Advertisement

Ubiquitous Manufacturing in the Age of Industry 4.0: A State-of-the-Art Primer

  • Pijush Kanti Dutta Pramanik
  • Bulbul Mukherjee
  • Saurabh Pal
  • Bijoy Kumar Upadhyaya
  • Shubhendu Dutta
Chapter
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

The industrial revolution has changed the socio-economic civilisation of mankind. It started dating back in the late 1700s and has been in continuous evolution since then. Presently, we are experiencing the latest industrial revolution, known as Industry 4.0. Among others, ubiquitous technologies probably have been the most influential in the implementation of Industry 4.0. This has led to a new manufacturing paradigm known as ubiquitous manufacturing. This chapter presents an in-depth discussion on different aspects of ubiquitous manufacturing. In addition to the history of industrial revolutions and the fundamentals of ubiquitous manufacturing, the topics such as production planning and scheduling, automated material handling system, and dynamic manufacturing are meticulously discussed from the perspective of the real-life scenarios, in the age of ubiquitous manufacturing. The ubiquitous technologies that have enabled ubiquitous manufacturing are reviewed in detail. Several other related and advanced manufacturing technologies such as cloud manufacturing, cloud robotics, global manufacturing, lean manufacturing, agile manufacturing, additive manufacturing, chaordic manufacturing, etc. are duly accentuated. A futuristic view on Industry 5.0 is also presented.

Keywords

Industrial revolution Lean manufacturing Cloud robotics Cloud manufacturing Industry 4.0 Industry 5.0 Ubiquitous technology Ubiquitous computing Production planning Real-time manufacturing AMHSR Cloud manufacturing Edge computing IIoT 

References

  1. 1.
    R. E. Crandall, “Industry 1.0 to 4.0: the Evolution of Smart Factories,” APICS, October 2017. [Online]. Available: http://www.apics.org/apics-for-individuals/apics-magazine-home/magazine-detail-page/2017/09/20/industry-1.0-to-4.0-the-evolution-of-smart-factories. [Accessed 7 April 2019].
  2. 2.
    E. Howard, “The Evolution of the Industrial Ages: Industry 1.0 to 4.0,” 5 September 2018. [Online]. Available: https://www.simio.com/blog/2018/09/05/evolution-industrial-ages-industry-1-0-4-0/. [Accessed 7 April 2019].
  3. 3.
    M. Aggarwal, “History of The Industrial Revolution,” [Online]. Available: http://www.historydiscussion.net/history/industrial-revolution/history-of-the-industrial-revolution/1784. [Accessed 7 May 2019].
  4. 4.
    Henry Ford in collaboration with Samuel Crowther, My Life and Work, Garden City, N.Y.: Page & Company, 1922.Google Scholar
  5. 5.
    C. Adams, The Sexual Politics of Meat, New York: Continuum, 1991.Google Scholar
  6. 6.
    G. D. Putnik and L. Wang, “Ubiquitous and cloud enterprise for manufacturing,” International Journal of Computer Integrated Manufacturing, vol. 30, no. 4–5, p. 344–346, 2017.Google Scholar
  7. 7.
    ICS & Cybersecurity, “The 4 industrial revolutions,” Sentryo, 23 February 2017. [Online]. Available: https://www.sentryo.net/the-4-industrial-revolutions/. [Accessed 7 April 2019].
  8. 8.
    P. Nissen, “Factory Automation from Industry 1.0 to Industry 4.0,” 2016. [Online]. Available: https://www.qubiqa.com/Qubiqa-EN/Blog/Per-Nissen-gives-a-quick-overview-of-factory-automation-from-Industry-1.0-to-Industry-4.0-%E2%80%93-and-the-future-of-automation. [Accessed 7 April 2019].
  9. 9.
    S. V. Ticaret, “A Brief History of Industry,” Bosch, 2017. [Online]. Available: http://www.sanayidegelecek.com/en/sanayi-4-0/tarihsel-gelisim/. [Accessed 7 April 2019].
  10. 10.
    J. Sheth, “The Industrial Revolution – From Industry 1.0 to Industry 5.0!,” Supply Chain Game Changer™, 24 March 2019. [Online]. Available: https://supplychaingamechanger.com/the-industrial-revolution-from-industry-1-0-to-industry-5-0/. [Accessed 7 April 2019].
  11. 11.
    A. Sanders, C. Elangeswaran and J. Wulfsberg, “Industry 4.0 Implies Lean Manufacturing: Research Activities in Industry 4.0 Function as Enablers for Lean Manufacturing,” J. Ind. Eng. Manag., vol. 9, no. 3, 2016.CrossRefGoogle Scholar
  12. 12.
    P. K. D. Pramanik, S. Pal and P. Choudhury, “Smartphone Crowd Computing: A Rational Solution towards Minimising the Environmental Externalities of the Growing Computing Demands,” in Emerging Trends in Disruptive Technology Management, R. Das, M. Banerjee and S. De, Eds., CRC Press, 2019.Google Scholar
  13. 13.
    P. K. D. Pramanik, S. Pal and P. Choudhury, “Green and Sustainable High-Performance Computing with Smartphone Crowd Computing: Benefits, Enablers, and Challenges,” Scalable Computing: Practice and Experience, vol. 20, no. 2, pp. 259-283, 2019.Google Scholar
  14. 14.
    P. K. D. Pramanik, B. Mukherjee, S. Pal, T. Pal and S. P. Singh, “Green Smart Building: Requisites, Architecture, Challenges, and Use Cases,” in Green Building Management and Smart Automation, A. Solanki and A. Nayyar, Eds., IGI Global, 2019.Google Scholar
  15. 15.
    AB&R, “RFID,” AB&R, 2019. [Online]. Available: https://www.abr.com/what-is-rfid-how-does-rfid-work/. [Accessed 11 May 2019].
  16. 16.
    Thomas, “Complete Guide to Actuators (Types, Attributes, Applications and Suppliers),” Thomas, 2019. [Online]. Available: https://www.thomasnet.com/articles/pumps-valves-accessories/types-of-actuators. [Accessed 11 May 2019].
  17. 17.
    Solanki, A., & Nayyar, A. (2019). Green Internet of Things (G-IoT): ICT Technologies, Principles, Applications, Projects, and Challenges. In Handbook of Research on Big Data and the IoT (pp. 379-405). IGI Global.Google Scholar
  18. 18.
    Batth, R. S., Nayyar, A., & Nagpal, A. (2018, August). Internet of Robotic Things: Driving Intelligent Robotics of Future-Concept, Architecture, Applications and Technologies. In 2018 4th International Conference on Computing Sciences (ICCS)(pp. 151-160). IEEE.Google Scholar
  19. 19.
    Nayyar, A., Puri, V., & Le, D. N. (2017). Internet of nano things (IoNT): Next evolutionary step in nanotechnology. Nanoscience and Nanotechnology, 7(1), 4-8.Google Scholar
  20. 20.
    P. K. D. Pramanik, S. Pal, A. Brahmachari and P. Choudhury, “Processing IoT Data: From Cloud to Fog. It’s Time to be Down-to-Earth,” in Applications of Security, Mobile, Analytic and Cloud (SMAC) Technologies for Effective Information Processing and Management, P. Karthikeyan and M. Thangavel, Eds., IGI Global, 2018, pp. 124-148.Google Scholar
  21. 21.
    Kaur, A., Gupta, P., Singh, M., & Nayyar, A. (2019). Data Placement in Era of Cloud Computing: a Survey, Taxonomy and Open Research Issues. Scalable Computing: Practice and Experience, 20(2), 377-398.Google Scholar
  22. 22.
    Microsoft, “What is cloud computing?,” Microsoft, 2019. [Online]. Available: https://azure.microsoft.com/en-in/overview/what-is-cloud-computing/. [Accessed 11 May 2019].
  23. 23.
    Singh, P., Gupta, P., Jyoti, K., & Nayyar, A. (2019). Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions. Scalable Computing: Practice and Experience, 20(2), 399-432.Google Scholar
  24. 24.
    Singh, S. P., Nayyar, A., Kaur, H., & Singla, A. (2019). Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platforms. Scalable Computing: Practice and Experience, 20(2), 433-456.Google Scholar
  25. 25.
    P. K. D. Pramanik and P. Choudhury, “IoT Data Processing: The Different Archetypes and their Security & Privacy Assessments,” in Internet of Things (IoT) Security: Fundamentals, Techniques and Applications, S. K. Shandilya, S. A. Chun, S. Shandilya and E. Weippl, Eds., River Publishers, 2018, pp. 37-54.Google Scholar
  26. 26.
    Singh, S. P., Nayyar, A., Kumar, R., & Sharma, A. (2019). Fog computing: from architecture to edge computing and big data processing. The Journal of Supercomputing, 75(4), 2070-2105.Google Scholar
  27. 27.
    P. K. D. Pramanik, B. Upadhyaya, S. Pal and T. Pal, “Internet of Things, Smart Sensors, and Pervasive Systems: Enabling the Connected and Pervasive Health Care,” in Healthcare Data Analytics and Management, N. Dey, A. Ashour, S. J. Fong and C. Bhatt, Eds., Academic Press, 2018, pp. 1-58.Google Scholar
  28. 28.
    P. K. D. Pramanik, S. Pal and M. Mukhopadhyay, “Healthcare Big Data: A Comprehensive Overview,” in Intelligent Systems for Healthcare Management and Delivery, N. Bouchemal, Ed., IGI Global, 2018, pp. 72-100.Google Scholar
  29. 29.
    M. Rouse, “big data analytics,” TechTarget, September 2018. [Online]. Available: https://searchbusinessanalytics.techtarget.com/definition/big-data-analytics. [Accessed 11 May 2019].
  30. 30.
    Qubole, “Overview of Big Data Analytics,” Qubole, 2019. [Online]. Available: https://www.qubole.com/big-data-analytics/. [Accessed 11 May 2019].
  31. 31.
    Nayyar, A., & Puri, V. (2017). Comprehensive Analysis & Performance Comparison of Clustering Algorithms for Big Data. Review of Computer Engineering Research, 4(2), 54-80.Google Scholar
  32. 32.
    B. Marr, “What Is The Difference Between Artificial Intelligence And Machine Learning?,” Forbes, 6 December 2016. [Online]. Available: https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#69fecbef2742. [Accessed 11 May 2019].
  33. 33.
    P. K. D. Pramanik, S. Pal and P. Choudhury, “Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Things,” in Cognitive Computing for Big Data Systems Over IoT: Frameworks, Tools and Application, A. K. Sangaiah, A. Thangavelu and V. M. Sundaram, Eds., Springer, 2018, pp. 1-37.Google Scholar
  34. 34.
    M. Rouse, “Cognitive computing,” TechTarget, July 2018. [Online]. Available: https://searchenterpriseai.techtarget.com/definition/cognitive-computing. [Accessed 11 May 2019].
  35. 35.
    Reality Technologies, “The Ultimate Guide to Understanding Augmented Reality (AR) Technology,” Reality Technologies, 2019. [Online]. Available: https://www.realitytechnologies.com/augmented-reality/. [Accessed 11 May 2019].
  36. 36.
    GE, “What is Additive Manufacturing?,” GE, 2019. [Online]. Available: https://www.ge.com/additive/additive-manufacturing. [Accessed 11 May 2019].
  37. 37.
    EOS GmbH, “Additive Manufacturing, Laser-Sintering and industrial 3D printing - Benefits and Functional Principle,” EOS GmbH, May 2018. [Online]. Available: https://www.eos.info/additive_manufacturing/for_technology_interested. [Accessed 11 May 2019].
  38. 38.
    SPI lasers, “Additive Manufacturing – a definition,” SPI lasers, 2019. [Online]. Available: https://www.spilasers.com/application-additive-manufacturing/additive-manufacturing-a-definition/. [Accessed 11 May 2019].
  39. 39.
    M. Weiser, “Hot topics-ubiquitous computing,” Computer, vol. 26, no. 10, pp. 71-72, 1993.CrossRefGoogle Scholar
  40. 40.
    M. Rouse, “IoT analytics guide: Understanding Internet of Things data,” TechTarget, November 2016. [Online]. Available: https://internetofthingsagenda.techtarget.com/definition/pervasive-computing-ubiquitous-computing. [Accessed 07 May 2019].
  41. 41.
    P. Peiris, “How IoT Strengthens Ubiquitous Computing,” 25 May 2017. [Online]. Available: https://dzone.com/articles/how-iot-strengthens-ubiquitous-computing. [Accessed 30 November 2018].
  42. 42.
    G. Banavar and A. Bernstein, “Software infrastructure and design challenges for ubiquitous computing applications,” Communications of the ACM, vol. 45, no. 12, pp. 92-96, 2002.Google Scholar
  43. 43.
    R. Want, “An Introduction to Ubiquitous Computing,” in Ubiquitous Computing Fundamentals, J. Krumm, Ed., Boca Raton, Florida: CRC Press, 2010, pp. 1-36.Google Scholar
  44. 44.
    J. L. V. Barbosa, “Ubiquitous computing: Applications and research opportunities,” in IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, 2015.Google Scholar
  45. 45.
    K. R. Prasanna and M. Hemalatha, “RFID GPS and GSM based logistics vehicle load balancing and tracking,” Procedia Engineering, vol. 30, pp. 726-729, 2012.CrossRefGoogle Scholar
  46. 46.
    T. Chen and H.-R. Tsai, “Ubiquitous manufacturing: Current practices, challenges, and opportunities,” Robotics and Computer-Integrated Manufacturing, vol. 45, pp. 126-132, 2017.CrossRefGoogle Scholar
  47. 47.
    X. Wang, S. K. Ong and A. Y. C. Nee, “A comprehensive survey of ubiquitous manufacturing research,” International Journal of Production Research, vol. 56, no. 1-2, pp. 604-628, 2017.CrossRefGoogle Scholar
  48. 48.
    Nayyar, A. (2011). INTEROPERABILITY OF CLOUD COMPUTING WITH WEB SERVICES. International Journal of ElectroComputational World & Knowledge Interface, 1(1).Google Scholar
  49. 49.
    Nayyar, A. (2011). Private Virtual Infrastructure (PVI) Model for Cloud Computing. International Journal of Software Engineering Research and Practices, 1(1), 10-14.Google Scholar
  50. 50.
    IBM Corporation, “Industrie 4.0 & Cognitive Manufacturing - Architecture Patterns, Use Cases & IBM Solution,” 28 September 2018. [Online]. Available: https://www.ibm.com/downloads/cas/YKEDY8RD. [Accessed 2019 April 12].
  51. 51.
    J. Barcus, “5 Benefits of Shifting to Smart Manufacturing,” Oracle, 29 March 2018. [Online]. Available: https://blogs.oracle.com/5-benefits-of-shifting-to-smart-manufacturing. [Accessed 07 May 2019].
  52. 52.
    C. Beebe, “The Benefits of Smart Manufacturing,” Fishman, 2019. [Online]. Available: https://www.fishmancorp.com/benefits-smart-manufacturing/. [Accessed 07 May 2019].
  53. 53.
    Infinite Uptime, “Smart Factory and Its benefits on manufacturing industry,” Infinite Uptime, 21 August 2018. [Online]. Available: https://infinite-uptime.com/blog/smart-factory-benefits-manufacturing/. [Accessed 07 May 2019].
  54. 54.
    W. Pao, “Smart manufacturing technology and how it benefits factories,” asmag.com, 02 10 2018. [Online]. Available: https://www.asmag.com/showpost/26303.aspx. [Accessed 07 May 2019].
  55. 55.
    Fathym, “5 powerful benifits of IoT for the manufacturing industry,” Fathym, 2018. [Online]. Available: https://fathym.com/2017/05/5-powerful-benefits-iot-manufacturing-industry/. [Accessed 07 May 2019].
  56. 56.
    MSRCosmos, “Challenges of the Manufacturing Industry, & Big Data Analytics,” 22 August 2017. [Online]. Available: https://www.msrcosmos.com/blog/challenges-of-the-manufacturing-industry-big-data-analytics/. [Accessed 07 May 2019].
  57. 57.
    TCS, “Big Data Study,” TCS, 2019. [Online]. Available: https://sites.tcs.com/big-data-study/manufacturing-big-data-benefits-challenges/. [Accessed 07 May 2019].
  58. 58.
    Big Data Value Association, “Big Data challenges in smart manufacturing,” BDVA, Bruxelles, 2018.Google Scholar
  59. 59.
    S. Wang, D. L. Jiafu Wan and C. Zhang, “Implementing Smart Factory of Industrie 4.0: An Outlook,” International Journal of Distributed Sensor Networks, 2016.Google Scholar
  60. 60.
    Q. Dai, R. Zhong, G. Q. Huang, T. Qub, T. Zhang and T. Y. Luo, “Radio frequency identification-enabled real-time manufacturing execution system: a case study in an automotive part manufacturer,” International Journal of Computer Integrated Manufacturing, vol. 25, no. 1, pp. 51-65, 2012.CrossRefGoogle Scholar
  61. 61.
    H. Luo, K. Wang, X. T. R. Kong, S. P. Lu and T. Qu, “Synchornised Production and Logistics via Ubiquitous Computing Technology,” Robotics and Computer Integrated Manufacturing, vol. 45, pp. 99-115, 2017.CrossRefGoogle Scholar
  62. 62.
    A. I. Corréa, A. Langevin and L.-M. Rousseau, “Scheduling and routing of automated guided vehicles: A hybrid approach,” Computers & Operations Research, vol. 34, no. 6, pp. 1688-1707, 2007.zbMATHCrossRefGoogle Scholar
  63. 63.
    R. Y. Zhong, G. Q. Huang, S. Lan, Q. Dai, T. Zhang and C. Xu, “A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing,” Advanced Engineering Informatics, pp. 799-812, 2015.CrossRefGoogle Scholar
  64. 64.
    Siemens, “Pictures of the Future,” 1996-2019. [Online]. Available: https://www.siemens.com/innovation/en/home/pictures-of-the-future/industry-and-automation/digitale-fabrik-rfid-in-industry.html. [Accessed 11 April 2019].
  65. 65.
    Identec Solutions, “Visibility of complex manufacturing and logistics operations delivers the fourth industrial revolution,” [Online]. Available: https://www.identecsolutions.com/industry-4-0/#identec-tab-feature-1. [Accessed 9 May 2019].
  66. 66.
    C. Lam and W. Ip, “An Integrated Logistics Routing and Scheduling Network Model with RFID-GPS Data for Supply Chain Management,” Wireless Personal Communications, vol. 105, no. 3, pp. 803-817, 2019.CrossRefGoogle Scholar
  67. 67.
    E. Arica and D. J. Powell, “A framework for ICT-enabled real-time production planning and control,” Advances in Manufacturing, vol. 2, no. 2, pp. 158-164, 2014.CrossRefGoogle Scholar
  68. 68.
    J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645-1660, 2013.CrossRefGoogle Scholar
  69. 69.
    Germany Trade & Invest (GTAI), “Industrie 4.0,” Germany Trade & Invest (GTAI), 2019. [Online]. Available: https://www.gtai.de/GTAI/Navigation/EN/Invest/Industries/Industrie-4-0/Industrie-4-0/industrie-4-0-what-is-it.html. [Accessed 5 April 2019].
  70. 70.
    The European Factories of the Future Research Association (EFFRA), “Factories of the Future,” The European Factories of the Future Research Association (EFFRA), 2019. [Online]. Available: https://www.effra.eu/factories-future. [Accessed 2 April 2019].
  71. 71.
    Center For Strategic & International Studies (CSIS), “Made in China 2025,” June 2015. [Online]. Available: https://www.csis.org/analysis/made-china-2025. [Accessed 10 April 2019].
  72. 72.
    C. Yang, W. Shen and X. Wang, “The Internet of Things in Manufacturing Key Issues and Potential Applications,” IEEE Systems, Man, & Cybernetics Magazine, vol. 4, no. 1, pp. 6-15, 2018.CrossRefGoogle Scholar
  73. 73.
    P. K. D. Pramanik, S. Pal, G. Pareek, S. Dutta and P. Choudhury, “Crowd Computing: The Computing Revolution,” in Crowdsourcing and Knowledge Management in Contemporary Business Environments, R. Lenart-Gansiniec, Ed., IGI Global, 2018, pp. 166-198.CrossRefGoogle Scholar
  74. 74.
    Q. Qi and F. Tao, “Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison,” IEEE ACCESS, vol. 6, pp. 3585-3593, 2018.CrossRefGoogle Scholar
  75. 75.
    P. Leita˜o, A. W. Colombo and S. Karnouskos, “Industrial automation based on cyber-physical systems technologies:Prototype implementations and challenges,” Computers in Industry, vol. 81, pp. 11-25, 2016.CrossRefGoogle Scholar
  76. 76.
    Guardian News & Media Limited, “Google Glass – hands-on review,” 2019. [Online]. Available: https://www.theguardian.com/technology/2013/jul/02/google-glass-review-augmented-reality. [Accessed 1 May 2019].
  77. 77.
    J. Lee, B. Bagheri and H.-A. Kao, “A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems,” Manufacturing Letters, vol. 3, pp. 18-23, 2015.CrossRefGoogle Scholar
  78. 78.
    K. Schwab, The Fourth Industrial Revolution, Penguin UK, 2017.Google Scholar
  79. 79.
    H. Kagermann, “Change Through Digitization—Value Creation in the Age of Industry 4.0,” in Management of Permanent Change, Springer Gabler, Wiesbaden, 2015, pp. 23-45.Google Scholar
  80. 80.
    ABB Group, “ABB unveils the future of human-robot collaboration: YuMi®,” 9 September 2014. [Online]. Available: https://new.abb.com/news/detail/13110/abb-unveils-the-future-of-human-robot-collaboration-yumir. [Accessed 21 April 2019].
  81. 81.
    Karen Lewis, “Adaptive robots with Watson IoT and KUKA,” IBM, 2 May 2017. [Online]. Available: https://www.ibm.com/blogs/internet-of-things/adaptive-robots-watson-kuka/. [Accessed 22 April 2019].
  82. 82.
    N.-A. David, H. M. Yip, Z. Wang, Y.-H. Liu, F. Zhong, T. Zhang and P. Li, “Automatic 3-D Manipulation of Soft Objects by Robotic Arms With an Adaptive Deformation Model,” IEEE Transactions on Robotics, vol. 32, no. 2, pp. 429-441, 2016.CrossRefGoogle Scholar
  83. 83.
    pi4, “Machine Vision and robotics - a brilliant combination pi4 has the expertise in both areas,” [Online]. Available: https://www.pi4.de/english/systems/workerbot/workerbot4tm.html.
  84. 84.
    D. Wu, J. L. Thames, D. W. Rosen and D. Schaefer, “Enhancing the Product Realization Process With Cloud-Based Design and Manufacturing Systems,” Journal of Computing and Information Science in Engineering, vol. 13, no. 4, 2013.Google Scholar
  85. 85.
    S. Wang, J. Wan, D. Li and C. Zhang, “Implementing Smart Factory of Industrie 4.0: An Outlook,” International Journal of Distributed Sensor Networks, vol. 12, no. 1, 2016.CrossRefGoogle Scholar
  86. 86.
    Xu and Xun, “From cloud computing to cloud manufacturing,” Robotics and Computer-Integrated Manufacturing, vol. 28, no. 1, pp. 75-86, 2012.CrossRefGoogle Scholar
  87. 87.
    J. Queiroz, P. Leitão and E. Oliveira, “Industrial Cyber Physical Systems Supported by Distributed Advanced Data Analytics,” in International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing (SOHOMA 2016), 2017.CrossRefGoogle Scholar
  88. 88.
    S. Raileanu, T. Borangiu, O. Morariu and I. Iacob, “Edge Computing in Industrial IoT Framework for Cloud-based Manufacturing Control,” in 22nd International Conference on System Theory, Control and Computing (ICSTCC), 2018.Google Scholar
  89. 89.
    Puri, V., Nayyar, A., & Raja, L. (2017). Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems, 20(4), 507-518.Google Scholar
  90. 90.
    Q. Qi, D. Zhao, T. W. Liao and F. Tao, “Modeling of cyber-physical systems and digital twin based on edge computing, fog computing and cloud computing towards smart manufacturing,” in 13th International Manufacturing Science and Engineering Conference, 2018.Google Scholar
  91. 91.
    C. Byers, “Fog computing for industrial automation,” CISCO, 8 March 2018. [Online]. Available: https://www.controleng.com/articles/fog-computing-for-industrial-automation/. [Accessed 1 April 2019].
  92. 92.
    P. K. D. Pramanik, A. Nayyar and G. Pareek, “WBAN: Driving E-Healthcare Beyond Telemedicine to Remote Health Monitoring. Architecture and Protocols,” in Telemedicine Technologies: Big data, Deep Learning, Robotics, Mobile and Remote Applications for Global Healthcare, D. J. Hemanth and V. E. Balas, Eds., Elsevier, 2019, pp. 89-119.Google Scholar
  93. 93.
    C.-C. Lin, D.-J. Deng, Z.-Y. Chen and K.-C. Chen, “Key Design of Driving Industry 4.0: Joint Energy-Efficient Deployment and Scheduling in Group-Based Industrial Wireless Sensor Networks,” Recent Advances in Green Industrial Networking, vol. 54, no. 10, pp. 46-52, 2016.CrossRefGoogle Scholar
  94. 94.
    M. Yi-Wei, C. Yung-Chiao and C. Jiann-Liang, “SDN-Enabled Network Virtualization for Industry 4.0 Based on IoTs and Cloud Computing,” in 19th International Conference on Advanced Communication Technology (ICACT), Bongpyeong, South Korea, 2017.Google Scholar
  95. 95.
    Reis, J. Zonichenn, Gonçalves and R. Franco, “The Role of Internet of Services (IoS) on Industry 4.0 Through the Service Oriented Architecture (SOA),” in Advances in Production Management Systems. Smart Manufacturing for Industry 4.0. APMS, 2018.CrossRefGoogle Scholar
  96. 96.
    J. Jung, B. Song, K. Watson and T. Usländer, “Design of Smart Factory Web Services Based on the Industrial Internet of Things,” in 50th Hawaii International Conference on System Sciences, 2017.Google Scholar
  97. 97.
    Amazon, “Manufacturing: Optimize production, speed time to market, and deliver innovative products and services with AWS Cloud,” Amazon, 2019. [Online]. Available: https://aws.amazon.com/manufacturing/. [Accessed 25 March 2019].
  98. 98.
    Deloitte, “Industry 4.0 and the digital twin Manufacturing meets its match,” Deloitte, May 2017. [Online]. Available: https://www2.deloitte.com/insights/us/en/focus/industry-4-0/digital-twin-technology-smart-factory.html. [Accessed 30 March 2019].
  99. 99.
    Oracle, “Digital Twins for IoT Applications A Comprehensive Approach to Implementing IoT Digital Twins,” Oracle White Paper, pp. 1-9, January 2017.Google Scholar
  100. 100.
    Datacore Software, “Storage Virtualization,” Datacore Software, 2019. [Online]. Available: https://www.datacore.com/storage-virtualization/. [Accessed 12 April 2019].
  101. 101.
    H. Enming, F. Bingyan, L. Chenhan, Y. Jianzhong and C. Jihong, “A design of CNC architecture based on cloud computing,” Journal of Engineering Manufacture, vol. 233, no. 4, pp. 1260-1268, 2018.Google Scholar
  102. 102.
    D. Mourtzis, N. Milas and N. Athinaios, “Towards Machine Shop 4.0: A General Machine Model for CNC machine-tools through OPC-UA,” in 6th CIRP Global Web Conference “Envisaging the future manufacturing,design, technologies and systems in innovation era”, 2018.Google Scholar
  103. 103.
    Gardner Business Media, Inc., “CNC Intro-The Key Concepts Of Computer Numerical Control,” May 2018. [Online]. Available: https://www.mmsonline.com/articles/cnc-intro-the-key-concepts-of-computer-numerical-control. [Accessed 31 March 2019].
  104. 104.
    HNC Electric, “Global Service,” 2014. [Online]. Available: http://www.hncelectric.com/en_download.aspx?cid=36&category_id=0. [Accessed 1 May 2019].
  105. 105.
    FANUC, “FANUC CNC,” February 2015. [Online]. Available: https://www.fanuc.eu/il/en/cnc. [Accessed 11 April 2019].
  106. 106.
    Siemens, “Premium class CNCs – delivering ultimate performance,” Siemens, 2019. [Online]. Available: https://new.siemens.com/global/en/products/automation/systems/cnc-sinumerik/automation-systems/sinumerik-840.html. [Accessed 1 April 2019].
  107. 107.
    S. Lemaignan, A. Siadat, J.-Y. Dantan and A. Semenenko, “MASON: A Proposal For An Ontology Of Manufacturing Domain,” in IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS’06), Prague, Czech Republic, 2006.Google Scholar
  108. 108.
    P. Leitao, A. Colombo and F. Restivo, “ADACOR: A Collaborative Production Automation and Control Architecture,” IEEE Intelligent Systems, pp. 58-66, 2005.CrossRefGoogle Scholar
  109. 109.
    I. Horrocks, P. F. Patel-Schneider, H. Boley, S. Tabet, B. Grosof and M. Dean, “SWRL: A Semantic Web Rule Language Combining OWL and RuleML,” 21 May 2004. [Online]. Available: https://www.w3.org/Submission/SWRL/.
  110. 110.
    H. Knublauch, “SPIN - Modeling Vocabulary,” 22 February 2011. [Online]. Available: https://www.w3.org/Submission/spin-modeling/.
  111. 111.
    E. Järvenpää, N. Siltala, O. Hylli and M. Lanz, “The development of an ontology for describing the capabilities of manufacturing resources,” Journal of Intelligent Manufacturing, pp. 959-978, 2019.CrossRefGoogle Scholar
  112. 112.
    L. Zhang, Y. Luo, F. Tao, B. H. Li, L. Ren, X. Zhang, H. Guo, Y. Cheng, A. Hu and Y. Liu, “Cloud manufacturing: A new manufacturing paradigm,” Enterprise Information Systems, vol. 8, no. 2, pp. 167-187, 2012.CrossRefGoogle Scholar
  113. 113.
    X. Xu, “From cloud computing to cloud manufacturing,” Robotics and Computer-Integrated Manufacturing, vol. 28, no. 1, pp. 75-86, 2012.CrossRefGoogle Scholar
  114. 114.
    D. Wu, M. J. Greer, D. W. Rosen and D. Schaefer, “Cloud manufacturing: Strategic vision and state-of-the-art,” Journal of Manufacturing Systems, vol. 32, no. 4, pp. 564-579, 2013.CrossRefGoogle Scholar
  115. 115.
    G. Hu, W. Tay and Y. Wen, “Cloud robotics: Architecture, challenges and applications,” IEEE Network, vol. 26, no. 3, pp. 21-28, 2012.CrossRefGoogle Scholar
  116. 116.
    J. Wan, S. Tang, H. Yan, D. Li, S. Wang and A. V. Vasilakos, “Cloud robotics: Current status and open issues,” IEEE Access, vol. 4, pp. 2797-2807, 2016.Google Scholar
  117. 117.
    Y. Fan, D. Zhao, L. Zhang, S. Huang and B. Liu, “Manufacturing grid: Needs, concept and architecture,” Lect. Notes Comput. Sci., vol. 3032, pp. 653-656, 2004.Google Scholar
  118. 118.
    M. R¨ußmann, M. Lorenz, P. Gerbert, M. Waldner, J. Justus, P. Engel and M. Harnisch, “Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries,” Boston Consulting Group, 2015.Google Scholar
  119. 119.
    J. Lee, B. Bagheri and C. Jin, “Introduction to cyber manufacturing,” Manufacturing Letters, vol. 8, pp. 11-15, 2016.CrossRefGoogle Scholar
  120. 120.
    P. Pontrandolfo and O. G. Okogbaa, “Global manufacturing: a review and a framework for planning in a global corporation,” International Journal of Produc-tion Economics, vol. 37, no. 1, pp. 1-19, 1999.zbMATHCrossRefGoogle Scholar
  121. 121.
    J. X. Jiao, X. You and A. Kumar, “An agent-based framework for collaborative negotiation in the global manufacturing supply chain network,” Robotics and Computer-Integrated Manufacturing, vol. 22, no. 3, pp. 239-255, 2006.CrossRefGoogle Scholar
  122. 122.
    C. Lee, C. S. Leem and I. Hwang, “PDM and ERP information methodolo-gy using digital manufacturing to support global manufacturing,” International Journal of Advanced Manufacturing Technology, vol. 53, no. 1-4, pp. 399-409, 2011.Google Scholar
  123. 123.
    V. G. Bharath and R. Pati, “Virtual Manufacturing: A Review,” International Journal of Engineering Research & Technology, pp. 355-364, 2015.Google Scholar
  124. 124.
    K. Kumar, D. Zindani and J. P. Davim, “Introduction to Virtual Manufacturing,” in Advanced Machining and Manufacturing Processes. Materials Forming, Machining and Tribology, Springer, Cham, 2018.Google Scholar
  125. 125.
    M. Olender and D. Krenczyk, “Practical application of game theory based production flow planning method in virtual manufacturing networks,” IOP Conference Series: Materials Science and Engineering, vol. 145, no. 022031, 2016.CrossRefGoogle Scholar
  126. 126.
    R. Shah and P. T. Ward, “Defining and developing measures of lean production,” Journal of operations management, vol. 25, no. 4, pp. 785-805, 2007.CrossRefGoogle Scholar
  127. 127.
    A. Gunasekaran, Y. Y. Yusuf, E. O. Adeleye, T. Papadopoulos, D. Kovvuri and D. G. Geyi, “Agile manufacturing: an evolutionary review of practices,” International Journal of Production Research, pp. 1-21, 2018.Google Scholar
  128. 128.
    V. Manivelmuralidaran, “Agile Manufacturing – An overview,” International Journal of Science and Engineering Applications, vol. 4, no. 3, pp. 156-159, 2015.CrossRefGoogle Scholar
  129. 129.
    S. Vaidya, P. Ambad and S. Bhosle, “Industry 4.0 - Glimpse,” Procedia Manufacturing, vol. 20, p. 233–238, 2018.CrossRefGoogle Scholar
  130. 130.
    W. Gao, Y. Zhang, D. Ramanujan, K. Ramania, Y. Chen, C. B.Williams, C. C. L. Wang, Y. C. Shin, S. Zhang and P. D. Zavattieri, “The status, challenges, and future of additive manufacturing in engineering,” Computer-Aided Design, vol. 69, pp. 65-89, 2015.CrossRefGoogle Scholar
  131. 131.
    U. M. Dilberoglu, B. Gharehpapagh, U. Yaman and M. Dolen, “The role of additive manufacturing in the era of industry 4.0.,” Procedia Manufacturing, vol. 11, pp. 545-554, 2017.CrossRefGoogle Scholar
  132. 132.
    F. v. Eijnatten, G. D. Putnik and A. Sluga, “Chaordic Systems Thinking for Novelty in Contemporary Manufacturing,” CIRP Annals - Manufacturing Technology, vol. 56, no. 1, pp. 447-450, 2007.CrossRefGoogle Scholar
  133. 133.
    F. v. Eijnatten, “Chaos and Complexity: An Overview of the ‘New Science’ in Organization and Management,” La Revue des Sciences de Gestion, vol. 40, pp. 123-165, 2004.Google Scholar
  134. 134.
    A. Hobbs, “Complete guide: 10 smart factory trends to watch in 2019,” 12 December 2018. [Online]. Available: https://internetofbusiness.com/complete-guide-10-smart-factory-trends-to-watch-in-2019/. [Accessed 7 May 2019].
  135. 135.
    P. Waterfield, “Fine watches, craft beer and the psychology of Industry 5.0,” 27 April 2018. [Online]. Available: https://enterpriseiotinsights.com/20180427/channels/fundamentals/the-psychology-of-industry-50-tag99. [Accessed 7 May 2019].

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pijush Kanti Dutta Pramanik
    • 1
  • Bulbul Mukherjee
    • 2
  • Saurabh Pal
    • 2
  • Bijoy Kumar Upadhyaya
    • 3
  • Shubhendu Dutta
    • 4
  1. 1.National Institute of TechnologyDurgapurIndia
  2. 2.Bengal Institute of TechnologyKolkataIndia
  3. 3.Tripura Institute of TechnologyAgartalaIndia
  4. 4.Aujas NetworksNew DelhiIndia

Personalised recommendations