Predictive Maintenance-Driven Digital Decarbonization in the Indian Cement Industry: An Equipment-Focused Framework

The cement sector is one of the most energy-intensive manufacturing industries and a significant contributor to greenhouse gas emissions. In India, the industry accounts for a considerable share of industrial carbon emissions due to its dependence on high-temperature processing equipment and energy-demanding grinding operations. While regulatory initiatives and cleaner fuel strategies have improved environmental performance, operational inefficiencies at the equipment level continue to impede deeper decarbonization. This paper examines the role of Artificial Intelligence (AI) and Industrial Internet of Things (IIoT)-enabled Predictive Maintenance (PdM) as a digital mechanism for reducing energy consumption and carbon emissions. An Equipment-Centric Predictive Maintenance and Decarbonization (ECPMD) framework is proposed to evaluate the influence of PdM on rotary kilns, pre-heaters, and grinding systems. Evidence synthesized from academic studies, industry reports, and sustainability disclosures indicates that predictive maintenance improves equipment reliability, stabilizes operations, lowers energy demand, and contributes to measurable emission reductions. The study highlights the strategic importance of predictive maintenance in supporting the Indian cement industry's transition toward sustainable and low-carbon production.