With its machine-learning software, Datapred helps industrial buyers of raw materials to centralize tens of internal and external data streams and make better buying decisions, saving up to 5% of their raw material spend, year over year.
Moreover, the solution provides industrial buyers with new opportunities to "trade around" their assets – that is, to purchase based on the real-time combination of market trend predictions and operational cost/constraint optimization.
The recently secured FIT Growth loan of CHF 500’000 will contribute to the further development of the company, as Datapred plans to expand internationally with a focus on Germany.
Machine learning to reduce costs in challenging times
Industrial buyers of raw materials operate in a challenging environment characterized by enormous pressure to reduce costs, high market volatility, and mountains of data to be analyzed, slowing execution down and leaving no time for strategic thinking. The current pandemic has even exacerbated these challenges for the energy and raw material markets.
“The Covid-19 crisis is having a violent impact on energy and raw material markets, but the traditional concept of price volatility doesn’t capture the many aspects of that instability, and is actually misleading in some cases. Chief Procurement Officers need finer insights to optimize buying decisions in the post Covid-19 world”, explains Thomas Oriol, director of Datapred.
Based at the EPFL Innovation Park in Lausanne, in the canton of Vaud, Datapred was founded in 2017, named a “Cool Vendor” by Gartner and won the international machine learning competition organized by Airbus in 2019, and ranks among the Top 50 Companies to Watch by Spend Matters in 2020.
