Grant: Alliance Grants

In modern manufacturing, mitigating environmental pollution and accurately reporting impacts is crucial for sustainable operations. ERA Environmental (ERA) offers software solutions that play a key role in quantifying and reporting manufacturing's environmental footprint, reflecting its critical importance and the global need for such technologies. On the one hand, the manufacturing data provides great potential to understand and forecast greenhouse gas emissions, leading to opportunities for emission reduction. On the other hand, the current industry practice and reliance on manual data entry poses risks due to potential human errors, which can lead to regulatory issues and impact environmental management. Hence, the project aims to tackle these issues by using artificial intelligence (AI) to automate anomaly detection in data entry, forecast greenhouse gas emissions, and identify potential areas for emission reduction. Objectives include reducing human error in environmental reports, streamlining ERA's software usage to boost efficiency and data integrity, and providing advice to reduce greenhouse gas emissions. Specifically, we will work closely with domain experts to facilitate both the functional and non-functional requirements of how to integrate anomaly detection and time series forecasting into an existing system and leverage a human-in-the-loop approach to integrate domain experts' knowledge in the result. Expected outcomes include enhanced data accuracy, minimized need for manual checks, and more dependable environmental reporting. This initiative will leverage software engineering research knowledge to seamlessly integrate AI into an existing platform initially developed without such capabilities. By embedding AI within ERA's software, the project tackles a key operational challenge and contributes to the broader objective of promoting sustainable manufacturing practices, placing this project at the cutting edge of environmental management innovations.