AI‐Based Prediction and Management of Automation Equipment Lifecycle Costs: A Pathway to Enhancing Customer Lifetime Value (CLV)

To enhance Customer Lifetime Value (CLV) within the lifecycle management of automated equipment, this study addresses the lack of foresight in traditional cost analysis by establishing an AI-based lifecycle cost modelling and CLV decision-making methodology grounded in multi-stage forecasting. Utilising operational and maintenance data, costs across distinct lifecycle phases are projected, with these forecasts integrated into a CLV optimisation model for empirical analysis.Results indicate this approach reduces cost prediction errors by approximately 30% and increases average equipment CLV by around 18%. The study concludes that decision mechanisms grounded in predictive costs effectively support value enhancement and refined management of automated equipment.