Nilesh Shedge

Industrial shop floors are among the highest consumers of energy and resources, yet significant inefficiencies persist due to limited real-time visibility and suboptimal utilization strategies. While automation has improved production processes, the integration of advanced energy and resource analytics remains an underutilized frontier for achieving substantial efficiency gains and sustainability improvements.
This paper categorizes industrial energy consumption into two primary patterns: (1) Utility Consumption Patterns, which analyze the correlation between plant-level production and overall energy/resource usage, and (2) Process Consumption Patterns, which focus on energy-intensive process parameters relative to production metrics. Through advanced data analytics, these patterns can be optimized to minimize waste, enhance energy efficiency, and drive reductions in greenhouse gas (GHG) emissions.

The study presents a real-world case from a highly energy-intensive chemical manufacturing facility in India, where an AI-driven, continuous data analytics platform—CarbonMinus—was deployed. By leveraging real-time monitoring, predictive analytics, and machine learning-driven insights, the platform identified inefficiencies, enabled proactive decision-making, and delivered tangible improvements in energy/resource efficiency. Key challenges, such as data fragmentation, lack of actionable insights, and resistance to change, were systematically addressed through a structured implementation approach.

This paper highlights the technical methodology, data-driven decision-making frameworks, and quantitative results achieved through digital energy analytics. It demonstrates how AI-enabled sustainability platforms can revolutionize industrial shop floor operations, ensuring not just cost savings but also long-term compliance with global sustainability standards and corporate net-zero goals.