A major refining company improves throughput and reduces costly operational delays by deploying AI-driven decision support that gives operators faster, clearer guidance in the control room.
At a glance
Success Highlights
- 15% improvement in overall operational efficiency
- 40% faster response to process deviations
Related Services and Solutions
- AI Roadmap
- AI Adoption
Challenge
A major refining company ran complex, continuous operations where small inefficiencies compounded into significant cost. Operators managed a constant stream of data from across the facility, but turning that data into timely, confident decisions was difficult, and the consequences of slow or suboptimal calls showed up directly in throughput and cost.
The existing approach left value on the table:
- Critical decisions relied on operator experience alone, with limited real-time guidance.
- Data was scattered across systems, making it hard to see the full operational picture quickly.
- Process deviations were often caught late, after they had already affected efficiency and cost.
Seeking to sharpen day-to-day decision-making without compromising safety, the client looked to put intelligent decision support directly in the hands of its operators.
Solution
AIHugger partnered with the client to design and roll out an AI-driven decision support capability, built around the realities of refinery operations and the operators who run them. Our approach centered on four pillars:
Use-Case Roadmap
- Identified the operational decisions where better, faster guidance would most improve efficiency.
- Prioritized use cases by value, feasibility, and safety impact.
- Established a clear baseline so improvements could be measured against it.
Data & Insight
- Unified data from across the facility into a single, real-time operational view.
- Developed models to surface deviations and recommend optimal adjustments.
- Validated outputs against operational constraints and historical performance.
Operator-First Design
- Delivered recommendations through clear interfaces that fit the control-room workflow.
- Kept operators firmly in control, with AI advising rather than acting autonomously.
- Built trust by making the reasoning behind each recommendation transparent.
Adoption & Refinement
- Trained operators to use and trust the decision support in live operations.
- Monitored performance and refined models as conditions changed.
- Transferred the capability so the organization could extend it to new processes on its own.
By putting clear, trustworthy guidance directly into the control room, the capability helped operators act faster and with greater confidence, without ever taking them out of the decision.
Outcomes
The decision support capability sharpened operations across the facility, turning scattered data into faster, better decisions:
15% efficiency improvement: Faster, better-informed decisions lifted overall operational efficiency across the refinery.
40% faster deviation response: Early detection and clear recommendations let operators address process deviations well before they affected cost.
Operators empowered, not replaced: Trusted, transparent guidance strengthened operator decision-making while keeping humans firmly in control.
A capability built to extend: With the approach transferred in-house, the organization could apply decision support to new processes and units over time.
By grounding AI in the realities of the control room, the organization turned everyday operational decisions into a lasting source of efficiency, setting a new standard for AI-driven operations in refining.