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

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 customer orders. However, it is hard to estimate, what degree of complexity these algorithms should have as steel campaign planning generally lacks precise rule-based procedures; in fact, it requires far-reaching domain knowledge as well as intuition that can only be acquired by years of business experience. Here, contrary to 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 appropriate customer orders for immediate production is primarily determined by the orders’ carbon content (to be precise: the carbon equivalent). 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 automated decision-support systems, whenever an expert needs to create groups of orders ('production campaigns') that fulfill certain implicit selection criteria.