Supply and trading teams operate in one of the most volatile environments in the industry. You’re balancing crude differentials, refinery margins, freight rates, geopolitical risk, storage constraints, and shifting product demand — all while markets move faster than traditional analytics can keep up with. Traders often rely on fragmented data sources and manual interpretation, which slows decision‑making and increases exposure. An AI‑driven market intelligence capability gives your teams a clearer, more timely view of market conditions so they can act with confidence.
What the Use Case Is
Supply and trading market intelligence uses machine learning to analyze market data, forecast price movements, and surface opportunities across crude, products, and freight. It sits between trading desks, supply planning, and risk management. You’re giving traders a decision support layer that synthesizes futures curves, spot prices, refinery runs, inventory levels, shipping data, and macroeconomic indicators.
This capability fits naturally into daily trading workflows. Traders review AI‑generated insights during morning strategy sessions, adjust positions based on predicted spreads, and monitor intraday alerts that flag emerging risks or arbitrage opportunities. Over time, the system becomes a steady companion that helps teams navigate complex markets with more clarity and less guesswork.
Why It Works
The model works because it processes variables that humans can’t track simultaneously. Market behavior is influenced by refinery outages, weather patterns, shipping delays, geopolitical events, and demand shifts across regions. AI models can ingest these signals continuously and surface patterns that would otherwise remain hidden.
This reduces friction across trading and supply workflows. Instead of reconciling conflicting data sources, everyone works from the same intelligence layer. It also improves throughput by reducing the time traders spend gathering data and increasing the time they spend evaluating strategy. The result is more consistent decision‑making and better alignment across the organization.
What Data Is Required
You need structured market and operational data. Futures curves, spot prices, crack spreads, freight rates, and inventory reports form the foundation. Refinery utilization, crude assays, and product yield data add operational context. Shipping data — vessel positions, port congestion, and freight costs — strengthens the model’s ability to detect arbitrage opportunities.
Real‑time data is essential. Weather updates, geopolitical alerts, refinery outages, and intraday price movements allow the model to adjust predictions as conditions shift. You also need metadata that ties each data point to its source and timestamp. Data freshness matters because market conditions can change within minutes.
First 30 Days
The first month focuses on scoping and validating the data needed for a reliable pilot. You start by selecting a specific market segment — crude differentials, product spreads, or freight — where historical data is strong. Data engineers clean price histories, reconcile shipping data, and align inventory reports with market movements. You also define the trading horizons that matter most for early testing.
A pilot model is trained and benchmarked against historical trading decisions. Traders review the insights during daily briefings to compare them with actual market outcomes. Early wins often come from identifying spread movements or arbitrage windows that were previously hard to detect in time. This builds confidence before integrating the system into live workflows.
First 90 Days
By the three‑month mark, you’re ready to integrate the model into real‑time trading and supply operations. This includes automating data ingestion, setting up dashboards for intraday alerts, and creating workflows for reviewing AI‑generated recommendations. You expand the pilot to additional market segments and incorporate more granular data sources such as refinery outage feeds or vessel‑tracking analytics.
Governance becomes essential. You define who reviews model outputs, how traders incorporate insights into positions, and how exceptions are handled. Cross‑functional teams meet weekly to review performance metrics such as forecast accuracy, spread capture, and realized margins. This rhythm ensures the capability becomes part of the operational fabric rather than a standalone analytics tool.
Common Pitfalls
Many operators underestimate the importance of clean historical price data. If settlement results or freight records are inconsistent, the model learns the wrong signals. Another common mistake is ignoring shipping data. Without vessel movement and congestion insights, the model misses key drivers of regional price spreads.
Some teams also deploy the system without clear decision‑making workflows. If traders don’t know when or how to use the insights, adoption slows. Finally, operators sometimes overlook intraday updates. A model that only refreshes once or twice a day can’t keep up with fast‑moving markets.
Success Patterns
The operators that succeed treat market intelligence as a decision support capability, not an automated trading engine. They involve traders early so the model reflects real market behavior. They maintain strong data hygiene, especially around price histories and shipping data. They also build simple workflows for reviewing and acting on insights, which keeps the system grounded in operational reality.
Successful teams refine the model regularly and incorporate new data sources as they become available. Over time, the capability becomes a trusted part of trading strategy, improving margins, reducing risk, and strengthening alignment across supply and commercial teams.
A strong market intelligence capability helps your teams see opportunities earlier, respond faster, and capture value that would otherwise slip through the cracks — and those gains compound across every trade you make.