SpringerLink
Log in

Artificial Intelligence and Automation for Industry 4.0

  • Chapter
  • First Online:
Computational Intelligence for Modern Business Systems

Abstract

The key premise of smart factories and enterprise 4.0 is the application of AI by employing robots to perform hard activities, lower fees, and improve the high-quality of products and solutions. Artificial intelligence (AI) is infiltrating the industrial sector with the help of cyber-bodily systems, fusing the physical and digital worlds. Artificial intelligence (AI) makes manufacturing smarter and more capable of co** with modern difficulties like customizable needs, faster time to market, and an expanding spectrum of sensors in equipment. The usage of bendy robots combined with artificial intelligence facilitates the production of a wide range of products. AI technologies can be used to analyse massive volumes of real-time data collected from a variety of sensors (such as data mining). AI is ushering in a new industrial revolution with intelligent automation, massive data, and networking. Time or place, data integration universally with networks evolves and allows completely automated supply chains, Industry 4.0 will bring the integration of horizontal and vertical systems with businesses, departments, features, and talents will become much more cohesive. Extra systems will be enhanced with embedded computers as the Internet of Things becomes more industrialized, and they will be connected using standard technologies. This allows machines to communicate and interact with one another, and a more centralized machine controller becomes increasingly vital. As cross-company, universal data-integration networks expand and enable totally automated value chains in Industry 4.0, horizontal and vertical system integration among firms, departments, functions, and capacities will become much more cohesive. Industrial auto solutions and the Internet of Things will also add embedded computing to more objects and connect them using standard standards.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Renjen P (2021) Deloitte in India recognised as a leading inclusiveworkplace.ClinMedNetPrints. https://www2.deloitte.com/content/dam/insights/us/articles/us32959-industry-4-0/DI_Industry4.0. Accessed 23 Aug 2021

  2. Hohnen P (2007) Corporate social responsibility an implementation guide for business. NetPrints. https://www.iisd.org. Accessed 24 Aug 2021

  3. Zhou J, Zhou Y, Wang B, Zang J (2019) Human–cyber–physical systems (HCPSs). In the context of new-generation intelligent manufacturing. Engineering 5(4):624–636. https://doi.org/10.1016/j.eng.2019.07.015

    Article  Google Scholar 

  4. Guo Q et al (2009) A novel approach for multi-agent-based Intelligent manufacturing system. Inf Sci 179(18):307–390. https://doi.org/10.1016/j.ins.2009.05.009

    Article  Google Scholar 

  5. Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems and systems architect. Manuf Lett 3:18–23. https://doi.org/10.1016/j.mfglet.2014.12.001

    Article  Google Scholar 

  6. Shi J, Wan J, Yan H, Suo H (2011) A survey of cyber-physical systems. In: Wireless communications and signal processing (WCSP). 2011 International conference on. IEEE, pp 1–6. https://doi.org/10.1109/WCSP.2011.6096958

  7. Tuptuk N, Hailes S (2018) Security of smart manufacturing systems. J Manuf Syst 47:93–106. https://doi.org/10.1016/j.jmsy.2018.04.007

    Article  Google Scholar 

  8. Bahrin MAK, Othman MF, Azli NHN, Talib MF (2016) Industry 4.0: a review on industrial automation and robotic. Jurnal Teknologi 78:137–143. https://doi.org/10.11113/jt.v78.9285

    Article  Google Scholar 

  9. Ben-Daya MH (2017) Internet of things and supply chain management: a literature review. Int J Prod Res. https://doi.org/10.1080/00207543.2017.1402140

    Article  Google Scholar 

  10. Autor D, Dorn D (2013) The growth of low-skill service jobs and the polarization of the US labor market. Am Eco Rev 103(5):1553–1597

    Article  Google Scholar 

  11. Branger J (2015) From automated home to sustainable healthy and manufacturing home a new story enabled by the Internet-of-Things and Industry 4.0. J Manag Anal 2(4):314–332. https://doi.org/10.1080/23270012.2015.1115379

    Article  Google Scholar 

  12. Baxter NE (1989) Research guidance: not giving it your best product testing with consumers.american society for testing and materials, pp 10–22. https://doi.org/10.1520/STP19490S

  13. Bughin J et al (2007) Artificial intelligence. The next digital frontier. Mc building change the manufacturing landscape: an Industry 4.0 perspective. Int J Mech 2–24. https://doi.org/10.5281/zenodo.1336426. Accessed on 28 Oct 2021

  14. Carvalho TP et al (2019) A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng. https://doi.org/10.1016/j.cie.2019.106024

    Article  Google Scholar 

  15. Gupta NA (2017) Literature survey on artificial intelligence. ClinMed Net Prints. https://www.ijert.org/research/a-literature-survey-on-artificial-intelligence-IJERTCONV5IS19015.pdf. Accessed on 18 Sept 2021

  16. Dallasega PR (2018) Industry 4.0 as an enabler of proximity for construction supply chains: a systematic literature review. Comput Ind 9:205–225. https://doi.org/10.1016/j.compind.2018.03.039

    Article  Google Scholar 

  17. Boyd D, Crawford K (2012) Critical questions for big data. Inf Commun Soc 15(5):662–679

    Article  Google Scholar 

  18. Brynjolfsson E, McAfee A (2014) The second machine age: work, progress, and prosperity in a time of brilliant technologies. W.W Norton, New York, NY

    Google Scholar 

  19. Brettel MF (2014) How virtualization decentralization and network building change the manufacturing landscape: an Industry 40 perspective. Int J Mech. https://doi.org/10.5281/zenodo.1336426

    Article  Google Scholar 

  20. Brennan LF (2015) Manufacturing in the world: Where next? Int J Oper Prod Manag 35(9):1253–1274. https://doi.org/10.1108/IJOPM-03-2015-0135

    Article  MathSciNet  Google Scholar 

  21. Thoben KD et al (2017) Industrie 4.0 and smart manufacturing—a review of research issues and application examples. Int J Autom Technol 11(1):4–16

    Article  Google Scholar 

  22. Lee J, Ardakani HD, Yang S, Bagheri B (2015) Industrial big data analytics and cyber- physical systems for future maintenance & service innovation. Procedia CIRP. https://doi.org/10.1016/J.PROCIR.2015.08.026

    Article  Google Scholar 

  23. Buer SV, Strandhagen JO, Chan FTS (2018) The link between Industry 40 and lean manufacturing: map** current research and establishing a research agenda. Int J Prod 56(8):2924–2940. https://doi.org/10.1080/00207543.2018.1442945

    Article  Google Scholar 

  24. Ford M (2009) The lights in the tunnel. automation, accelerating technology and the economy of the future. Acculant Publishing, Wayne, PA

    Google Scholar 

  25. Intel gov (2011) Principles of artificial intelligence ethic for the intelligence community clinmednetprint. https://www.intelligence.gov/images/AI/Principles_of_.AI_Ethics_for_the_Intelligence_Community.pdf

  26. Tao F, Cheng Y, Da Li X, Zhang L, Li BH (2014) Cloud computing and Internet of things-based cloud manufacturing service system. IEEE Trans Ind Inform 10(2):1435–1442. https://doi.org/10.1109/TII.2014.2306383 Accessed 23 Aug 2021

  27. Kirtan Jha AD (2019) A comprehensive review on automation in agriculture using artificial intelligence. Artif Intell Agric 2:112–114. https://doi.org/10.1016/j.aiia.2019.05.004

    Article  Google Scholar 

  28. Christensen CM (1997) The innovator’s dilemma: when new technologies cause great firms to fail. Harvard Business School Press, Boston, Mass

    Google Scholar 

  29. Stolcke A (1997) Linguistic knowledge and empirical methods in speech recognition. AI Mag. https://doi.org/10.1609/aimag.v18i4.1319

    Article  Google Scholar 

  30. Judit Nagy JO (2018) The role and impact of industry 4.0 and the internet of things on the business strategy of the value chain—the case of hungary. Sustainability 10:1–24. https://doi.org/10.3390/su10103491

    Article  Google Scholar 

  31. Bekman E, Bound J, Machin S (1998) Implications of skill-biased technological change: international evidence. Quart J Econ 113(4):1245–1279

    Article  Google Scholar 

  32. Heathcote J, Storesletten K, Violante G (2010) The macroeconomic implications of rising wage inequality in the United States. J Polit Econ 118(4):681–722

    Article  Google Scholar 

  33. Autor D (2014) Polanyi’s paradox and the shape of employment growth. Paper presented at the Kansas City’s economic policy symposium on ‘‘Re- Evaluating Labor Market Dynamics”, Jackson Hole, WY

    Google Scholar 

  34. Levy F, Murnane R (2013) Dancing with robots: human skills for computerized work, Third Way Next. Fresh Thinking

    Google Scholar 

  35. Eleanor Bird JFS (2020) The ethics of artificial intelligence: issues and initiatives. ClinMedNetPrints. https://www.europarl.europa.eu/thinktank/en/document

  36. Acemoglu D, Autor D (2013) Skills, tasks and technologies: implications for employment and earnings. In: Card D, Ashenfelter O (eds) Handbook of labor economics, vol 4B. Elsevier, Amsterdam, pp 1043–1171. Accessed 09 Oct 2021

    Google Scholar 

  37. Eleanor B et al (2020) Future of science and technology (STOA).STOA | Panel for the future of science and technology. pp 4–124: NetPrint. https://www.europarl.europa.eu/RegData/etudes/STUD/2020/634452/EPRS_STU(2020)634452_EN.pdf

  38. Ribeiro J et al (2021) Robotic process automation and artificial intelligence in industry 4.0—a literature review.51–58. Accessed 28 Aug 2021

    Google Scholar 

  39. Silva RL, Rudek M, Szejka AL, Junior OC (2018) Machine vision systems for industrial quality control inspections. In: Product lifecycle management to support industry 4.0: 15th IFIP WG 5.1 international conference, PLM 2018, Turin, Italy, July 2–4, 2018, Proceedings 15. Springer International Publishing, pp 631–641

    Google Scholar 

  40. Nilsson N (1980) Principles of artificial inteligence. Tioga Press, Menlo Park CA, p 366

    Google Scholar 

  41. Newell A (1980) A textbook that points the way review of Nils Nilsson. In: A. Newell. California: Tioga Press

    Google Scholar 

  42. Unknown (2021) Automation anywhere robotic process automation to ERP automation anywhere. NetPrints. https://www.automationanywhere.com/lp/automate-any-erp-process-with-rpa Accessed 25 Aug 2021

  43. Lacity M, Willcocks LP (2016) A new approach for automating services. MIT Sloan Manag Rev 58:40–49

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Christ University, Gaziabad, India

    Amrita Chaurasia

  2. Vellore Institute of Technology University, Bhopal, India

    Bhakti Parashar

  3. LBEF Campus, Kathmandu, Nepal

    Sandeep Kautish

Authors
  1. Amrita Chaurasia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bhakti Parashar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sandeep Kautish
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bhakti Parashar .

Editor information

Editors and Affiliations

  1. LBEF Campus, Kathmandu, Nepal

    Sandeep Kautish

  2. Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India

    Prasenjit Chatterjee

  3. Faculty of Organizational Sciences, Department of Operations Research and Statistics, University of Belgrade, Belgrade, Serbia

    Dragan Pamucar

  4. Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India

    N. Pradeep

  5. Symbiosis Centre for Management Studies, Symbiosis International (Deemed University), Pune, India

    Deepmala Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chaurasia, A., Parashar, B., Kautish, S. (2024). Artificial Intelligence and Automation for Industry 4.0. In: Kautish, S., Chatterjee, P., Pamucar, D., Pradeep, N., Singh, D. (eds) Computational Intelligence for Modern Business Systems . Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore. https://doi.org/10.1007/978-981-99-5354-7_18

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-5354-7_18

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5353-0

  • Online ISBN: 978-981-99-5354-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation