Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives

Process Safety and Environmental Protection

Volumn 117, Issue , 2018, Pages 408-425

  Kamble, Sachin S ^a   Gunasekaran, Angappa ^b   Gawankar, Shradha A ^a  

^a NATIONAL INSTITUTE OF INDUSTRIAL ENGINEERING NITIE   (India)

^b CALIFORNIA STATE UNIVERSITY   (United States)

Author keywords

Augmented reality; Big data; Industry 4.0; Internet of things; Process safety; Smart manufacturing; Sustainability

Indexed keywords

ACCIDENT PREVENTION; AUGMENTED REALITY; BIG DATA; FLOW CONTROL; INTERNET OF THINGS; SUSTAINABLE DEVELOPMENT;

AUTOMATED MANUFACTURING ENVIRONMENTS; HUMAN MACHINE INTERACTION; ORGANIZATIONAL PERFORMANCE; PROCESS SAFETY; RESEARCH AND DEVELOPMENT; SMART MANUFACTURING; SUSTAINABLE INDUSTRIES; SYSTEMATIC LITERATURE REVIEW;

INDUSTRY 4.0;

EID: 85047528617     PISSN: 09575820     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.psep.2018.05.009     Document Type: Article

References (111)

1. Adolph, Lars, et al. German Standardization Roadmap: Industry 4.0. Version 2. 2016, DIN, Berlin.
2. Alexandre, B., Salguero, J., Peralta-Alvarez, M.E., Aguayo-Gonzalez, F., Ares, E., Application of Industry 4.0 technologies to the design and manufacturing of handicraft products. DYNA 92:4 (2017), 435–441, 10.6036/8169.
3. Arunachalam D., Kumar N., Kawalek J.P., 2017, Understanding big data analytics capabilities in supply chain management: unravelling the issues, challenges and implications for practice, Transp. Res. Part E xxx (2017) (In press).
4. Bahrin, M.A.K., Othman, M.F., Azli, N.H.N., Talib, M.F., Industry 4.0: a review on industrial automation and robotic. Jurnal Teknologi, 78(6–13), 2016.
5. Bogle, I.D.L., A perspective on smart process manufacturing research challenges for process systems engineers. Engineering, 3(2), 2017, 10.1016/J.ENG.2017.02.003.
6. Bastian, Mathieu, Sebastien Heymann, Mathieu Jacomy, Gephi: an open source software for exploring and manipulating networks. Icwsm 8 (2009), 361–362.
7. Carvalho, N., Chaim, O., Cazarini, E., Gerolamo, M., Manufacturing in the fourth industrial revolution: a positive prospect in sustainable manufacturing. Procedia Manuf. 21 (2018), 671–678.
8. Chen, T., Lin, Y.C., Feasibility evaluation and optimization of a smart manufacturing system based on 3D printing: a review. Int. J. Intell. Syst., 32(4), 2017, 10.1002/int.21866.
9. Choi, S.S., Jung, K., Kulvatunyou, B., Morris, K.C., An analysis of technologies and standards for designing smart manufacturing systems. J. Res. Natl. Inst. Stand. Technol., 121, 2016, 10.6028/jres.121.021.
10. Chromjakova, F., Process stabilisation-key assumption for implementation of Industry 4.0 concept in industrial company. J. Syst. Integr., 8(1), 2017, 3.
11. Davis, J., Edgar, T., Porter, Bernaden J., Sarli, M., Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng. 47 (2012), 145–156, 10.1016/j.compchemeng.2012.06.037.
12. De Sousa Jabbour, A.B.L., Jabbour, C.J.C., Foropon, C., Godinho Filho, M., When titans meet–can Industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technol. Forecast. Soc. Change 132 (2018), 18–25, 10.1016/j.techfore.2018.01.017.
13. Duarte, S., Cruz-Machado, V., Exploring linkages between lean and green supply chain and the industry 4.0. International Conference on Management Science and Engineering Management, Springer, Cham, 2017, 1242–1252 July.
14. Fernandes, I., Assuncao, E., Industry 4.0: training for automation in Europe. Welding 96:7 (2017), 50–52.
15. Gentner, S., Industry 4.0: reality, future or just science fiction? How to convince today's management to invest in tomorrow's future! Successful strategies for Industry 4.0 and manufacturing IT. Chimia, 70(9), 2016, 10.2533/chimia.2016.628.
16. Gephi, Gephi—Makes Graphs Handy. 2013 https://gephi.org.
17. Brian, Guo, Tak Wing Yiu, González, Vicente A., Predicting safety behavior in the construction industry: development and test of an integrative model. Safety Science 84 (2016), 1–11.
18. He, J.F., Jia, G.Y., Han, G.J., Wang, H., Yang, X., Locality-aware replacement algorithm in flash memory to optimize cloud computing for smart factory of Industry 4.0. IEEE Access, 5, 2017, 10.1109/ACCESS.2017.2740327.
19. Stefan Heck, SMatt Rogers, Paul Carroll, Resource revolution: how to capture the biggest business opportunity in a century. Houghton Mifflin Harcourt, 2014.
20. Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., Ivanova, M., A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0. Int. J. Prod. Res., 54(2), 2016, 10.1080/00207543.2014.999958.
21. Johannes, Cornelis de Man, Strandhagen, Jan Ola, Research into the potential revenue models for Industry 4.0 supported sustainable products. Procedia CIRP 63 (2017), 721–726.
22. Kamigaki, T., Object-oriented RFID with IoT: a design concept of information systems in manufacturing. Electronics, 6(1), 2017, 10.3390/electronics6010014.
23. Karakose, M., Yetis, H., A cyberphysical system based mass-customization approach with integration of Industry 4.0 and smart city. Wireless Commun. Mobile Comput. 2017 (2017), 1–9, 10.1155/2017/1058081.
24. Kersten, T., Industry 4.0: self-sufficient production prevents standstill with smart processes in a productive and sustainable future – the smart meat factory. FLEISCHWIRTSCHAFT, 96(1), 2016.
25. Kibira, D., Morris, K.C., Kumaraguru, S., Methods and tools for performance assurance of smart manufacturing systems. J. Res. Natl. Inst. Standards Technol., 121, 2016, 10.6028/jres.121.013.
26. Kim, D.B., Denno, P.O., Jones, A.T., A model-based approach to refine process parameters in smart manufacturing. Concurrent Eng.-Res. Appl., 23(4), 2016, 10.1177/1063293X15591038.
27. Lamba, K., Singh, S.P., Big data in operations and supply chain management: current trends and future perspectives. Prod. Plann. Control 28:11–12 (2017), 877–890, 10.1080/09537287.2017.1336787.
28. Lao, L.F., Ellis, M., Christofides, P.D., Smart manufacturing: handling preventive actuator maintenance and economics using model predictive control. AIChE J., 60(6), 2014, 10.1002/aic.14427.
29. Lao, L.F., Ellis, M., Durand, H., Christofides, P.D., Real-time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control. AIChE J., 61(10), 2015, 10.1002/aic.14960.
30. Lee, H.W., Liu, C.H., High precision optical sensors for real-time on-line measurement of straightness and angular errors for smart manufacturing. Smart Sci., 4(3), 2016, 10.1080/23080477.2016.1207407.
31. Lee, Ju Yeon, Yoon, Joo Seong, Kim, Bo-Hyun, A big data analytics platform for smart factories in small and medium-sized manufacturing enterprises: an empirical case study of a die casting factory. Int. J. Precis. Eng. Manuf., 18(10), 2017, 10.1007/s12541-017-0161-x.
32. Lee, M.X., Lee, Y.C., Chou, C.J., Essential implications of the digital transformation in Industry 4.0. J. Sci. Ind. Res., 76(8), 2017.
33. Li, D., Tang, H., Wang, S.Y., Liu, C.L., A big data enabled load-balancing control for smart manufacturing of Industry 4.0. Cluster Comput. J. Netw. Softw. Tools Appl., 20(2), 2017, 10.1007/s10586-017-0852-1.
34. Lin, C.C., Deng, D.J., Chen, Z.Y., Chen, K.C., Key design of driving Industry 4.0: joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks. IEEE Commun. Mag., 54(10), 2016.
35. Liu, Y.K., Xu, X., Industry 4.0 and cloud manufacturing: a comparative analysis. J. Manuf. Sci. Eng.-Trans. ASME Cyber-Phys. Syst. Of-the-art Big Data Chall., 139(3), 2017, 10.1115/1.4034667.
36. Lobo, F.A., Industry 4.0: what does it mean to the semiconductor industry?. Solid State Technol., 59(8), 2016.
37. Lotzmann, T., Wenzel, F., Karsunke, U., Kozak, K., For Industry 4.0, visualization and machine learning can be combined to enhance laser processing. Laser Focus World, 53(1), 2017.
38. Lu, Y.J., Cecil, J., An IoT (IoT)-based collaborative framework for advanced manufacturing. Int. J. Adv. Manuf. Technol., 84(5–8), 2016, 10.1007/s00170-015-7772-0.
39. Luthra, Sunil, Mangla, Sachin Kumar, Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Saf. Environ. Prot. 117 (2018), 168–179, 10.1016/j.psep.2018.04.018.
40. Marques, M., Agostinho, C., Zacharewicz, G., Jardim-Goncalves, R., Decentralized decision support for intelligent manufacturing in Industry 4.0. J. Ambient Intell. Smart Environ., 9(3), 2017, 10.3233/AIS-170436.
41. Menasce, D.A., Krishnamoorthy, M., Brodsky, A., Autonomic smart manufacturing. J. Decis. Syst., 24(2), 2015, 10.1080/12460125.2015.1046714.
42. Mishra, D., Gunasekaran, A., Papadopoulos, T., Childe, S.J., Big data and supply chain management: a review and bibliometric analysis. Ann. Oper. Res., 2016, 1–24.
43. Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., Ueda, K., Cyber-physical systems in manufacturing. CIRP Ann. 65:2 (2016), 621–641.
44. Monostori, L., Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP 17 (2014), 9–13.
45. Moreno, A., Velez, G., Ardanza, A., Barandiaran, I., de Infante, A.R., Chopitea, R., Virtualisation process of a sheet metal punching machine within the Industry 4.0 vision. Int. J. Interact. Des. Manuf.–IJIDEM, 11(2), 2017, 10.1007/s12008-016-0319-2.
46. Mosterman, P.J., Zander, J., Industry 4.0 as a cyber-physical system study. Softw. Syst. Model., 15(1), 2016, 10.1007/s10270-015-0493-x.
47. Moyne, J., Iskandar, J., Big data analytics for smart manufacturing: case studies in semiconductor manufacturing. PROCESSES, 5(3), 2017, 10.3390/pr5030039.
48. Mueller, E., Chen, X.L., Riedel, R., Challenges and requirements for the application of Industry 4.0: a special insight with the usage of cyber-physical system. Chin. J. Mech. Eng., 30(5), 2017, 10.1007/s10033-017-0164-7 9.
49. Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., Lin, Y., Big data analytics in supply chain management: a state-of-the-art literature review. Comput. Oper. Res., 2017, 1–11.
50. Oesterreich, T.D., Teuteberg, F., Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry. Comput. Ind., 83, 2016, 10.1016/j.compind.2016.09.006.
51. Paelke, V., Augmented reality in the smart factory supporting workers in an industry 4.0 environment. IEEE- 2014 IEEE Emerging Technology and Factory Automation (ETFA), 2014.
52. Papazoglou, M.P., van den Heuvel, W.J., Mascolo, J.E., A reference architecture and knowledge-based structures for smart manufacturing networks. IEEE Softw., 32(3), 2015.
53. Park, H.S., Tran, N.H., Development of a cloud based smart manufacturing system. J. Adv. Mech. Des. Syst. Manuf., 9(3), 2015.
54. Parlanti, R., Smart Shopfloors and Connected Platformes in Industry 4.0| Electronics World. 2017, 123.
55. Pei, F.Q., Tong, Y.F., He, F., Li, D.B., Research on design of the smart factory for forging enterprise in the Industry 4.0 environment. Mechanika, 23(1), 2017, 10.5755/j01.mech.23.1.13662.
56. Persson, J.-G., Current trends in product development. Procedia CIRP 50 (2016), 378–383, 10.1016/j.procir.2016.05.088.
57. Pfeiffer, S., Robots, Industry 4.0 and humans, or why assembly work is more than routine work. Societies, 6(2), 2016, 10.3390/soc6020016.
58. Pfliegl, R., Keller, H., Mobility Governance-digitisation of transport in the context of Industry 4.0 and society's responsibility for sustainable mobility. Elektrotechnik Und Informationstechnik, 132(7), 2015, 10.1007/s00502-015-0345-y.
59. Posada Jorge, Carlos Toro, Iñigo Barandiaran, David Oyarzun, Didier Stricker, Raffaele de Amicis, Eduardo Pinto, B., Peter Eisert, Jürgen Döllner, Ivan Vallarino, Visual computing as a key enabling technology for industrie 4. 0 and industrial internet. IEEE Computer Graphics and Applications 35:no. 2 (2015), 26–40.
60. Prause, G., Atari, S., On sustainable production networks for Industry 4.0. Entrepreneurship Sustain. Issues, 4(4), 2017, 10.9770/jesi.2017.4.4(2).
61. Preuveneers, D., Ilie-Zudor, E., The intelligent industry of the future: a survey on emerging trends, research challenges and opportunities in Industry 4.0. J. Ambient Intell. Smart Environ., 9(3), 2017, 10.3233/AIS-170432.
62. Qin, J., Liu, Y., Grosvenor, R., A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP 52 (2016), 173–178.
63. Qu, T., Lei, S.P., Wang, Z.Z., Nie, D.X., Chen, X., Huang, G.Q., IoT-based real-time production logistics synchronization system under smart cloud manufacturing. Int. J. Adv. Manuf. Technol., 84(1–4), 2016, 10.1007/s00170-015-7220-1.
64. Qian, F., Zhong, W.M., Du, W.L., Fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Engineering 3:2 (2017), 154–160, 10.1016/J.ENG.2017.02.011.
65. Quintas, J., Menezes, P., Dias, J., Information model and architecture specification for context awareness interaction decision support in cyber-physical human-machine systems. IEEE Trans. Hum.-Mach. Syst., 47(3), 2017, 10.1109/THMS.2016.2634923.
66. Ramadan, M., Al-Maimani, H., Noche, B., RFID-enabled smart real-time manufacturing cost tracking system. Int. J. Adv. Manuf. Technol., 89(1–4), 2017, 10.1007/s00170-016-9131-1.
67. Ramos‐Rodríguez, Antonio‐Rafael, José Ruíz‐Navarro, Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal, 1980-2000. Strategic Management Journal 25:no.10 (2004), 981–1004.
68. Reis, M.S., Gins, G., Industrial process monitoring in the big Data/Industry 4.0 era: from detection to diagnosis, to prognosis. Processes, 5(3), 2017, 10.3390/pr5030035.
69. Reniers, G., On the future of safety in the manufacturing industry. Procedia Manuf. 13 (2017), 1292–1296.
70. Rubmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M., Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. 2015, The Boston Consulting Group, Boston, MA (Accessed 28 August 2016) https://www.bcgperspectives.com/content/articles/engineered_products_project_business_industry_40_future_productivity_growth_manufacturing_industries/.
71. Roblek, V., Mesko, M., Krapez, A., A complex view of Industry 4.0. Sage Open, 6(2), 2016, 10.1177/2158244016653987.
72. Sackey, S.M., Bester, A., Adams, D., Industry 4.0 learning factory didactic design parameters for industrial engineering education in South Africa. S. Afr. J. Ind. Eng., 28(1), 2017, 10.7166/28-1-1584.
73. Sanders, A., Elangeswaran, C., Wulfsberg, J., Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing. J. Ind. Eng. Manage.-JIEM, 9(3), 2016, 10.3926/jiem.1940.
74. Saunders, M., Lewis, P., Thornhill, A., Research Methods for Business Students. 2016, Pearson Education Limited, Pearson Education UK, 10.1017/CBO9781107415324.004.
75. Schlechtendahl, J., Keinert, M., Kretschmer, F., Lechler, A., Verl, A., Making existing production systems Industry 4.0-ready Holistic approach to the integration of existing production systems in Industry 4.0 environments. Prod. Eng.-Res. Dev., 9(1), 2015, 10.1007/s11740-014-0586-3.
76. Schmidt, R., Möhring, M., Härting, R.-C., Reichstein, C., Neumaier, P., Jozinović P., Industry 4.0 – potentials for creating smart products: empirical research results. Abramowicz, W., (eds.) Business Information Systems, 2015, 2015, Springer International Publishing, 16–27.
77. Schuh, G., Potente, T., Varandani, R., Schmitz, T., Global footprint design based on genetic algorithms – an Industry 4.0 perspective. CIRP Ann.-Manuf. Technol., 63(1), 2014, 10.1016/j.cirp.2014.03.121.
78. Schuh, G., Potente, T., Wesch-Potente, C., Weber, A.R., Prote, J.P., Collaboration Mechanisms to Increase Productivity in the Context of Industrie 4.0. 2014, Elsevier, Germany.
79. Shafiq, S.I., Sanin, C., Toro, C., Szczerbicki, E., Virtual engineering object (VEO): toward experience-based design and manufacturing for industry 4.0. Cybern. Syst., 46(1–2), 2015, 10.1080/01969722.2015.1007734.
80. Shafiq, S.I., Sanin, C., Szczerbicki, E., Toro, C., Virtual engineering factory: creating experience base for industry 4.0. Cybern. Syst., 47(1–2), 2016, 10.1080/01969722.2016.1128762.
81. Shamim, S., Cang, S., Yu, H.N.A., Li, Y., Examining the feasibilities of Industry 4.0 for the hospitality sector with the lens of management practice. Energies, 10(4), 2017, 10.3390/en10040499.
82. Sommer, L., Industrial revolution – Industry 4.0: are german manufacturing SMEs the first victims of this revolution?. J. Ind. Eng. Manage.-JIEM, 8(5), 2015, 10.3926/jiem.1470.
83. Stock, T., Seliger, G., Opportunities of sustainable manufacturing in Industry 4.0. Procedia CIRP 40 (2016), 536–541.
84. Strange, R., Zucchella, A., Industry 4.0 global value chains and international business. Multinatl. Bus. Rev., 25(3), 2017, 10.1108/MBR-05-2017-0028.
85. Tao, F., Zuo, Y., Xu, L.D., Zhang, L., IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans. Ind. Inf., 10(2), 2014, 10.1109/TII.2014.2306397.
86. Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, C., Lundholm, T., Lennartson, B., An event-driven manufacturing information system architecture for Industry 4.0. Int. J. Prod. Res., 55(5), 2017, 10.1080/00207543.2016.1201604.
87. Thramboulidis, K., Christoulakis, F., UML4IoT-A UML-based approach to exploit IoT in cyber-physical manufacturing systems. Comput. Ind., 82, 2016, 10.1016/j.compind.2016.05.010.
88. Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br. J. Manage. 14:3 (2003), 207–222, 10.1111/1467-8551.00375.
89. Trstenjak, M., Cosic, P., Industry 4.0 readiness factor calculation-problem structuring. International Conference Management of Technology–Step to Sustainable Production (MOTSP 2017), 2017 June.
90. Uhlemann, Uhlemann, Thomas H-J., Christian Lehmann, Rolf Steinhilper, The digital twin: realizing the cyber-physical production system for industry 4. 0. Procedia CIRP 61 (2017), 335–340.
91. Waibel, M.W., Steenkamp, M.W., Moloko, L.P., Oosthuizen, G.A., Investigating the effects of Smart Production Systems on sustainability elements. Procedia Manuf. 8 (2017), 731–737.
92. Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 165 (2015), 234–246.
93. Wan, J.F., Yi, M.L., Li, D., Zhang, C.H., Wang, S.Y., Zhou, K.L., Mobile services for customization manufacturing systems: an example of Industry 4.0. IEEE Access, 4, 2016, 10.1109/ACCESS.2016.2631152.
94. Wang, L., Thorngren, M., Onori, M., Current status and advancement of cyber-physical systems in manufacturing. J. Manuf. Syst. 37:Pt. 2 (2015), 517–527.
95. Wang, S., Wan, J.D., Zhang, C., Implementing smart factory of Industrie 4.0: an outlook. Int. J. Distrib. Sens. Netw., 2016, 2015 p. 10.
96. Wang, S.Y., Wan, J.F., Zhang, D.Q., Li, D., Zhang, C.H., Towards smart factory for Industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput. Netw., 101, 2016, 10.1016/j.comnet.2015.12.017.
97. Wang, W.S., Zhu, X.X., Wang, L.Y., Qiu, Q., Cao, Q.X., Ubiquitous robotic technology for smart manufacturing system. Computational Intelligence and Neuroscience, 2016.
98. Wang, S., Wan, J., Li, D., Zhang, C., Implementing smart factory of industrie 4. 0: an outlook. Int. J. Distrib. Sens. Netw., 12(1), 2016, 3159805.
99. Wang, C., Jiang, P.Y., Ding, K., A hybrid-data-on-tag-enabled decentralized control system for flexible smart workpiece manufacturing shop floors. Proc. Inst. Mech. Eng. Part C-J. Mech. Eng. Sci., 231(4), 2017, 10.1177/0954406215620452.
100. Witkowski Krzysztof, Internet of things, big data, industry 4. 0?innovative solutions in logistics and supply chains management. Procedia Engineering 182 (2017), 763–769.
101. Wittenberg, C., Human-CPS interaction – requirements and human-machine interaction methods for the Industry 4.0. IFAC-Papersonline 49–19 (2016), 420–425.
102. Wolf, M., Serpanos, D., Safety and security of cyber-physical and internet of- things systems. Proc. IEEE, 105(6), 2017, 10.1109/JPROC.2017.2699401.
103. Wong, K.S., Kim, M.H., Privacy protection for data-driven smart manufacturing system. Int. J. Web Serv. Res., 14(3), 2017, 10.4018/IJWSR.2017070102.
104. Wu, D.Z., Jennings, C., Terpenny, J., Gao, R.X., Kumara, S., A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J. Manuf. Sci. Eng.-Trans. ASME, 139(7), 2017, 10.1115/1.4036350.
105. Wyrwicka, M.K., Mrugalska, B., “Industry 4.0”—towards opportunities and challenges of implementation. DEStech Transactions on Engineering and Technology Research ICPR, 2017.
106. Xu, L., He, W., Li, S., IoT in industries: a survey. IEEE Trans. Ind. Inf. 10:4 (2014), 2233–2243.
107. Yang, C., Lan, S.L., Shen, W.M., Huang, G.Q., Wang, X.B., Lin, T.Y., Towards product customization and personalization in IoT-enabled cloud manufacturing. Clust. Comput. J. Netw. Softw. Tools Appl., 20(2), 2017, 10.1007/s10586-017-0767-x|.
108. Yu, Zhenhua, Ouyang, Jie, Li, Sisi, Peng, Xia, Formal modeling and control of cyber-physical manufacturing systems. Adv. Mech. Eng., 9(10), 2017.
109. Yuan, Z.H., Qin, W.Z., Zhao, J.S., Smart manufacturing for the oil refining and petrochemical industry. Engineering, 3(2), 2017, 10.1016/J.ENG.2017.02.012.
110. Yue, X., Cai, H., Yan, H., Zou, C., Zhou, K., Cloud-assisted industrial cyber-physical systems: an insight. Microprocess. Microsyst. 39:8 (2015), 1262–1270.
111. Zawadzki, P., Zywicki, K., Smart product design and production control for effective mass customization in the Industry 4.0 concept. Manage. Prod. Eng. Rev., 7(3), 2016, 10.1515/mper-2016-0030.