Challenge
The manufacturer wanted to reduce scrap generation, improve tube quality consistency, and minimize unplanned production stoppages across its tube mill operations. However, recurring ovality and scarf defects, improper roller alignment, and limited visibility into machine health were impacting operational efficiency, maintenance responsiveness, and overall manufacturing performance.
Solution
Through close collaboration between manufacturing, maintenance, and digital engineering teams, Tata Technologies helped the customer transform its tube mill operations with an AI-powered condition monitoring solution. We identified critical failure modes, defined machine health monitoring parameters, and developed connected data pipelines to enable continuous real-time visibility into equipment performance and production conditions.
To improve operational reliability and reduce production losses, we installed and commissioned sensors across critical equipment and developed intuitive real-time dashboards for operators and maintenance teams. These dashboards enabled faster monitoring of machine conditions, improved decision-making, and proactive maintenance planning across the plant environment.
We also developed AI-powered anomaly detection models to identify potential machine failures before breakdowns occurred, helping reduce unplanned downtime and improve manufacturing stability. To further accelerate troubleshooting and maintenance responsiveness, we implemented a GenAI-powered PlantIQ Assistant that enabled faster diagnosis of maintenance issues and AI-driven recommendations for corrective actions. The solution empowered plant teams with intelligent insights to improve product quality, reduce scrap generation, and drive more efficient and resilient manufacturing operations.