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Precise pricing strategy is increasingly vital for wholesale fuel operators in today’s volatile market. With product margins tightening and supply chain complexities mounting, teams need to synthesize vast and fragmented pricing data across regions, product lines, and terminals. This case study demonstrates how AI-driven analysis enabled a fuel distributor to pinpoint where pricing power resided, identify volatility threats, and streamline terminal and product strategy—without manual reporting or data wrangling. The story reveals how agentic automation can decode multifactor business drivers in an industry where every cent counts.
With Scoop’s agentic automation, the pricing team achieved an unprecedented clarity on the interlocked dynamics of product, region, supplier, and timing. Notably, the analysis exposed the magnitude of regional price disparities and the predictability of supplier-driven premiums, informing negotiation and distribution choices. The platform's ML-powered insight into terminal selection and pricing implementation workflows enabled refinements in supply chain management and pricing agility—key to securing margin and customer satisfaction in a volatile market. Core metrics from the analysis underscored just how much revenue and risk could be missed with conventional, manual approaches.
The highest unit price identified in the dataset, highlighting the risk exposure associated with outlier terminal, region, or product scenarios.
Both highway-use and off-road diesel average identical prices, but are affected by remarkable volatility: ranging over a 6.8-unit spread.
Both highway-use and off-road diesel average identical prices, but are affected by remarkable volatility: ranging over a 6.8-unit spread.
Average prices in Montana exceeded those in Idaho and Washington by at least 1.10 units per gallon, framing clear regional segmentation and premium capture.
Major oil company terminals, despite representing nearly three-quarters of supply points, consistently charged 0.30 units more than regional distributors.
Wholesale fuel distributors operate in a highly competitive sector where margins are slim, supply relationships are complex, and market pricing changes rapidly across geographies. Data is often siloed—spread across account records, terminal logs, fuel product catalogs, and periodic price notices—making it difficult to build a unified view of product-level profitability or regional trends. Traditional BI tools struggle to reconcile the interplay between fuel type, supplier, geography, and customer segments, leaving pricing analysts with manual workflows, limited visibility into price volatility, and reactive rather than proactive pricing strategies. As new pricing pressures and supply chain risks emerge in the market, the need for real-time, predictive, and deeply granular insights has grown acute.
Automated Dataset Scanning & Metadata Inference: Instantly identified the key domains, metrics, and relationships—fuel type hierarchies, terminal operators, region/city structures, and date semantics—enabling downstream analysis free from manual configuration.
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Scoop’s agentic ML revealed non-intuitive drivers of price and terminal behavior that evade traditional dashboarding. The consistent pricing hierarchy—Clear Diesel > Red Diesel > E10 Gasoline > Non-Ethanol—was observable across all regions and terminals, but only advanced modeling uncovered that this structure held irrespective of supplier, city, or notice timing, suggesting systemic tax and supply chain drivers. Most notably, terminal selection was not just a matter of proximity or volume, but aligned precisely with specific city-region and account profiles: for instance, certain cities routed all supply to one terminal (e.g., Great Falls/Calumet), while key accounts in Montana exhibited exclusive supplier relationships based on account tier. Scoop's analysis further identified that immediate price changes (zero days’ notice) trended overwhelmingly toward midnight implementations, but with unique exceptions at certain terminals handling specialized products. Volatility, especially the nearly 7-unit price range for similar diesel types, was quantitatively linked to both regional and supplier patterns, enabling predictive risk mapping beyond descriptive averages. These multilayered dependencies would require sustained data science effort to detect manually, but Scoop surfaced them for immediate action.
Armed with precise, AI-extracted insights, the organization was able to refine its terminal negotiation strategy—prioritizing high-premium regions for margin optimization and targeting competitive suppliers for volume procurement. The identification of city- and account-level exclusivity patterns informed targeted sales efforts and highlighted where deeper supply agreements or diversification would yield greatest strategic advantage. The exposure of significant price volatility within diesel products led to a review of hedging and inventory approaches. Next phases include expanding the data model to encompass real-time market and competitor feeds, automating alerting on volatility spikes, and piloting Scoop-driven optimization for both pricing implementation timing and supplier selection. The expectation is a measurable uplift in margin capture, risk mitigation, and supply chain agility.