ABB Ability Performance Optimization Service forCold Rolling Mills
ABB has launched its new ABB Ability Performance Optimization Service for cold rolling mills, offering steel, aluminium and other metals manufacturer’s
ABB has launched its new ABB Ability Performance Optimization Service for cold rolling mills, offering steel, aluminium and other metals manufacturer’s opportunities to reach new levels of operational performance through technology, boosting their processes and profitability. The new service, part of ABB’s metals digital portfolio and Collaborative Operations for Metals suite, combines continuous performance monitoring using ABB Ability Data Analytics for cold rolling mills, with real time support from ABB experts. ABB will work alongside customers with the vision of continuing to transform the metals industry. The data analytics component uses process-specific algorithms based on a century of metals domain expertise to collect high frequency data from mill control systems and discover trends, benchmarks and other performance factors, sending alerts to operators and maintenance when opportunities to optimize performance are identified.
ABB will provide greater insights into the cold rolling process using continuous monitoring, data analytics and real time expertise. The most important productivity, quality and yield KPIs, filtered by time range, material grade or strip dimension and allowing for quick analysis, benchmarking and drilldown to detailed KPIs for individual coil or coil set
Alongside this, ABB experts are available to provide onsite or offsite support, recommending actions to ensure the mill maintains its performance targets against key performance indicators (KPIs) for productivity, quality and yield. Leveraging the collective strengths of metals producers and ABB experts, access to dashboards is shared, enabling all parties to drill down to individual coil level.
In addition, ABB experts can provide customers with detailed reports at regular intervals describing areas for improvement, identified trends, or problem areas found in historical data, allowing for continuous improvement over time.