Process automation at MMK delivers RUB 660 million in savings

Since 2018, the total savings from the incorporation of robotic process automation (RPA) software at Magnitogorsk Iron and Steel Works (MMK) has amounted to RUB 660 million, including RUB 283.8 million last year. Overall, MMK has automated 310 business processes across nearly all of its divisions.

The extensive development and application of RPA technologies at the plant began in 2018; the main supplier of RPA solutions is the Magnitogorsk-based company Next RPA.

“Today, MMK is a recognised leader when it comes to robotic process automation, which is an integral part of the Company’s digitalisation strategy. Software robots are successfully performing hundreds of business processes, freeing employees from routine, repetitive actions. RPA solutions help to increase the speed of key production tasks and reduce the number of errors, especially during peak activity – for example, at the end of reporting periods,” said Andrei Eremin, MMK’s Director of Economics.

The first software robot was installed in MMK’s Commercial Department, where it helped automate the purchase of scrap metal: the programme processes letters from counterparties, checks the status of railcars in Russian Railways databases and transmits data to MMK employees. In essence, the robot has now developed into an entire digital office to support the supply of scrap metal.

Thanks to its automation drive, the Company no longer uses workforce hours to generate reports manually; to compare data in various records systems; to search for specific documents on federal portals; to prepare orders, agreements and certificates; to process temporary transfers; or to carry out numerous other operations.

MMK is actively rolling out deep analytics technologies for process mining and task mining that will be able to map any business process based on data from the customer’s information system. MMK used process mining to complete a comprehensive project for end-to-end management of one of its procurement processes, which enabled the Company to optimise working capital, shorten the procurement cycle and reduce labour costs.