The importance of prediction methods in industry 4.0 on the example of steel industry

• Autorzy: Gajdzik Bożena

Identyfikatory

DOI

10.2478/mape-2019-0028

• Języki publikacji: EN

Abstrakty

EN

This paper presents the importance of the prediction of steel production in industry 4.0 along with forecasts for steel production in the world until 2022. In the last two decades, the virtual world has been increasingly entering production. Today’s manufacturing systems are becoming faster and more flexible – easily adaptable to new products. Steel is the basic structural material (base material) for many industrial sectors. Industries such as automotive, mechanical engineering, construction and transport use steel in their production processes. Prediction methods in cyber-physical production systems are gaining in importance. The task of prediction is to reduce risk in the decision-making process. In autonomous manufacturing systems in industry 4.0 the role of prediction is more active than passive. Forecasts have the following functions: warning, reaction, prevention, normative, etc. The growing number of customized solutions in industry 4.0 translates into new challenges in the production process. Manufacturers must respond to individual customer needs more quickly, be able to personalize products while reducing energy and resource costs (saving energy and resources can increase the product competitiveness). The modern market becomes increasingly unpredictable. Production prediction under such conditions should be carried out continuously, which is possible because there is more empirical data and access to data. Information from the ongoing monitoring of the company’s production is directly transferred to the prospective evaluation. In view of the contemporary reciprocal use of automation, data processing, data exchange and manufacturing techniques, there is greater access to external data, e.g. on production in different target markets and with global, international, national, regional coverage. Companies can forecast in real time, and the forecasts obtained give the possibility to quickly change their production. Industry 4.0 (from the business objective point of view) aims to provide companies with concrete economic benefits – primarily by reducing manufacturing costs, standardizing and stabilizing quality, increasing productivity. Industry 4.0 aims to create a given autonomous smart factory system in which machines, factory components and services communicate and cooperate with each other, producing a personalized product. The aim of this paper is to present new challenges in the production processes in relation to steel production, as well as to prepare and present forecasts of (quantitative) steel production of territorial, global and temporary range until 2022, taking into account the applied production technologies (BOF and EAF). For forecasting purposes, classic trend models and adaptive trend models were used. This methodology was used to build separate forecasts for: total steel production, BOF steel and EAF steel. Empirical data is world steel production in 2000-2017 (annual production volume in Mt).

Słowa kluczowe

EN

steel production; Industry 4.0; prediction; forecasts

• Wydawca: STE GROUP ; De Gruyter
• Czasopismo: Multidisciplinary Aspects of Production Engineering
• Rocznik: 2019
• Tom: Vol. 2, Iss. 1
• Strony: 283--295
• Opis fizyczny: Bibliogr. 32 poz., fig., tab.

Twórcy

autor

Gajdzik Bożena

• Silesian University of Technology, Poland

Bibliografia

• 1. Bauernhansl, T., Ten Hompel, M. and Vogel-Henser, B. (2014). Industrie 4.0 in Produkten. Automatisierung und Logistik. Wiesbaden: Springer Fachmedien.
• 2. Dittmann, P. (2011). Prognozowanie w zarządzaniu sprzedażą i finansami przedsiębiorstwa. Warszawa: Oficyna a Wolters Kluwer business.
• 3. Dittmann, P. (2016). Prognozowanie w przedsiębiorstwie. Metody i ich zastosowanie. Kraków: Wydawnictwo Nieoczywiste.
• 4. Furman, J., Kuczyńska-Chałada, M., Pawlak, S. and Grabowska, S. (2017). The influence of Lean Manufacturing tools on the product quality in the casting process – case study. In: METAL 2017: 26th Anniversary International Conference on Metallurgy and Materials, Ostrava: Tanger, pp. 2127.
• 5. Gajdzik, B. (2018). Models of production function for the steel industry after restructuring process with forecasts and scenarios of changes in volume of steel production. Gliwice: The Silesian University of Technology.
• 6. Gajdzik, B. (2017). Prognostic modeling of total global steel production. Metalurgija 1-2 (56), pp. 279-282.
• 7. Gajdzik, B. (2013). The road of Polish steelworks towards market success – changes after restructuring process. Metalurgija, 3, pp. 421-424.
• 8. Gajdzik, B. (2019). Visions and directions for the development of logistics 4.0 in context 4.0 industrial revolution (level 4.0 - L.4.0). In: Production management and packaging. Food safety and industry 4.0, ed. A. Walaszczyk, I. Jałmużna and 1J. Lewandowski. Łódź: Wydaw. Politechniki Łódzkiej, pp. 69-79. Monografie Politechniki Łódzkiej, 2305. Available at : http://cybra.lodz.pl/dlibra/docmetadata?id=16169&from=publication
• 9. Gajdzik, B. and Gawlik, R. (2018) Choosing the production function model for an optimal measurement of the restructuring efficiency of the Polish metallurgical sector in years 2000–2015. Metals, 8(23), pp. 2-11.
• 10. Gajdzik, B. and Sitko, J. (2014). An analysis of the causes of complaints about steel sheets in metallurgical products quality management systems. Metalurgija, 1 (53), January-March, pp. 135-138.
• 11. Gajdzik, B. and Sitko, J. (2016). Steel mill product analysis using quality methods. Metalurgija, 4 (55), pp. 807-810.
• 12. Gajdzik, B. and Sroka, W. (2012). Analytic study of the capital restructuring process in metallurgical enterprises around the World and in Poland. Metalurgija 2 (51), pp. 265-268.
• 13. Gerbert, P., Lorenz, M., Rüßmann, M., Waldner, M., Justus, J., Engel, P. and Harnisch, M. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries [online].Available at: www.bcg.com. [Accessed: 9 Apr.2015].
• 14. Grabowska, S. (2016). Business model metallurgical company built on the competitive advantage. METAL 2016. 25th Anniversary International Conference on Metallurgy and Materials, May 25th-27th, 2016. Brno, Czech Republic. Ostrava: Tanger Ltd., pp. 1800-1807.
• 15. Grabowska, S. (2018). Implement of the Heat Treatment Process in the Industry 4.0 Context, in 27th International Conference on Metallurgy and Materials. Metal 2018. Brno: Tanger Ltd., pp. 1985-1990.
• 16. Green, W.H. (2003). Econometric Analysis. 5 ed. Upper Saddle River, NJ, Prentice Hall.
• 17. Jasperneite, J. (2012). Was hinter Begriffen wie Industrie 4.0 steckt. Computer & Automation. 19 Dec. 2012 [Accessed 27 Jan. 2016].
• 18. Kagermann, H., Wahlster W. and Helbig J., eds. (2013). Recommendations for implementing the strategic initiative Industrie 4.0: Final report of the Industrie 4.0 Working Group.
• 19. Kramarz, M. (2012). Strategie adaptacyjne przedsiębiorstw flagowych sieci dystrybucji z odroczoną produkcją: dystrybucja wyrobów hutniczych. Gliwice: Wydawnictwo Politechniki Śląskiej.
• 20. Report PWC: Przemysł 4.0 czyli wyzwania współczesnej produkcji. [online] Available at: https://www.pwc.pl/pl/pdf/przemysl-4-0-raport.pdf [Accessed: 20 May 2018].
• 21. Saniuk, A., Witkowski, K. and Saniuk, S. (2013). Management of production orders in metalworking production / // W: 22nd International Conference on Metallurgy and Materials – METAL 2013. Brno: Tanger Ltd., pp. 2057-2062.
• 22. Schwab, K. (2016). The Fourth Industrial Revolution [Accessed 27 Jan. 2016].
• 23. Sendler, U. (2013). Industrie 4.0 – Die Beherrschung der industrieller Komplexität mit SysLM. Berlin: Springer Vieweg.
• 24. Sitko, J. (2015).The intelligent process of initiating new product in aspect problems of management. 15th International Multidisciplinary Scientific GeoConference SGEM 2015. Ecology, economics, education and legislation, 18-24, June, 2015, Albena, Bulgaria. Conference proceedings. Vol. 3, Environmental Economics, Education & Accreditation in Geosciences. Sofia: STEF92 Technology, pp. 689-696.
• 25. Sitko, J. Mikus, R., Bożek, P. (2018) Analysis of device failure in the mechanical production plant. MAPE 2018. XV International Conference Multidisciplinary Aspects of Production Engineering, 05-08 September 2018, Zawiercie, Poland. Conference proceedings, 1(1), pp. 93-99.
• 26. Snarska, A. (2005). Statistics, Econometrics, Prognosis. Warsaw: Placet.
• 27. Sroka, W., Cygler, J., Gajdzik, B. (2014). Knowledge transfer in networks – the case of steel enterprises in Poland. Metalurgija, 1 (53), January-March, pp. 101-104.
• 28. StatSoft (2012). Prediction in business. Cracow: StatSoft.
• 29. Steel Statistical Yearbook. World Steel Association.[online] Available at: https://www.worldsteel.org/internet-2017/steel-by-topic/statistics/steel-statistical-yearbook-html] and other reports from 2016 to 2001.
• 30. Time for 4.0. Special Report. (2018). Newsletter [Accessed 26 Feb. 2019]. Automatyka 10/2018. [online] Available at: wnp.pl, https://automatykaonline.pl/Artykuly/Przemysl-4.0/Czas-na-cztery-zero.-Raport-specjalny-2018].
• 31. World Steel Association report: Top steelmakers in 2017.
• 32. Zeliaś, A. (1997). Theory of forecast. Warsaw: PWE.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).