en.Wedoany.com Reported - Aarti Industries Limited (AIL) of India has achieved energy cost savings of over 20% over the past 2-3 years by deploying predictive manufacturing technology based on Real-Time Manufacturing Insights (RTMI) and digital twins. The company believes that the reactive maintenance model long used in the chemical industry—repairing equipment after failure, adjusting processes after deviations, and managing energy consumption after the fact—can no longer meet current market demands for cost efficiency and sustainability.
The foundation of this system lies in the integration of Real-Time Manufacturing Insights and digital twin technology. Acting as a dynamic virtual replica of the physical plant, the digital twin simulates thermodynamic and chemical processes with high fidelity; when paired with RTMI, a continuous data loop enables the system to identify minor deviations in temperature, pressure, and flow rate, triggering interventions or self-corrections before safety incidents or production losses occur.
On the data front, AIL has leveraged long-accumulated historical data to build predictive models and developed an end-to-end manufacturing analytics platform that integrates data engineering, data science, and data interpretation into a unified tool, allowing operations teams to conduct advanced data analysis without relying on dedicated engineers for each of these functions. Machine learning algorithms can handle multivariate interactions that are difficult to analyze with traditional rule-based logic, dynamically providing setpoints that maximize output while minimizing energy consumption.
In equipment maintenance, this system replaces both the "fix-when-broken" and "replace-on-schedule" approaches. AIL has deployed Industrial Internet of Things (IIoT) sensors to collect high-frequency condition data such as vibration, acoustics, and thermal imaging, correlating it with RTMI process data. For example, by cross-referencing vibration changes in pumps with the viscosity profile of the fluid being processed, the system can predict seal failure days to weeks in advance, allowing maintenance to be scheduled during planned downtime, thereby avoiding catastrophic failures and improving Overall Equipment Effectiveness (OEE).
Energy optimization is the most direct area of impact for this system. AIL monitors energy consumption in real time and uses predictive models to optimize boiler and chiller operations, achieving energy cost savings of over 20%. The company states that this result stems not from large-scale equipment replacement, but from the combination of digital tools and process engineering expertise. The optimized processes consume fewer resources and generate less waste and carbon emissions.
The energy savings are also reflected in sustainability ratings. AIL has been included in the S&P Global Sustainability Yearbook 2026, ranking as the highest-scoring Indian chemical company; its EcoVadis 2026 Platinum rating score of 87/100 places it in the top 1% of assessed companies globally, marking the company's first Platinum rating.
Predictive manufacturing is also transforming how personnel work. Automated monitoring has reduced "firefighting" maintenance, allowing engineers and operators to shift toward process innovation and strategy optimization. AIL received the Gallup Exceptional Workplace Award (GEWA) this year, attributing the recognition to improved employee engagement driven by enhanced job content.
AIL states that the journey toward autonomous plants is still evolving, with more advanced AI and edge computing set to further enhance the predictive capabilities of its facilities. The company believes that digital autonomy is a necessary path for the Indian chemical industry to maintain global competitiveness.
