A Fuzzy-Guided Genetic Algorithm for Quality Enhancement in the Supply Chain

Tang, Cassandra X. H.; Lau, Henry C. W.

doi:10.5220/0001865100860090

Cited by 1 publication

(1 citation statement)

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“…The application of AI in supply chain management extends beyond performance enhancement, with studies emphasizing its role in quality enhancement, fraud prediction, and resilience analytics within supply chains (Tang & Lau, 2009;Lokanan & Maddhesia, 2022;Goodarzian et al, 2021). Moreover, AI has been recognized as an enabler for sustainable supply chains, offering solutions for risk mitigation and sustainable financing streams (Naz et al, 2021;Pawlicka & Bal, 2022).…”

Section: Introductionmentioning

confidence: 99%

AI in supply chain optimization: A comparative review of USA and African Trends

Akoh Atadoga,

Ogugua Chimezie Obi,

Femi Osasona

et al. 2024

Int. J. Sci. Res. Arch.

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The integration of Artificial Intelligence (AI) in supply chain management has emerged as a critical driver of efficiency and competitiveness in global markets. This paper provides a comparative review of AI trends in supply chain optimization between the United States and African regions, shedding light on the unique challenges and opportunities faced by each. In the United States, AI adoption in supply chain optimization has been robust, with a focus on predictive analytics, machine learning, and advanced automation technologies. American companies leverage AI to enhance demand forecasting, optimize inventory management, and streamline logistics processes. The integration of AI-driven solutions has allowed U.S. businesses to achieve higher accuracy in demand predictions, reduce lead times, and minimize operational costs. Furthermore, the use of AI algorithms in route optimization has significantly improved delivery efficiency, leading to enhanced customer satisfaction. Contrastingly, African countries are experiencing a more gradual but steadily increasing adoption of AI in supply chain optimization. Limited access to advanced technology infrastructure, coupled with resource constraints, has posed challenges for African businesses. However, innovative approaches are being explored, such as the use of mobile technologies and cloud-based solutions to overcome infrastructure limitations. AI applications in African supply chains focus on improving visibility, minimizing waste, and ensuring timely delivery. The continent's diverse supply chain landscape, encompassing agriculture, mining, and manufacturing, presents a unique set of challenges that AI aims to address. Both the United States and African nations recognize the potential of AI to transform supply chain management. While the U.S. is at the forefront of AI implementation, Africa is forging ahead with tailored solutions that align with its specific context. Collaboration and knowledge exchange between these regions could pave the way for a globalized approach to AI in supply chain optimization. This review underscores the importance of understanding regional nuances in adopting AI technologies, fostering collaboration for mutual benefit, and advancing the global evolution of AI-driven supply chain management.

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