Conceptual Framework for Maintenance Planning Based on Spare Parts Volatility: Case Study of Wheel Loader Maintenance in an Open Pit Mine in Morocco

Maintenance policies are crucial in industrial asset management, aiming to sustain operational efficiency while reducing downtime and costs. Effective maintenance planning requires a comprehensive approach that considers various factors, including spare parts availability, workforce skills, and complex logistical constraints. However, a significant challenge lies in moving beyond traditional models to include the dynamic factor of Remaining Useful Life as a direct decision variable, thereby enabling more adaptive and predictive maintenance strategies. This work presents the methodological framework adopted to develop an integrated maintenance optimisation model that incorporates the relative competence to perform tasks, their criticality-specifically, the Remaining Useful Life (RUL) as a direct decision variable-and addresses sustainability challenges by integrating logistical constraints related to spare parts inventories, which involve prognostic uncertainty parameters. To address this, we propose a new approach that combines accurate RUL prediction with maintenance planning. In a case study of two wheel loaders at a phosphate mine, we developed a planning system using two optimisation techniques: a deterministic method, assuming data certainty, and a stochastic process, which explicitly considers uncertainties such as spare parts availability, operator skills, logistical risks including delays, and the importance of operations relative to the production schedule and the RUL of the mine loaders' components.