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Concept of Reconfigurability in Interoperation Manufacturing Buffers for Smart Factory

-6, Yang, H.L., Chang, T.W., Choi, Y., 2018. Exploring the Research Trend of Smart Factory with Topic Modeling. Sustainability 10(8: 2779), https://doi.org/10.3390/su10082779

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The Challenges of Industry 4.0 for Small and Medium Enterprises in Poland and Slovakia

Abstract

In the paper an analysis of the state of preparation of small and medium-sized enterprises in the metal industry in Poland and Slovakia was presented. Based on the conducted surveys, the challenges of industry 4.0, which will have to be met by small and medium enterprises, have been identified. Opportunities and threats for enterprises from the SME sector have been also defined. It was found that the biggest threats was lack of capital and lack of appropriate specialists, as well as high costs of staff preparation. Opportunities for enterprises are increased productivity and productivity, faster response to changes to customer requirements.

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Industry 4.0, M2m, Iot&S – All Equal?

, Wolfgang, 2012 Industry 4.0: From Smart Factories to Smart Products, availabale at http://www.business-meetsresearch.lu/bmr/content/download/4929/41471/version/1/file/Industry_4_0From_Smart_Factories_to_Smart_Products.pdf. http://m2m-summit.com/files/m2m_summit_2013_vatm_e_030913.pdf http://www.internet-of-things.no/iot.html http://hutgrip.com/blogs/market-opportunity-for-the-next-big-thing/

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Engineer 4.0 in a metallurgical enterprise

Abstract

Along with the growing dynamics of technological changes in production in the perspective of the development of 4.0 industry, there are changes in the structure of employment and professional qualifications of employees. The development of cyber-physical production systems (CPPS) entails an increase in the demand for engineers. Industry 4.0 is a new megatrend in production. In the second decade of this century, the concept of Industry 4.0 gained importance thanks to the policy of the German government and gradually penetrated into other countries. Enterprises, in addition to traditional production organization, started realizing of cyberphysical production lines as well as smart factories. New production solutions based on IT and robotics technologies using IoT the need for new employee competencies. On the market there is still a growing demand for IT specialists, and there is a demand for engineers 4.0, that is employees with new technical competences, able to control and service CPPS.This publication attempts to present the scope of changes in employment and presents the profile of professional qualifications of engineer 4.0 in a metallurgical enterprise. The list of new skills for an engineer 4.0 employed in an metallurgical enterprise is presented in this article by authors.

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

Abstract

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).

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Environmental Protection in Industry 4.0. Opportunities and Threats in Selected Areas

: www.mdpi.com/journal/sustainabili Burke, R., Mussomeli, A., Laaper, S., Hartigan, M. and Sniderman, B. (2017). The smart factory: Responsive, adaptive, connected manufacturing. Deloitte Insights, August, 31, pp. 1-19. Bujak, A. (2017). Rewolucja przemysłowa – 4.0 i jej wpływ na logistykę XXI wieku. Autobusy, 6, pp. 1338-1344. Efektywność wykorzystania energii w latach 2006-2016/Efficiency of energy using (in Polish). [online] Statistics Poland. Warsaw. Available at: https

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Marketing principles for Industry 4.0 — a conceptual framework

business management, and involve a development of smart factories that communicate in real time via the Internet of Things in an ecosystem composed of machinery, a network of factories, and people ( Kagermann et al., 2013 ). Cloud technologies and the ability to perform an intelligent analysis of large data volumes also enable the integration of value chains, both vertical — occurring inside companies — and horizontal — involving other market participants ( Jarocka and Wang, 2018 ; Saucedo-Martínez et al., 2017 ). This phenomenon has a direct impact on the changes in

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Fourth industrial revolution: a way forward to attain better performance in the textile industry

. Therefore, the fourth industrial revolution is key to the promotion of organisational performance. Industry 4.0 has important elements, such as big data, cyber-physical systems (CPS), the interoperability, the Internet of Things (IoT) and a smart city. The industrial revolution is mostly based on these five factors. However, the current study examined the effect of three major factors, namely, CPS, the interoperability and a smart city (a smart factory, a smart product) on the production and services of textile companies in Malaysia. These three elements of Industry 4

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The Influence of Industry 4.0 on the Enterprise Competitiveness

Mazur, M. (2016). Implementation of Logic Flow in Planning and Production Control. “Management and Production Engineering Review”, 7(1), pp. 89-94. Wang, S., Wan, J., Zhang, D., Li, D. and Zhang, Ch. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, Vol. 101, pp. 158-168. Zhong, R.Y., Xu, X., Klotz, E. and Newman, S.T. (2017). Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, Vol. 3 (5), pp. 616-630.

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Performance of an automated process model discovery – the logistics process of a manufacturing company

). Secondly, the particular integrations are joined by concepts of smart factories, smart product, new business models and new customer services ( Qin et al., 2016 ). Thirdly, there are several leading technological solutions with a major impact on production and services: Cyber-Physical Systems (CPS), big data analytics, cloud computing, autonomous machines, simulations, augmented reality, IoT etc. ( Pan et al., 2015 ; Kolberg & Zühlke, 2015 ), where the use of all of such technologies leads towards further digitisation and computerisation of production, service and

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