a shuffling-aided network method to assess the complexity of the selection patterns established by a human expert. This technique allows us to formalize and represent the tacit knowledge that enters the campaign planning. As a result of the network analysis, we have discovered that the choice of production orders is primarily determined by the orders' carbon content. Surprisingly, trace elements like manganese, silicon, and titanium have a lesser impact on the selection decision than assumed by the pertinent literature. Our approach can serve as an input to a range of decision-support systems, whenever a human expert needs to create groups of orders ('campaigns') that fulfill certain implicit selection criteria.