INTEGRATION OF ARTIFICIAL INTELLIGENCE AND PROCESS MINING FOR CUTOVER PLANNING IN CONSUMER PRODUCT ENTERPRISES

The enterprise cutover – the timeframe when legacy systems are retired and new enterprise resource planning (ERP) environments come online – continues to be a critical period of digital transformation risk for consumer product companies. A traditional method of developing cutover plans involves static run-books, expert judgment and rule-based scheduling, all of which have a difficult time capturing the dynamic dependencies and hidden bottlenecks that emerge when the plan is put into practice at scale. This paper presents an integrated framework that integrates process mining to discover, monitor and verify whether the actual cutover execution conforms to a set of planned processes, with AI techniques such as predictive analytics, natural language processing, and reinforcement learning, to dynamically forecast delays, extract task dependencies and optimize scheduling decisions. It introduces a four-layer architecture from data capture to process mining, the use of AI for decision support, and the delivery of integrated dashboards. Conceptual evaluation of the framework is provided using comparative key performance indicators from three anonymized case profiles of consumer product companies around the world. The results suggest that AI-process mining integration can shorten cutover by more than 33%, cut down on task delay incidents by 50%, and significantly increase the utilization of resources compared with its traditional counterparts. The paper provides an empirical methodology that can be replicated to plan a cutover, in a structured way, that connects academic process mining research with practical ERP transformation management processes and activities, and offers suggestions for future empirical validation.