The Future of AI in American Supply Chain Management

AI integration in supply chain management enhances efficiency and competitiveness in global marketplaces. This research compares AI developments in supply chain optimization in the US and Africa, highlighting specific difficulties and potential for each location. AI is widely used in supply chain optimization in the US, particularly for predictive analytics, machine learning, and advanced automation. American corporations use AI to improve demand forecasting, inventory management, and logistical processes. AI-powered solutions have helped U.S. businesses improve demand prediction accuracy, reduce lead times, and cut operational costs.

AI-powered route optimization improves delivery efficiency and customer

Satisfaction. In contrast, African countries are gradually but steadily adopting AI for supply chain optimization. African businesses face hurdles due to a lack of advanced technology infrastructure and resources. Innovative alternatives, including mobile and cloud-based solutions, are being investigated to address infrastructure limitations. AI applications in African supply chains aim to increase visibility, reduce waste, and ensure timely delivery. The continent's complex supply chain landscape, including agriculture, mining, and industry, provides distinct difficulties that AI can handle. AI has the ability to alter supply chain management, as recognized by both the US and Africa. Africa is leading the way in tailoring AI solutions to its own setting, while the United States is at the forefront of deployment. Collaboration and knowledge exchange across these regions could enable a multinational approach to AI in supply chain optimization. This analysis highlights the significance of understanding regional differences when implementing AI technologies, enabling collaboration for mutual benefit, and driving the global expansion of AI-driven supply chain management.The comparative research aims to assess AI integration in the US and identify potential trends and concerns in African countries. This includes identifying opportunities for collaboration and improvement. This study tries to apply ideas from AI adoption in the US to the African setting. Additionally, it aims to identify specific difficulties and possibilities in African supply chains to create specialized AI integration plans. This review compares two regions to promote knowledge exchange and collaboration for better supply chain optimization using AI technologies.

AI integration in supply chain management has gained attention from 

Researchers and practitioners for its potential to improve operational efficiency, resilience, and sustainability. AI technology enables enhanced data analysis, decision-making, and optimization, altering traditional supply chain procedures (Mohsen, 2023). Research has shown that AI can improve supply chain resilience, company performance, and digital transformation (Mohsen, 2023; Modgil et al., 2021; Sullivan & Wamba, 2022). AI-driven innovations can improve supply chain finance and sustainability, especially in industries like food and drink (Olan et al., 2021; Pawlicka & Bal, 2022). AI applications in supply chain management include quality enhancement, fraud prediction, and resilience analytics (Tang & Lau, 2009; Lokanan & Maddhesia, 2022; Goodarzian et al., 2021). AI is acknowledged as a key facilitator for sustainable supply chains, including risk mitigation and financing solutions (Naz et al., 2021; Pawlicka & Bal, 2022). Scholars are exploring the potential of AI and Machine Learning (ML) to digitally change supply chains, with an emphasis on uncovering new contexts and applications (Rana & Daultani, 2022).AI plays a crucial role in supply chain management by improving firm resilience to disruptions and optimizing the entire supply chain, making it a key tool for digital transformation (Sullivan & Wamba, 2022; Trong & Kim, 2020). AI integration with Blockchain and IoT is a growing research area, especially for sustainability and smart city efforts (WU et al., 2022). AI has the capacity to handle supply chain concerns, including dynamism and COSupply chain optimization, which is crucial for firms looking to improve efficiency and minimize costs. Strategic management involves overseeing the flow of goods and services from raw material procurement to final product delivery to consumers.

The incident has underlined the importance of optimizing

Supply chains to ensure resilience and agility (Pupavac et al., 2021). According to Li (2020), optimizing the supply chain is crucial for increasing competitiveness and meeting consumer needs. According to Fahimnia et al. (2011), effective supply chain management requires integrating logistics planning and optimization.Several optimization strategies and algorithms have been developed to improve supply chain management. Zhang et al. (2016) developed distance clustering analysis techniques to optimize customer orders and reduce unused container volume in logistics orders. Research has shown that evolutionary algorithms may optimize multi-objective logistics distribution paths, underlining their importance in addressing complex supply chain difficulties.Research on the VID-19 pandemic has shown that it can improve supply chain resilience and performance (Belhadi et al., 2021; Naz et al.,2022).The literature provides a complete review of AI's applications and impacts in supply chain management, with a focus on its potential to enable sustainable and digitally transformed supply networks. This comparative research examines AI use in supply chains in the USA and Africa, providing significant insights into the worldwide landscape.

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