  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.
