Generative AI in asset maintenance planning can improve equipment uptime, reduce maintenance costs, and enhance operational efficiency.

Issue / Opportunity

In mining and oil and gas operations, maintenance planning helps prevent premature equipment failure, costly repairs and replacements, and extends the life of an asset. Facing near- and long-term constraints and factors, maintenance plans and the subsequent downstream processes can be changed to align with production, in response to resource availability, or because of unexpected events. Making maintenance plan alterations, however, can be costly and labor intensive.

How Gen AI can help

Continuous improvement

Generative AI can be used to reconcile lessons learned from prior shutdowns, identify opportunities for maintenance alignment, provide planners with the information needed to challenge assumptions on maintenance alignment, and develop strategies to minimize the impact across the system.

Optimal maintenance scheduling

Generative AI helps optimize maintenance schedules by weighing operational factors (e.g., equipment use, production requirements, and maintenance costs), recommending the most efficient and cost-effective schedules, and analyzing equipment use and performance data to minimize downtime and maximize equipment availability.

Simulation and optimization

Generative AI can simulate maintenance scenarios and evaluate the impact of maintenance strategies on equipment performance, productivity, and operational efficiency. This helps reveal the most effective maintenance approaches and optimizes resource allocation for maintenance activities

Managing risk and promoting trust

Robust and reliable

Generative AI applications for asset maintenance planning depend on the quality of the data. Data that is incorrect, incomplete, or is not representative of the current operational environment or maintenance practices can lead to a suboptimal and potentially inappropriate maintenance plans that may even be detrimental to asset health management and future maintenance planning activities.

Accountable

There is no machine substitute for a human asset maintenance planners' knowledge, experience, and expertise. Overreliance on AI-generated outputs without critical human review may lead to important contextual factors and valuable insights being overlooked.

Safe and secure

Generative AI models may struggle to account for the uncertainties inherent in asset maintenance planning, like unexpected equipment failures or changing production requirements. Suboptimal or unrealistic Generative AI recommendations due to overfitting can lead to inaccuracies or poor performance when applied to real-world maintenance scenarios. The degree of human intervention and oversight needed must be considered in the design phase of the solution. This is especially true in complex maintenance scenarios with interdependent systems or intricate operational constraints may also prevent Generative AI from providing accurate and feasible solutions.