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

Process Safety and Environmental Protection - Tập 117 - Trang 408-425 - 2018
Sachin S. Kamble1, Angappa Gunasekaran2, Shradha A. Gawankar1
1Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Mumbai, 400087, India
2School of Business and Public Administration, California State University, Bakersfield, 9001 Stockdale Highway, 20BDC/140, Bakersfield, CA 93311-1022, USA

Tài liệu tham khảo

Adolph, 2016 Alexandre, 2017, Application of Industry 4.0 technologies to the design and manufacturing of handicraft products, DYNA, 92, 435, 10.6036/8169 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). Bahrin, 2016, Industry 4.0: a review on industrial automation and robotic, Jurnal Teknologi, 78 Bogle, 2017, A perspective on smart process manufacturing research challenges for process systems engineers, Engineering, 3, 10.1016/J.ENG.2017.02.003 Bastian, Mathieu, 2009, Gephi: an open source software for exploring and manipulating networks, Icwsm, 8, 361, 10.1609/icwsm.v3i1.13937 Carvalho, 2018, Manufacturing in the fourth industrial revolution: a positive prospect in sustainable manufacturing, Procedia Manuf., 21, 671, 10.1016/j.promfg.2018.02.170 Chen, 2017, Feasibility evaluation and optimization of a smart manufacturing system based on 3D printing: a review, Int. J. Intell. Syst., 32, 10.1002/int.21866 Choi, 2016, An analysis of technologies and standards for designing smart manufacturing systems, J. Res. Natl. Inst. Stand. Technol., 121, 10.6028/jres.121.021 Chromjakova, 2017, Process stabilisation-key assumption for implementation of Industry 4.0 concept in industrial company, J. Syst. Integr., 8, 3 Davis, 2012, Smart manufacturing, manufacturing intelligence and demand-dynamic performance, Comput. Chem. Eng., 47, 145, 10.1016/j.compchemeng.2012.06.037 De Sousa Jabbour, 2018, When titans meet–can Industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors, Technol. Forecast. Soc. Change, 132, 18, 10.1016/j.techfore.2018.01.017 Duarte, 2017, Exploring linkages between lean and green supply chain and the industry 4.0, 1242 Fernandes, 2017, Industry 4.0: training for automation in Europe, Welding, 96, 50 Gentner, 2016, 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, 10.2533/chimia.2016.628 Gephi, 2013 Brian, 2016, Predicting safety behavior in the construction industry: development and test of an integrative model, Safety Science, 84, 1, 10.1016/j.ssci.2015.11.020 He, 2017, Locality-aware replacement algorithm in flash memory to optimize cloud computing for smart factory of Industry 4.0, IEEE Access, 5, 10.1109/ACCESS.2017.2740327 Stefan Heck, 2014, Resource revolution: how to capture the biggest business opportunity in a century, Houghton Mifflin Harcourt Ivanov, 2016, A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0, Int. J. Prod. Res., 54, 10.1080/00207543.2014.999958 Johannes, 2017, Research into the potential revenue models for Industry 4.0 supported sustainable products, Procedia CIRP, 63, 721, 10.1016/j.procir.2017.03.315 Kamigaki, 2017, Object-oriented RFID with IoT: a design concept of information systems in manufacturing, Electronics, 6, 10.3390/electronics6010014 Karakose, 2017, A cyberphysical system based mass-customization approach with integration of Industry 4.0 and smart city, Wireless Commun. Mobile Comput., 2017, 1, 10.1155/2017/1058081 Kersten, 2016, Industry 4.0: self-sufficient production prevents standstill with smart processes in a productive and sustainable future – the smart meat factory, FLEISCHWIRTSCHAFT, 96 Kibira, 2016, Methods and tools for performance assurance of smart manufacturing systems, J. Res. Natl. Inst. Standards Technol., 121, 10.6028/jres.121.013 Kim, 2016, A model-based approach to refine process parameters in smart manufacturing, Concurrent Eng.-Res. Appl., 23, 10.1177/1063293X15591038 Lamba, 2017, Big data in operations and supply chain management: current trends and future perspectives, Prod. Plann. Control, 28, 877, 10.1080/09537287.2017.1336787 Lao, 2014, Smart manufacturing: handling preventive actuator maintenance and economics using model predictive control, AIChE J., 60, 10.1002/aic.14427 Lao, 2015, Real-time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control, AIChE J., 61, 10.1002/aic.14960 Lee, 2016, High precision optical sensors for real-time on-line measurement of straightness and angular errors for smart manufacturing, Smart Sci., 4, 10.1080/23080477.2016.1207407 Lee, 2017, 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, 10.1007/s12541-017-0161-x Lee, 2017, Essential implications of the digital transformation in Industry 4.0, J. Sci. Ind. Res., 76 Li, 2017, A big data enabled load-balancing control for smart manufacturing of Industry 4.0, Cluster Comput. J. Netw. Softw. Tools Appl., 20 Lin, 2016, Key design of driving Industry 4.0: joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks, IEEE Commun. Mag., 54, 10.1109/MCOM.2016.7588228 Liu, 2017, 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 Lobo, 2016, Industry 4.0: what does it mean to the semiconductor industry?, Solid State Technol., 59 Lotzmann, 2017, For Industry 4.0, visualization and machine learning can be combined to enhance laser processing, Laser Focus World, 53 Lu, 2016, An IoT (IoT)-based collaborative framework for advanced manufacturing, Int. J. Adv. Manuf. Technol., 84 Luthra, 2018, Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies, Process Saf. Environ. Prot., 117, 168, 10.1016/j.psep.2018.04.018 Marques, 2017, Decentralized decision support for intelligent manufacturing in Industry 4.0, J. Ambient Intell. Smart Environ., 9 Menasce, 2015, Autonomic smart manufacturing, J. Decis. Syst., 24, 10.1080/12460125.2015.1046714 Mishra, 2016, Big data and supply chain management: a review and bibliometric analysis, Ann. Oper. Res., 1 Monostori, 2016, Cyber-physical systems in manufacturing, CIRP Ann., 65, 621, 10.1016/j.cirp.2016.06.005 Monostori, 2014, Cyber-physical production systems: roots, expectations and R&D challenges, Procedia CIRP, 17, 9, 10.1016/j.procir.2014.03.115 Moreno, 2017, Virtualisation process of a sheet metal punching machine within the Industry 4.0 vision, Int. J. Interact. Des. Manuf.–IJIDEM, 11 Mosterman, 2016, Industry 4.0 as a cyber-physical system study, Softw. Syst. Model., 15, 10.1007/s10270-015-0493-x Moyne, 2017, Big data analytics for smart manufacturing: case studies in semiconductor manufacturing, PROCESSES, 5, 10.3390/pr5030039 Mueller, 2017, 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, 10.1007/s10033-017-0164-7 Nguyen, 2017, Big data analytics in supply chain management: a state-of-the-art literature review, Comput. Oper. Res., 1 Oesterreich, 2016, 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, 10.1016/j.compind.2016.09.006 Paelke, 2014, Augmented reality in the smart factory supporting workers in an industry 4.0 environment, IEEE- 2014 IEEE Emerging Technology and Factory Automation (ETFA), 10.1109/ETFA.2014.7005252 Papazoglou, 2015, A reference architecture and knowledge-based structures for smart manufacturing networks, IEEE Softw., 32, 10.1109/MS.2015.57 Park, 2015, Development of a cloud based smart manufacturing system, J. Adv. Mech. Des. Syst. Manuf., 9, 10.1299/jamdsm.2015jamdsm0030 Parlanti, 2017, 123 Pei, 2017, Research on design of the smart factory for forging enterprise in the Industry 4.0 environment, Mechanika, 23 Persson, 2016, Current trends in product development, Procedia CIRP, 50, 378, 10.1016/j.procir.2016.05.088 Pfeiffer, 2016, Robots, Industry 4.0 and humans, or why assembly work is more than routine work, Societies, 6, 10.3390/soc6020016 Pfliegl, 2015, Mobility Governance-digitisation of transport in the context of Industry 4.0 and society's responsibility for sustainable mobility, Elektrotechnik Und Informationstechnik, 132 Posada Jorge, 2015, Visual computing as a key enabling technology for industrie 4. 0 and industrial internet, IEEE Computer Graphics and Applications, 35, 26, 10.1109/MCG.2015.45 Prause, 2017, On sustainable production networks for Industry 4.0, Entrepreneurship Sustain. Issues, 4 Preuveneers, 2017, 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 Qin, 2016, A categorical framework of manufacturing for industry 4.0 and beyond, Procedia CIRP, 52, 173, 10.1016/j.procir.2016.08.005 Qu, 2016, IoT-based real-time production logistics synchronization system under smart cloud manufacturing, Int. J. Adv. Manuf. Technol., 84 Qian, 2017, Fundamental theories and key technologies for smart and optimal manufacturing in the process industry, Engineering, 3, 154, 10.1016/J.ENG.2017.02.011 Quintas, 2017, Information model and architecture specification for context awareness interaction decision support in cyber-physical human-machine systems, IEEE Trans. Hum.-Mach. Syst., 47, 10.1109/THMS.2016.2634923 Ramadan, 2017, RFID-enabled smart real-time manufacturing cost tracking system, Int. J. Adv. Manuf. Technol., 89 Ramos‐Rodríguez, 2004, Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal, 1980-2000, Strategic Management Journal, 25, 981, 10.1002/smj.397 Reis, 2017, Industrial process monitoring in the big Data/Industry 4.0 era: from detection to diagnosis, to prognosis, Processes, 5 Reniers, 2017, On the future of safety in the manufacturing industry, Procedia Manuf., 13, 1292, 10.1016/j.promfg.2017.09.057 Rubmann, 2015 Roblek, 2016, A complex view of Industry 4.0, Sage Open, 6, 10.1177/2158244016653987 Sackey, 2017, Industry 4.0 learning factory didactic design parameters for industrial engineering education in South Africa, S. Afr. J. Ind. Eng., 28 Sanders, 2016, Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing, J. Ind. Eng. Manage.-JIEM, 9 Saunders, 2016 Schlechtendahl, 2015, 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, 10.1007/s11740-014-0586-3 Schmidt, 2015, Industry 4.0 – potentials for creating smart products: empirical research results, 2015, 16 Schuh, 2014, Global footprint design based on genetic algorithms – an Industry 4.0 perspective, CIRP Ann.-Manuf. Technol., 63, 10.1016/j.cirp.2014.03.121 Schuh, 2014 Shafiq, 2015, Virtual engineering object (VEO): toward experience-based design and manufacturing for industry 4.0, Cybern. Syst., 46 Shafiq, 2016, Virtual engineering factory: creating experience base for industry 4.0, Cybern. Syst., 47 Shamim, 2017, Examining the feasibilities of Industry 4.0 for the hospitality sector with the lens of management practice, Energies, 10, 10.3390/en10040499 Sommer, 2015, Industrial revolution – Industry 4.0: are german manufacturing SMEs the first victims of this revolution?, J. Ind. Eng. Manage.-JIEM, 8 Stock, 2016, Opportunities of sustainable manufacturing in Industry 4.0, Procedia CIRP, 40, 536, 10.1016/j.procir.2016.01.129 Strange, 2017, Industry 4.0 global value chains and international business, Multinatl. Bus. Rev., 25 Tao, 2014, IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing, IEEE Trans. Ind. Inf., 10 Theorin, 2017, An event-driven manufacturing information system architecture for Industry 4.0, Int. J. Prod. Res., 55, 10.1080/00207543.2016.1201604 Thramboulidis, 2016, UML4IoT-A UML-based approach to exploit IoT in cyber-physical manufacturing systems, Comput. Ind., 82, 10.1016/j.compind.2016.05.010 Tranfield, 2003, Towards a methodology for developing evidence-informed management knowledge by means of systematic review, Br. J. Manage., 14, 207, 10.1111/1467-8551.00375 Trstenjak, 2017, Industry 4.0 readiness factor calculation-problem structuring, International Conference Management of Technology–Step to Sustainable Production (MOTSP 2017) Uhlemann, 2017, The digital twin: realizing the cyber-physical production system for industry 4. 0, Procedia CIRP, 61, 335, 10.1016/j.procir.2016.11.152 Waibel, 2017, Investigating the effects of Smart Production Systems on sustainability elements, Procedia Manuf., 8, 731, 10.1016/j.promfg.2017.02.094 Wamba, 2015, How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study, Int. J. Prod. Econ., 165, 234, 10.1016/j.ijpe.2014.12.031 Wan, 2016, Mobile services for customization manufacturing systems: an example of Industry 4.0, IEEE Access, 4, 10.1109/ACCESS.2016.2631152 Wang, 2015, Current status and advancement of cyber-physical systems in manufacturing, J. Manuf. Syst., 37, 517, 10.1016/j.jmsy.2015.04.008 Wang, 2015, Implementing smart factory of Industrie 4.0: an outlook, Int. J. Distrib. Sens. Netw., 2016 Wang, 2016, Towards smart factory for Industry 4.0: a self-organized multi-agent system with big data based feedback and coordination, Comput. Netw., 101, 10.1016/j.comnet.2015.12.017 Wang, 2016, Ubiquitous robotic technology for smart manufacturing system Wang, 2016, Implementing smart factory of industrie 4. 0: an outlook, Int. J. Distrib. Sens. Netw., 12, 3159805, 10.1155/2016/3159805 Wang, 2017, 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, 10.1177/0954406215620452 Witkowski Krzysztof, 2017, Internet of things, big data, industry 4. 0?innovative solutions in logistics and supply chains management, Procedia Engineering, 182, 763, 10.1016/j.proeng.2017.03.197 Wittenberg, 2016, Human-CPS interaction – requirements and human-machine interaction methods for the Industry 4.0, IFAC-Papersonline, 49–19, 420, 10.1016/j.ifacol.2016.10.602 Wolf, 2017, Safety and security of cyber-physical and internet of- things systems, Proc. IEEE, 105, 10.1109/JPROC.2017.2699401 Wong, 2017, Privacy protection for data-driven smart manufacturing system, Int. J. Web Serv. Res., 14, 10.4018/IJWSR.2017070102 Wu, 2017, A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests, J. Manuf. Sci. Eng.-Trans. ASME, 139, 10.1115/1.4036350 Wyrwicka, 2017, “Industry 4.0”—towards opportunities and challenges of implementation, DEStech Transactions on Engineering and Technology Research ICPR Xu, 2014, IoT in industries: a survey, IEEE Trans. Ind. Inf., 10, 2233, 10.1109/TII.2014.2300753 Yang, 2017, Towards product customization and personalization in IoT-enabled cloud manufacturing, Clust. Comput. J. Netw. Softw. Tools Appl., 20 Yu, 2017, Formal modeling and control of cyber-physical manufacturing systems, Adv. Mech. Eng., 9, 10.1177/1687814017725472 Yuan, 2017, Smart manufacturing for the oil refining and petrochemical industry, Engineering, 3, 10.1016/J.ENG.2017.02.012 Yue, 2015, Cloud-assisted industrial cyber-physical systems: an insight, Microprocess. Microsyst., 39, 1262, 10.1016/j.micpro.2015.08.013 Zawadzki, 2016, Smart product design and production control for effective mass customization in the Industry 4.0 concept, Manage. Prod. Eng. Rev., 7