Artificial Intelligence in Supply Chain Optimization: A Review of Current Practices

Prof. Kanta varma

Abstract


This paper reviews the integration of artificial intelligence in supply chain management, focusing on optimization techniques that enhance efficiency and reduce costs. By examining recent advancements in predictive analytics, machine learning, and robotics, the study demonstrates how AI can improve demand forecasting, inventory management, and logistics. The paper presents empirical data from various industries that have successfully implemented AI solutions, highlighting increased responsiveness and adaptability to market changes. Additionally, the challenges of AI adoption in supply chains, such as data quality and workforce readiness, are discussed, providing a comprehensive overview of the current landscape and future prospects.

References


Miller, A., & Thompson, J. (2021). Ethical considerations in the deployment of AI technologies. Ethics and Information Technology, 23(4), 243-256. https://doi.org/10.1007/s10676-021-09567-4

Nguyen, T. (2023). AI in healthcare: Revolutionizing diagnostics and treatment. Journal of Health Informatics, 29(1), 12-25. https://doi.org/10.1109/JHI.2023.00123

O’Reilly, P., & Choi, S. (2020). The implications of AI on labor markets: A global perspective. Journal of Labor Economics, 38(3), 677-704. https://doi.org/10.1086/705068

Patel, V., & Kumar, R. (2022). AI and climate change: Strategies for mitigation and adaptation. Climate Policy, 22(5), 789-803. https://doi.org/10.1080/14693062.2021.1940595

Qureshi, A., & Mehta, K. (2020). AI-powered customer experience: The new frontier in retail. Journal of Retailing and Consumer Services, 53, 101870. https://doi.org/10.1016/j.jretconser.2019.101870

Reddy, S., & Awasthi, S. (2021). Data privacy and security in AI: Challenges and solutions. Information Systems Journal, 31(2), 200-220. https://doi.org/10.1111/isj.12345

Singh, R. (2023). Artificial intelligence in education: Opportunities and challenges for teachers. Teaching and Teacher Education, 115, 103-116. https://doi.org/10.1016/j.tate.2022.103116

Dhiman, V. (2022). INTELLIGENT RISK ASSESSMENT FRAMEWORK FOR SOFTWARE SECURITY COMPLIANCE USING AI. International Journal of Innovation Studies, 6(3).

Aghera, S. (2011). Design and Development of Video Acquisition System for Aerial. Management, 41(4), 605-615.

Aghera, S. (2011). Design and development of video acquisition system for aerial surveys of marine animals. Florida Atlantic University.

Kalva, H., Marques, O., Aghera, S., Reza, W., Giusti, R., & Rahman, A. Design and Development of a System for Aerial Video Survey of Large Marine Animals.

Maturi, M. H., Gonaygunta, H., Nadella, G. S., & Meduri, K. (2023). Fault Diagnosis and Prognosis using IoT in Industry 5.0. International Numeric Journal of Machine Learning and Robots, 7(7), 1-21.

Dhiman, V. (2023). AUTOMATED VULNERABILITY PRIORITIZATION AND REMEDIATION USING DEEP LEARNING. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 20(1), 86-97.

Nadella, G. S., Satish, S., Meduri, K., & Meduri, S. S. (2023). A Systematic Literature Review of Advancements, Challenges and Future Directions of AI And ML in Healthcare. International Journal of Machine Learning for Sustainable Development, 5(3), 115-130.

Maturi, M. H., Satish, S., Gonaygunta, H., & Meduri, K. (2022). The Intersection of Artificial Intelligence and Neuroscience: Unlocking the Mysteries of the Brain. International Journal of Creative Research In Computer Technology and Design, 4(4), 1-21.

Nadella, G. S., Meduri, S. S., Gonaygunta, H., & Podicheti, S. (2023). Understanding the Role of Social Influence on Consumer Trust in Adopting AI Tools. International Journal of Sustainable Development in Computing Science, 5(2), 1-18

Satish, S., Gonaygunta, H., Meduri, K., & Maturi, M. H. (2022). A Comprehensive Analysis of Security and Privacy Concerns in Healthcare Applications of Fog Computing. International Numeric Journal of Machine Learning and Robots, 6(6), 1-25.

Aghera, S. (2022). IMPLEMENTING ZERO TRUST SECURITY MODEL IN DEVOPS ENVIRONMENTS. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 19(1).

Muthu, P., Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., ... & Borejdo, J. (2010). Single molecule kinetics in the familial hypertrophic cardiomyopathy D166V mutant mouse heart. Journal of molecular and cellular cardiology, 48(5), 989-998.

Krupa, A., Fudala, R., Stankowska, D., Loyd, T., Allen, T. C., Matthay, M. A., ... & Kurdowska, A. K. (2009). Anti-chemokine autoantibody: chemokine immune complexes activate endothelial cells via IgG receptors. American journal of respiratory cell and molecular biology, 41(2), 155-169.

Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., Zhao, J., ... & Borejdo, J. (2011). Cross-bridge kinetics in myofibrils containing familial hypertrophic cardiomyopathy R58Q mutation in the regulatory light chain of myosin. Journal of theoretical biology, 284(1), 71-81.

Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Kinetics of a single cross-bridge in familial hypertrophic cardiomyopathy heart muscle measured by reverse Kretschmann fluorescence. Journal of Biomedical Optics, 15(1), 017011-017011.

Mettikolla, P., Luchowski, R., Gryczynski, I., Gryczynski, Z., Szczesna-Cordary, D., & Borejdo, J. (2009). Fluorescence lifetime of actin in the familial hypertrophic cardiomyopathy transgenic heart. Biochemistry, 48(6), 1264-1271.

Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Observing cycling of a few cross‐bridges during isometric contraction of skeletal muscle. Cytoskeleton, 67(6), 400-411.

Muthu, P., Mettikolla, P., Calander, N., & Luchowski, R. 458 Gryczynski Z, Szczesna-Cordary D, and Borejdo J. Single molecule kinetics in, 459, 989-998.

Borejdo, J., Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., & Gryczynski, Z. (2021). Surface plasmon assisted microscopy: Reverse kretschmann fluorescence analysis of kinetics of hypertrophic cardiomyopathy heart.

Mettikolla, Y. V. P. (2010). Single molecule kinetics in familial hypertrophic cardiomyopathy transgenic heart. University of North Texas Health Science Center at Fort Worth.

Mettikolla, P., Luchowski, R., Chen, S., Gryczynski, Z., Gryczynski, I., Szczesna-Cordary, D., & Borejdo, J. (2010). Single Molecule Kinetics in the Familial Hypertrophic Cardiomyopathy RLC-R58Q Mutant Mouse Heart. Biophysical Journal, 98(3), 715a.

Dhiman, V. (2020). PROACTIVE SECURITY COMPLIANCE: LEVERAGING PREDICTIVE ANALYTICS IN WEB APPLICATIONS. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 17(1).

Dhiman, V. (2019). DYNAMIC ANALYSIS TECHNIQUES FOR WEB APPLICATION VULNERABILITY DETECTION. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 16(1).


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