AI for Optimizing Supply Chain Management

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed various sectors, with supply chain management (SCM) being a notable beneficiary. This research paper explores the impact of AI on optimizing supply chain processes, focusing on its applications in predictive analytics, natural language processing, robotics and automation, blockchain, and the Internet of Things (IoT). Through a mixed-method approach involving quantitative surveys, qualitative interviews, and secondary data analysis, the study evaluates how AI-driven solutions enhance key performance indicators (KPIs) such as inventory turnover, lead time, order accuracy, and cost reduction. The results indicate substantial improvements in these KPIs post-AI implementation, highlighting AI's role in achieving greater operational efficiency, accuracy, and cost-effectiveness in SCM. Challenges associated with AI adoption, including data quality, integration with legacy systems, and skill gaps, are also discussed. The paper provides actionable insights and recommendations for organizations aiming to leverage AI technologies to optimize their supply chain operations. This research contributes to the growing body of knowledge on AI in SCM and offers practical guidance for industry practitioners

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2024-11-13
DOI
https://doi.org/10.22214/ijraset.2024.65059
Citations
2
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.70
Controls
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article

AI for Optimizing Supply Chain Management

Prince Kumar
2 citations
International Journal for Research in Applied Science and Engineering Technology
Scheduling and Optimization Algorithms
0.70
article

AI for Optimizing Supply Chain Management

Prince Kumar
article en
2 citations

Abstract

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed various sectors, with supply chain management (SCM) being a notable beneficiary. This research paper explores the impact of AI on optimizing supply chain processes, focusing on its applications in predictive analytics, natural language processing, robotics and automation, blockchain, and the Internet of Things (IoT). Through a mixed-method approach involving quantitative surveys, qualitative interviews, and secondary data analysis, the study evaluates how AI-driven solutions enhance key performance indicators (KPIs) such as inventory turnover, lead time, order accuracy, and cost reduction. The results indicate substantial improvements in these KPIs post-AI implementation, highlighting AI's role in achieving greater operational efficiency, accuracy, and cost-effectiveness in SCM. Challenges associated with AI adoption, including data quality, integration with legacy systems, and skill gaps, are also discussed. The paper provides actionable insights and recommendations for organizations aiming to leverage AI technologies to optimize their supply chain operations. This research contributes to the growing body of knowledge on AI in SCM and offers practical guidance for industry practitioners

International Journal for Research in Applied Science and Engineering TechnologyVol. 12(11)
Industry, innovation and infrastructure
Openalex Percentile: Top 25%
Scheduling and Optimization Algorithms
0.70
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AI for Optimizing Supply Chain Management — Prince Kumar · International Journal for Research in Applied Science and Engineering Technology (2024) | TGRS Research Map | TGRS