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Digitization for reliable and efficient manufacturing

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Abstract

Manufacturing is an important economic activity and all manufacturers are fiercely vying to grab the scarce market space using quality and price competitiveness to achieve their objectives. The paper highlights digitization as a means to achieve greater reliability and efficiency in manufacturing operations. Mathematical models capable of analyzing enormous data using statistics and optimization algorithms along with the development of affordable electronics and software algorithms does translate manufacturing from traditional to one that is data driven. Digitization does lead to enhanced efficiency in manufacturing operations. The shortcoming of these is that, it needs cultural change, which is difficult, but not impossible to implement in a manufacturing system. The methodologies explained might enable practicing managers to translate their manufacturing systems into ones that are data driven, reliable and efficient. Though there are evidences that suggest the use of data analytics in other domains, such as e-commerce, but the technology has not yet been exploited for data-based manufacturing. The manuscript gives an insight into how digitization can act as a driver for higher reliability and efficiency in manufacturing domain. The paper attempts to fill this gap. The methodologies explained in the manuscript may act as a good guide for practicing operational managers.

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References

  • Bhanot N, Rao PV, Deshmukh SG (2016) Identifying the perspectives for sustainability enhancement: a text mining approach for a machining process. J Adv Manag Res 13(3):244–270

    Article  Google Scholar 

  • Bokrantz J, Ylipää T, Skoogh A (2014) Lean principles and engineering tools in maintenance organizations–a survey study. Swedish Production Symposium, Gothenburg

    Google Scholar 

  • Brodsky A, Shao G, Riddick F (2016) Process analytics formalism for decision guidance in sustainable manufacturing. J Intell Manuf 27(3):561–580

    Article  Google Scholar 

  • Da Xu L (2016) An internet-of-things initiative for one belt one road (OBOR). Front Eng Manag 3(3):206–223

    Article  Google Scholar 

  • Davenport TH, Harris JG (2007) Competing on analytics: the new science of winning. Harvard Business Press, Brighton

    Google Scholar 

  • Gutowski T, Murphy C, Allen D, Bauer D, Bras B, Piwonka T, Sheng P, Sutherland J, Thurston D, Wolff E (2005) Environmentally Benign Manufacturing: observations from Japan, Europe and the United States. J Clean Prod 13:1–17

    Article  Google Scholar 

  • Jain S, Shao G (2014) Virtual factory revisited for manufacturing data analytics. In: Proceedings of the 2014 Winter Simulation Conference, IEEE Press, pp 887–898

  • Jena J, Fulzele V, Gupta R, Sherwani F, Shankar R, Sidharth S (2016) A TISM modeling of critical success factors of smartphone manufacturing ecosystem in India. J Adv Manag Res 13(2):203–224

    Article  Google Scholar 

  • Jonsson K, Holmstrom J, Leven P (2010) Organizational dimensions of e-maintenance: a multi-contextual perspective. Int J Syst Assur Eng Manag 1(3):210–218

    Article  Google Scholar 

  • Kordonowy DN (2002) A power assessment of machining tools. BS Thesis in Mechanical Engineering MIT, Cambridge, Massachusetts, USA

  • Lade P, Ghosh R, Srinivasan S (2017) Manufacturing analytics and industrial internet of things. IEEE Intell Syst 32(3):74–79

    Article  Google Scholar 

  • Lechevalier D, Narayanan A, Rachuri S (2014) Towards a domain-specific framework for predictive analytics in manufacturing. IEEE International Conference on Big Data, IEEE, pp 987–995

  • Lee J, Bagheri B, Kao HA (2013) Recent advances and trends in predictive manufacturing systems in big data environment. Manufac Lett 1(1):38–41

    Article  Google Scholar 

  • Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufac Lett 3(1):18–23

    Article  Google Scholar 

  • Logeais G (2008) The internet of things in the context of manufacturing. http://docbox.etsi.org/workshop/2008/200812_WIRELESSFACTORY/SAP_LOGEAIS.pdf. Accessed 25 May 2017

  • Manyika J, Sinclair J, Dobbs R, Strube G, Rassey L, Mischke J, Remes J, Roxburgh C, George K, O’Halloran D, Ramaswamy S (2012) Manufacturing the future: The next era of global growth and innovation. Mckinsey Global Institute

  • Sherrill A, Neumann J (2017) US Manufacturing: Federal Programs Reported Providing Support and Addressing Trends. Report No. GAO-2017-240, United States Government Accountability Office

  • Shin SJ, Woo J, Rachuri S (2014) Predictive analytics model for power consumption in manufacturing. Proc CIRP 15:153–158

    Article  Google Scholar 

  • Ündey C, Ertunç S, Mistretta T, Looze B (2010) Applied advanced process analytics in biopharmaceutical manufacturing: challenges and prospects in real-time monitoring and control. J Process Control 20(9):1009–1018

    Article  Google Scholar 

  • Zhong RY, Xu C, Chen C, Huang GQ (2017) Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors. Int J Prod Res 55(9):2610–2621

    Article  Google Scholar 

Download references

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Correspondence to Piyush Gupta.

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Gupta, S., Gupta, P. Digitization for reliable and efficient manufacturing. Life Cycle Reliab Saf Eng 7, 245–250 (2018). https://doi.org/10.1007/s41872-018-0051-y

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  • DOI: https://doi.org/10.1007/s41872-018-0051-y

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