SnoFox Sciences develops a thermodynamically accurate digital twin of industrial refrigeration facilities by connecting to existing control systems. This physics-based platform derives real-time data to generate actionable insights for operations and management teams. The resulting analysis helps reduce energy costs through peak shaving and load shifting while predicting equipment failures to improve maintenance efficiency.
Funding
$6.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
TVFounders
Product
Problem
Industrial refrigeration systems often operate inefficiently due to a lack of real-time, comprehensive data on thermodynamic performance. This leads to increased energy consumption, higher operational costs, and potential system failures that disrupt the cold chain. Traditional monitoring methods require additional hardware and manual data collection, making it difficult to identify and address inefficiencies proactively.
Solution
SnoFox Sciences offers a data analytics platform that enhances cold chain monitoring and preventative maintenance by leveraging existing refrigeration control systems. The platform creates a thermodynamically accurate digital twin of the facility, providing real-time insights into system performance without requiring additional hardware. By analyzing data from the control system, SnoFox identifies inefficiencies, predicts potential failures, and enables facilities to optimize energy usage and operational performance. The platform generates actionable insights for various stakeholders, from facility operators to the board room, facilitating data-driven decision-making and improved cold chain management.
Target Audience
SnoFox Sciences targets enterprise customers in the food cold supply chain, including cold storage facilities, food processors, and distributors seeking to reduce energy consumption and improve operational efficiency.
Features
- Thermodynamically accurate digital twin creation based on facility refrigeration schematics
- Real-time data analysis using existing refrigeration control system inputs
- Identification of system inefficiencies and prediction of future failures
- Peak shaving and load shifting recommendations to reduce energy costs
- Customizable dashboards for facility, operations, and management teams
- Integration with existing refrigeration control systems without requiring additional hardware