Integrated Human-AI Forecasting for Preventive Maintenance Task Duration Estimation

Maintenance task duration estimations help manage shipyard resource usage and allow planners to decide on maintenance priorities within a limited time frame. Better estimated task durations help produce more robust resource schedules, perform more tasks in facilities such as shipyards, reduce resource idling time and increase ship operational availability. However, task duration estimations have until now been historically performed by human experts with essentially no artificial intelligence-based forecasting for shipyard operations. The analysis of historical data is also not a common practice to complement any expert-driven forecasting. To explore opportunities for using AI in this work domain, and to improve on human estimations for task durations, we propose a novel hybrid Human-AI approach that involves integrating human forecasts with data-driven models. Our empirical data comes from two fleet maintenance facilities in Canada, containing more than 13,000 anonymized historical ship work orders (WO) ranging from 2017 to 2022. We used supervised learning algorithms to forecast the preventive maintenance task duration on this data, with and without expert task duration estimates and the results demonstrate that hybrid models perform better than both human expert model and historical data alone. An average of 8.6% improvement from Hybrid human-AI model over human expert model is observed based on R2 evaluation metric. Results suggest that human forecasts, which tend to rely on a broader contextual knowledge than the inputs captured in a historical database, remain key for effective task duration estimation, yet can be fine-tuned by the pattern recognition capabilities of machine learning algorithms.