Abstract

Outage and maintenance planning have long been constrained by fragmented data, static scheduling, and reactive execution. TerraTolga’s AI-Driven Planning Framework introduces a new paradigm: a predictive, data-integrated approach that uses machine learning to prioritize, sequence, and simulate maintenance activities. The result: optimized schedules, reduced downtime, and measurable gains in reliability and cost efficiency.

Main Article

In industrial operations, maintenance is both a necessity and a challenge. Unplanned outages, task overlaps, and resource bottlenecks often arise not from technical limitations, but from planning blind spots caused by disconnected data systems and static work management processes.
TerraTolga’s AI-Driven Planning Framework closes this gap by embedding intelligence directly into the planning and scheduling workflow.

The system ingests data from Computerized Maintenance Management Systems (CMMS), inspection histories, and operational performance logs to forecast work demand and dynamically rank activities based on risk, cost, and resource impact.

Core Components of TerraTolga’s AI Planning Architecture:

1. Predictive Work Identification – Machine learning models analyze asset reliability trends, sensor data, and historical failure modes to automatically flag high-risk components and forecast upcoming maintenance needs.

2. Smart Prioritization Engine – Integrates reliability risk ranking (RBI, RCM, FMEA) with scheduling parameters such as manpower, equipment availability, and production constraints to produce optimized task hierarchies.

3. Simulation & Scenario Optimization – AI models simulate multiple “what-if” outage scenarios, quantifying the impact of timing, sequencing, and resource changes before execution.

4. Adaptive Scheduling Interface – The system generates actionable schedules compatible with Primavera P6, MS Project, or SAP, while learning continuously from execution feedback to improve future plans.

The outcome is not merely automation, but decision augmentation.
Planners and engineers gain the ability to model risk, cost, and performance together, turning maintenance strategy into a predictive science rather than an operational scramble.
Early case studies show reductions in turnaround duration by up to 18%, and cost savings of 12–15%, achieved through intelligent task consolidation and data-driven timing.

AI-Driven Planning exemplifies TerraTolga’s philosophy of integrating intelligence at every operational layer—from data readiness to maintenance optimization—creating systems that learn, adapt, and continuously improve plant reliability.

Key Takeaway

By merging predictive analytics with advanced scheduling logic, TerraTolga’s AI-Driven Planning Framework transforms outages and routine maintenance from reactive necessities into proactive, data-optimized events: reducing cost, risk, and downtime while increasing asset availability and organizational foresight.