AI-Driven Maintenance Planning Platform Delivers Over $4.5M Annual Savings for a Major Gas Producer

The client operates a large-scale gas production and processing infrastructure comprising more than 500,000 pieces of equipment across upstream and midstream facilities. Maintaining this asset base required approximately 1 million man-hours of maintenance work annually, coordinated by a centralized planning department of over 25 specialists relying heavily on manual data entry and Excel-based workflows.
The operational challenges were substantial:

• Maintenance planning involved thousands of interdependent tasks, each influenced by seasonality, equipment availability, workforce specialization, and asset criticality.
• Manual planning was time-consuming, error-prone, and difficult to scale.
• A single planning error led to an unplanned equipment shutdown costing the company approximately $15 million per day in lost production.
• Existing enterprise systems, including SAP PM, were unable to support the complexity and performance requirements of large-scale, constraint-based planning.

An attempt to solve the problem through SAP customization lasted nearly two years without delivering a viable outcome, prompting the decision to seek an alternative, purpose-built solution.

The client defined the following objectives:

1. Eliminate manual, Excel-based maintenance planning for large equipment fleets.
2. Optimize long-term and short-term maintenance schedules under real operational constraints.
3. Reduce equipment downtime and maintenance backlog.
4. Lower total maintenance and subcontractor costs while improving planning accuracy.
5. Ensure seamless integration with existing ERP and asset-management systems.

After evaluating commercial planning platforms such as Primavera and Microsoft Project, it became clear that none could meet the industry-specific requirements related to scale, performance, and maintenance logic. A decision was made to design and implement a custom intelligent planning system.

System Architecture & Modules

The solution consisted of two tightly integrated planning modules:
1. Long-Term Planning Module (5 - 10 years horizon)
Designed to optimize strategic maintenance planning and resource allocation, this module calculates the optimal combination of:

• Workforce size and specialization
• Equipment and tooling availability
• Maintenance cycles and regulatory intervals

The model accounts for:

• Cyclic maintenance schedules
• Seasonal constraints and varying work efficiency
• Interdependent maintenance activities
• Asset accessibility and operational windows

2. Short-Term Planning Module (1 - 8 weeks horizon)
Focused on operational execution and unplanned maintenance, this module dynamically prioritizes work based on:

• Urgency and criticality of failures
• Workforce availability by skill
• Equipment and auxiliary resource readiness
• Multi-shift execution scenarios

Both modules were integrated with existing ERP systems (including SAP) to ensure data consistency and traceability.

Following full deployment, the client achieved measurable and sustained improvements:
Operational Impact

• Equipment downtime significantly reduced through improved long-term planning and workload leveling.
• Maintenance backlog reduced from ~6 weeks to ~2 weeks for unplanned work.
• Planning errors virtually eliminated, preventing high-impact shutdown events.

Financial Impact

• >$3 million annual savings from optimized long-term maintenance planning and resource utilization.
• >$1.5 million additional annual savings from faster execution of unplanned maintenance and reduced production losses.
• Reduced reliance on subcontractors, overtime, and idle equipment.

Productivity & Governance

• Maintenance planners transitioned from manual scheduling to scenario-based decision-making.
• Planning cycles shortened from days to hours.
• Improved auditability and transparency of maintenance decisions.