LAWS GOVERNED ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN SUPPLY CHAIN MANAGEMENT

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Published: Jun 25, 2023
Keywords:
Artificial intelligence machine learning legal environment supply chain

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ABIDA HAFEEZ, MADIHA, FAHAD ASGHAR, WAJID ALI, MUHAMMAD RASHID, WAHAJ ALI

Abstract

The current investigation used a quantitative research plan as its guide, with the application of AI and ML in the process of supply chain automation serving as the primary focus of the investigation. The primary research interests in this work were supply chain automation trends, the impact that technologies and legal environment brings to machine learning and artificial intelligence and have impact on supply chain results, and the nature of technologies that contribute to supply chain enhancements. In addition, the study also explored the impact that technologies like machine learning and artificial intelligence have on supply chain results. The research study arrive at trustworthy conclusions by using a sample size that was sufficient in size (100 people) and the most appropriate according to the objectives of the study. These individuals were requested to complete an online questionnaire that included both survey questions and demographic questions. Because of this, the study is able to arrive at the conclusion that machine learning is utilized extensively throughout the supply chains of the UAE, whereas artificial intelligence is conspicuously absent from the country. According to the findings, this could be due to the fact that legal environment has an impact and AI is still in its early stages as a technology and still requires significant advancement before it can be useful in the current supply chain setting. In conclusion, the researchers suggest that under the legal boundaries, supply networks make use of technology to augment rather than replace human labor.

Article Details

Issue
Vol. 11 No. 4 (2023)
Section
Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Author Biography

ABIDA HAFEEZ, MADIHA, FAHAD ASGHAR, WAJID ALI, MUHAMMAD RASHID, WAHAJ ALI

DR. ABIDA HAFEEZ1, MADIHA2, FAHAD ASGHAR3, WAJID ALI4, MUHAMMAD RASHID5, WAHAJ ALI6

1Assistant Professor, Department of Economics, Division of Management and Administrative Science, University of Education, Lahore. 

2Institute of Business Information Technology, University of the Punjab, Lahore. 

3Department of Business Administration, Thal University Bhakkar

4PhD Scholar, Faculty of Management Sciences, International Islamic University Islamabad, Pakistan. 

5Department of Technology Management. International Islamic University Islamabad. E

6Lecturer, Department of Information Technology, The Islamia University of Bahawalpur.

References

Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993-1004.

Baryannis, G., Dani, S., Validi, S., & Antoniou, G. (2019). Decision support systems and artificial intelligence in supply chain risk management. In Revisiting supply chain risk (pp. 53-71). Springer, Cham.

Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202.

Browne, M. (2021). Artificial intelligence data-driven internet of things systems, real-time process monitoring, and sustainable industrial value creation in smart networked factories. Journal of Self-Governance and Management Economics, 9(2), 21-31.

Cobbe, J., & Singh, J. (2021). Artificial intelligence as a service: Legal responsibilities, liabilities, and policy challenges. Computer Law & Security Review, 42, 105573.

Darvazeh, S. S., Vanani, I. R., & Musolu, F. M. (2020). Big data analytics and its applications in supply chain management. New Trends in the Use of Artificial Intelligence for the Industry, 4, 175.

Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3), 43-53.

Dodds, S., & Hess, A. C. (2020). Adapting research methodology during COVID-19: lessons for transformative service research. Journal of Service Management.

Gray-Hawkins, M., & Lăzăroiu, G. (2020). Industrial artificial intelligence, sustainable product lifecycle management, and internet of things sensing networks in cyber-physical smart manufacturing systems. Journal of Self-Governance and Management Economics, 8(4), 19-28.

Gupta, R. K., & Kumari, R. (2017). Artificial intelligence in public health: Opportunities and challenges. JK Science, 19(4), 191–192.

Hofmann, E., Sternberg, H., Chen, H., Pflaum, A., & Prockl, G. (2019). Supply chain management and Industry 4.0: conducting research in the digital age. International Journal of Physical Distribution & Logistics Management.

Kothari, C. R. (2004). Research methodology: Methods and techniques. New Age International.

Kupriyanovsky, V.P., Dobrynin, A.P., Sinyagov, S.A. 2017. A holistic model of transformation in digital economy – how to become digital leaders. International Journal of Open Information Technologies, 5(1), 26-33.

Lei, Y., Qiaoming, H., & Tong, Z. (2023). Research on Supply Chain Financial Risk Prevention Based on Machine Learning. Computational Intelligence and Neuroscience, 2023.

Manavalan E, Jayakrishna K (2019) A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Comput Ind Eng 127:925–953

Narwane, V. S., Raut, R. D., Yadav, V. S., Cheikhrouhou, N., Narkhede, B. E., & Priyadarshinee, P. (2021). The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. Journal of enterprise information management, 34(5), 1452-1480.

Pandian, A. P. (2019). Artificial intelligence application in smart warehousing environment for automated logistics. Journal of Artificial Intelligence, 1(02), 63-72.

Plastino, E., & Purdy, M. (2018). Game changing value from Artificial Intelligence: eight strategies. Strategy & Leadership.

Reyes, P. M., Visich, J. K., & Jaska, P. (2020). Managing the dynamics of new technologies in the global supply chain. IEEE Engineering Management Review, 48(1), 156-162.

Reza Tizhoosh, H., & Pantanowitz, L. (2018). Artificial intelligence and digital pathology: Challenges and opportunities. Journal of Pathology Informatics, 9(1).

Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 104926.

Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517.

Yuan, G., Wu, S., & Wang, B. (2023). Supply chain management model based on machine learning. Neural Computing and Applications, 35(6), 4319-4335.

Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2022). Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 1-28.

Zatarain, J. M. N. (2017). The role of automated technology in the creation of copyright works: The challenges of artificial intelligence. International Review of Law, Computers and Technology, 31(1), 91–104.