Use Case

Demand Sensing & Distribution Planning

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The Planning Challenge

This global energy company operates a nationwide fuel distribution network supplying more than 2,300 service stations, alongside industrial clients and logistics partners.

Ensuring reliable fuel availability requires precise coordination between depots, transportation fleets, and retail stations.

However, demand patterns are increasingly volatile. Fuel consumption varies significantly due to factors such as:

• Weather conditions
• Public holidays and long weekends
• Traffic patterns and travel peaks
• Local events affecting station activity

Historically, forecasting relied mainly on recent historical sales data. While effective during stable periods, this approach struggled to capture sudden demand fluctuations.

Improving forecasting accuracy became essential to maintain service levels and optimize distribution operations.

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Results at a Glance

• 86% of forecasting models reaching 75–93% accuracy
• Some daily demand predictions achieving up to 98–100% accuracy
• Improved reliability of service station replenishment
• Reduced stock shortages caused by inaccurate demand forecasts
• More efficient scheduling of fuel deliveries

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The Operational Environment

Fuel distribution requires constant coordination between supply, transportation, and retail operations.

Dispatch teams must anticipate station demand in order to schedule deliveries, allocate truck capacity, and avoid both stock shortages and unnecessary transportation costs.

At the same time, demand can change rapidly depending on external factors such as holidays, weather events, or regional traffic patterns.

Managing these dynamics requires forecasting systems capable of integrating both internal operational data and external demand drivers.

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The Transformation

The company implemented the Sunstice platform to strengthen its demand forecasting capabilities.

Using AI-driven demand sensing models, demand predictions are generated at the product-station level, providing precise visibility into consumption patterns across thousands of service stations.

The forecasting system integrates external variables such as weather, traffic conditions, and public holidays, allowing demand predictions to adapt dynamically to real-world conditions.

These predictions are recalculated continuously, enabling dispatch teams to optimize delivery schedules and improve operational responsiveness.

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Customer Perspective

“The objective was to move from basic forecasting methods to a predictive approach capable of integrating external variables and improving the reliability of our sales predictions.”

Project Manager
Demand Forecast Transformation

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Structured Agility™ in Action

This transformation illustrates Structured Agility™, Sunstice’s operating framework for planning in environments of constant volatility.

By integrating demand sensing, external data signals, and high-frequency forecasting models, the company strengthened its ability to anticipate demand fluctuations and coordinate distribution operations more effectively.

Planning becomes a predictive capability capable of supporting operational resilience and service excellence.