Simulation Game Concept For AI-Enhanced Teaching Of Advanced Value Stream Analysis and Design

Abstract

Value stream analysis and design is employed globally by improvement teams within industrial settings to maximize value creation and eliminate waste. For ending methodical time-centricity, research expanded the methodology to incorporate diverse facets like material flow cost accounting, information logistics, and external influence factors. These enhancements, along with increasing data volumes, are prompting a re-evaluation of how professional improvement teams should think and operate. Consequently, a transformation of the pedagogical approach used for educating students and professionals necessitates novel solutions. Conventional teaching methods such as expository lectures are widely considered inadequate in promoting knowledge retention and engagement. So far, existing research has not yet resulted in a solution that can effectively impart the methodological complexity of advanced value stream analysis and design in a motivating and vivid fashion. To address this gap, this paper applies a tailored CRISP gamification framework to develop a simulation game concept. These concept enables AI-enhanced teaching of advanced value stream analysis and design focusing on identification of multi-stage resource-efficient optimization strategies. Through integration of game-based learning with AI a trained reinforcement learning agent can act either competitively or cooperatively, creating a unique form of teaching accounting the aspects personalization, adaptive feedback, content creation, and analysis and assessment.