Synergizing ChatGPT and experiential learning: unravelling TOC based production planning and control variants through the dice game

Abstract

Amidst debates around the impact of Artificial Intelligence (AI) technologies like ChatGPT in education, our study explores their role in enhancing the ‘theory of experiential learning’, particularly in Production and Operations Management (POM). We demonstrate how Goldratt’s Dice Game, as an experiential learning aid, allows undergraduate students in a senior-level production planning and control (PPC) course to apply knowledge and skills in a dynamic, interactive setting. This study presents how these students, supported by ChatGPT's insights, gain a deeper understanding of the DBR system, focusing on buffer management, internal (i.e. a dominant capacity constraint), external (i.e. market demand constraint), and interactive decision-making processes. We detail manual and Excel-based simulation models for Drum-Buffer-Rope (DBR) variants, reflecting on experiential learning outcomes. Concluding with managerial implications, our research advocates for the synergy of ChatGPT-aided theoretical learning with experiential models, presenting a comprehensive approach for understanding POM fundamentals such as Production Planning & Control (PPC) systems.