Nevertheless, Tata Steel’s executives considered whether they should outfit the plant with instruments and sensors to monitor equipment and processes. Analytics technologies were then years away from becoming readily applicable to businesses. Tata Steel’s multiyear effort to develop analytics capabilities at the Kalinganagar plant can be traced to a decision that executives made in 2005, when the plant was first being designed and built. Preparing for analytics, well ahead of its time By building these capabilities, the plant would raise its performance to world-class levels and earn acclaim from the World Economic Forum in July 2019 as one of 44 Lighthouse sites that are leading in the adoption of Fourth Industrial Revolution technologies. They concluded that the company would need to further cultivate employees’ analytics skills and transform their way of working. Managers also realized that classroom training alone would not prepare employees to sustain those performance improvements. Similar difficulties arose with the models being used elsewhere in the plant.ĭespite the setback, managers had seen how the advanced-analytics models reduced costs and raised output. Employees who had gone through the classroom training tried to fix the model but found the problems too complex to correct using the basic skills they’d learned. But as the mix of steel orders from customers began to differ from the mix of orders recorded in the model’s training data, the model started to generate faulty recommendations-which frontline operators ceased to follow. The secondary-metallurgy station at the Kalinganagar plant used analytics to increase throughput by better controlling the superheating processĪt first, the superheating model proposed settings that consistently improved the strike rate. In addition, managers arranged for some employees to receive classroom training in data science, data engineering, and other advanced-analytics disciplines. Data scientists from Tata Steel and McKinsey used historical information from the plant to build and “train” a superheating-optimization model, which would examine real-time operational data and recommend process set points conducive to a higher strike rate. In early 2017, they devised a plan for building analytics models that would help frontline operators get better results from the superheating process and several other activities. The opportunity stood out to the plant managers, who had been given a mandate to improve the plant’s performance with advanced analytics. Bringing the strike rate up to 85 percent or so would result in 28 to 30 heats per day, enough to boost throughput by roughly 8 to 12 percent, or 600 to 900 daily tonnes. That “strike rate” allowed them to complete 25 “heats” per day, but it also left room for improvement. Most of the time, they heated two of every three batches of steel into the optimal range. They would consider prompts from control systems, which were loaded with standard formulas, and then decide what set points to apply so the steel temperature would end up in the target range. The frontline operators at the secondary-metallurgy station were used to running the superheating process based on past experiences. If the steel isn’t hot enough, it can “freeze” before it has been cast, which compromises its quality. If the steel comes out too hot, the equipment operators must slow the casting step. Steel reaches more than 1600 degrees Celsius during superheating, and the ideal temperature range spans only 15 degrees. Shortly before molten steel is cast into solid shapes at Tata Steel’s plant in Kalinganagar, India, frontline operators put the metal through a process known as superheating, which is necessary to bring the steel to the proper temperature for casting.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |