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Significance of digital technology in manufacturing sectors: Examination of key factors during Covid-19

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  • Mohapatra, Biswajit
  • Tripathy, Sushanta
  • Singhal, Deepak
  • Saha, Rajnandini

Abstract

The Covid-19 pandemic has been the center of human existential chaos throughout the world, which also has affected the manufacturer in an extraordinary and unexpected way. With the decline in demand, supply, and workforce the industries are driven into the gloom. The concerned research objective is to explore the factors which impact manufacturing throughout the world during the epidemic of Covid-19. Further, it delineates the usage of advanced digital technologies like artificial intelligence (AI), big data analytics (BDA), and internet of things (IoT) to bring on solutions/approaches to evolving to pandemic-constrained manufacturing. An overall of twelve key factors is determined from extensive literature reviews which are categorized into challenges and solutions. Here, ISM methodology has been used to establish the interrelationship among identified twelve challenges and solutions. Further, MICMAC analysis has categorized them according to their driving and dependence power. The consequences display the absence of autonomous factors whilst efficient supply chain, centralized decision making, product diversification, and JIT along with revenue generation turn out to be significant dependant factors. The facilitators like digital technologies are the pre-cursors to the ultimate solution of revenue generation and termed preliminary solutions. The outcomes of this research will suggest eventual policy recommendations for industry leaders to progress manufacturing within Covid-19 constraints. It will offer a sturdy base for manufacturers around the world to tune to the new digital transformation of the production scenario.

Suggested Citation

  • Mohapatra, Biswajit & Tripathy, Sushanta & Singhal, Deepak & Saha, Rajnandini, 2022. "Significance of digital technology in manufacturing sectors: Examination of key factors during Covid-19," Research in Transportation Economics, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:retrec:v:93:y:2022:i:c:s0739885921001062
    DOI: 10.1016/j.retrec.2021.101134
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    Cited by:

    1. Chun-Yan Zhu & Dong-Liang Zhang, 2022. "An Empirical Study on the Mechanism of Dynamic Capacity Formation in the Supply Chain," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    2. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.

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