Artificial intelligence means using computer systems to do tasks that usually require human thinking. In supply chain management, AI uses complex rules, machine learning, language processing, robots, and data analysis to look at large amounts of data and make decisions that improve processes. AI learns from data patterns to predict demand, manage inventory, plan transportation, and improve buying accuracy. All of these are important for healthcare organizations.
In U.S. healthcare, where it is very important to have medical supplies, medicines, and equipment on time, AI can help a lot. AI can make operations smoother, reduce waste, control inventory better, and make supply chains quicker to respond to patient needs and unexpected healthcare demands.
Supply chain management has four parts: logistics, marketing, production, and supply chain planning. AI helps improve all these parts in different ways.
Logistics in healthcare means managing how medical supplies and equipment move and are stored. AI helps by making warehouse work better, choosing the best paths for workers, and finding the fastest routes for delivery vehicles. AI systems can watch inventory levels in real time and change deliveries as needed.
One example is AI-powered robots inside warehouses. These robots can pick and sort items, which helps the warehouse run better while lowering mistakes and reducing worker fatigue. In the future, fully autonomous trucks could carry large shipments. These trucks would cost less and deliver on time.
Blume Global, a company that uses AI for supply chains, shows how machine learning and natural language processing help make logistics better. By checking contract papers and supplier messages, AI helps managers find repeated problems and blockages. This leads to ongoing improvements in healthcare supply services.
In healthcare supply chains, marketing is about managing demand. This means understanding and predicting what medical supplies are needed and when. AI helps by looking at past order data and outside factors like seasons, economic changes, or sudden health crises.
Good demand forecasting with machine learning lowers the chance of having too much or too little important items like vaccines, medicines, or surgery supplies. This stops waste and makes sure patient care keeps going without problems. AI finds patterns that people might miss and updates predictions quickly when conditions change.
Dr. Ali Nazarpour’s work with AI in inventory and sales planning gives useful examples of how U.S. healthcare facilities can use AI tools to meet patient needs better and keep stock at the right levels.
Production means making and checking the quality of medical products. AI helps by using computer vision to find defects automatically. This helps keep product quality high and lowers returns or recalls. This is very important because medical supplies must be safe and exact.
AI also creates simulation tools, called digital twins, that let healthcare makers test changes to production or adjust output without stopping real operations. These virtual models help plan for more capacity or new products.
AI also helps healthcare supply chains be more environmentally friendly. It tracks where supplies come from to check if they follow ethical rules and environmental standards, which is becoming more important in healthcare buying.
Planning means coordinating all activities related to supplies. This includes buying, inventory control, schedule transportation, and managing risks. AI helps by giving real-time data, predictions, and automated decisions.
For healthcare leaders and IT managers in the U.S., AI gives useful business information. AI can predict supply problems, find the right time to order again, and balance inventory costs with service needs. For example, AI quickly checks purchase orders, supplier dependability, and contract details. This lowers mistakes and saves time on routine jobs.
One important move is combining Industry 4.0 technologies—such as the Internet of Things (IoT), cloud computing, blockchain, and big data—with AI to create smart supply chains. Dr. Vahid Sohrabpour’s research shows these technologies working together make supply networks clearer, more flexible, and quick to respond, which is key for healthcare in the U.S.
Workflow efficiency is very important in healthcare because supply delivery must be quick and accurate. AI not only improves parts of supply chain management, but also helps automate workflows to lower manual work, reduce mistakes, and improve teamwork.
In medical offices, AI can automate repeated tasks like matching purchase orders, analyzing spending, and checking suppliers. Companies like JAGGAER use AI tools that include machine learning, language processing, robotic process automation (RPA), and optical character recognition (OCR) to pull contract information and manage risks. This helps buying teams focus on bigger goals.
AI chatbots and smart assistants answer supplier and patient questions quickly, making communication faster and better. These AI tools can find supply problems or shortages early so staff can act fast.
AI also helps check safety and compliance using sensors and computer vision. In warehouses and distribution centers, AI systems make sure safety gear is used and find hazards. These automatic checks make workplaces safer and reduce injuries, which is very important for healthcare facilities.
Additionally, AI can predict when equipment in warehouses or delivery trucks will fail. This lowers the chance of breakdowns and keeps supplies moving. This prediction is key for healthcare settings that handle sensitive items like temperature-controlled medicines.
AI-based data analysis lets supply chain managers run “what-if” tests using digital twins. They can try changes in workflows, supplier choices, or delivery plans without disrupting real work. These tests help plan strategies and redesign workflows to be more efficient.
Even with the benefits, adding AI to healthcare supply chains in the U.S. has some challenges. The quality and cleanliness of data affect how well AI works. Healthcare groups must keep data correct and updated to use AI tools well.
Costs for AI setup, training, and maintenance can be high. Small medical offices might find these costs hard to handle without good plans or partners.
Also, AI does not replace human judgment and relationships, especially with suppliers and service providers. Effective AI use means combining machine results with human knowledge.
Researchers like Dr. Vahid Sohrabpour, Dr. Ali Nazarpour, and Madani Abdu Alomar show facts about AI’s effect on supply chain performance. For example, Madani Abdu Alomar’s study on AI optimization reached a supply chain performance score of 94.12%, showing big improvements in capacity, quality, and cost savings.
Companies such as Oracle use AI in their Fusion Cloud Supply Chain & Manufacturing suite to improve buying, transportation, and inventory management. These serve as examples for healthcare organizations aiming to update their supply chains. Likewise, Blume Global’s AI tools in logistics highlight how AI helps improve U.S. healthcare supply and delivery.
Because of these efforts, healthcare managers and IT leaders in the U.S. get tools to build supply chains that are strong, efficient, and centered on patients. AI, combined with Industry 4.0 technologies, keeps giving medical organizations ways to improve supply chains in ways that save money and meet healthcare needs better in a difficult environment.
The paper aims to identify the contributions of artificial intelligence (AI) to supply chain management (SCM) by systematically reviewing existing literature to address current scientific gaps and suggest potential AI techniques for improving SCM.
The study covers (1) prevalent AI techniques in SCM, (2) potential AI techniques for SCM, (3) current AI-enhanced SCM subfields, and (4) subfields with high potential to be improved by AI.
The paper employs a systematic literature review using specific inclusion and exclusion criteria to identify and examine papers across four SCM fields: logistics, marketing, supply chain, and production.
Key researchers include Reza Toorajipour (business model innovation, SCM), Dr. Vahid Sohrabpour (Industry 4.0 technologies, IoT, AI in SCM), Dr. Ali Nazarpour (operations and supply chain management), and Dr. Pejvak Oghazi (business studies and industrial marketing).
He worked in the construction sector and automotive industry holding roles such as Sales Supervisor, Marketing and Sales Planning Chief, and Inventory Management Project Manager.
He integrates Internet of Things (IoT), Cloud Computing, Block Chain, Big Data, and Artificial Intelligence to promote Smart Supply Chain, Smart Manufacturing, and Smart Products.
AI can improve efficiency, optimization, and decision-making within SCM processes by analyzing large data sets, forecasting demands, and automating logistics, which traditional methods may struggle to handle effectively.
The four SCM fields analyzed are logistics, marketing, supply chain, and production.
The paper identifies scientific gaps that require further research to better understand and implement AI techniques effectively across various SCM subfields.
It synthesizes and analyzes current knowledge to provide insights on effective AI techniques, enabling academics and practitioners to understand which AI methods are most beneficial to specific SCM subfields and where future research should focus.
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