DIGITALISATION
Results of design-sessions Beginning of 2017
Only top managers participate
a real Hot-Dip Galvanising Line at NLMK’s Lipetsk site. The system allows the plant to receive and analyse information on changes in equipment operation and all personnel movements in real time. Events, which are recorded and stored in the database, analyse employees’ actions and prevent accidents, reduce safety risks and improve operating efficiency. At the beginning of the shift an employee puts a tracker into his chest pocket. This tracker can show where this employee is at any particular moment. The tracker is additionally equipped with accelerometer, help button and vibration sensor, which turns on to warn a person, if, for example, he enters a hazardous, gas contaminated place. Many companies produce such systems nowadays, but a real production facility imposes a number of process restrictions. First of all, in production conditions, due to shielding and radio interference, the signal of the device is unstable. Secondly, due to the low capacity of the battery, the tracker often needed recharging. Thirdly, in order to have the right positioning, we had to know where an employee was within a half-metre accuracy, which most technical solutions could not provide. We had several design-sessions to elaborate possible scenarios of the system operation: crossing a hazardous area, activation of the help button on the
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FUTURE STEEL FORUM
Beginning of 2018
Top managers and operational personnel participate
tracker, fall or loss of consciousness of an employee, methane or carbon monoxide emission detected. With the assistance of SAP specialists and the National Centre of Internet of Things, a process-oriented partner in the framework of this project, a required technology was selected - UWB (Ultra-Wideband). Positioning accuracy and resistance to the rough conditions of production premises, such as radio interference and shielding, characterise UWB. We developed a new solution using a SAP Cloud Platform, RTLS-UWB positioning system; LoRaWan 3D-visualisation and wireless data transfer technologies. Today a number of positioning products are present on the market and this is far from being an innovation. However, we are the first who combined a RTLS positioning system and data collection from LoRaWAN environment sensors in the SAP Cloud platform with visualisation of all the data on a 3D-model in the Unity game engine. Such a seemingly complex software and hardware package can be easily replicated and upgraded according to the needs of any division concerned. Innovations in machine learning In 2017 NLMK Group was the first in Russia to switch over to the SAP S/4HANA, a most advanced IT-platform. It will not only
speed up the current business processes, but also implement cloud solution technologies, Internet of Things and machine learning into production without installation of any additional systems. Currently, the SAP S/4HANA solution is integrated with more than 20 information and production systems of Russian and European companies within the NLMK Group. More than 6,500 users are working with it. On the basis of this new platform a number of projects were implemented within the NLMKSAP joint innovation laboratory. We developed a model that compares invoices with bank statements and automates financial routines. Across the company’s foreign assets the operational accuracy of the model was 90%. But at the Russian sites the programme had to learn the word “advance payment”. In the group’s Russian divisions around 25% of all the payments are advance payments. The model under testing did not “know” about this and for each payment tried to find an open account. Such comparison was not possible, which led to mistakes. We changed the current model so that it corresponded to the needs and special aspects of the company. And now, based on linguistic analysis of the text in a bank transaction (purpose of payment), the model identifies the advance payment correctly in 95% of cases. The same procedure was followed during implementation of the cash flow forecasting project. We had a request from the financial division of the company, employees of which wanted to know when a contractor was going to pay a certain invoice over a onemonth period. The task was not easy, but we managed to implement it. Having analysed historical data regarding contractors’ payments in the previous periods, the model could forecast with a 90% probability when the contractor was going to pay his next invoices. Forecast accuracy of the model can be improved using more complicated types of machine learning model, for example, recurrent neural networks. Another project implemented with machine learning is the creation of a predictive model, which can predict failure of one of the HSM 2000 units and thus significantly reduce costs of unscheduled
Steel Times International
16/05/2018 10:35:14