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Benefits of Industry 4.0 for Logistics and Decision-making of Managers

://iot-analytics.com/the-leading-industry-4-0-companies-2019/ . [7] Microsoft Azure product portfolio. (2019). Retrieved September 02, 2019, from https://azure.microsoft.com/en-us/ . [8] Migalani, S. (2018). Microsoft – Industry 4.0, smart factories with IoT and AI. Retrieved September 10, 2019, from https://blogs.msdn.microsoft.com/msind/2018/04/27/iiot-smart-factories-ai-azure-iot-edge/ . [9] Müller, J. & Däschle, S. (2018). Business Model Innovation of Industry 4.0 Solution Providers towards customer process innovation. Processes. 6(12), 260. [10] PTC – augmented reality, use

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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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Exploration of Land Development Intensity Index of Port Container Logistics Park Based on Quantitative Algorithm and Pent Analysis Method

, Guo, Z, Zeng, and J, Tian, “Uncovering the strategies of green development in a Chinese province driven by reallocating the emission caps of multiple pollutants among industries,” Science of the Total Environment, Vol. 607-608, pp. 1487, 2017. 7. S. Choo, D. Sohn, and M. Park, “Mobility characteristics of the elderly: A case for Seoul Metropolitan Area,” Ksce Journal of Civil Engineering, Vol. 20, No. 3, pp. 1023-1031, 2016. 8. Y. S. Kang, I. H. Park, and S. Youm, “Performance Prediction of a MongoDB-Based Traceability System in Smart

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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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The Millennials’ Effect: How Can Their Personal Values Shape the Future Business Environment of Industry 4.0?

. (2004). Value congruence and job satisfaction among nurses: A human relations perspective. International Journal of Nursing Studies, 41 , 599-605. https://doi.org/10.1016/j.ijnurstu.2003.12.011 Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101 , 158–168. https://doi.org/10.1016/j.comnet.2015.12.017 Weber, J. (2017). Discovering the Millennials’ Personal Values Orientation: A Comparison to Two

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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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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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