United StatesManufacturing2023 – ongoingEnerSys
Machine learning for sustainability data extraction from utility bills
EnerSys used machine learning to extract utility-bill data across 180 sites
Problem
EnerSys needed traceable emissions and resource-consumption data from utility bills collected across its worldwide operations.
Approach
Site contacts uploaded utility-bill PDFs to ESG Flo. Its machine learning extracted consumption and billing information and flagged unusual values for the sustainability team.
Results
Takeaway
The system retained a link between reported sustainability data and the underlying bills, supporting audit work alongside extraction.
Categories
- Size
- Large enterprise
- Industry
- Manufacturing
- Business function
- Finance, risk and legal
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Vendor solution
- Evidence
- Company's own claim
Sources
Source summaries
EnerSys's sustainability manager described using ESG Flo to extract utility-bill information across 180 sites. She reported improved accuracy and auditability, without quantifying those improvements.
Full details are in the original sources.
