Managing Complexity Across Global Operations
JSW Steel operates a large, complex network of production facilities, spanning multiple plants, processes, and thousands of critical assets.
Initial deployments began in India, with a focus on validating whether predictive insights could consistently translate into real-world maintenance outcomes.
As confidence grew, the approach expanded across additional plants and regions, including operations in the Americas, with the goal of improving reliability at scale without increasing operational burden.
The Gap Between Insight and Action
At scale, the challenge wasn’t a lack of data, but rather the consistency in how decisions were made. Across sites and teams:
- Data was available, but interpretation carried
- Maintenance actions were not always clearly prioritised
- Insights didn’t always translate into confident, timely decisions on the ground.
This created a familiar gap between detection and action, where teams could see issues emerging, but not always with enough clarity to act early and effectively.
Introducing the Prescriptive Intelligence Layer
JSW Steel implemented PlantOS as a prescriptive intelligence layer, designed to sit across operations and support clearer decision-making.
Rather than stopping at identifying issues, the focus shifted to translating asset behaviour into clear, prioritised recommendations that teams could act on with confidence. These recommendations were embedded directly into workflows through digital work orders and guided actions, helping standardise how maintenance was planned and executed across sites.
At the same time, a closed-loop system was established, where outcomes were validated and used to continuously improve future recommendations. This created a more consistent pathway from signal to action, reducing reliance on manual interpretation and individual judgement, and enabling teams to respond earlier with greater confidence.
From Reactive Work to Repeatable Outcomes
As adoption increased, the shift became operational, not just technical. Teams moved away from reacting to faults and interpreting data in isolation, towards acting on clear, prescriptive recommendations. Decisions that once relied on individual judgement became more consistent, guided by validated actions that could be applied across sites.
This move was supported by digital workflows and automated prescriptions, improving execution discipline and reducing variability in how maintenance was planned and delivered. With continuous monitoring in place, teams were able to intervene earlier, with greater confidence in both the issue and the recommended response.
Prescriptive insights were applied across a wide range of critical equipment and processes, from pellet plant systems such as ball mills and conveyors, through to blast furnace components, rolling mills, and rotating equipment across production lines.
Despite the diversity of assets, the approach remained consistent: early signals were identified, clear actions were guided, and outcomes were validated. Over time, this created a more repeatable model for improving reliability, not just within individual plants, but across global operations.
The impact was measurable across reliability and operational performance.
Operational outcomes:
- 36,108 hours of unplanned downtime avoided
- 6,917 hours MTBF achieved across critical assets
- 2.5% increase in utilisation in mill operations
Execution outcomes:
- 6,000+ prescriptive actions executed
- High implementation rates across plant teams
- Consistent translation from insight to action to outcome.
