Performance assessment methodology for AI-supported decision-making in production management
Peter Burggräf, Johannes Wagner, Benjamin Koke, Milan Bamberg
Abstract
Artificial intelligence (AI) gains importance in many domains and may soon be transferred decision-making responsibilities in production management from production managers. For the future, it will be vital to identify each entity’s domain of decision-making superiority. Therefore, this paper proposes and applies a model to assess AI performance in contrast to human decision-making. Relying on reinforcement learning and item response theory, the approach describes a minimum viable setup for AI systems to identify opportunities for AI systems in manufacturing. The model is based on operative production management decisions (job-shop scheduling) and validated through a series of academic scheduling instances.