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Comprehensive Study of Artificial Intelligence Tools in Supply Chain

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Advances in Industrial and Production Engineering

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

Abstract

The global supply chain has become more complex in recent years, and the advent of artificial intelligence tools is set to improve the functioning of supply chain. This paper examines the effect of artificial intelligence tools on key parameters of supply chain such as cost, quality, pace, reliability, and sustainability. The Blockchain, the internet of things, the big data technologies, and the machine learning are the new potential enablers of sustainable manufacturing supply chain. This study reviews the current state-of-art research efforts and provides a systematic overview of the current and potential research directions to recognize the market trend in the adoption of these new technologies and some of the challenges as well.

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References

  1. Mistry, I., Tanwar, S., Tyagi, S., Kumar, N.: Blockchain for 5G-enabled IoT for industrial automation: A systematic review, solutions, and challenge. Mech. Syst. Sign. Process. 135 (2020)

    Google Scholar 

  2. Blockchain technology for enhancing supply chain Resilience: Hockey Min. Bus. Horiz. 62, 35–45 (2019)

    Article  Google Scholar 

  3. Azzia, R., Kilany Chamouna, R., Sokhnb, M.: The power of a blockchain-based supply chain. Comput. Indus. Eng. 135, 582–592 (2019)

    Google Scholar 

  4. Czachorowski, K., Solesvik, M., Kondratenko, Y.: The application of Blockchain technology in the Maritime Industry. Springer Nature Switzerland AG (2019)

    Google Scholar 

  5. Chang, S.E., Chen, Y.C., Lu, M.F.: Supply chain re-engineering using blockchain technology: A case of smart contract-based tracking process. Technol Forecast Soc Change 144, 1–11 (2019)

    Google Scholar 

  6. Kamblea, S.S., Gunasekaran, A., Gawankar, S.A..: Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. Int. J. Prod. Econ. 219, 179–194 (2019)

    Google Scholar 

  7. Lee, M.L., Yoo, J., Kim, S.W., Lee, J.H., Hong, J.: Autonomic machine learning platform. Int. J. Inf. Manage. 49, 491–501 (2019)

    Google Scholar 

  8. Niu, X., Li, Z.: Research on Supply Chain Management Based on Blockchain Technology. IOP J. Phys. 1176 (2019)

    Google Scholar 

  9. Manavalan, E., Jayakrishna, K.: A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements (2019)

    Google Scholar 

  10. Nawaz, F, Janjua, N. K., Hussain, O.K.: Predictive complex event processing and reasoning for IoT-enabled supply chain (2019)

    Google Scholar 

  11. Baryannisa, G., Danib, S.: Grigoris Antonioua, Predicting supply chain risk using machine learning. Thetrade-off between performance and interpretability (2019)

    Google Scholar 

  12. Coronado Mondragon, A. E., Coronado, C.E.: Investigating the applicability of distributed ledger/blockchain technology in manufacturing and perishable goods supply chains. In IEEE 6th International conference on Industrial Engineering and Applications (2019)

    Google Scholar 

  13. Settemsdal, S., Siemens:Machine Learning and Artificial Intelligence as a Complement to Condition Monitoring in a Predictive Maintenance Setting (2019)

    Google Scholar 

  14. Bhandari, B.:Supply Chain Management, Blockchains and Smart Contracts. NYU School of Law (2019)

    Google Scholar 

  15. Mushtaq, A., Ul Haq, I.: Implications of Blockchain In Industry 4.O. Pakistan Inst. of Engineering and Applied Sciences (PIEAS) (2018)

    Google Scholar 

  16. Koens, T., Poll, E.: The Drivers Behind Blockchain Adoption: The Rationality of Irrational Choices, Radboud University, The Netherlands (2018)

    Google Scholar 

  17. Sharma, P.K., Kumar, N., Park, J. H.: Blockchain-based distributed framework for automotive industry in a smart city. IEEE Trans. Indus. Inf. (2018)

    Google Scholar 

  18. Kottler, F.: Potential and barriers to the implementation of blockchain technology in supply chain management. University of Hamburg (2018)

    Google Scholar 

  19. Li, S., Xu, L.D., Zhao, S.:5G Internet of Things: A survey IEEE (2018)

    Google Scholar 

  20. Novo, O.: Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT (2018)

    Google Scholar 

  21. Li, S., Xu, L.D., Zhao, S.: J. Indus. Inf. Integr. (2018)

    Google Scholar 

  22. Sisinni, E., Saifullah, A., Han, S., Mikael Gidlund, U.: Industrial Internet of Things: Challenges, Opportunities, and Direction. IEEE (2018)

    Google Scholar 

  23. Rejeb, A., Keogh, J.G., Treiblmaier, H.: Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management. (2018)

    Google Scholar 

  24. Panarello, A., Tapas, N., Merlino, G, Longo, F., Puliafito, A.: Blockchain and IoT Integration: A Systematic Survey (2018).

    Google Scholar 

  25. Zabihi Naeini, E.: A machine learning approach to quantitative interpretation. Ikon Science (2018)

    Google Scholar 

  26. Deloitte.: Continuous interconnected supply chain with Blockchain and Internet of Things in supply chain traceability (2018)

    Google Scholar 

  27. Ball, K., Energy, D., Arbus, T., Odi, U., Sneed, J.: The Rise of the Machines, Analytics, and the Digital Oilfield: Artificial Intelligence in the Age of Machine Learning and Cognitive Analytics (2017)

    Google Scholar 

  28. O'Byrne.: Blockchain Technology is Set to Transform the Supply Chain (2017)

    Google Scholar 

  29. Cecere, L.: Moving Blockchain Forward: Seven use cases for hyperledger in supply chain (2017)

    Google Scholar 

  30. Rosic, A.: Smart Contracts: The Blockchain Technology That Will Replace Lawyers. Blockgeeks (2016)

    Google Scholar 

  31. Cao, Q., Banerjee, R., Gupta, S., Li, J., Zhou, W.: B, Jeyachandra. Data driven production forecast using machine learning, Schlumberger. (2016)

    Google Scholar 

  32. Weber, I., Xu, X, Riveret, R., Governatori, G., Ponomarev, A., Mendlin, J.: Untrusted Business Process Monitoring and Execution Using Blockchain, School of Computer Science and Engineering, UNSW, Australia (2016)

    Google Scholar 

  33. Xu, L. D., He, W., Li, S.: Internet of Things in Industries: A Survey: Transaction on Industrial Informatics IEEE (2014)

    Google Scholar 

  34. Subrahmanya, N., Peng, X.U., El-Bakry, A., Reynolds, C.: Advanced machine learning methods for production data pattern recognition (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Amity University Uttar Pradesh, Noida, India

    Manish Kumar Ojha, Bal Krishna Sharma, Rajat Rana, Sumit Kumar & Sumit Gupta

  2. Graphic Era Hill University, Bhimtal Campus, Bhimtal, Uttarakhand, India

    Poonam Ojha

Authors
  1. Manish Kumar Ojha
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  2. Bal Krishna Sharma
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  3. Rajat Rana
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  4. Sumit Kumar
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  5. Sumit Gupta
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  6. Poonam Ojha
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Corresponding author

Correspondence to Manish Kumar Ojha .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Amity School of Engineering and Technology, Noida, Uttar Pradesh, India

    Rakesh Kumar Phanden

  2. Department of Mechanical Engineering, Amity School of Engineering and Technology, Noida, Uttar Pradesh, India

    K. Mathiyazhagan

  3. Department of Mechanical Engineering, Amity School of Engineering and Technology, Noida, Uttar Pradesh, India

    Ravinder Kumar

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

    J. Paulo Davim

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Cite this paper

Ojha, M.K., Sharma, B.K., Rana, R., Kumar, S., Gupta, S., Ojha, P. (2021). Comprehensive Study of Artificial Intelligence Tools in Supply Chain. In: Phanden, R.K., Mathiyazhagan, K., Kumar, R., Paulo Davim, J. (eds) Advances in Industrial and Production Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-33-4320-7_16

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  • DOI: https://doi.org/10.1007/978-981-33-4320-7_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4319-1

  • Online ISBN: 978-981-33-4320-7

  • eBook Packages: EngineeringEngineering (R0)

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