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Literature review of Industry 4.0 and related technologies

Abstract

Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

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Fig. 1

Reproduced with permission from BCMCOM (2017)

Fig. 2

Reproduced with permission from Oztemel (2010)

Fig. 3

Reproduced with permission from Oztemel and Tekez (2009a)

Fig. 4

Reproduced with permission from Boston Consulting Group (2016)

Fig. 5

Reproduced with permission from Lichtblau et al. (2016)

Fig. 6
Fig. 7

Reproduced with permission from GTAI (2017)

Fig. 8
Fig. 9

Reproduced with permission from Remon (2017)

Fig. 10

Reproduced with permission from Jaehne and KalalChelvan (2017)

Fig. 11

Reproduced with permission from Gaurav (2017)

References

  1. Aalaei, A., & Davoudpour, H. (2016). Revised multi-choice goal programming for incorporated dynamic virtual cellular manufacturing into supply chain management: A case study. Engineering Applications of Artifical Intelligence,47, 3–15.

    Google Scholar 

  2. Abdoa, J., & Demerjianb, J. (2017). Evaluation of mobile cloud architectures. Pervasive and Mobile Computing,39, 284–303.

    Google Scholar 

  3. Aburaia, M., Markl, E., & Stuje, K. (2015). New concept for design and control of 4 axis robot using the additive manufacturing technology. Procedia Engineering,100, 1364–1369.

    Google Scholar 

  4. Accenture. (2016). Industry 4.0 revolution report. https://www.accenture.com/us-en/insight-digital-industry-impact. Available on August 28, 2017.

  5. Accorsi, R., Bortolini, M., Baruffaldi, G., Pilati, F., & Ferrari, E. (2017). Internet-of-things paradigm in food supply chains control and management. Procedia Manufacturing,11, 889–895.

    Google Scholar 

  6. Ackermann, R. (2013). M2M, internet of things and industry 4.0An industry perspective. http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Second_German-Indian-Workshop/India_01_13_Industrie40_m2m_Ackermann_SAP.pdf. Available on August 28, 2017.

  7. Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering,101, 528–543.

    Google Scholar 

  8. Adeyeri, S., Kanisuru, M., Khumbulani, M., & Olukorede T. (2015). Integration of agent technology into manufacturing enterprise: A review and platform for industry 4.0. In Proceedings of the 2015 international conference on industrial engineering and operations management Dubai, United Arab Emirates (UAE) (pp. 1625–1635).

  9. Agency, M. (2008). The medical products agency’s working group on medical information systems. National Board of Health and Welfare in the regulations on quality management systems in health care. https://lakemedelsverket.se/upload/foretag/medicinteknik/en/Medical-Information-Systems-Report_2009-06-18.pdf. Available on August 22, 2017.

  10. Ahmed, E., & Kohno, R. (2017). Error control coding and decoding with medical QoS constraints for Wban end to end connection via UMTS channel. ICT Express. https://doi.org/10.1016/j.icte.2018.01.016.

    Article  Google Scholar 

  11. AIR-LIQUIDE. (2016). https://www.airliquide.com/media/france-air-liquide-plant-future-project-certified-technological-showcase-industry-future-alliance. Available on August 30, 2017.

  12. Akoka, J., Wattiau, I., & Laoufi, N. (2016). Research on big data—A systematic mapping study. Neurocomputing,2, 1023–1041.

    Google Scholar 

  13. Al-Ali, A., & Aburukba, R. (2015). Role of internet of things in the smart grid technology. Journal of Computer and Communications,3, 229–233.

    Google Scholar 

  14. Alam, K., & Saddik, A. (2015). C2PS: A digital twin architecture reference model for the cloud-based cyber-physical system. IEEE Access,5, 25–35.

    Google Scholar 

  15. Alanso-Martin, F., Castro, A., Malfaz, M., & Castillo, J. (2017). Identification and distance estimation of users and objects by means of electronic beacons in social robotics. Expert Systems with Applications,86, 247–257.

    Google Scholar 

  16. Alatoibi, Y. (2016). Business process modelling challenges and solutions: A literature review. Journal of Intelligent Manufacturing,27, 701–723.

    Google Scholar 

  17. Alayaa, M., Banouara, D., Monteila, S., Chassota, Z., & Drira, T. (2014). OM2M: Extensible ETSI-compliant M2M service platform with self-configuration capability. Computer Science,32, 1079–1086.

    Google Scholar 

  18. Albert, A., Bartosz, G., Tobias, P., Viktoriia, B., & Tobias, S. (2016). Procedure for defining the system of objectives in the initial phase of an industry 4.0 project focusing on intelligent quality control systems. Reconfigurable & Virtual Production,52, 262–267.

    Google Scholar 

  19. Albodour, R., James, A., & Yaacob, N. (2015). QoS within business grid quality of service (BGQoS). Future Generation Computer Systems,50, 22–37.

    Google Scholar 

  20. Aleina, S. C., Viola, N., Fusara, R., Saccoccia, G., & Vercella, V. (2018). Using the ESA exploration technology roadmaps in support of new mission concepts and technology prioritization. Acta Astronautica. https://doi.org/10.1016/j.actaastro.2018.04.035.

    Article  Google Scholar 

  21. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols and applications. IEEE Communications Surveys and Tutorials,17(4), 2347–2376.

    Google Scholar 

  22. Alharthi, A., Krotov, V., & Bowman, M. (2017). Adressing bariers to big data. Business Horizons,60, 285–292.

    Google Scholar 

  23. Alkhamisi, A., & Monowar, M. (2013). Rise of augmented reality: Current and future application areas. International Journal of Internet and Distributed Systems,1, 25–34.

    Google Scholar 

  24. Alkoc, E., & Erbatur, F. (1997). Productivity improvement in concreting operations through simulation models. Building Research and Information,25(2), 83–95.

    Google Scholar 

  25. Amatoa, F., & Moscato, F. (2017). Exploiting cloud and workflow patterns for the analysis of composite cloud services. Future Generation Computer Systems,67, 255–265.

    Google Scholar 

  26. Anderl, R. (2014). Industries 4.0-advanced engineering of smart products and smart production. In 19th International seminar on high technology, technological innovations in the product development, Piracicaba, Brazil. https://www.researchgate.net/publication/270392830_Industrie_40_-_Advanced_Engineering_of_Smart_Products_and_Smart_Production_09_October_2014. Available on December 28, 2017.

  27. Andrade, A., Pereira, A., Walter, S., Almeida, R., Loureiro, R., Compagna, D., et al. (2014). Bridging the gap between robotic technology and health care. Biomedical Signal Processing and Control,10, 65–78.

    Google Scholar 

  28. Angeles, R. (2005). RFID technologies: Supply-chain applications and implementation issues. Information Systems Management,22, 51–65.

    Google Scholar 

  29. Ângelo, A., Barata, J., da Cunha, P. R., & Almeida, V. (2017). Digital transformation in the pharmaceutical compounds supply chain: Design of a service ecosystem with e-labeling. In European, Mediterranean, and Middle Eastern conference on information systems (pp. 307–323).

  30. Anitha, R., & Mukherjee, S. (2017). ‘MaaS’: Fast retrieval of E-file in cloud using metadata as a service. Journal of Intelligent Manufacturing,28, 1871–1891.

    Google Scholar 

  31. ARIZ. (2017). https://www.festo.com/group/en/cms/12690.htm. Available on August 30, 2017.

  32. Armentia, A., Gangoiti, U., Orive, D., & Marcos, M. (2017). Dynamic QoS management for flexible multimedia applications. IFAC PapersOnLine,50, 5920–5925.

    Google Scholar 

  33. Atanasov, I., Nikolov, A., Pencheva, E., Dimova, R., & Ivanov, M. (2015). An approach to data annotation for internet of things. International Journal of Information Technology and Web Engineering (IJITWE),10(4), 1–19.

    Google Scholar 

  34. Atif, Y., Dinga, J., & Jeusfelda, M. A. (2016). Internet of things approach to cloud-based smart car parking. Computer Science,98, 193–198.

    Google Scholar 

  35. Atzori, L., Morabito, G., & Lera, A. (2017). Understanding the internet of things: Definition. Ad Hoc Networks,56, 122–140.

    Google Scholar 

  36. AWS. (2017). https://aws.amazon.com/iot-platform/. Available on August 30, 2017.

  37. Azevedo, P., Azevedo, C., & Romão, M. (2014). Application integration: Enterprise resource planning (ERP) systems in the hospitality industry. Procedia Technology,16, 52–58.

    Google Scholar 

  38. Backhaus, J., & Reinhart, G. (2017). Digital description of products, processes and resources for task-oriented programming of assembly systems. Journal of Intelligent Manufacturing,28, 1787–1800.

    Google Scholar 

  39. Badawi, H., Dong, H., & El Saddika, Abdulmotaleb. (2017). Mobile cloud-based physical activity advisory system using biofeedback sensors. Future Generation Computer Systems,66, 59–70.

    Google Scholar 

  40. Bagheri, B., Yang, S., Kao, H. A., & Lee, J. (2015). Cyber-physical systems architecture for selfaware machines in industry 4.0 environment. IFAC-PapersOnLine,48(3), 1622–1627.

    Google Scholar 

  41. Baheti, R., & Gill, H. (2011). Cyber-physical systems. The İmpact of Control Technology,12, 161–166.

    Google Scholar 

  42. Balina, S., Baumgarte, D., & Salna, E. (2017). Cloud based cross-system integration for small and medium-sized enterprises. Computer Science,104, 127–132.

    Google Scholar 

  43. Bartezzaghi, E., & Ronchi, S. (2003). Internet supporting the procurement process lessons from four case studies. Integrated Manufacturing Systems,14, 632–641.

    Google Scholar 

  44. Bauer, W., Schlund, S., Marrenbach, D., & Ganschar, O. (2014). Industry 4.0Volkswirtschaftliches Potenzial für Deutschland, BITKOM company. http://www.produktionsarbeit.de/content/dam/produktionsarbeit/de/documents/Studie-Industrie-4-0-Volkswirtschaftliches-Potential-fuer-Deutschland.pdf. Available on August 28, 2017 (in German).

  45. Bauernhansl, T. (2014). Die vierte Industrylle Revolution. Der Weg in ein wertschaffendes Produktionsparadigma,4, 3–35. (in German).

    Google Scholar 

  46. Bauernhansl, T., ten Hompel, M., & Vogel-Heuser, B. (Eds.) (2014). Industry 4.0 in Produktion, Automatisierung und Logistik. Anwendung, Technologien und Migration 8, 30–40 (in German).

  47. Baygin, M., Yetis, H., Karakose, M., & Akin, E. (2016). An effect analysis of industry 4.0 to higher education. In 2016 15th international conference on information technology based higher education and training (ITHET), July 10–12, 2017, Ohrid, Macedonia.

  48. BCMCOM. (2017). Industry 4.0 technologies for new trends and developments for industry delivering quality. http://www.bcmcom.com/solutions_application_industry40.htm. Available on August 28, 2017.

  49. Beckera, T., & Sterna, H. (2016). Future trends in human work area design for cyber-physical production systems. Procedia CIRP,57, 404–409.

    Google Scholar 

  50. Bellini, P., Bruno, I., Cenni, D., & Nesi, P. (2017). Managing cloud via smart cloud engine and knowledge base. In 2015 IEEE 8th international conference on cloud computing (CLOUD), 27 June–2 July 2015, New York, NY, USA.

  51. Bello, O., Zeadally, S., & Badra, M. (2017). Network layer inter-operation of device-to-device communication technologies in internet of things (IoT). Ad Hoc Networks,57, 52–62.

    Google Scholar 

  52. Bently, C. (2016). The manufacturer industry 4.0 UK readiness report. Oracle Company Report. https://www.oracle.com/webfolder/s/delivery_production/docs/FY16h1/doc19/Industry-Report.pdf. Available on August 28, 2017.

  53. Bergera, C., Heesa, A., Braunreuthera, S., & Reinharta, G. (2016). Characterization of cyber-physical sensor systems. Manufacturing System,41, 638–643.

    Google Scholar 

  54. Berryman, D. (2012). Augmented reality: A review. Medical Reference Services Quarterly,31(2), 212–218.

    Google Scholar 

  55. Bertacchini, F., Bilotta, E., & Pantano, P. (2017). Shopping with a robotic companion. Computers in Human Behavior,77, 382–395.

    Google Scholar 

  56. Biral, A., Centenaro, M., Zanella, A., Vangelista, L., & Zorzi, M. (2015). The challenges of M2M massive access in wireless cellular networks. Digital Communications and Networks,1(1), 1–19.

    Google Scholar 

  57. BMBF. (2014). Bundesministerium für Bildung und Forschung, 2014: Zukunftsbild Industry 4.0. http://www.bmbf.de/pubRD/Zukunftsbild_Industry_40.pdf. Available on August 28, 2017 (in German).

  58. Boston Consulting Group. (2016). Industry 4.0: The future of productivity and growth in manufacturing industries. https://www.bcgperspectives.com/content/articles/engineered_products_project_business_industry_40_future_productivity_growth_manufacturing_industries/?chapter=4#chapter4_section2. Available on August 28, 2017.

  59. Bourke, R., & Mentis, M. (2014). An assessment framework for inclusive education: Integrating assessment approaches. Assesment in Education,21(4), 384–397.

    Google Scholar 

  60. Bouwers, E., & Vis, R. (2009). Multidimensional software monitoring applied to ERP. Electronic Notes in Theoretical Computer Science,233, 161–173.

    Google Scholar 

  61. Boveta, G., & Hennebertb, J. (2013). Energy-efficient optimization layer for event-based communications on Wi-Fi thing. Computer Science,19, 256–264.

    Google Scholar 

  62. Bower, M., Howe, C., McCredie, N., Robinson, A., & Grover, D. (2014). Augmented reality in education—Cases, places and potentials. Educational Media International,51(1), 1–15.

    Google Scholar 

  63. Brandmeier, M., Bognera, E., Brossoga, M., & Frankea, J. (2016). Product design improvement through knowledge feedback of cyber-physical systems. Procedia CIRP,50, 186–191.

    Google Scholar 

  64. Brennera, A., & Hummela, V. (2016). A seamless convergence of the digital and physical factory aiming in personalized Product Emergence Process (PPEP) for smart products within ESB Logistics Learning Factory at Reutlingen University. Procedia CIRP,54, 227–232.

    Google Scholar 

  65. Brettel, M., Klein, M., & Friederichsen, N. (2016). The relevance of manufacturing flexibility int he context of industries 4.0. Procedia CIRP,41, 105–110.

    Google Scholar 

  66. Brioto, M., Hoque Z., Steinke R., & Willner A. (2016). Towards programmable fog nodes in smart factories. In 2016 IEEE 1st international workshops on foundations and applications of self systems, Augsburg, Germany, 12–16 September 2016.

  67. Brunete, A., Gambao, E., Koskinen, J., Heikkila, T., Kaldestad, K., Tyapin, I., et al. (2017). Hard material small-batch industrial machining robot. Robotics and Computer-Integrated Manufacturing. https://doi.org/10.1016/j.rcim.2017.11.004.

    Article  Google Scholar 

  68. Bryner, M. (2012). Smart manufacturing: The next revolution. CEP Magazine,7, 1090–1098.

    Google Scholar 

  69. Bui, D., Yoon, Y., Huh, E., Jun, S., & Lee, S. (2013). Energy efficiency for cloud computing system based on predictive optimization. Journal of Parallel and Distributed Computing,102, 103–114.

    Google Scholar 

  70. Bungart, S. (2014). Industrial internet versus industry 4.0. Produktion—Technik und Wirtschaft für die deutsche Industry. Retrieved from http://www.produktion.de/automatisierung/industrial-internet-versus-Industry-4-0/print. Available on August 28, 2017.

  71. Bunse, B. (2016). Industry: Based on “German Industry 4.0” report. Journal of Applied Business and Economics,18, 40–50.

    Google Scholar 

  72. Bürger, T., & Tragl, K. (2014). SPS-Automatisierung mit den Technologien der IT-Welt verbinden. Technologien und Migration (pp. 559–569) (in German).

  73. Burke, M., Quigley, N., & Speed, C. (2013). The internet of things: Pink jumpers and Hungarian eggs in digital spaces. Procedia Computer Engineering,9, 152–157.

    Google Scholar 

  74. Calderona, M., Delgadilloa, S., & Antonio, J. (2016). A more human-centric Internet of Things with temporal and spatial context. Computer Science,83(2016), 553–559.

    Google Scholar 

  75. Candra, S. (2012). ERP implementation success and knowledge capability. International Congress on Interdisciplinary Business and Social Science,65, 141–149.

    Google Scholar 

  76. Canedoa, A., & Richterb, J. (2014). Architectural design space exploration of cyber-physical systems using the functional modeling compiler. Engineering Services,21, 46–51.

    Google Scholar 

  77. Carboneras, M., Insa, C., & Salort, E. (2003). ERP implementation in the stone industry: Special difficulties and solutions in the production area. In Emerging technologies and factory automation, 2003. Proceedings. ETFA’03. IEEE conference. Lisbon, Portugal.

  78. Carniani, E., Darenzo, D., Lazouski, A., Martinelli, A., & Mori, P. (2016). Usage control on cloud systems. Future Generation Computer Systems,63, 37–55.

    Google Scholar 

  79. Carrera, C., & Asensio, C. (2016). Landscape interpretation with augmented reality and maps to improve spatial orientation skill. Journal of Geography in Higher Education,41(1), 119–133.

    Google Scholar 

  80. Carstensen, J., Carstensen, T., Pabs, M., Schulz, F., Friederichs, J., Aden, S., et al. (2016). Condition monitoring and cloud-based energy analysis for autonomous mobile manipulation—Smart factory concept with LUHbots. Procedia Technology,26(2016), 560–569.

    Google Scholar 

  81. Chang, H., Kim, J., & Park, J. (2014). IT convergence security. Journal of Intelligent Manufacturing,25, 213–215.

    Google Scholar 

  82. Chang, H., Ma, J., Loke, S., Zimmermann, H., & Li, Z. (2012). Intelligent ubiquitous IT policy and its industrial services. Journal of Intelligent Manufacturing,23, 913–915.

    Google Scholar 

  83. Chang, V., Ramachandranb, M., Wills, G., Walters, R., Li, C., & Watters, P. (2016). Editorial for FGCS special issue: Big Data in the cloud. Future Generation Computer Systems,65, 73–75.

    Google Scholar 

  84. Chatterjee, S. (2015). ERP failure in developing countries: A case study in India. In India conference (INDICON), 2015 Annual IEEE, 17–20 December 2015, New Delhi, India.

  85. Chelloug, S. (2015). Energy-efficient content-based routing in internet of things. Journal of Computer and Communications,3, 9–20.

    Google Scholar 

  86. Chen, G., & Liu, Y. (2012). Performance evaluation of ERP implementation based on uncertainty measurement theory. In 2012 International conference on information management, innovation management and industrial engineering, 20–21 October 2012, Sanya, China.

  87. Chen, G., & Wang, J. (2010). Analysis on performance evaluation system of ERP implementation. In 2010 International conference of information science and management engineering, 7–8 August 2010, China.

  88. Chen, T. C. (2018). Cloud intelligence in manufacturing. Journal of Intelligent Manufacturing,28, 1057–1059.

    Google Scholar 

  89. Chen, T., & Chiu, M. (2017). Development of a cloud-based factory simulation system for enabling ubiquitous factory simulation. Robotics and Computer-Integrated Manufacturing,45, 133–143.

    Google Scholar 

  90. Chen, T., & Wu, C. (2017). A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory. Journal of Intelligent Manufacturing,28, 1095–1107.

    Google Scholar 

  91. Chen, X., Zhao, Y., Zhang, C., Wang, X., & Chen, L. (2018). Robot needle-punching for manufacturing composite preforms. Robotics and Computer-Integrated Manufacturing,50, 132–139.

    Google Scholar 

  92. Chen, X., & Jin, Z. (2012). Research on key technology and applications for internet of things. Physics Procedia,33(2012), 561–566.

    Google Scholar 

  93. Cheng, G., Lıu, L., & Quıang, Z. (2016). Industry 4.0 development and application of intelligent manufacturing. In 2016 International conference on information system and artificial intelligence, 24–26 June 2016, Hong Kong, China.

  94. Cheng-Yu, W., Pi-Cheng, T., & Chyun-Chau, F. (2010). Development of an automatic arc welding system using an adaptive sliding mode control. Intelligent Manufacturing,21(4), 355–362.

    Google Scholar 

  95. Chi, X., Zhang, J., & Ma, L. (2011). Queuing theory based service performance evaluation under H2H and M2M blending traffic arriving. Procedia Environmental Science,11(Part A), 478–485.

    Google Scholar 

  96. Chien, C., Gen, M., & Shi, Y. (2014). Manufacturing intelligence and innovation for digital manufacturing and operational excellence. Journal of Intelligent Manufacturing,25, 845–847.

    Google Scholar 

  97. Chlen, C., Kim, K., Liu, B., & Gen, M. (2012). Advanced decision and intelligence technologies for manufacturing and logistics. Journal of Intelligent Manufacturing,22, 2133–2135.

    Google Scholar 

  98. Chu, C., Weidong, L., & Jiao, R. (2013). Design chain management: bridging the gap between engineering and management. Journal of Intelligent Manufacturing,24, 541–544.

    Google Scholar 

  99. Cooper, S. (2017). Designing a UK industrial strategy for the age of industry 4.0. Rethink Manufacturing (pp. 1–27).

  100. Corcio, M. (2016). Manufacturing intelligence, group manager: Automation, MES & Electricity. http://www.iiconsortium.org/smart-factory-forum/MIGUEL-CORCIO-Keynote_IIC-MC-Smart_Manufacturing.pdf. Available on August 28, 2017.

  101. Dagli, C. (2016). Engineering cyber physical systems: Applying theory to practice. Procedia Computer Science,95, 7–8.

    Google Scholar 

  102. Daim, T., Yoon, B., Linderberg, J., Grizzi, R., & Estep, J. (2018). Strategic roadmapping of robotics echnologies for the power industry: A multicriteria technology assessment. Technological Forecasting and Social Change,131, 49–66.

    Google Scholar 

  103. Damle, A., Damle, R., Flahive, J., Schlussel, A. T., Davids, J., Sturrock, P. R., et al. (2017). Diffusion of technology: Trends in robotic-assisted colorectal surgery. The American Journal of Surgery,214, 820–824.

    Google Scholar 

  104. Dasgupta, A., Nagaraj, R., & Nagamani, K. (2016). An internet of things platform with Google. Journal of Software Engineering and Applications,9, 291–295.

    Google Scholar 

  105. Davali, I., Belli, L., Cilfone, A., & Ferrari, G. (2016). Integration of Wifi mobile nodes in a web of things tested. ICT Express,2(3), 96–99.

    Google Scholar 

  106. Dechene, D., & Shami, A. (2013). Energy efficient QoS constrained scheduler for SC-FDMA uplink. Physical Communication,8, 81–90.

    Google Scholar 

  107. Decker, M., Fischer, M., & Ott, I. (2017). Service robotics and human labor: A first technology assessment of substitution and cooperation. Robotics and Autonomous Systems,87, 348–354.

    Google Scholar 

  108. DEF. (2016). https://www.economie.gouv.fr/files/files/PDF/web-dp-indus-ang.pdf. Available on August 30, 2017.

  109. Deja, M., & Siemiaatkowski, M. (2013). Feature-based generation of machining process plans for optimised parts manufacture. Journal of Intelligent Manufacturing,24, 831–846.

    Google Scholar 

  110. Dener, M., & Bostancıoğlu, C. (2015). Smart technologies with wireless sensor networks. Social and Behavioral Sciences,195, 1915–1921.

    Google Scholar 

  111. Deng, G., Chen, D., & Yao, M. (2015). Value structure analysis for cloud service ecosystem. International Journal of Services, Technology and Management,21(4/5/6), 228–237.

    Google Scholar 

  112. Ding, L., Liu, Y., Han, B., & Zhang, S. (2017a). HB-file: An efficient and effective high-dimensional big data storage structure based on US-ELM. Proceedings of ELM,1, 489–500.

    Google Scholar 

  113. Ding, Y., Yaoa, G., & Haoa, K. (2017b). Fault-tolerant elastic scheduling algorithm for workflow in cloud systems. Future Generation Computer Systems,393, 47–65.

    Google Scholar 

  114. Do, H., Minh, P., Sheng, W., Yang, D., & Liu, M. (2018). RiSH: A robot-integrated smart home for elderly care. Robotics and Autonomous Systems,101, 74–92.

    Google Scholar 

  115. Dong, H.-S. (2016). Anatomy of big data developmental process. Telecommunication Policy,40(9), 837–854.

    Google Scholar 

  116. Drath, H., & Horch, A. (2014). Industry 4.0: Hit or hype? Industry forum. IEEE Industrial Electronics Magazine,8(2), 56–58.

    Google Scholar 

  117. Du, C., Tan, L., & Dong, Y. (2015). Period selection for integrated controller tasks in cyber physical systems. Aeronautics China,28(3), 894–902.

    Google Scholar 

  118. Du, Z., He, L., Chen, Y., Xiao, Y., Gao, P., & Wang, T. (2017). Robot cloud: Bridging the power of robotics and cloud computing. Future Generation Computer Systems,74, 337–348.

    Google Scholar 

  119. Duan, Q. (2017). Cloud service performance evaluation: status, challenges, and opportunities—A survey from the system modeling perspective. Computer Science,3(2), 101–111.

    Google Scholar 

  120. Dudek, J., Auersperg, J., Pantou, R., & Rzepka, S. (2015). Thermal and mechanical behavior of an RFID based smart system embedded in a transmission belt determined by FEM simulations for industry 4.0 applications. In 2015 16th international conference on Fraunhofer ENAS, 19–22 April 2015, Budapest, Hungary.

  121. Dworschak, B., & Zaiser, H. (2014). Competencies for cyber-physical systems in manufacturing—First findings and scenarios. Procedia CIRP,25, 345–350.

    Google Scholar 

  122. EEF. (2017). The 4th industrial revolutionA primer for manufacturers. Technical report, EEF the manufacturers Organization, UK.

  123. e-factory. (2017). https://tr3a.mitsubishielectric.com/fa/tr/solutions/efactory. Available on August 30, 2017.

  124. Elmangousha, A., Coricib, A., Steinkeb, R., Coricib, M., & Magedanz, T. (2015). A framework for handling heterogeneous M2M traffic. Procedia Computer Science,63, 112–119.

    Google Scholar 

  125. Elmonem, M. A., Geith, M., Nasr, E., & Geith, M. (2017). Benefits and challenges of cloud ERP systems—A systematic literature review. Future Computing and Informatics Journal,1(1–2), 1–9.

    Google Scholar 

  126. Elmonem, M., Nasr, E., & Geith, M. (2016). Benefits and challenges of cloud ERP systems: A systematic literature view. Future Computing and Informatics Journal,1(1–2), 1–9.

    Google Scholar 

  127. Elragal, A. (2014). ERP and big data: The inept couple. Procedia Technology,16, 242–249.

    Google Scholar 

  128. Enget, K. (2016). A big data case. Journal of Accounting Education,39, 1–84.

    Google Scholar 

  129. ENTOC. (2017). https://www.festo.com/group/en/cms/12827.htm. Available on August 30, 2017.

  130. EPRS. (2015). http://www.europarl.europa.eu/RegData/etudes/BRIE/2015/568337/EPRS_BRI(2015)568337_EN.pdf. Available on August 30, 2017.

  131. Epstein, B., & Givoni, M. (2016). Analyzing the gap between the QOS demanded by PT users and QOS supplied by service operators. Transportation Research Part A,94, 622–637.

    Google Scholar 

  132. Ermilova, E., & Afsarmanesh, E. (2007). Modeling and management of profiles and competencies in VBEs. Intelligent Manufacturing,18, 561–586.

    Google Scholar 

  133. Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible industry 4.0: A scenario-based approach to learning for the future of production. Procedia CIRP,54, 13–18.

    Google Scholar 

  134. Esfahbodi, A., Zhang, Y., & Watson, G. (2016). Sustainable supply chain management in emerging economies: Trade-offs between environmental and cost performance. International Journal of Production Economics,181, 350–366.

    Google Scholar 

  135. ESIMA. (2017). Industry 4.0 project. https://www.esima-projekt.de/. Available on August 28, 2017 (in Germany).

  136. Eslava, H., Rojas, L., & Pereira, R. (2014). Implementation of machine-to-machine solutions using MQTT protocol in internet of things (IoT) environment to improve automation process for electrical distribution substations in Colombia. Journal of Power and Energy Engineering,3, 92–96.

    Google Scholar 

  137. Evans, A., & Annunziata, B. (2012). Industrial internet: Pushing the boundaries of minds and machines. https://www.ge.com/docs/chapters/Industrial_Internet.pdf. Available on May 28, 2017.

  138. Fallera, C., & Feldmüllera, D. (2015). Industry 4.0 learning factory for regional SMEs. In The 5th conference on learning factories 2015 (Vol. 32, pp. 88–91).

  139. Fanjiang, Y., Syu, Y., & Kuo, J. (2016). Search based approach to forecasting QoS attributes of web services using genetic programming. Information and Software Technology,80, 158–174.

    Google Scholar 

  140. Fariss, M., Asaidi, H., & Bellouki, M. (2018). Comparative study of skyline algorithms for selecting Web Services based on QoS. The First International Conference On Intelligent Computing in Data Sciences,127, 408–415.

    Google Scholar 

  141. Feldmann, A. (2011). A strategic perspective on plants in manufacturing networks. Division of Production Economics Department of Management and Engineering, Vol. 1, pp. 581–583. ISBN: 978-91-7393-134-2.

  142. Filaretov, V. F., & Pryyanichnikov, V. E. (2015). Autonomous mobile university robots AMUR: Technology and applications to extreme robotics. Procedia Engineering,100, 269–277.

    Google Scholar 

  143. Filippi, S., & Barattin, D. (2012). Classification and selection of prototyping activities for interaction design. Intelligent Information Management,4, 147–156.

    Google Scholar 

  144. Finin, T., Labrou, Y., & Mayfied, J. (1995). KQML as an agent communication language. In J. M. Bradshaw (Ed.), Software agents. Cambridge: MIT Press. ISBN 9780262522342.

  145. Flammini, E., & Sisinni, E. (2012). Wireless sensor networking in the internet of things and cloud computing era. Procedia Engineering,87(2014), 672–679.

    Google Scholar 

  146. Fleisch, E., Weinberger, M., & Wortmann, F. (2014). Business models and the internet of things. Bosch IoT Lab Whitepaper, University of St. Gallen. http://cocoa.ethz.ch/downloads/2014/10/2090_EN_Bosch%20Lab%20White%20Paper%20GM%20im%20IOT%201_2.pdf. Available on May 28, 2017.

  147. Flores-Abad, A., Ma, Q., Pham, K., & Ulrich, S. (2014). A review of space robotics technologies for on-orbit servicing. Progress in Aerospace Sciences,68, 1–26.

    Google Scholar 

  148. Foehr, M., Vollmar, J., Calà, A., Leitão, P., Karnouskos, S., & Colombo A. W. (2017). Engineering of next generation cyber-physical automation system architectures. In MultiDisciplinary Engineering for Cyber-Physical Production Systems, pp. 185–206. https://doi.org/10.1007/978-3-319-56345-9_8.

    Google Scholar 

  149. Foerstl, K., Azadegan, A., Leppelt, T., & Hartmann, E. (2015). Drivers of supplier sustainability: Moving beyond compliance to commitment. Journal of Supply Chain Management,51(1), 67–92.

    Google Scholar 

  150. Forti, T., & Munteanub, V. (2017). Topics in cloud incident management. Future Generation Computer Systems,72, 163–164.

    Google Scholar 

  151. Foster, K., Smith, G., Ariyachandra, T., & Frolick, M. (2015). Business intelligence competency center: Improving data and decisions. Information Systems Management,32(3), 229–233.

    Google Scholar 

  152. Framinan, J., & Pierreval, H. (2012). Special issue on pull strategies in manufacturing systems and supply chains: Recent advances. Journal of Intelligent Manufacturing,23, 1–3.

    Google Scholar 

  153. Francis, H., & Kusiak, A. (2017). Prediction of engine demand with a data-driven approach. Procedia Computer Science,103, 28–35.

    Google Scholar 

  154. Friedberg, I., McLaughlin, K., Smith, P., Laverty, D., & Seze, S. (2016). STPA-SafeSec: Safety and security analysis for cyber-physical systems. Journal of Information Security and Applications,2(2), 123–133.

    Google Scholar 

  155. FUSION. (2016). http://fusion-edu.eu/FUSION/. Available on August 30, 2017.

  156. Gabrel, V., Manouvrier, M., Moreau, K., & Murat, C. (2018). QoS-aware automatic syntactic service composition problem: Complexity and resolution. Future Generation Computer Systems,80, 311–321.

    Google Scholar 

  157. Gaikwad, P. P., Gabhane, J. P., & Golait, S. S. (2015). A survey based on Smart Homes system using Internet-of-Things. In Computation of power, energy information and communication (ICCPEIC) (pp. 0330–0335).

  158. Gajos, K., Weisman, L., & Shrobe, H. (2001). Design principles for resource management systems for intelligent spaces. International Workshop on Self-Adaptive Software,36, 198–215.

    Google Scholar 

  159. Galaske, N., & Anderl, R. (2016). Disruption management for resilient processes in cyber-physical production systems. Procedia CIRP,50, 442–447.

    Google Scholar 

  160. Gao, Y., Yang, T., & Bo, H. (2014). Improving the transmission reliability in smart factory through spatial diversity with ARQ. In IEEE/CIC international conference on communication in China, 27–29 July 2016, Chengdu, China.

  161. Gash, D., Ariyachandra, T., & Frolick, M. (2011). Looking to the clouds for business intelligence. Journal of Internet Commerce,10(4), 261–269.

    Google Scholar 

  162. Gaurav, D. (2017). What is the difference between digital manufacturing and virtual manufacturing, Quora. https://www.quora.com/What-is-the-difference-between-Digital-Manufacturing-and-Virtual-Manufacturing. Available on August 28, 2017.

  163. Gawanda, H., & Roya, K. (2015). Online monitoring of a cyber physical system against control aware cyber attacks. Engineering Services,70, 238–244.

    Google Scholar 

  164. Gay, S., & Nieuwoudt, L. (2010). Results of a trade simulation model for the South African fresh orange industry. Agrekon,38(4), 707–715.

    Google Scholar 

  165. Ge, M., Hong, J., Guttman, W., & Kim, D. (2014). A framework for automating security analysis of the internet of things. Procedia Technology,83, 12–27.

    Google Scholar 

  166. Geeta, R. B., Totad, G., Reddy, P., & Shobha, R. B. (2015). Big data structure and usage mining coalition. International Journal of Services, Technology and Management,21(6), 252–271.

    Google Scholar 

  167. Gelbmann, U., & Hammerl, B. (2015). Integrative re-use systems as innovative business models for devising sustainable product–service-systems. Journal of Cleaner Production,97, 50–60.

    Google Scholar 

  168. Gen, M., & Hwang, H. (2011). Advanced models and optimization in manufacturing and logistics systems. Journal of Intelligent Manufacturing,22, 343–344.

    Google Scholar 

  169. German Ministry of Education. (2016). Industry 4.0 platform, recommendations of industry 4.0 applications. http://www.din.de/blob/65354/f5252239daa596d8c4d1f24b40e4486d/roadmap-i4-0-e-data.pdf. Available on August 28, 2017.

  170. Gharbic, G., Guermoucheb, N., & Monteil, T. (2014). Timed verification of machine-to-machine communications. Procedia Computer Science,32, 1071–1078.

    Google Scholar 

  171. Giasiranis, S., & Sofos, L. (2016). Production and evaluation of educational material using augmented reality for teaching the module of “representation of the information on computers” in junior high school. Creative Education,7, 1270–1291.

    Google Scholar 

  172. Giusto, D., Lera, A., Morabito, G., & Atzori, L. (Eds.) (2010). The internet of things: 20th Tyrrhenian workshop on digital communications. Springer. ISBN-10: 1441916733.

  173. Gjeldum, N., Mladineoa, M., & Vezaa, I. (2016). Transfer of model of innovative smart factory to croatian economy using lean learning factory. Procedia CIRP,54, 158–163.

    Google Scholar 

  174. Gökalp, M., Kayabay, K., Akyol, M., Eren, E., & Kocyigit. A. (2016). Big data for industry 4.0: A conceptual framework. In 2016 International conference on computational science and computational intelligence, 15–17 December 2016, Las Vegas, NV, USA.

  175. Golova, N., & Rönnbäck, L. (2016). Big data normalization for massively parallel processing database. Computer Standard,54(Part 2), 86–93.

    Google Scholar 

  176. Golparvar-Fard, M., Peña-Mora, F., & Savarese, S. (2009). D4AR—A 4-dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication. Journal of Information Technology in Construction,14, 81–97.

    Google Scholar 

  177. Gonzales-Coma, J. P., Joham, M., Castro, P., & Castedo, L. (2018). QoS constrained power minimization in the multiple stream MIMO broadcast channel. Signal Processing,143, 48–55.

    Google Scholar 

  178. Gorecky, D., Schmitt, M., Loskyll, M., & Zühlke, D., (2014). Human–machine-interaction in the industry 4.0 era. In 12th IEEE international conference on industrial informatics (INDIN) (pp. 289–294).

  179. Granell, C., Havlik, D., Schade, S., Sabeur, Z., Delaney, C., Pielorz, J., et al. (2016). Future internet technologies for environmental applications. Enviromental Modelling and Software,78, 1–15.

    Google Scholar 

  180. Greenyera, J., Gritznera, D., Katzb, G., Marronb, A., Gladea, N., Gutjahra, T., et al. (2016). Distributed execution of scenario-based specifications of structurally dynamic cyber-physical systems. Engineering Services,26, 552–559.

    Google Scholar 

  181. Grzenda, M., Bustillo, A., & Zawistowski, P. (2012). A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. Intelligent Manufacturing Systems,23(5), 1733–1743.

    Google Scholar 

  182. GTAI. (2017). Industries 4.0, http://www.gtai.de/GTAI/Navigation/EN/Invest/Industries/Industrie-4-0/Industrie-4-0/industrie-4-0-what-is-it.html#overviewAnker. Available on November 11, 2017.

  183. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems,29, 1645–1660.

    Google Scholar 

  184. Gudfinnsson, K., Strand, M., & Berntsson, M. (2015). Analyzing business intelligence maturity. Journal of Decision Systems,24(1), 37–54. https://doi.org/10.1080/12460125.2015.994287.

    Article  Google Scholar 

  185. Guide, V. D. R., Jr., & Van Wassenhove, L. N. (2009). OR FORUM—The evolution of closedloop supply chain research. Operations Research,57(1), 10–18.

    Google Scholar 

  186. Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications. International Journal of Production Research,45(12), 2819–2840.

    Google Scholar 

  187. Guo, K., Liang, Z., Tang, Y., & Chi, T. (2016). SOR: An optimized semantic ontology retrieval algorithm for heterogeneous multimedia big data. International Journal of Information,4, 25–35.

    Google Scholar 

  188. Guoa, Z., Zhanga, Z., & Li, W. (2012). Establishment of intelligent identification management platform in railway logistics system by means of the internet of things. Procedia Engineering,29, 726–730.

    Google Scholar 

  189. Gupta, M., & George, J. (2016). Toward the development of a big data analytics capability. Information Management,53(8), 1049–1064.

    Google Scholar 

  190. Gursoy, M. C., Qiao, D., & Velipasalar, S. (2008). Analysis of energy efficiency in fading channels under QoS constraints. IEEE Transactions on Wireless Communications,8, 1276–1536.

    Google Scholar 

  191. Haddara, M., & Elragal, A. (2015). The readiness of ERP systems for the factory of the future. Procedia Computer Science,64, 721–728.

    Google Scholar 

  192. Haquea, S., & Aziz, S. (2013). False alarm detection in cyber-physical systems for healthcare applications. Engineering Services,5, 54–61.

    Google Scholar 

  193. Hardy, K., & Maurushat, A. (2016). Opening up government data for Big Data analysis and public benefit. Journal of Business Research,33(1), 30–37.

    Google Scholar 

  194. Hartunga, R., Hakanssonb, A., & Moradianc, E. (2015). A prescription for cyber physical systems. Manufacturing System,5, 4–9.

    Google Scholar 

  195. Hashem, I., Chang, V., Anuar, N., Adewole, K., Yaquub, I., Gani, A., et al. (2016). The role of big data in smart city. International Journal of information,36(5), 748–758.

    Google Scholar 

  196. Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., et al. (2015). Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: Opportunities and challenges. In IEEE international conference on services computing (SCC) (pp. 285–292).

  197. Hayyolalam, V., & Kazem, A. (2018). A systematic literature review on QoS-aware service composition and selection in cloud environment. Journal of Network and Computer Applications,110, 52–74.

    Google Scholar 

  198. Hazen, B., Boone, C., Farmer, L. A., & Ezell, J. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. Internal Journal of Production,154, 72–80.

    Google Scholar 

  199. Hazen, B. T., Skipper, J. B., Ezell, J. D., & Boone, C. A. (2016). Big Data and predictive analytics for supply chain sustainability: A theory-driven research agenda. Computers & Industrial Engineering,101, 592–598.

    Google Scholar 

  200. He, J., Chen, H., & Hu, F. (2015). ERP: An enhanced read policy for HDFS to improve read performance for files under construction. In 2015 IEEE international conference on progress in informatics and computing (PIC), 18–20 December 2015, Nanjing, China.

  201. He, K., & Li, X. (2016). A quantitative estimation technique for welding quality using local mean decomposition and support vector machine. Journal of Intelligent Manufacturing,27, 525–533.

    Google Scholar 

  202. Hea, Y., Chena, L., & Wang, L. (2016). An improved direct anonymous attestation scheme for M2M networks. Computer Science,15, 1481–1486.

    Google Scholar 

  203. Hecklau, F., Galeitzkea, M., Flachsa, S., & Kohl, H. (2015). Holistic approach for human resource management in industry 4.0. In Conference on learning factories, 10–11 October 2009, Changsha, Hunan, China.

  204. Heng, S., Slomka, L., Ag, D. B., & Hoffmann, R. (2014). Industry 4.0. Upgrading of Germany’s industrial capabilities on the horizon. Frankfurt am Main: Deutsche Bank Research. SSRN: https://ssrn.com/abstract=2656608.

  205. Henriques, C. I., Sobreiro, V. A., & Kimura, H. (2018). Science and technology park: Future challenges. Technology in Society,53, 144–160.

    Google Scholar 

  206. Heragu, S., & Kusiak, A. (1987). Analysis of expert systems in manufacturing design. IEEE Transactions on Systems, Man, and Cybernetics,17(6), 898–912.

    Google Scholar 

  207. Hermann, M., Tobias, P., & Otto, B. (2016). Design principles for industry 4.0 scenarios. http://www.thiagobranquinho.com/wp-content/uploads/2016/11/Design-Principles-for-Industrie-4_0-Scenarios.pdf. Available on August 28, 2017.

  208. Herron, J. (2016). Augmented reality in medical education and training. Journal of Electronic Resources in Medical Libraries,13(2), 51–55.

    Google Scholar 

  209. Herterich, M., Uebernickel, F., & Brenner, W. (2015). The impact of cyber-physical systems on industrial services in manufacturing. Procedia CIRP,30, 323–328.

    Google Scholar 

  210. Higashinoa, W., Capretz, M., & Bittencourt, L. (2017). CEPSim: Modelling and simulation of complex event processing systems in cloud environments. Future Generation Computer Systems,65, 122–139.

    Google Scholar 

  211. Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry,89, 23–34.

    Google Scholar 

  212. Holm, A., Wang, L., & Brewster, R. (2016). Localizing operators in the smart factory: A review of existing techniques and systems. In 2016 International symposium on flexible automation, 1–3 August 2016, Cleveland, Ohio, USA.

  213. Hong, E.-K., Baek, J., Jang, Y., Na, J., & Kim, K. (2017). QoS-guaranteed scheduling for small cell networks. ICT Express. https://doi.org/10.1016/j.icte.2017.11.017.

    Article  Google Scholar 

  214. Hossain, M. S., & Muhammad, G. (2016). Cloud-assisted industrial internet of things (iiot)—Enabled framework for health monitoring. Computer Networks,101, 192–202.

    Google Scholar 

  215. Houda, K., & Lakel, R. (2015). Synchronized communication in a set of autonomous mobile robots using bluetooth technology. Procedia Computer Science,73, 154–161.

    Google Scholar 

  216. Hsiao, M. (2018). A conceptual framework for technology-enabled and technology dependent user behavior toward device mesh and mesh app. Future Business Journal,4, 130–138.

    Google Scholar 

  217. Hu, T., Xiao, M., Hu, C., Gao, G., & Wang, B. (2017). A QoS-sensitive task assignment algorithm for mobile crowdsensing. Pervasive and Mobile Computing,41, 333–342.

    Google Scholar 

  218. Huang, C., Liang, W., & Yi, S. (2017). Cloud-based design for disassembly to create environmentally friendly products. Journal of Intelligent Manufacturing,28, 1203–1218.

    Google Scholar 

  219. Hubert, C., & Chan, Y. (2015). Internet of things business models. Journal of Service Science and Management,50, 1020–1030.

    Google Scholar 

  220. Huckle, S., Bhattacharya, R., White, M., & Beloff, N. (2016). Internet of things blockchain, shared economy applications. Procedia Computer Science,98(2016), 461–466.

    Google Scholar 

  221. Hufnagel, J., & Vogel-Heuser, B. (2015). Data integration in manufacturing industry model-based integration of data distributed from ERP to PLC. In 13th International conference on industrial informatics (INDIN), 22–24 July 2015, Cambridge, UK.

  222. Hwang, G., Lee, J., Park, J., & Chang, T. (2016). Developing performance measurement system for Internet of Things and smart factory environment. International Journal of Production Research,55(9), 2590–2602.

    Google Scholar 

  223. I4MTS. (2016). http://www.the-mtc.org/pdf/Industry-4-Report-2016-e.pdf. Available on August 30, 2017.

  224. Iavazzo, C., & Gkegkes, I. (2017). Cost–benefit analysis of robotic surgery in gynaecological oncology. Best Practice & Research Clinical Obstetrics and Gynaecology,45, 7–18.

    Google Scholar 

  225. ICV. (2016). International controller association report. http://integratedreporting.org/wp-content/uploads/2013/08/137_International-Controller-Association-Discussion-Paper.pdf. Available on August 28, 2017.

  226. Iera, A., Floerkemeier, C., Mitsugi, J., & Morabito, G. (2010). The internet of things. IEEE Wireless Communications,17, 8–9.

    Google Scholar 

  227. Ignaccolo, M. (2003). A simulation model for airport capacity and delay analysis. Transportation Planning and Technology,26(2), 135–170.

    Google Scholar 

  228. IIC. (2016). http://www.process-worldwide.com/usa-industry-40-the-american-way-a-536602/. Available on August 30, 2017.

  229. Ince, H., Imamoglu, S. Z., Keskin, H., Akgun, A., & Efe, M. A. (2013). The impact of ERP systems and supply chain management practices on firm performance: Case of Turkish companies. International Strategic Management Conference,99, 1124–1133.

    Google Scholar 

  230. Inderfurth, K., de Kok, A. G., & Flapper, S. D. P. (2001). Product recovery in stochastic remanufacturing systems with multiple reuse options. European Journal of Operational Research,133, 130–152.

    Google Scholar 

  231. INESA. (2016). http://journal.jp.fujitsu.com/en/2016/10/31/01/. Available on August 30, 2017.

  232. InGlobe. (2017). http://www.inglobetechnologies.com/smart-manufacturing-ar-industry-4-0. Available on August 30, 2017.

  233. Intel IOT Report. (2016). Developing solutions for the internet of things. http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/developing-solutions-for-iot.pdf. Available on August 28, 2017.

  234. Iqbal, A., Zhang, H., Kong, L., & Hussain, G. (2015). A rule-based system for trade-off among energy consumption, tool life, and productivity in machining process. Journal of Intelligent Manufacturing,26, 1217–1232.

    Google Scholar 

  235. Issa, H., Regenbrecht, H., & Hale, R. (2012). Augmented reality applications in rehabilitation to improve physical outcomes. Physical Therapy Reviews,17(1), 16–28.

    Google Scholar 

  236. Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2015). A dynamic model and an algorithm for short term supply chain scheduling in the smart factory industry 4.0. International Journal of Production Research,54(2), 386–402.

    Google Scholar 

  237. Jäckel, M., Falk, T., & Landgrebe, D. (2016). Concept for further development of self-pierce riveting by using cyber physical systems. Procedia CIRP,44, 293–297.

    Google Scholar 

  238. Jaehne, J., & KalalChelvan, S. (2017). Towards a connected world of supply chainIndustry 4.0 presentation. https://www.slideshare.net/sarathygurushankar1/shaping-towards-a-connected-world-of-supply-chain-industrie-40. Available on August 22, 2017.

  239. Jannsenn, M., Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision making quality. Journal of Business Research,70, 338–345.

    Google Scholar 

  240. Jararweha, Y., Al-Ayyoub, M., Darabseh, A., Benkhelifa, E., Vouk, M., & Rindos, A. (2017). Software defined cloud: Survey, system and evaluation. Future Generation Computer Systems,58, 56–74.

    Google Scholar 

  241. Jatzkowskia, J., & Kleinjohanna, B. (2016). Towards self-reconfiguration of real-time communication within cyber-physical systems. Manufacturing Systems,15, 54–61.

    Google Scholar 

  242. Jayanthi, S., Roth, V., Kristal, M., & Venu, L. (2009). Strategic resource dynamics of manufacturing firms. Management Science,55(6), 1060–1076.

    Google Scholar 

  243. Jeang, A. (2015a). Robust product design and process planning in using process capability analysis. Intelligent Manufacturing Systems,26(3), 459–470.

    Google Scholar 

  244. Jeang, A. (2015b). Robust product design and process planning in using process capability analysis. Journal of Intelligent Manufacturing,26, 459–470.

    Google Scholar 

  245. Jeng, T., Tzeng, S., Tseng, C., & Liu, Y. (2016). The design and fabrication of a temperature diagnosis system for the intelligent rotating spindle of industry 4.0. Smart Science,4, 38–43.

    Google Scholar 

  246. Jernigan, D., Fernandez, S., Pensyl, R., & Shangping, L. (2009). Digitally augmented reality characters in live theatre performances. International Journal of Performance Arts and Digital Media,5(1), 35–49.

    Google Scholar 

  247. Jeschke, S., Brecher, C., Meisen, T., Özdemir, D., & Eschert, T. (2017). Industrial internet of things and cyber manufacturing systems. In Industrial internet of things, international publishing (pp. 3–19).

  248. Ji, Z., Ganchev, I., O’Droma, M., Zhao, L., & Zhang, X. (2014). A cloud-based car parking middleware for IoT-based smart cities: Design and implementation. Sensors,14, 22372–22393.

    Google Scholar 

  249. Jianjuna, S., Xub, W., Jizhenc, G., & Yangzhou, C. (2016). The analysis of traffic control cyber-physical systems. Social and Behavioral Science,96, 2487–2496.

    Google Scholar 

  250. Jiao, B., Zhou, Y., Du, J., Huang, C., Wang, J., & Li, B. (2015). A heuristic nonlinear operator for the aggregation of incomplete judgment matrices in group decision making. Journal of Intelligent Manufacturing,26, 1253–1266.

    Google Scholar 

  251. Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks,20, 2481–2501.

    Google Scholar 

  252. Johansson, B., Alajbegovic, A., & Alexopoulos, V. (2015). Cloud ERP adoption opportunities and concerns: The role of organizational size, system sciences (HICSS). In 2015 48th Hawaii international conference on system sciences (pp 1530–1605), 5–8 January 2015, Kauai, HI, USA.

  253. Jones, A., Vidalis, S., & Abouzakhar, N. (2016). Information security and digital forensics in the world of cyber physical systems. In 2016 Eleventh international conference on digital information management (ICDIM), 19–21 September, Porto, Portugal.

  254. Jourdan, Z., Rainer, K., & Marshall, T. (2008). Business intelligence: An analysis of the literature. Information Systems Management,25(2), 121–131.

    Google Scholar 

  255. Junghanns, P., Fabian, B., & Ermakova, T. (2016). Engineering of secure multi-cloud storage. Computers in Industry,83, 108–120.

    Google Scholar 

  256. Kagermann, H. (2014). Chancen von Industry 4.0 nutzen. In Bauernhansl, T., M. ten Hompel and B. Vogel-Heuser, Vol. 4, pp. 603–614 (in German).

  257. Kagermann, H., Lukas, W., & Wahlster, W. (2011). Industry 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. Industryllen Revolution. VDI nachrichten, Vol. 13, pp. 1090–1100.

  258. Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative Industry 4.0. Final report of the industry 4.0 working group, http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf. Available on August 22, 2017.

  259. Kaidanren. (2016). Toward realization of the new economy and society. Japan Business Federation (p. 8). http://www.keidanren.or.jp/en/policy/2016/029_outline.pdf. Available on August 22, 2017.

  260. Karakus, M., & Durresi, A. (2017). Quality of service (QoS) in software defined networking (SDN): A survey. Journal of Network and Computer Applications,80, 200–218.

    Google Scholar 

  261. Kba, S. (2015). Cloud based health system. Computer Science,18, 989–1000.

    Google Scholar 

  262. Ke, Y., Wang, P., Chen, Y., Gu, B., Qi, H., Zhou, P., et al. (2015). Concurrent mental activities affect ERPs and impair performance of ERP-spellers. In 2015 7th International IEEE/EMBS conference on neural engineering (NER), 22–24 April 2015, Montpellier, France.

  263. Kermorgant, O. (2018). A magnetic climbing robot to perform autonomous welding in the shipbuilding industry. Robotics and Computer Integrated Manufacturing,53, 178–186.

    Google Scholar 

  264. Khan, R., Khan, S. U., Zaheer, R., Khan, S. (2012). Future internet: The internet of things architecture, possible applications and key challenges. In 10th International conference on frontiers of information technology (FIT) (pp. 257–260).

  265. Kiel, D., Arnold, C., & Voigt, K. I. (2017). The influence of the Industrial Internet of Things on business models of established manufacturing companies—A business level perspective. Technovation,68, 4–19.

    Google Scholar 

  266. Kim, H., Lee, S., Park, H., & Lee, G. (2005). A model for a simulation-based shipbuilding system in a shipyard manufacturing process. International Journal of Computer Integrated Manufacturing,18(6), 427–441.

    Google Scholar 

  267. Kim, J., Kim, H., Lakshmanan, K., & Rajkumar, R. R. (2013). Parallel scheduling for cyber-physical systems: Analysis and case study on a self-driving car. In Proceedings of the ACM/IEEE 4th international conference on cyber-physical systems (pp. 31–40).

  268. Kim, J., Lee, S., Seo, J., & Kamat, V. (2018). Modular data communication methods for a robotic excavator. Automation in Construction,90, 166–177.

    Google Scholar 

  269. Kim, W., & Jo, O. (2015). Cost-optimized configuration of computing instances for large sized cloud systems. Computer Science,5, 20–30.

    Google Scholar 

  270. Kim, Y., & Suzuki, K. (2015). Social context representation in product-service systems with internet of things. Open Journal of Social Sciences,3, 187–193.

    Google Scholar 

  271. Kirthica, S., & Sridhar, R. (2016). CIT: A cloud inter-operation toolkit to enhance elasticity and tolerate shut down of external clouds. Journal of Network and Computer Applications,85, 32–46.

    Google Scholar 

  272. Klaus, H. (2016). Siemens industry 4.0 report for german industry and applications. On the way industry 4.0. https://www.siemens.com/press/pool/de/events/2015/digitalfactory/2015-04-hannovermesse/presentation-e.pdf. Available on August 22, 2017.

  273. Klimeš, J. (2014). Using formal concept analysis for control in cyber-physical systems. Engineering Services,69, 1518–1522.

    Google Scholar 

  274. Kokuryo, D., Kaihara, T., Suginouchi, S., & Kuik, S. (2016). A study on value co-creative design and manufacturing system for tailor-made rubber shoes production. In 2016 International symposium on flexible automation, 1–3 August 2016, Ohio, USA.

  275. Kolberg, D., Berger, C., Pirvu, B., Franke, M., & Michniewicz, J. (2016). Insights from a framework for designing cyber-physical systems in production environments. Procedia CIRP,57, 32–37.

    Google Scholar 

  276. Koo, D., Piratla, K., & Matthews, J. (2015). Towards sustainable water supply: Schematic development of big data collection using internet of things. Procedia Computer Engineering,4, 45–55.

    Google Scholar 

  277. Koseleva, N., & Ropaite, G. (2017). Big data in building energy efficiency: Understanding of big data and main challenges. Procedia Engineering,172(2017), 544–549.

    Google Scholar 

  278. Kothandaraman, D., & Chellappan, C. (2016). Direction detecting system of indoor Smartphone users using BLE in IoT. Circuits and Systems,7, 1492–1503.

    Google Scholar 

  279. Kowalska, M., Pazdzior, M., & Maziopa, A. (2018). Erratum to: Implementation of QFD method in quality analysis of confectionery products. Journal of Intelligent Manufacturing,29, 449–450.

    Google Scholar 

  280. Kozhirbayev, Z., & Sinnott, R. (2017). A performance comparison of container-based technologies for the cloud. Future Generation Computer Systems,68, 175–182.

    Google Scholar 

  281. Krawatzeck, R., & Dinter, B. (2015). Agile business intelligence: Collection and classification of agile business intelligence actions by means of a catalog and a selection guide. Information Systems Management,32(3), 177–191.

    Google Scholar 

  282. Kumar, J., & Zaveri, M. (2016). Hierarchical clustering for dynamic and heterogeneous internet of things. Computer Science,93, 276–282.

    Google Scholar 

  283. Kurth, M., & Syleyer, C. (2016). Smart factory and education. In 2016 IEEE 11th conference on industrial electronics and applications (ICIEA) (pp. 110–119), 5–7 June 2016, Hefei, China.

  284. Kusiak, A. (2009). Short-term prediction of wind farm power: A data mining approach. Wind Energy Journal,12(3), 275–293.

    Google Scholar 

  285. Kusiak, A. (2012). A data-mining approach to monitoring wind turbines. Transactions on Sustainable Energy,3(1), 150–165.

    Google Scholar 

  286. Kusiak, A. (2013). Innovation: The living laboratory perspective. Computer-Aided Design and Applications,4(6), 196–206.

    Google Scholar 

  287. Kusiak, A. (2017a). Smart manufacturing. International Journal of Production Research. https://doi.org/10.1080/00207543.2017.1351644.

    Article  Google Scholar 

  288. Kusiak, A. (2017b). Smart manufacturing must embrace big data. Nature,544(7648), 23–25.

    Google Scholar 

  289. Kusiak, A., Zheng, H., & Song, Z. (2010). Power optimization of wind turbines with data mining and evolutionary computation. Renewable Energy,35(3), 695–702.

    Google Scholar 

  290. Kyriazisa, D., & Varvarigoua, T. (2013). Smart, autonomous and reliable Internet of Things. Computer Science,21(2013), 442–448.

    Google Scholar 

  291. Lakhmi, C. J., & Nguyen, N. T. (2009). Knowledge processing and decision making in agent-based systems. Berlin: Springer. ISBN 978-3-540-88048-6.

  292. Lakshimi, R., Babu, S., & Bhalaji, N. (2017). Analysis of clustered QoS routing protocol for distributed wireless sensor network. Computers & Electrical Engineering,64, 173–181.

    Google Scholar 

  293. Lasi, H., Fettke, P., Kemper, G., Feld, T., & Hoffmann, M. (2014). Industry 4.0: Bedarfssog und Technologiedruck als Treiber der vierten Industrillen Revolution. The İnternational Journal of Wirtschaftsinformatik,56, 261–264. (in German).

    Google Scholar 

  294. Layuan, L., & Chunlin, L. (2002). A multicast routing protocol supporting multiple QoS constraints. In 10th IEEE international conference on networks (Vol. 2). https://doi.org/10.1109/icon.2002.1033285.

  295. Lee, A. (2008). Cyber physical systems: Design challenges. In 11th IEEE symposium on object oriented real-time distributed computing (ISORC), 5–7 May 2008, Orlando, FL, USA.

  296. Lee, D. (2014). Robots in the shipbuilding industry. Robotics and Computer-Integrated Manufacturing,30, 442–450.

    Google Scholar 

  297. Lee, H., Leu, J., & Huang, Y. (2015c). Implementation of enterprise resource planning using the value engineering and system dynamics methods. In 2015 2nd International conference on information science and control engineering (ICISCE), 24–26 April 2015, Shanghai, China.

  298. Lee, J., & Shin, K. (2017). Development and use of a new task model for cyber-physical systems: A real-time scheduling perspective. Journal of System,126, 45–56.

    Google Scholar 

  299. Lee, J., Ardakani, H., Yang, S., & Bagheri, B. (2015a). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP,38, 3–7.

    Google Scholar 

  300. Lee, J., Bagheri, B., & Kao, H. (2015b). A cyber systems architecture for industry 4.0 based manufacturing systems. Manufacturing Letters,3, 18–23.

    Google Scholar 

  301. Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp,16, 3–8.

    Google Scholar 

  302. Lee, J., & Lapira, E. (2014). Industry 4.0 environment. Asset Condition Management,15, 54–61.

    Google Scholar 

  303. Lee, J., Lapira, E., Bagheri, B., & Kao, H. A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters,1(1), 38–41.

    Google Scholar 

  304. Lee, H., Yoo, S., & Kim, Y. (2016). An energy management framework for smart factory on context awareness. In 18th International conference on advanced communication technology (ICACT), 31 January–3 February 2016, Pyeongchang, South Korea.

  305. Lei, C., Wan, K., & Man, K. (2013). Developing a smart learning environment in universities via cyber-physical systems. Information Technology and Quantitative Management,17, 583–585.

    Google Scholar 

  306. Leloglu, E. (2017). A review of security concerns in internet of things. Journal of Computer and Communications,5, 121–136.

    Google Scholar 

  307. Leppelt, T., Foerstl, K., Reuter, C., & Hartmann, E. (2013). Sustainability management beyond organizational boundaries–sustainable supplier relationship management in the chemical industry. Journal of Cleaner Production,56, 94–102.

    Google Scholar 

  308. Li, B., Song, A. M., & Song, J. (2012). A distributed QoS-constraint task scheduling scheme in cloud computing environment: Model and algorithm. Advances in information Sciences and Service Sciences (AISS),4, 283–291.

    Google Scholar 

  309. Li, G., Zhang, D., Zheng, K., Ming, X., Pan, H., & Jiang, K. (2013). A kind of new multicast routing algorithm for application of internet of things. Journal of Applied Research and Technology,11(4), 578–585.

    Google Scholar 

  310. Li, J., Xie, T., & Du, S. (2011). Requirements analysis on flexibility of ERP system of medium and small publishers. Procedia Engineering,15, 5493–5497.

    Google Scholar 

  311. Li, Z., Shen, H., Li, H., Xia, G., Gamba, P., & Zhang, L. (2017). Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery. Remote Sensing of Environment,191, 342–358.

    Google Scholar 

  312. Lia, B., & Yub, B. (2011). Research and application on the smart home based on component technologies and Internet of Things. Procedia Engineering,15, 2087–2092.

    Google Scholar 

  313. Liang, H., & Du, Y. (2017). Dynamic service selection with QoS constraints and inter-service correlations using cooperative coevolution. Future Generation Computer Systems,76, 119–135.

    Google Scholar 

  314. Lian-yue, W. (2012). Think of construction lean SCM based on IOT. In IEEE Symposium on Robotics and Applications (ISRA) (pp. 436–438).

  315. Liao, T. (2015). Augmented or admented reality? The influence of marketing on augmented reality technologies. Information, Communication and Society,18(3), 310–326.

    Google Scholar 

  316. Liao, Y., Deschamps, F., Loures, E., & Ramos, L. (2017). Past, present and future of Industry 4.0—A systematic literature review and research agenda proposal. International Journal of Production Research,55(12), 3609–3629.

    Google Scholar 

  317. Lichtblau, K., Stıch, V., Bertenrath, R., Blum, R., Bleider, M., Millack, A., et al. (2016). IMPULS, Industry 4.0 readiness, VDMA. http://industrie40.vdma.org/documents/4214230/5356229/Industrie%204.0%20Readiness%20Study%20English.pdf/f6de92c1-74ed-4790-b6a4-74b30b1e83f0. Available on August 28, 2017.

  318. Lilis, G., Conus, G., Asadi, N., & Kayal, M. (2017). Towards the next generation of intelligent building: An assessment study of current automation and future IoT based systems with a proposal for transitional design. Sustainable Cities and Society,28, 473–481.

    Google Scholar 

  319. Lim, M. K., Tseng, M. L., Tan, K. H., & Bui, T. D. (2017). Knowledge management in sustainable supply chain management: Improving performance through an interpretive structural modelling approach. Journal of Cleaner Production,162, 806–816.

    Google Scholar 

  320. Lin, T., Chen, M., Yang, D., & Chen, Y. (2016). New method for industry 4.0 machine status prediction—A case study with the machine of a spring factory. In 2016 International computer symposium, 15–17 December 2016, Chiayi, Taiwan.

  321. Lin, Y. C., Hung, M. H., Huang, H. C., Chen, C. C., Yang, H. C., Hsieh, Y. S., et al. (2017a). Development of advanced manufacturing cloud of things (AMCoT)—A smart manufacturing platform. IEEE Robotics and Automation Letters,2, 1809–1816.

    Google Scholar 

  322. Lin, D., Lee, C., Lau, H., & Yang, Y. (2017c). Strategic response to Industry 4.0: An empirical investigation on the Chinese automotive industry. Industrial Management & Data Systems, 118(3), 589–605.

    Google Scholar 

  323. Lin, B., Lin, F., & Tung, L. (2016b). The roles of 5G mobile broadband in the development of IoT, big data, cloud and SDN. Communications and Network,8, 9–21.

    Google Scholar 

  324. Lin, C., Wnag, K., & Deng, G. (2017b). A QoS-aware routing in SDN hybrid networks. Procedia Computer Science,110, 242–249.

    Google Scholar 

  325. Linton, J. D., Klassen, R., & Jayaraman, V. (2007). Sustainable supply chains: An introduction. Journal of operations management,25, 1075–1082.

    Google Scholar 

  326. Liu, D., & Hu, X. (2006). Firm real-time system scheduling based on a novel QoS constraint. IEEE Transactıons on Computers,55, 1–14.

    Google Scholar 

  327. Liu, J., & Tonga, W. (2012). Device service networks maintenance based on components migration in the internet of things. Procedia Engineering,29, 3418–3423.

    Google Scholar 

  328. Liu, M., Ma, J., Lin, L., Ge, M., Wang, Q., & Liu, C. (2017b). Intelligent assembly system for mechanical products and key technology based on internet of things. Journal of Intelligent Manufacturing,28(2), 271–299.

    Google Scholar 

  329. Liu, X., Guo, X., Chen, L., Zhou, Y., & Xin, C. (2014). The use of three-dimensional integrated design system in smart substation design. Journal of Power and Energy Engineering,2, 632–638.

    Google Scholar 

  330. Liu, Z., Choo, K. K. R., & Zhao, M. (2017a). Practical-oriented protocols for privacy-preserving outsourced big data analysis: Challenges and future research directions. Computers and Security,69, 97–113.

    Google Scholar 

  331. Lokers, R., Knapen, K., Sander, J., Randen, Y., & Jansen, J. (2016). Analysis of big data technologies for use in agro-environmental science. Modelling Software,4, 1090–1105.

    Google Scholar 

  332. Lom, M., Pribyl, O., & Svitek, M. (2016). Industry 4.0 as a part of smart cities. Smart Cities Symposium, 26–27 May 2016, Prague, Czech Republic.

  333. Longo, F., Nicoletti, L., & Padovano, A. (2017). Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Computers & Industrial Engineering,113, 144–159.

    Google Scholar 

  334. Lorenc, A., & Szkoda, M. (2015). Customer logistic service in the automotive industry with the use of the SAP ERP system. In 2015 4th International conference on advanced logistics and transport (ICALT), 20–22 May 2015, Valenciennes, France.

  335. Loseto, G., Ieva, S., Gramegna, F., Ruta, M., Scioscia, F., & Sciascio, E. (2016). Linking the web of things: LDP-CoAP mapping. Computer Science,83, 1182–1187.

    Google Scholar 

  336. Lucke, A. (2008). Manufacturing systems and technologies for the new frontier. In The 41st CIRP conference on manufacturing systems, Tokyo, Japan (Vol 2, pp. 115–118).

  337. Maansman, J., Böcker, S., Rettberg, F., Wietfeld, C., & Rehtanz, C. (2014). Renewable energies in smart factories with electric vehicle fleets. In 49th International universities power engineering conference (UPEC), Cluj-Napoca, Romania.

  338. Macabee, S., Landis, R., & Burke, M. (2017). Inductive reasoning: The promise of big data. Human Resource Management,27(2), 277–290.

    Google Scholar 

  339. Machowiak, W. (2012). Risk management—Unappreciated instrument of supply chain strategy. LogForum,8, 277–285.

    Google Scholar 

  340. Madani, S. R., & Rasti-Barzoki, M. (2017). Sustainable supply chain management with pricing, greening and governmental tariffs determining strategies: A game-theoretic approach. Computers & Industrial Engineering,105, 287–298.

    Google Scholar 

  341. Magdić, J., & Car, Z. (2013). A company model supporting ERP and CRM software development and implementation processes. In 12th International conference on telecommunications (ConTEL), 26–28 June 2013, Zagreb, Croatia.

  342. Majeed, A. A., & Rupasinghe, T. D. (2017). Internet of things (IoT) embedded future supply chains for industry 4.0: An assessment from an ERP-based fashion apparel and footwear industry. International Journal of Supply Chain Management,6, 25–40.

    Google Scholar 

  343. Marron, J. S. (2014). Big data in context and robustness against heterogeneity. Computer Science,2, 73–80.

    Google Scholar 

  344. Martin, P., & Dantan, J. (2011). Virtual manufacturing: Prediction of work piece. International Journal of Computer Integrated Manufacturing,24, 620–626.

    Google Scholar 

  345. Martinez, G., & Munizaga, M. (2016). Workshop 5 report: Harnessing big data. Research in Transportation economics,59, 236–241.

    Google Scholar 

  346. Matena, V., Bures, T., Gerostathopoulos, I., & Hnetynka, P. (2016). Model problem and testbed for experiments with adaptation in smart cyber-physical systems. In Software engineering for IEEE/ACM, 11th international symposium on adaptive and self-managing systems (SEAMS), 16–17 May 2016, Austin, TX, USA.

  347. Matutinovic, I., Salthe, S., & Ulanowicz, R. (2016). The mature stage of capitalist development: Models, signs, policy, implications. Structural Change and Economic Dynamics,39, 17–30.

    Google Scholar 

  348. Mawlawi, B., Dore, J., Lebedev, N., & Gorce, J. (2014). Performance evaluation of multiband CSMA/CA with RTS/CTS or M2M. In International conference on selected topics in mobile and wireless networking, Rome, Italy (Vol. 40, pp. 108–115).

  349. Mayer, S., Verborgh, R., Kovatsch, M., & Mattern, F. (2016). Smart configuration on smart environments. IEEE Transactions on Automation Science and Engineering,13(3), 1247–1255.

    Google Scholar 

  350. McCullough, A., Gempesaw, C., Daniels, W., & Bacon, R. (2008). Simulating the economic viability of crawfish production: A two stage modeling approach. Aquaculture Economics and Management,5(2), 69–79.

    Google Scholar 

  351. Mckinsey. (2016). Industry 4.0: How to navigate digitization of the manufacturing sector. https://www.mckinsey.de/files/mck_industry_40_report.pdf. Available on August 22, 2017.

  352. McKinsey. (2017). China develops from ‘sponge’ into innovation leader. https://www.your-bizbook.com/en/Club-China-News/mckinsey-china-develops-from-sponge-into-innovation-leader. Available on November 19, 2017.

  353. Meddeb, M., Alaya, S., Monteil, T., Dhraief, A., & Drira, K. (2014). M2M platform with autonomic device management service. Computer Science,32, 1063–1070.

    Google Scholar 

  354. MESA. (2009). Smart manufacturing in industry 4.0 systems, mesa international report for industry 4.0 systems. http://www.mesa.org/en/resources/MESAWhitePaper52-SmartManufacturing-LandscapeExplainedShortVersion.pdf. Available on August 22, 2017.

  355. MetamoFAB. (2017). https://www.festo.com/group/en/cms/10275.htm. Available on August 30, 2017.

  356. Meziane, F., Vadera, S., Kobbacy, K., & Proudlove, N. (2014). Intelligent systems in manufacturing: Current developments and future prospects. Integrated Manufacturing Systems,11(4), 218–238.

    Google Scholar 

  357. Michniewicza, J., & Reinharta, G. (2016). Cyber-physical robotics—Automated analysis, programming and configuration of robot cells based on cyber-physical-systems. Engineering Services,15, 566–575.

    Google Scholar 

  358. Michona, E., Gossa, J., Genaud, S., Unbekandt, L., & Kherbache, V. (2017). Schlouder: A broker for IaaS clouds. Future Generation Computer Systems,69, 11–23.

    Google Scholar 

  359. Mikusz, M. (2014). Towards an understanding of cyber-physical systems as industrial software-product-service systems. Procedia CIRP,16, 385–389.

    Google Scholar 

  360. Miloslavskaya, N., & Tolstoy, A. (2017). Big data, fast data and data lake concepts. Procedia Engineering,88(2016), 300–305.

    Google Scholar 

  361. Ming, B., Shuo, T., Mingsan, M., Jiaojiao, J., & Weiyun, X. (2015). Big data applications in traditional Chinese medicine research. International Journal of Services, Technology and Management,21(4), 294–300.

    Google Scholar 

  362. Mirsanei, H. S., Zandieh, M., Moayed, M. J., & Khabbazi, M. R. (2011). A simulated annealing algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times. Journal of Intelligent Manufacturing,22, 965–978.

    Google Scholar 

  363. Miškuf, M., & Zolotová, I. (2016). Comparison between multi-class classifiers and deep learning with focus on industry 4.0. Cybernetics & Informatics (pp. 1–5), 2–5 February 2016.

  364. Mohammed, A., & Wang, L. (2018). Brainwaves driven human–robot collaborative assembly. CIRP Annals Manufacturing Technology,1781, 1–4.

    Google Scholar 

  365. Mokhtar, B., & Eltoweissy, M. (2017). Big data and semantics management system. Ad Hoc Networks,57, 32–51.

    Google Scholar 

  366. Monostori, L. (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. Procedia CIRP,17, 9–13.

    Google Scholar 

  367. Monteiroa, V., Ferreirab, J., & Afonso, J. (2014). Smart platform towards batteries analysis based on internet-of-things. Procedia Computer Egineering,17(2014), 520–527.

    Google Scholar 

  368. Moon, S., Kang, S., Jeon, J., & Chun, I. (2016). Simulation modeling of sewing process, for evaluation, of production schedule in smart factory. In 2016 International conference on industrial engineering, management science and application (ICIMSA), 23–26 May 2016, Jeju, South Korea.

  369. Moregård, A., Haubenwallera, A., & Vandikasb, K. (2015). Computations on the edge in the internet of things. Computer Science,52, 29–34.

    Google Scholar 

  370. Mourtzis, D., Zogopoulos, V., & Vlachou, E. (2017). Augmented reality application to support remote maintenance as a service in the robotics industry. Procedia CIRP,63, 46–51.

    Google Scholar 

  371. Mucci, H., Sharaf, M., & Weyns, D. (2016). Self-adaptation for cyber-physical systems: A systematic literature review. In 2016 IEEE/ACM 11th international symposium on software engineering for adaptive and self-managing systems (SEAMS), 16–17 May 2016, Austin, TX, USA.

  372. Müller, R. (2016). Planning and developing cyber-physical assembly systems by connecting virtual and real worlds. Procedia CIRP,52, 35–40.

    Google Scholar 

  373. Munera, E., Luis, L., Lujan, P., Luis, J., Yagüe, P., Simo, J., et al. (2015). Control kernel in smart factory, environments, smart resources integration. In The 5th annual IEEE international conference on cyber technology in automation, 8–12 June 2015, Shenyang, China.

  374. Murray, T. (1999). Authoring intelligent tutoring systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education,10, 98–129.

    Google Scholar 

  375. Nawrocki, P., & Reszelewski, W. (2017). Resource usage optimization in mobile cloud computing. Journal Computer Communications,99(C), 1–12.

    Google Scholar 

  376. Nazarko, L. (2017). Future-oriented technology assessment. Procedia Engineering,182, 504–509.

    Google Scholar 

  377. Negash, B., Rahmani, A., Westelund, T., Liljeberg, P., & Tenhunen, H. (2015). LISA: Lightweight internet of things service bus architecture. Computer Science,52(2015), 436–443.

    Google Scholar 

  378. Neisse, R., Steri, G., & Favino, I. (2014). A model based security toolkit for the IOT. In 9th International conference on availability, reliability and security (ARES), 8–12 September 2014, Fribourg, Switzerland (pp.78–87).

  379. Netland, T. (2016). Augmented reality: Ready for manufacturing industries. Better Operations, The Routledge Companion to Lean Management. http://better-operations.com/2016/10/07/augmented-reality-manufacturing/. Available on August 28, 2017.

  380. Nguyen, P., Shaukat, A., & Tao, Y. (2017). Model-based security engineering for cyber-physical systems: A systematic mapping study. Information Software,83, 116–135.

    Google Scholar 

  381. Ning, H., & Liu, H. (2012). Cyber-physical-social based security architecture for future internet of things. Advances in Internet of Things,2, 1–7.

    Google Scholar 

  382. Nishioka, Y. (2016). https://iv-i.org/en/docs/doc_160428_hannover.pdf. Available on August 30, 2017.

  383. Nofal, M., & Yusof, Z. (2013). Integration of business intelligence and enterprice resource planning within organizations. Procedia Technology,11, 658–665.

    Google Scholar 

  384. Nordahla, M., & Magnussona, B. (2015). A lightweight data interchange format for Internet of Things in the PalCom middleware framework. Computer Science,56(2015), 284–291.

    Google Scholar 

  385. NTIO. (2017). Smart Cities Taiwan: Opportunities for Dutch companies. https://www.rvo.nl/sites/default/files/2017/05/taiwan-ambition-and-development-of-smart-citiesv2.pdf.

  386. Nuñez, D., Fernández, G., & Luna, J. (2017). Cloud system. Procedia Computer Engineering,62, 149–164.

    Google Scholar 

  387. Oesterreich, D. T., & Teuteberg, F. (2016). Understanding the implications of digitalization and automation in the context of Industry 4.0. Computers in Industry,83, 121–139.

    Google Scholar 

  388. Ojha, T., Misra, S., & Raghuwanshi, N. (2017). Sensing-cloud: Leveraging the benefits for agricultural applications. Computers and Electronics in Agriculture,135, 96–107.

    Google Scholar 

  389. Olszak, C. (2016). Toward better understanding and use of business intelligence in organizations. Information Systems Management,32(2), 105–123.

    Google Scholar 

  390. Ong, S. K., Yuan, M. L., & Nee, A. Y. C. (2008). Augmented reality applications in manufacturing: A survey. International Journal of Production Research,46, 2707–2742.

    Google Scholar 

  391. Onime, C., & Abiona, O. (2016). 3D mobile augmented reality interface for laboratory experiments. International Journal of Communications, Network and System Sciences,9, 67–76.

    Google Scholar 

  392. OPAK. (2017). A industry 4.0 project “open engineering platform for autonomous mechatronic automation components in a function-oriented architecture”. https://www.automation.com/automation-news/industry/festo-to-demonstrate-opak-industry-40-research. Available on August 28, 2017.

  393. Orasız, S., & Yörök, G. (2012). Key performance indicators used in ERP performance measurement applications. In IEEE 10th jubilee international symposium on intelligent systems and informatics (SISY) (pp.43–48), 20–22 September 2012, Subotica, Serbia.

  394. Ospennikova, A., Ershov, M., & Iljin, I. (2015). Educational robotics as an inovative educational technology. Social and Behavioral Sciences,214, 18–26.

    Google Scholar 

  395. Ou, C. S., Liu, F. C., Hung, Y. C., & Yen, D. C. (2010). A structural model of supply chain management on firm performance. International Journal of Operations & Production Management,30, 526–545.

    Google Scholar 

  396. Oztemel, E. (2010). Intelligent manufacturing systems. In L. Benyoucef & B. Grabot (Eds.), Artificial intelligence techniques for networked manufacturing enterprises management, chapter 1. Berlin: Springer. ISBN 978-1-84996-118-9.

    Google Scholar 

  397. Oztemel, E. (2015). Special issue on “Current progress of intelligent technologies, for manufacturing society”. Journal of Intelligent Manufacturing,26, 959–960.

    Google Scholar 

  398. Oztemel, E., & Tekez, K. (2009a). A general framework of a reference model for intelligent integrated manufacturing systems (REMIMS). Engineering Applications of Artificial Intelligence,22(6), 855–864.

    Google Scholar 

  399. Oztemel, E., & Tekez, E. (2009b). Integrating manufacturing systems through knowledge exchange protocols within an agent based knowledge network. Robotics and Computer-Integrated Manufacturing,25(1), 235–245.

    Google Scholar 

  400. Oztemel, E., & Tekez, E. (2009c). Knowledge protocols. In M. M. Cunha, E. F. Olivera, A. J.Tavares, & L. G.Ferreira (Eds.), Handbook of research on social dimensions of semantic technologies and web services (pp. 304–324). ISBN: 978-1-60566-650-1, Chapter 15, IGI Global, USA, PA.

  401. Paelke, V. (2014). Augmented reality in the smart factory: Supporting workers in an industry 4.0. Environment, emerging technology and factory automation (ETFA) (pp. 1–4).

  402. Pagell, M., & Shevchenko, A. (2014). Why research in sustainable supply chain management should have no future. Journal of Supply Chain Management,50(1), 44–55.

    Google Scholar 

  403. Palanisamy, R. (2008). Organizational culture and knowledge management in ERP implementation: An empirical study. Journal of Computer Information Systems,48(2), 100–120.

    Google Scholar 

  404. Pan, M., & Kraft, M. (2015). Applying industry 4.0 to the Jurong Island eco-park. Energy Procedia,75, 1536–1541.

    Google Scholar 

  405. Pandey, R. K., & Panda, S. S. (2015). Optimization of bone drilling using Taguchi methodology coupled with fuzzy based desirability function approach. Journal of Intelligent Manufacturing,26, 1121–1129.

    Google Scholar 

  406. Pandya, A., Siadat, M., & Auner, G. (2005). Design, implementation and accuracy of a prototype for medical augmented reality. Computer Aided Surgery,10(1), 23–35.

    Google Scholar 

  407. Pang, Z. (2013). Technologies and architectures of the ınternet-of-things (IoT) for health and well-being. Doctoral dissertation, KTH Royal Institute of Technology. https://pdfs.semanticscholar.org/222d/206e8fc758c19ac06680db61a555fd6b71ed.pdf.

  408. Pang, Z., Chen, Q., & Zheng, L. (2012). Value creation, sensor portfolio and information fusion of internet-of-things solutions for food supply chains. Information Systems Frontiers, Information Systems Fronties,17, 289–319.

    Google Scholar 

  409. Papadakis, L., Schober, A., & Zaeh, M. (2013). Considering manufacturing effects in automotive structural crashworthiness: A simulation chaining approach. International Journal of Crashworthiness,18(3), 276–287.

    Google Scholar 

  410. Park, H., Kim, H., Joo, H., & Song, J. (2016). Recent advancement in the IOT related standards a one M2M perspective. ICT Express,2(3), 126–129.

    Google Scholar 

  411. Park, J. (2010). A smart factory operation method for a smart grid, information systems engineering. In 2010 40th international conference on computers and industrial engineering (CIE), 25–28 July 2010, Awaji, Japan.

  412. Park, S. (2016). Development of innovative strategies for the Korean manufacturing industry by use of the connected smart factory. Computer Science,91(2016), 744–750.

    Google Scholar 

  413. Parkhi, S., Joshi, S., Gupta, S., & Sharma, M. (2015). a study of evolution and future of supply chain management. Supply Chain Management,9, 95–106.

    Google Scholar 

  414. ParsiFAI. (2017). https://www.festo.com/group/en/cms/12002.htm. Available on August 30, 2017.

  415. Pence, H. (2010). Smartphones, smart objects, and augmented reality. The Reference Librarian,52(1), 136–145.

    Google Scholar 

  416. Peng, Q., Chung, C., Yu, C., & Luan, T. (2007). A networked virtual manufacturing system for SMEs. International Journal of Computer Integrated Manufacturing,20, 71–79.

    Google Scholar 

  417. Peng, Y., Xie, D., & Shemshadi, A. (2013). A network storage framework for internet of things. Computer Science,19, 1136–1141.

    Google Scholar 

  418. Peres, R., Parreira-Rocha, M., Rocha, A., Barbosa, J., Leitão, P., & Barata, J.(2016). Selection of a data exchange format for industry 4.0 manufacturing systems, industrial electronics society. In IECON 201642nd annual conference of the IEEE (pp. 5723–5728), 23–26 October 2016, Florence, Italy.

  419. Perkinsa, C., & Mullera, G. (2015). Using discrete event simulation to model attacker interactions with cyber and physical security systems. Procedia Computer Science,61, 221–226.

    Google Scholar 

  420. Persson, M., & Håkansson, A. (2015). A communication protocol for different communication technologies in cyber-physical system. Engineering Services,60, 1697–1706.

    Google Scholar 

  421. Petnga, L., & Austin, M. (2013). Ontologies of time and time-based reasoning for MBSE of cyber-physical systems. Procedia Computer Science,16, 403–412.

    Google Scholar 

  422. Pfohl, H., & Yahsi, B. (2016). The impact of industry supply chain. Published in: Innovations and strategies for logistics an Wolfgang Kersten, Thorsten Blecker and Christian M. Ri, Vol. 2, pp. 120–131, Proceedings of the Hamburg International Conference of Logistics (HICL) ISBN (online): 978-3-7375-4059-9, 4430.

  423. Piccialli, F., Benedusi, P., & Amato, F. (2017). S-InTime: A social cloud analytical service oriented system. Future Generation Computer Systems,45, 699–705.

    Google Scholar 

  424. Pimenov, D. Y., Bustillo, A., & Mikolajczyk, T. (2018). Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. Journal of Intelligent Manufacturing,29, 1045–1061.

    Google Scholar 

  425. Pisching, M. A., Junqueira, F., Santos Filho, D. J., & Miyagi, P. E., (2015). An architecture for organizing and locating services to the industry 4.0. In Proceedings of 23rd ABCM international congress of mechanical engineering (pp. 1–4).

  426. Plansee. (2017). Industry 4.0 project. https://www.plansee.com/en/news-archive/News/detail/research-project-on-industry-40-shaping-the-future-together.html. Available on August 28, 2017.

  427. Plattform Industry 4.0. (2014). Plattform industry 4.0. http://ec.europa.eu/information_society/newsroom/image/document/2016-27/10__pi40_diemer_16494.pdf. Available on August 28, 2017.

  428. PNC. (2016). PNC industry 4.0 report. https://www.pnc.com/content/dam/pnc-ideas/articles/insurance-industry-article.pdf. Available on August 28, 2017.

  429. Poghosyana, G., Pefkianakisb, I., Guyadecc, P., & Christophidesd, V. (2016). Mining usage patterns in residential intranet of things. Computer Science,83(2016), 988–993.

    Google Scholar 

  430. Pokharel, S., & Mutha, A. (2009). Perspectives in reverse logistics: A review. Resources, Conservation and Recycling,53, 175–182.

    Google Scholar 

  431. Pollock, N., & Cornford, J. (1999). Customizing manufacturing system for universities. International Journal of Mass Customization,4(3), 171–194.

    Google Scholar 

  432. Potts, J., & Cunningham, S. (2008). Four models of creative industries. International Journal of Cultural Policy,14(3), 233–247.

    Google Scholar 

  433. Prajogo, D., Chowdhury, M., Yeung, A. C., & Cheng, T. C. E. (2012). The relationship between supplier management and firm’s operational performance: A multi-dimensional perspective. International Journal of Production Economics,136, 123–130.

    Google Scholar 

  434. Prinz, C., Morlock, F., Freith, S., Kreggenfeld, N., Kreimeier, D., & Kuhlenkötter, B. (2016). Learning factory modules. Procedia CIRP,54, 113–118.

    Google Scholar 

  435. Puttonen, J., Lobov, A., Soto, M., & Lastra, M. L. (2016). Cloud computing as a facilitator for web service composition in factory automation. Journal of Intelligent Manufacturing,27, 689–700.

    Google Scholar 

  436. Qiao, D. (2009). The impact of QoS constraints on the energy efficiency of fixed-rate wireless transmissions. IEEE Transactions on Wireless Communications,8, 5957–5969.

    Google Scholar 

  437. Qin, J., Liu, Y., & Grosvenor, R. (2016). A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP,52, 173–178.

    Google Scholar 

  438. Qiu, X., Luo, H., Xu, G., Zhong, R., & Huang, G. Q. (2015). Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP). International Journal of Production Economics,159, 4–15.

    Google Scholar 

  439. Qiuping, W., Shunbinga, Z., & Chunquan, D. (2011). Study on key technologies of internet of things perceiving mine. Procedia Engineering,2011, 2326–2333.

    Google Scholar 

  440. Radziwon, A., Bilberg, A., Bogers, M., & Madsen, E. S. (2014). The smart factory: Exploring adaptive and flexible manufacturing solutions. Procedia Engineering,69, 1184–1190.

    Google Scholar 

  441. Rago, F. (2015). A smart adaptable architecture based on contexts for cyber physical systems. Engineering Services,61, 301–306.

    Google Scholar 

  442. Ramezani, J., & Jassbi, J. (2017). A hybrid expert decision support system based on artificial neural networks in process control of plaster production—An industry 4.0 perspective, technological innovation for smart systems. IFIP advances in information and communication technology (Vol 499, pp. 55–71).

  443. Ranjan, A., & Hussain, M. (2016). Terminal authentication in M2M communications in the context of internet of things. Computer Science,89(2016), 34–42.

    Google Scholar 

  444. Rashid, M., Riaz, Z., Turan, E., Haskilic, V., Sunje, A., & Khan, N. (2012). Smart factory: E-business perspective of enhanced ERP in aircraft manufacturing industry. In 2012 Proceedings of technology management for emerging technologies (PICMET’12) (pp. 3262–3275), 29 July–2 August 2012, Vancouver, BC, Canada.

  445. Raza, S., Misra, P., He, Z., & Voigt, T. (2017). Building the internet of things with bluetooth smart. AdHoc Networks,57, 19–31.

    Google Scholar 

  446. Remon, D. (2017). Smart factory: Reducing maintenance costs and ensuring quality in the manufacturing process. http://www.libelium.com/smart-factory-reducing-maintenance-costs-ensuring-quality-manufacturing-process/. Available on August 22, 2017.

  447. Rennunga, F., Luminosua, C., & Draghicia, A. (2016). Service provision in the framework of industry 4.0. Behavioral Science,221, 372–377.

    Google Scholar 

  448. Reuter, T. (2016). Kuka industry 4.0 research, KUKA Aktiengesellschaft Zugspitzstraße 140, Augsburg, Vol. 1, pp. 1–50 (in German).

  449. Richert, A., Shehadeh, M., Plumanns, M, Groß, K., Schuster, K., & Jeschke, S. (2016). Educating engineers for industry 4.0: Virtual worlds and human–robot-teams empirical studies towards a new educational age. In Global engineering education conference (EDUCON), 2016 IEEE, 10–13 April 2016, Abu Dhabi, United Arab Emirates.

  450. Riedl, M., Zipper, H., Meier, M., & Diedric, C. (2014). Cyber-physical systems alter automation architectures. Annual Reviews in Control,38, 123–133.

    Google Scholar 

  451. Riel, A., & Flatscher, M. (2017). A design process approach to strategic production planning for industry 4.0. In European conference on software process improvement (pp. 323–333).

  452. Rihab, C., Ellouze, F., Koubaa, A., Qureshi, B., Preira, N., Youssef, H., et al. (2016). Cyber-physical systems clouds: A survey. Computer Networks,108, 260–278.

    Google Scholar 

  453. Risso, N. A., Neyem, A., Benedetto, J., Carillo, M., Farias, A., Gajordo, M., et al. (2016). A cloud-based mobile system to improve respiratory therapy services at home. Journal of Biomedical Informatics,94, 467–479.

    Google Scholar 

  454. Rosas, J. C., Aguilar, J. A., Tripp-Barba, C., Espinosa, R., & Aguilar P. (2017). A mobile sensor fire prevention system based on the internet of things. In International conference on computational science and its applications (pp. 274–283).

  455. Rosendahl, R., Schmidt, N., Lüder, A., & Ryashentseva, D. (2016). Industry 4.0 value networks in legacy systems. In IEEE 20th conference on emerging technologies & factory automation (ETFA) (pp. 1–4), 8–11 September 2015, Luxembourg.

  456. RTI. (2014). https://www.slideshare.net/RealTimeInnovations/342488-io-t-influence. Available on August 30, 2017.

  457. Ruivo, P., Johansson, B., Oliveira, T., & Netoa, M. (2013). Commercial ERP systems and user productivity: A study across European SMEs. Procedia Technology,9(2013), 84–93.

    Google Scholar 

  458. Ruivo, P., Mestrea, A., Johanssonb, B., & Oliveira, T. (2014). Defining the ERP and CRM integrative value. In Conference on enterprise information systems (CENTERIS) (Vol 16, pp. 704–709).

  459. Ruivo, P., Oliveira, T., & Neto, M. (2012). ERP post-adoption: Value impact on firm performance. In 7th Iberian conference on information systems and technologies (CISTI) (pp. 1–6), 20–23 June 2012, Madrid, Spain.

  460. Ruiz, A., Canovas, O., & Lopez-de-Teruel, P. (2013). A vision-enhanced multi-sensor LBS suitable for augmented reality applications. Journal of Location Based Services,7(3), 145–164.

    Google Scholar 

  461. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., et al. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group. https://www.bcg.com/publications/2015/engineered_products_project_business_industry_4_future_productivity_growth_manufacturing_industries.aspx. Available on December 28, 2017.

  462. Sacala, I., & Moisescu, M. (2015). Cyber physical systems oriented robot development platform. Engineering Services,65, 203–209.

    Google Scholar 

  463. Sachsenmeier, P. (2016). Industry 5.0—The relevance and implications of bionics and synthetic biology. Engineering,2, 225–229.

    Google Scholar 

  464. Sadrzadehrafieia, S., Chofrehb, S., Hosseinia, N., & Sulaimana, R. (2013). The benefits of enterprise resource planning (ERP) system implementation in dry food packaging industry. International Conference on Electronics Engineering and Informatics,11, 220–226.

    Google Scholar 

  465. Safari, H., Faraji, Z., & Majidian, S. (2016). Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR. Journal of Intelligent Manufacturing,27, 475–486.

    Google Scholar 

  466. Sah, P. (2016). Saving environment using internet of things: Challenges and the possibilities. Advances in Internet of Things,6, 55–64.

    Google Scholar 

  467. Saikrishna, P., & Pasumarthy, R. (2016). Multi-objective switching controller for cloud computing systems. Control Engineering Practice,57, 72–83.

    Google Scholar 

  468. Samani, A., Ghenniva, H., & Wahaishi, A. (2015). Privacy in internet of things: A model and protection framework. Computer Science, Lecture Notes in Computer Science,52, 606–613.

    Google Scholar 

  469. Samaniego, M., & Deters, R. (2016). Management and internet of things. Computer Science,94, 137–143.

    Google Scholar 

  470. Sampaio, A. Z., & Rosário, D. (2012). Virtual reality technology applied on maintenance of painted walls of buildings. Journal of Software Engineering and Applications,5, 297–303.

    Google Scholar 

  471. Sangmahachai, K. (2015). Kasetsart energy and technology management center. http://www.wise.co.th/wise/Knowledge_Bank/References/Everything_4/Revolution_to_Industry_4.pdf. Available on August 30, 2017.

  472. Sangregorio, P., Cologni, A. L., Owen, F. C., & Previdi, F. (2015). Remote maintenance system for semi-automated manufacturing machines. In 2015 IEEE 1st international forum on research and technologies for society and industry leveraging a better tomorrow (RTSI) (pp. 457–461), 16–18 September 2015, Turin, Italy.

  473. Santosa, A., Macedoa, J., Costaa, A., & Nicolau, M. (2014). Internet of things and smart objects for M-health monitoring and control. Procedia Technology,16, 1351–1360.

    Google Scholar 

  474. Sasikala, B., Rajanarajana, M., & Geethavani, B. (2017). Internet of things: A survey on security issues analysis and countermeasures. International Journal of Engineering and Computer Science,6(5), 21435–21442.

    Google Scholar 

  475. Scheer, S. (2013). Industry 4.0: Wie sehen Produktionsprozesse im Jahr 2020, e-book, published by AWS-Institute for Digitized Products and Processes, ISBN: 978-398-1583-328 (in Germany).

  476. Scheuermann, C., Verclas, S., & Bruegge, B. (2015). Agile factory—An example of an industry 4.0 manufacturing process, cyber-physical systems. In IEEE 3rd international conference on networks, and applications (CPSNA) (pp. 43–47), 19–21 August 2015, Hong Kong, China.

  477. Schlick, J. (2014). Industry 4.0 in der praktischen Anwendung. In T. Bauernhansl, M. ten Hompel, & B. Vogel-Heuser (Eds.), Industry 4. 0 in Produktion, Automatisierung und Logistik (Vol. 4, pp. 57–84). Anwendung, Technologien und Migration (in German).

  478. Schouh, G., Gartzen, T., & Marks, A. (2015). Promoting work-based learning through industry 4.0. CIRP Conference on Learning Factorie,32, 82–87.

    Google Scholar 

  479. Schuh, G., Pitscha, M., Rudolfa, S., Karmanna, W., & Sommera, M. (2014a). Modular sensor platform for service-oriented cyber-physical systems in the European tool making industry. Engineering Services,17, 374–379.

    Google Scholar 

  480. Schuh, G., Potente, T., Wesch-Potente, C., Weber, A. R., & Prote, J. P. (2014b). Collaboration mechanisms to increase productivity in the context of industrie 4.0. Procedia CIRP,19, 51–56.

    Google Scholar 

  481. Schuhmacher, J., & Hummel, V. (2016). Decentralized control of logistic processes in cyber-physical production systems at the example of ESB logistics learning factory. Procedia CIRP,54, 19–24.

    Google Scholar 

  482. Schumacher, A., Erol, S., & Sihna, W. (2016). A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. Reconfigurable and Virtual Production,52, 161–166.

    Google Scholar 

  483. Schumann, A. (1999). Integrated production control for batch plants. European Control Conference. https://doi.org/10.23919/ECC.1999.7100101.

    Article  Google Scholar 

  484. Schweer, D., & Sahl, J. C. (2017). The digital transformation of industry—The benefit for Germany. In The drivers of digital transformation (Vol. 10, pp. 23–31). Springer.

  485. Sedera, D., & Gable, G. G. (2010). Knowledge management competence for enterprise system success. The Journal of Strategic Information Systems,19(4), 296–306.

    Google Scholar 

  486. Seethamraju, R., & Sundar, D. (2013). Influence of ERP systems on business process agility. Management Review,25(3), 137–149.

    Google Scholar 

  487. Seitza, K., & Nyhuis, P. (2015). Cyber-physical production systems combined with logistic models—A learning factory concept for an improved production planning and control. In The 5th conference on learning factories (Vol. 32, pp. 92–97).

  488. Sena, D., Ozturk, M., & Vayvay, O. (2016). An overview of big data for growth in SMEs. Social and Behavioral Sciences,235, 159–167.

    Google Scholar 

  489. Seok, H., & Nof, S. (2018). Intelligent information sharing among manufacturers in supply networks: Supplier selection case. Journal of Intelligent Manufacturing,29, 1097–1113.

    Google Scholar 

  490. Shafiq, S. I., Sanin, C., Toro, C., & Szczerbicki, E. (2015). Virtual engineering object (VEO): Toward experience-based design and manufacturing for industry 4.0. Cybernetics and Systems,46, 35–50.

    Google Scholar 

  491. Shah, M. (2016). Big data and the internet of things. In Big data analysis: New algorithms for a new society (pp. 207–237). Springer.

  492. Shah, L. A., Etienne, A., Siadat, A., & Vernadat, F. (2016). Decision-making in the manufacturing environment using a value-risk graph. Journal of Intelligent Manufacturing,27, 617–630.

    Google Scholar 

  493. Shahabi, C., Kashani, F., Khoshgozaran, A., Nocera, L., & Xing, S. (2010). GeoDec: A framework to effectively visualize and query geospatial data for decision-making. IEEE Multi Media,10(99), 1–11.

    Google Scholar 

  494. Shaikh, F. K., Zeadally, S., & Exposito, E. (2017). Enabling technologies for green internet of things. IEEE Systems Journal,11(2), 983–994.

    Google Scholar 

  495. Shallock, B., Rybski, C., Jochem, R., & Kohl, H. (2018). Learning factory for industry 4.0 to provide future skills beyond technical training. Procedia Manufacturing,23, 27–32.

    Google Scholar 

  496. Shamsuzzoha, A., Ferreira, F., Azevado, A., & Helo, P. (2016). Collaborative smart process monitoring within virtual factory environment: An implementation issue. International Journal of Computer Integrated Manufacturing,30(1), 167–181.

    Google Scholar 

  497. Shaoshuai, F., Wenxiao, S., Nan, W., & Yan, W. (2011). MODM-based evaluation model of service quality in the internet of things. Procedia Environmental Sciences,11(Part A), 63–69.

    Google Scholar 

  498. Shariatzadeh, N., Lundholma, T., Lindberga, L., & Sivarda, G. (2016). Integration of digital factory with smart factory based on Internet of Things. CIRP,50(2016), 512–517.

    Google Scholar 

  499. Sharma, A., & Gupta, S. (2014). Identifying the role of ERP in enhancing operational efficiency and supply chain mobility in aircraft manufacturing industry. In 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (pp. 330–333), 7–8 February 2014, Ghaziabad, India.

  500. Sharma, Y., Javadi, B., Si, W., & Sun, D. (2016). Reliability and energy efficiency in cloud computing systems: Survey and taxonomy. Journal of Network and Computer Applications,74, 66–85.

    Google Scholar 

  501. Sherbini, K., & Krawczyk, R. (2004). Overview of intelligent architecture. In 1st ASCAAD international conferencee-design in architecture KFUPM (pp. 137–152), December 2004, Dhahran, Saudi Arabia.

  502. Shi, Y., Lin, L., Zhou, C., Zhu, M., Xie, L., & Chai, G. (2017). A study of an assisting robot for mandible plastic surgery based on augmented reality. Minimally Invasive Therapy and Allied Technologies,26(1), 23–30.

    Google Scholar 

  503. Shrimali, R., Shah, H., & Chauhan, R. (2017). Proposed caching scheme for optimizing trade-off between freshness and energy consumption in name data networking based IoT. Advances in Internet of Things,7, 11–24.

    Google Scholar 

  504. Shrouf, F., & Miragliotta, G. (2015). Energy management based on Internet of Things: Practices and framework for adoption in production management. Journal of Cleaner Production,100, 235–246.

    Google Scholar 

  505. Shrouf, F., Ordieres, J., & Miragliotta, G. (2014). Smart factories in industry 4.0: A review of the concept and of energy management approached in production based on the internet of things paradigm. In IEEE international conference onindustrial engineering and engineering management (IEEM) (pp. 697–701), 9–12 December 2014, Sunway, Malaysia.

  506. Siddiqa, A., Hassem, A., Yaqoob, A., Marjani, M., Shamshirband, S., Gani, A., et al. (2016). A survey of big data management: Taxonomy and state of the art. IEEE Network,29(5), 6–9.

    Google Scholar 

  507. Silva, E., & Maló, P. (2014). IoT testbed business model. Advances in Internet of Things,4, 37–45.

    Google Scholar 

  508. Singh, A., & Chatterjee, K. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications,79, 88–115.

    Google Scholar 

  509. Sipsas, K., Alexopoulos, K., Xanthakis, V., & Chryssolouris, G. (2016). Collaborative maintenance in flow-line manufacturing environments: An Industry 4.0 approach. Research and Innovation for Future Production,55, 236–241.

    Google Scholar 

  510. Smara, M., Aliouat, M., Pathan, A., & Aliout, Z. (2017). Acceptance test for fault detection in component-based cloud computing and systems. Future Generation Computer Systems,70, 74–93.

    Google Scholar 

  511. SmartFactoryKL. (2014). Keyfinder production line. http://smartfactory.dfki.uni-kl.de/en/content/demo/technological-demo/plant-industry4. Available on August 28, 2017.

  512. Smirnova, A., Kashevnika, A., & Ponomarev, A. (2015). Multi-level self-organization in cyber-physical-social systems: Smart home cleaning scenario. Manufacturing System,30, 329–334.

    Google Scholar 

  513. SMLC. (2011). Implementing 21st century smart manufacturing. Workshop summary report, https://smartmanufacturingcoalition.org/sites/default/files/implementing_21st_century_smart_manufacturing_report_2011_0.pdf. Available on August 28, 2017.

  514. SMT. (2017). http://www.asm-smt.com/en/asm-smt/smart-factory. Available on August 30, 2017.

  515. Sogoti. (2014). Industry 4.0 report. https://www.fr.sogeti.com/globalassets/global/downloads/reports/vint-research-3-the-fourth-industrial-revolution. Available on August 22, 2017.

  516. Song, T., Li, R., Mei, B., Yu, J., Xing, X., & Cheng, X. (2017). A privacy preserving communication protocol for IoT applications in smart homes. IEEE Internet of Things Journal,4, 1844–1852.

    Google Scholar 

  517. Song, Z., & Niu, D. (2017). Focus on the current competitiveness of local coal industry in china. Resources Policy,51, 172–182.

    Google Scholar 

  518. Sookhak, M., Gani, A., Khan, M., & Buyya, R. (2017). Dynamic remote data auditing for securing big data storage in cloud computing. Information Science,380, 101–116.

    Google Scholar 

  519. SOPHIE. (2017). Industry 4.0 project. https://www.simplan.de/en/press/press-reports/562-pr15-research-project-sopie.html. Available on August 28, 2017.

  520. Sotiriadis, S., & Bessis, N. (2017). An inter-cloud bridge system for heterogeneous cloud platforms. Future Generation Computer Systems,54, 180–194.

    Google Scholar 

  521. Spath, D., Gerlach, S., Hämmerle, M., Schlund, S., & Strölin, T. (2013). Cyber-physical system for self-organised and flexible labour utilisation. https://blog.iao.fraunhofer.de/images/blog/paper-cps.pdf. Available on August 28, 2017.

  522. Spezzano, G., & Vinci, A. (2015). Pattern detection in cyber-physical systems. Engineering Services,52, 1016–1021.

    Google Scholar 

  523. Steele, R., & Clarke, A. (2013). The internet of things and next-generation public health information systems, communications and network, robot in industry 4.0 environment. Procedia CIRP,5, 4–9.

    Google Scholar 

  524. Stergiou, C., Psannis, K. E., Kim, B., & Gupta, B. (2018). Secure integration of IoT and cloud computing. Future Generation Computer Systems,78(3), 964–975.

    Google Scholar 

  525. Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP,40, 536–541.

    Google Scholar 

  526. Strozzi, F., Colicchia, C., Creaazza, A., & Noe, C. (2017). Literature review on the ‘Smart Factory’ concept using bibliometric tools. International Journal of Production Research. https://doi.org/10.1080/00207543.2017.1326643.

    Article  Google Scholar 

  527. Suh, Y., & Lee, H. (2017). Developing ecological index for identify roles of ICT Industry in mobile ecosystems. Telematics İnformatics,34(1), 425.

    Google Scholar 

  528. Sun, C. (2012). Application of RFID technology for logistics on internet of things. Procedia Computer Science,1, 106–111.

    Google Scholar 

  529. Sun, H., Ni, W., & Lam, R. (2015). A step-by-step performance assessment and improvement method for ERP implementation: Action case studies in Chinese companies. Computers in Industry,68, 40–52.

    Google Scholar 

  530. Sundmaeker, H., Guillemin, P., Friess, P., & Woelfflé, S. (2010). Vision and challenges for realising the Internet of Things. Cluster of European Research Projects on the Internet of Things, European Commision,3, 34–36.

    Google Scholar 

  531. Tajiki, M., Akbari, B., & Mokari, N. (2017). Optimal Qos-aware network reconfiguration in software defined cloud data centers. Computer Networks,120, 71–86.

    Google Scholar 

  532. Tamang, P., & Kumar, P. (2015). A DBMS based inventory model and its timeframe study in automobile spare parts import management. In 9th International conference on software, knowledge, information management and applications (SKIMA) (pp. 321–328), 15–17 December 2015, Kathmandu, Nepal.

  533. Tang, H., Li, X., Guo, S., Liu, S., Lang, L., & Huang, L. (2017). An optimizing model to solve the nesting problem of rectangle pieces based on genetic algorithm. Journal of Intelligent Manufacturing,28, 1817–1826.

    Google Scholar 

  534. Tao, C., & Gao, J. (2017). On building a cloud based mobile testing infrastructure service system. Journal of Systems and Software,124, 39–55.

    Google Scholar 

  535. Tarimoradi, M., Zarandi, M. H., Zaman, H., & Turksan, B. (2017). Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: An agent-based optimization state of the art. Journal of Intelligent Manufacturing,28, 1551–1579.

    Google Scholar 

  536. Tavana, M., Mirzagoltabar, H., Mirhedayatian, S. M., Saen, R. F., & Azadi, M. (2013). A new network epsilon-based DEA model for supply chain performance evaluation. Computers & Industrial Engineering,66(2), 501–513.

    Google Scholar 

  537. Tekez, E., & Taşdeviren, G. (2016). A model to assess leanness capability of enterprises. Procedia Computer Science,100, 776–781.

    Google Scholar 

  538. Testa, F., & Iraldo, F. (2010). Shadows and lights of GSCM (Green Supply Chain Management): Determinants and effects of these practices based on a multi-national study. Journal of Cleaner Production,18, 953–962.

    Google Scholar 

  539. Thames, L., & Schaefer, D. (2016). Software-defined cloud manufacturing for industry 4.0. Reconfigurable & Virtual Production,52, 12–17.

    Google Scholar 

  540. Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, C., Lundholm, T., et al. (2016). An event-driven manufacturing information system architecture for Industry 4.0. International Journal of Production Research,55, 1297–1311.

    Google Scholar 

  541. Thompsona, K., & Kadiyalab, R. (2016). Making water systems smarter using M2M technology. Computer Science,89, 437–443.

    Google Scholar 

  542. Thuemmler, C., & Bai, C. (2017). Health 4.0: Application of industry 4.0 design principles in future asthma management. In Health 4.0: How virtualization and big data are revolutionizing healthcare (pp. 23–37).

  543. Tong, L., Yancun, W., & Junjiaou, W. (2016). Capacity analysis of an iron foundry fettling-shop, using virtual manufacturing technology. International Journal of Cast Metals Research,16, 329–332.

    Google Scholar 

  544. Trieu, V. (2017). Getting value from business intelligence systems: A review and research agenda. Decision Support Systems,93, 117–124.

    Google Scholar 

  545. Tripathy, A., & Tripathy, P. (2018). Fuzzy QoS requirement-aware dynamic service discovery andadaptation. Applied Soft Computing,68, 136–146.

    Google Scholar 

  546. Tsai, C., Lin, W., & Ke, S. (2016). Big data mining with parallel computing: A comparison of distributed and MapReduce methodologies. The Journal of Systems and Software,122, 83–92.

    Google Scholar 

  547. Tsai, W., Chu, P., Chang, T., Lee, H., & Huang, H. (2015). The impact of IT governance on performance of IFRS conversion under ERP systems. In 2015 IEEE international conference on industrial engineering and engineering management (IEEM) (pp. 626–630), 6–9 December 2015, Singapore.

  548. Tsai, W., Tsaur, T., Chou, T., Liu, T., & Hsu, J. (2009). Evaluating the information systems success of ERP implementation in Taiwan’s industries. In IEEE international conference on industrial engineering and engineering management (pp. 1815–1819), 8–11 December 2009, Hong Kong, China.

  549. Tuncel, C., & Polat, A. (2016). Sectoral system of innovation and sources of technological change in machinery industry: An investigation on Turkish machinery industry. Innovation and Business Management,229, 214–225.

    Google Scholar 

  550. TUSIAD. (2016). Tusiad industry 4.0 in turkey as an imperative for global competitiveness an ermerging market perspective. http://tusiad.org/tr/yayinlar/raporlar/item/download/7848_180faab86b5ec60d04ec929643ce6e45. Available on August 28, 2017.

  551. UK Government Office. (2016). Education report of IOT technology. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/409774/14-1230-internet-of-things-review.pdf. Available on August 28, 2017.

  552. UNITY. (2015). Industry 4.0 report. https://www.unity.de/fileadmin/Insights/OPPORTUNITY/OPPORTUNITY_Seize_OPPORTUNITY_Industrie_4.0.pdf. Available on August 28, 2017.

  553. Vachon, S., & Klassen, R. D. (2008). Environmental management and manufacturing performance: The role of collaboration in the supply chain. International Journal of Production Economics,111, 299–315.

    Google Scholar 

  554. Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0—Glimpse. Procedia Manufacturing,20, 233–238.

    Google Scholar 

  555. Vallsa, M., Calva, C., Puenteb, J., & Alonsob, A. (2017). Adjusting middleware knobs to assess scalability limits of distributed cyber-physical systems. Computer Standards,51, 95–103.

    Google Scholar 

  556. Vandaie, R. (2008). The role of organizational knowledge management in successful ERP implementation projects. Knowledge-Based Systems,21, 920–926.

    Google Scholar 

  557. Verdouw, C., Wolfert, J., & Beulens, A. (2015). Virtualization of food supply chain with internet of things. Journal of Food Engineering,176, 128–136.

    Google Scholar 

  558. Vermesan, O., & Friess, P. (2013). Internet of things: Converging technologies for smart environments and integrated ecosystems. River Publisher http://www.internet-of-things-research.eu/pdf/Converging_Technologies_for_Smart_Environments_and_Integrated_Ecosystems_IERC_Book_Open_Access_2013.pdf. Available on November 28, 2017.

  559. Villani, V., Pini, F., Leani, F., & Secchi, C. (2018). Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications. Mechatronics. https://doi.org/10.1016/j.mechatronics.2018.02.009.

    Article  Google Scholar 

  560. Vincent, H., Wells, L., Tarazaga, P., & Camelio, J. (2015). Trojan detection and side-channel analyses for cyber-security in cyber-physical manufacturing systems. Procedia Manufacturing,1, 77–85.

    Google Scholar 

  561. Virkki, J., & Chen, L. (2013). Personal perspectives: Individual privacy in the IOT. Advances in Internet of Things,3, 21–26.

    Google Scholar 

  562. Viswanadham, N. (2002). The past, present, and future of supply-chain automation. IEEE Robotics and Automation Magazine,9, 48–56.

    Google Scholar 

  563. Vlacheas, P., Giaffreda, R., Stavroulaki, V., Kelaidonis, D., Foteinos, V., Poulios, G., et al. (2013). Enabling smart cities through a cognitive management framework for the internet of things. IEEE Communications Magazine,51, 102–111.

    Google Scholar 

  564. Vogel-Heuser, B., & Hess, D. (2016). Guest editorial: Industry 4.0–prerequisites and visions. IEEE Transactions on Automation Science and Engineering,13, 411–413.

    Google Scholar 

  565. Vogel-Heuser, B., Rösch, S., Fischer, J., Simon, T., Ulewicz, S., & Folmer, J. (2016). Fault handling in PLC-based industry 4.0 automated production systems as a basis for restart and self-configuration and its evaluation. Journal of Software Engineering and Applications,9, 1–43.

    Google Scholar 

  566. Wamba, S., Gunasekaran, A., Akter, S., Ren, S., Dubey, R., & Childe, S. (2017). Big data analytics and firm performance. Journal of Business Research,70, 356–365.

    Google Scholar 

  567. Wan, J., Suo, H., Yan, H., & Liu, J. (2011). A general test platform for cyber-physical systems: Unmanned vehicle with wireless sensor network navigation. In 2011 International conference on advances in engineering, Procedia Engineering (Vol. 24, pp. 123–127).

  568. Wang, F., & Chen, K. (2013). Virtual manufacturing to design a manufacturing technology for components made of a multiphase perfect material. Computer-Aided Design and Applications,40(7), 837–846.

    Google Scholar 

  569. Wang, H., Xu, Z., & Witold, A. (2017a). An overview on the roles of fuzzy set techniques in big data processing: Trends, challenges and opportunities. Knowledge-Based Systems,118, 15–30.

    Google Scholar 

  570. Wang, H., Yang, D., Qi, Yu., & Tao, Y. (2018b). Integrating modified cuckoo algorithm and creditability evaluation for QoS-aware service composition. Knowledge Based Systems,140, 64–81.

    Google Scholar 

  571. Wang, S., Wan, S., Zhang, D., Li, D., & Zhang, C. (2015b). 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.

    Google Scholar 

  572. Wang, S., Zhang, C., & Wan, J. (2016). A smart factory solution to hybrid production of multi-type products with reduced intelligence. In Information technology, networking, electronic and automation control conference, IEEE (pp. 848–853), 20–22 May 2016, Chongqing, China.

  573. Wang, S., Zhao, Y., Huang, L., Xu, J., & Hsu, C. (2018c). QoS prediction for service recommendations in mobile edge computing. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2017.09.014.

    Article  Google Scholar 

  574. Wang, T., & Wen, Q. (2017). A key agreement protocol based-on object identifier for Internet of Things. Advanced in Control Engineering and Information Science,15, 1787–1791.

    Google Scholar 

  575. Wang, X., & Chen, R. (2009). An experimental study on collaborative effectiveness of augmented reality potentials in urban design. Co-Design,5(4), 229–244.

    Google Scholar 

  576. Wang, X., Lv, J., Huang, M., Li, K., Li, J., & Ren, K. (2018a). Energy-efficient ICN routing mechanism with QoS support. Computer Networks,131, 38–51.

    Google Scholar 

  577. Wang, X., Zhu, Y., Ha, Y., Qui, M., Huang, T., Si, X., et al. (2017b). An energy-efficient system on a programmable chip platform for cloud applications. Journal of Systems Architecture,76, 117–132.

    Google Scholar 

  578. Wang, W., Tian, Y., Gong, X., Qi, Q., & Hu, Y. (2015a). Software defined autonomic QoS mode for future Internet. The Journal of Systems and Software,110, 122–135.

    Google Scholar 

  579. Wanka, J. (2015). Industrie 4.0Innovationnen für die produktion von morgen. https://www.bmbf.de/pub/Industrie_4.0.pdf. Available on November 28, 2017 (in German).

  580. Wardell, D., Mills, R., Peterson, G., & Oxley, M. (2016). A method for revealing and addressing security vulnerabilities in cyber-physical systems by modeling malicious agent interactions with formal verification. Engineering Services,95, 24–31.

    Google Scholar 

  581. WEF. (2015). Deep shift technology tipping points and societal impact, world economic forum. Survey report. http://www3.weforum.org/docs/WEF_GAC15_Technological_Tipping_Points_report_2015.pdf. Available on November 28, 2017.

  582. Weiss, S., Dhurandhar, A., Baseman, R., White, B., Logan, R., Wislow, J., et al. (2016). Continuous prediction of manufacturing performance throughout the production lifecycle. Journal of Intelligent Manufacturing,27, 751–763.

    Google Scholar 

  583. Weyer, S., Schmitt, M., Ohmer, M., & Gorecky, D. (2015). Towards Industry 4.0—Standardization as the crucial challenge for highly modular, multi-vendor production systems. IFAC Papers Online,48(3), 579–584.

    Google Scholar 

  584. Wille, E., Mellia, M., Leonardi, E., & Marsan, M. (2006). IP network design with end-to-end QoS constraints: The VPN Case. Computer Networks,50, 1086–1110.

    Google Scholar 

  585. Wu, H., Yue, K., Li, B., Zhang, B., & Hsu, C. (2018). Collaborative QoS prediction with context-sensitive matrix factorization. Future Generation Computer Systems,82, 669–678.

    Google Scholar 

  586. Wua, W., Lib, W., Lawa, D., & Naa, W. (2015). Improving data center energy efficiency using a cyber-physical systems approach: Integration of building information modeling and wireless sensor networks. Engineering Services,118, 1266–1273.

    Google Scholar 

  587. Xia, M., & Hea, Y. (2016). Functional connectomics from a “big data” perspective. Neuroimage,7(11), 1448–1454.

    Google Scholar 

  588. Xiaoyinga, S., & Huanyan, Q. (2011). Design of wetland monitoring system based on the internet of things. Procedia Environmental Sciences,10(Part B), 1046–1051.

    Google Scholar 

  589. Xinga, Y., Malcolm, R., Hornera, W., El-Harama, M., & Bebbingtonb, J. (2009). A framework model for assessing sustainability impacts of urban development. Accounting Forum,33(3), 209–224.

    Google Scholar 

  590. Xu, Y., Fang, G., Na, Lv, Chen, S., & Zou, J. (2015). Computer vision technology for seam tracking in robotic GTAW and GMAW. Robotics and Computer-Integrated Manufacturing,32, 25–36.

    Google Scholar 

  591. Xu, Y., Jiang, R., Yan, S., & Xiong, D. (2011). The research of safety monitoring system applied in school bus based on the internet of thing. Procedia Engineering,15, 2464–2468.

    Google Scholar 

  592. Yang, C., Liu, J., Chen, S., & Huang, K. (2016). Virtual machine management system based on the power saving algorithm in cloud. Journal of Network and Computer Applications,80, 165–180.

    Google Scholar 

  593. Yang, J., Wang, H., Lv, Z., Wei, W., Song, H., Kantarci, M., et al. (2017). Multimedia recommendation and transmission system based on cloud platform. Future Generation Computer Systems,70, 94–103.

    Google Scholar 

  594. Yang, K., & Hirohide, Haga. (2015). Vocabulary game using augmented reality—Expressing elements in virtual world with objects in real world. Open Journal of Social Sciences,3, 25–30.

    Google Scholar 

  595. Yang, X., Malak, R., Lauer, C., Weidig, C., Hagen, H., Hamann, B., et al. (2013). Manufacturing system design with virtual factory tools. International Journal of Computer Integrated Manufacturing,28(1), 25–40.

    Google Scholar 

  596. Yao, G., Ding, Y., Ren, L., Hao, K., & Lei, C. (2016). An immune system-inspired rescheduling algorithm for workflow in Cloud systems. Knowledge-Based Systems,99, 39–50.

    Google Scholar 

  597. Yaseen, M., Anjum, A., Rana, O., & Hill, R. (2017). Cloud-based scalable object detection and classification in video streams. Future Generation Computer Systems,10(99), 1–11.

    Google Scholar 

  598. Yeh, J. (2006). Evaluating ERP performance from user perspective. In Proceedings of the 2006 IEEE Asia-Pacific conference on services computing (APSCC’06) (pp. 311–314), 12–15 December 2006, Guangdong, China.

  599. Yu-fang, L., & Jin-xing, S. J. (2011). Using the internet of things technology constructing digital mine. Procedia Environmental Sciences,10(Part B), 1104–1108.

    Google Scholar 

  600. Yun, J., Won, D., Jeong, E., Park, K., Yang, J., & Park, J. (2016). The relationship between technology, business model, and market in autonomous car and intelligent robot industries. Technological Forecasting and Social Change,103, 142–155.

    Google Scholar 

  601. Yusof, M., Othman, M., Omar, Y., & Yusof, M. (2013). The study on the application of business intelligence in manufacturing: A review. IJCSI International Journal of Computer Science Issues,10(1), 317–324.

    Google Scholar 

  602. Zailani, S., Govindan, K., Shaharudin, M. R., & Kuan, E. E. L. (2017). Barriers to product returnmanagement in automotive manufacturing firms in Malaysia. Journal of Cleaner Production,141, 22–40.

    Google Scholar 

  603. Zarte, M., & Pechmann, A. (2016). Building an Industry 4.0-compliant lab environment to demonstrate connectivity between shop floor and IT levels of an enterprise. In IECON 201642nd annual conference of the IEEE (pp. 6590–6595), 23–26 October 2016, Florence, Italy.

  604. Zezulka, F., Marcon, P., Vesely, I., & Sajdl, O. (2016). Industry 4.0—An introduction in the phenomenon. IFAC,49(25), 8–12.

    Google Scholar 

  605. Zhai, L., & Zhang, S. (2009). The feature model of general ERP system for discrete manufacturing industry. In International conference on electronic commerce and business intelligence, 2009. ECBI 2009 (pp.12–15), 6–7 June 2009, Beijing, China.

  606. Zhang, J., Ong, S., & Nee, A. (2010b). RFID-assisted assembly guidance system in an augmented reality environment. International Journal of Production Research,49(13), 3919–3938.

    Google Scholar 

  607. Zhang, L., & Jiao, J. (2009). Modeling production configuration using nested colored object-oriented Petri-nets with changeable structures. Intelligent Manufacturing,20, 359–378.

    Google Scholar 

  608. Zhang, L., Luo, Y., Tao, F., Li, B. H., Ren, L., Zhang, X., et al. (2014). Cloud manufacturing: A new manufacturing paradigm. Enterprise Information Systems,8, 167–187.

    Google Scholar 

  609. Zhang, P., Jin, H., He, Z., Leung, H., Song, W., & Jiang, Y. (2018). IgS-wBSRM: A time-aware Web Service QoS monitoring approach in dynamic environments. Information and Software Technology,96, 14–26.

    Google Scholar 

  610. Zhang, X. (2016). The transformation and upgrading of the Chinese manufacturing. Journal of Applied Business and Economics,18(5), 97–105.

    Google Scholar 

  611. Zhang, Y., Jiang, P., Huang, G., Qu, T., Zhou, G., & Hong, J. (2010a). RFID-enabled real-time manufacturing information tracking infrastructure for extended enterprises. Intelligent Manufacturing,23(6), 2357–2366.

    Google Scholar 

  612. Zhang, Y., Zhang, G., Liu, Y., & Hu, D. (2017). Research on services encapsulation and virtualization access model of machine for cloud manufacturing. Journal of Intelligent Manufacturing,28, 1109–1123.

    Google Scholar 

  613. Zhao, L., Chien, C., & Gen, M. (2018). A bi-objective genetic algorithm for intelligent rehabilitation scheduling considering therapy precedence constraints. Journal of Intelligent Manufacturing,29, 973–988.

    Google Scholar 

  614. Zheng, S. (2015). Research on mobile learning based on augmented reality. Open Journal of Social Sciences,3, 179–182.

    Google Scholar 

  615. Zhou, K., Fu, C., & Yang, S. (2016a). Big DATA driven smart energy management: From big data to big insights. Modelling Software,56, 215–225.

    Google Scholar 

  616. Zhou, K., Liu, T., & Zhou, L., (2015). Industry 4.0: Towards future industrial opportunities and challenges. In 2015 12th international conference fuzzy systems and knowledge discovery (FSKD) (pp. 2147–2152).

  617. Zhou, L., Pan, S., Wang, J., & Vasilakos, A. (2016b). Machine learning on big data: Opportunities and challenges. Neurocomputing,47, 563–569.

    Google Scholar 

  618. Zhou, W., Feng, D., Tan, Z., & Zheng, Y. (2017). Improving big data storage performance in hybrid environment. Computer Science,9, 40–50.

    Google Scholar 

  619. Zhu, S., & Dong, H. (2010). Researching on the implementation theory and methodology for the service industry ERP system. In 2010 International conference on electrical and control engineering (ICECE) (pp. 4764–4768), 25–27 June 2010, Wuhan, China.

  620. Zujevs, A., Osadcuks, V., & Ahrendt, P. (2015). Trends in robotic sensor technologies for fruit harvesting: 2010–2015. Procedia Computer Science,77, 227–233.

    Google Scholar 

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Oztemel, E., Gursev, S. Literature review of Industry 4.0 and related technologies. J Intell Manuf 31, 127–182 (2020). https://doi.org/10.1007/s10845-018-1433-8

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Keywords

  • Industry 4.0
  • Smart factory
  • Internet of things (IoT)
  • Cyber-physical systems
  • Cloud systems
  • Big data