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Manufacturing Intelligence in the Context of Indian SME’s

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Advances in Intelligent Manufacturing

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

As many thriving industries are planning to walk on the roadmap of Industrial Revolution 4.0, it has become essential for them to acquaint with essential requirements for deployment of Internet of Things (IoT), Robotics, Virtual Reality, and Artificial Intelligence. Manufacturing Intelligence can be vertically divided into two subgroups as Intelligent Manufacturing and Data Intelligence. Smart manufacturing is the backbone of intelligent manufacturing with the involvement of process automation and automated machine tools. Whereas data intelligence is related to data acquisition, storage, and analysis for smart decision-making. It is possible with the integration of the latest IoT gadgets, cloud computing, and various Enterprise Resource Planning (ERP) packages. The selection varies as per the diversified requirements of the manufacturing units. Quality consultancy is required for the selection of tools and techniques in order to excel the business performance. It is followed by the critical phase of deployment with the requirement of high-level expertise. This paper proposes a framework for the deployment of manufacturing intelligence specifically in Small Medium Enterprise (SME). This framework will be the guiding tool for those SMEs who want to apply manufacturing intelligence to their units.

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References

  1. Dirican, C. (2015). The impacts of robotics, artificial intelligence on business and economics. Procedia—Soc. Behav. Sci., 195, 564–573.

    Article  Google Scholar 

  2. Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. J. Manuf. Syst. 48, 157–169.

    Google Scholar 

  3. Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. J. Manuf. Syst. 1–13.

    Google Scholar 

  4. Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. 3, 616–630.

    Google Scholar 

  5. May, G. S., & Member, S. (2004). Intelligent SOP manufacturing. 27(2), 426–437.

    Google Scholar 

  6. Sethi, P., & Sarangi, S. R. (2017). Internet of Things : Architectures, protocols, and applications. 2017.

    Google Scholar 

  7. Shen, W., Member, S., Lang, S. Y. T., & Wang, L. (2005). iShopFloor : An internet-enabled agent-based intelligent shop floor. 35(3), 371–381.

    Google Scholar 

  8. Li, B., Hou, B., Yu, W., Lu, X., & Yang, C. (2017). Applications of artificial intelligence in intelligent manufacturing: A review. 18(1), 86–96.

    Google Scholar 

  9. Daniel, P., et al. (2018). Technologies to enable a manufacturing execution system. J. Manuf. Syst.

    Google Scholar 

  10. Wubbolt, C., & Patterson, J. T. (2012). Manufacturing execution systems.

    Google Scholar 

  11. Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing smart factory of industrie 4.0: An outlook. 2016.

    Google Scholar 

  12. Tolk, A., Diallo, S. Y., Ryzhov, I. O., Yilmaz, L., Buckley, S., & Miller, J. A. (2014). Virtual factory revisited for manufacturing data analytics. 887–898.

    Google Scholar 

  13. Taylor, P., et al. (2014). Real-time information capturing and integration framework of the internet of manufacturing things. Int. J. Comput. Integ. Manuf. 37–41.

    Google Scholar 

  14. Swan, E. (2008). All Electric Injection Molding Machines: How Much Energy Can You Save.

    Google Scholar 

  15. Solutions, D. C., & Applications, M. Data Capture Solutions for Manufacturing Applications.

    Google Scholar 

  16. Poonkodi, M., Manohar, S., Rajkumar, H., & Aarthi, R. (2018). A framework for enterprise manufacturing intelligence. 7(3), 6–12.

    Google Scholar 

  17. Peschi, M., Link, N., Hoffmeister, M., Gonçalves, G., & Fernando, L. F. (2013). Designing and implementation of an intelligent manufacturing system. 4(4), 718–745.

    Google Scholar 

  18. Munguía, J., Lloveras, J., Llorens, S., & Laoui, T. (2011). Advice system r P Fo r R w On ly.

    Google Scholar 

  19. Kadadevaramath, R. S., Chen, J. C. H., Sangli, M., & Raj, R. K. (2014). Industrial engineering and management a study on implementation of IT tools in SME’ S in India. 3(4).

    Google Scholar 

  20. Imran, S., Sanin, C., Szczerbicki, E., Toro, C. (2017). Towards an experience based collective computational intelligence for manufacturing. Futur. Gener. Comput. Syst. 66, 89–99.

    Google Scholar 

  21. Bogle, L. (2017). A perspective on smart process manufacturing research challenges for process systems engineers. Engineering 3(2), 161–165.

    Google Scholar 

  22. Coyte, R., Ricceri, F., & Guthrie, J. (2012). The management of knowledge resources in SMEs: An Australian case study. 16(5), 789–807.

    Google Scholar 

  23. Chan, F. T. S., Jiang, B., & Tang, N. K. H. (2000). The development of intelligent decision support tools to aid the design of # exible manufacturing systems. 65, 73–84.

    Google Scholar 

  24. Beynon, M. J., Jones, P., & Pickernell, D. (2018). SME development strategy and product/service innovation intention: A NCaRBS analysis of the role of uncertainty. 2006.

    Google Scholar 

  25. Bao, J., Guo, D., Li, J., & Zhang, J. (2018). The modelling and operations for the digital twin in the context of manufacturing. 7575.

    Google Scholar 

  26. Zhang, Y., Zhang, G., Wang, J., Sun, S., Si, S., & Yang, T. (2014). Real-time information capturing and integration framework of the internet of manufacturing things. International Journal of Computer Integrated Manufacturing, 28(8), 811–822.

    Google Scholar 

  27. Coronado, P. D. U., Lynn, R., Louhichi, W., Parto, M., Wescoat, E., & Kurfess, T. (2018). Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system. Journal of Manufacturing Systems, 48, 25–33.

    Google Scholar 

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Acknowledgements

This work is supported by the inputs given by various SMEs located in Chakan MIDC, Pune. Special thanks to Shivam Enterprises for allowing to conduct a case study in their unit.

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Authors and Affiliations

  1. School of Mechanical Engineering, Lovely Professional University, Phagwara, Punjab, India

    Pravin M. Kinge & U. C. Jha

Authors
  1. Pravin M. Kinge
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  2. U. C. Jha
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Corresponding author

Correspondence to U. C. Jha .

Editor information

Editors and Affiliations

  1. Mechanical Engineering, Opole University of Technology, Opole, Poland

    Grzegorz Krolczyk

  2. School of Mechanical Engineering, Lovely Professional University, Phagwara, India

    Chander Prakash

  3. Centre for Nanofibers & Nanotechnology, National University of Singapore, Singapore, Singapore

    Sunpreet Singh

  4. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    Joao Paulo Davim

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Kinge, P.M., Jha, U.C. (2020). Manufacturing Intelligence in the Context of Indian SME’s. In: Krolczyk, G., Prakash, C., Singh, S., Davim, J. (eds) Advances in Intelligent Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4565-8_3

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  • DOI: https://doi.org/10.1007/978-981-15-4565-8_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4564-1

  • Online ISBN: 978-981-15-4565-8

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