Over the last ten years, many people have shown interest in using AI to improve supply chain management (SCM). Supply chains cover many steps \u2014 from buying and choosing suppliers, to managing inventory, and delivering products. AI can change these steps by offering predictions, doing routine jobs automatically, and helping make decisions quickly. This is very important in healthcare where getting supplies like medicines and devices on time can affect patient care.<\/p>\n
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A review of research papers from 2012 to 2023 shows that AI’s role in supply chains is growing. Researchers use models like MACO (Motivation, Application, Capability, Outcome) to explain why organizations start using AI, how they use it, what technology they need, and what results they get. AI helps healthcare supply chains by reducing waste, managing inventory better, and reacting faster to problems like shortages or sudden high demand.<\/p>\n
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Even with these improvements, the use of AI in healthcare supply chains is still new. Many studies say more research is needed to understand how healthcare groups can use AI well. This is important for doctors, administrators, and IT managers who handle complicated buying processes and supply risks.<\/p>\n
Research divides the main reasons for using AI into three groups: economic, technological, and social. Economically, organizations want to cut costs and work better. Technologically, new tools like machine learning and data analysis make AI easier and more reliable to use. Socially, people want supply chains to be open and more sustainable.<\/p>\n
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The effects of using AI include saving money, competing better, and making organizations stronger. This helps with new ideas and better operations, which matter a lot in healthcare where supply chains affect how well services run. In the US, AI helps keep supplies steady by predicting shortages and helping find other sources.<\/p>\n
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But, healthcare supply chains are complicated by rules and safety needs. Generic AI supply chain models may not solve all these problems. More research is needed on how AI can help US healthcare meet rules from groups like the FDA and stay safe while also making supply chains stronger.<\/p>\n
Lack of Healthcare-Specific AI Supply Chain Models<\/b>
\nMost studies look at AI in factories or stores where rules and safety are very different from healthcare. There is little research on how AI can help with medical supply buying, managing, or risk with rules like FDA guidelines in mind.<\/p>\n<\/li>\n
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Long-Term Outcomes and Impact Assessment<\/b>
\nMany studies focus on short-term benefits like saving money or working better. Few studies look at how AI affects supply chain strength and healthcare service over many years in the US.<\/p>\n<\/li>\n
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Integration Challenges and Organizational Factors<\/b>
\nThe research talks a lot about technology but not much about company culture, staff readiness, and how to handle change, which are very important in healthcare. More study is needed on how administrators and IT managers can get their groups ready for AI.<\/p>\n<\/li>\n
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Ethical and Transparency Concerns<\/b>
\nAI raises questions about decision-making openness, responsibility, and patient data privacy, especially in buying and supply decisions. Research should create rules to make sure AI is used responsibly in healthcare supply chains.<\/p>\n<\/li>\n
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Scalability of AI Solutions in Varied Healthcare Settings<\/b>
\nThere is not much knowledge about how AI tools can be used in different sizes and types of US healthcare groups, from small clinics to big hospitals. This makes it hard to create solutions that balance cost, difficulty, and usefulness.<\/p>\n<\/li>\n
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Interplay with Other Emerging Technologies<\/b>
\nAI often works together with other technologies like blockchain for tracking or IoT for real-time monitoring. Research on how these combined tools affect medical supply chains in US healthcare is still limited.<\/p>\n<\/li>\n
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Risk Management and Supply Chain Resilience<\/b>
\nAI helps find and reduce supply risks, but studies say there is a need for new risk management plans that use AI better in healthcare. This became important during supply problems like those seen in the COVID-19 pandemic.<\/p>\n<\/li>\n<\/ul>\n
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