How Certas Energy uses AI to manage supply and demand 


Certas Energy (CE), a leading distributor of oil, gas, and lubricants, recognises the pivotal role of weather patterns in influencing demand across various regions in the UK. For companies in this space this insight is crucial for optimising supply chain management, creating targeted marketing strategies and implementing dynamic pricing models. For instance, warm summers often lead to less demand for heating homes, whilst cold snaps in winter can cause sharp increases in demand, challenging the supply chain and offering opportunities to maximise revenue.  

However, historical and forecast weather data covering a wide range of variables and locations was unavailable, which was hindering the company’s ability to make important business decisions. As a result, CE was in search of a solution that would enable the integration of extensive weather data into their decision-making processes. Implementing AI would help CE see the bigger picture, manage supply and demand more effectively and introduce dynamic pricing, all with the aim of maintaining its leadership position in the distribution industry. 


CE turned to Valcon for its deep expertise in AI and data science and its experience in working with Microsoft Fabric, an AI-powered analytics platform. In collaboration with a selected weather information provider, carefully chosen to align with CE’s specific requirements, Valcon crafted a customised weather information service, designed to cater to the company’s needs. 

This solution deployed APIs (application protocol interfaces) to collect data from 121 geographical locations, spanning the breadth of CE’s operations. The raw weather data underwent a thorough cleansing and transformation process, ensuring it was customised to fit CE’s demands and was compatible with its machine learning algorithms and reporting frameworks. 

The processed data was compiled into a central repository, enhancing its accessibility, and facilitating seamless integration with CE’s existing analytics infrastructure. This customised approach not only addressed CE’s immediate needs for weather data but also underscored Valcon’s commitment to delivering robust solutions that integrate seamlessly with the client’s operational ecosystem. 



  • Improved predictive analytics: CE leverages historical weather data and customer behaviour, allowing for more precise anticipation of customer needs and improved sales conversion rates. This also enables informed business decisions regarding product offerings and promotions. 
  • Dynamic pricing optimisation: CE has enhanced its dynamic pricing by analysing weather patterns and customer actions, adjusting prices based on demand shifts, such as increased heating oil sales in cold weather. This boosts revenue and aligns with benefits seen in sectors using effective data management, like better operational efficiency, enhanced risk management, and informed decision-making with centralised, accurate data. 
  • Maintaining competitive edge: AI has enabled CE to make informed business decisions, manage supply and demand and introduce dynamic pricing. The new AI strategy enhances revenue through gains in operational efficiency, risk management and decision-making through centralised data management. 

Case Studies