Leveraging AI and Machine Learning for Optimizing Supply Chain Management in Healthcare: A Predictive and Prescriptive Approach
Abstract
The healthcare industry relies on efficient supply chain management to ensure the timely delivery of critical medical supplies, equipment, and medications. However, traditional supply chain systems often face challenges such as demand variability, supply disruptions, and inefficiencies in inventory management. This paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing supply chain management within the healthcare sector. We present a comprehensive framework that integrates predictive analytics for forecasting demand and prescriptive analytics for decision-making, enabling real-time adjustments to supply chain operations. The study delves into key AI and ML techniques, such as neural networks, reinforcement learning, and decision trees, to address specific challenges like demand forecasting, route optimization, and inventory control. Through case studies and simulations, the paper demonstrates how AI-driven models can improve supply chain resilience, reduce operational costs, and enhance patient care outcomes. We also discuss ethical considerations, data security, and the scalability of these technologies in diverse healthcare environments. This research highlights the critical role of AI and ML in shaping the future of healthcare supply chains, offering actionable insights for practitioners, policymakers, and researchers.
References
Davuluri, M. (2024). AI in Healthcare Fraud Detection: Ensuring Integrity in Medical Billing. International Machine learning journal and Computer Engineering, 7(7).
Davuluri, M. (2024). AI in Geriatric Care: Supporting an Aging Population. International Numeric Journal of Machine Learning and Robots, 8(8).
Davuluri, M. (2023). AI for Healthcare Workflow Optimization: Reducing Burnout and Enhancing Efficiency. International Numeric Journal of Machine Learning and Robots, 7(7).
Davuluri, M. (2023). AI in Surgical Assistance: Enhancing Precision and Outcomes. International Machine learning journal and Computer Engineering, 6(6).
Davuluri, M. (2022). AI in Mental Health: Transforming Diagnosis and Therapy. International Machine learning journal and Computer Engineering, 5(5).
Davuluri, M. (2021). AI for Chronic Disease Management: Improving Long-Term Patient Outcomes. International Journal of Machine Learning and Artificial Intelligence, 2(2).
Davuluri, M. (2021). AI in Personalized Oncology: Revolutionizing Cancer Care. International Machine learning journal and Computer Engineering, 4(4).
Davuluri, M. (2020). AI-Driven Drug Discovery: Accelerating the Path to New Treatments. International Journal of Machine Learning and Artificial Intelligence, 1(1).
Davuluri, M. (2020). AI in Pediatric Healthcare: Transforming Care for Younger Patients. International Numeric Journal of Machine Learning and Robots, 4(4).
Boppiniti, S. T. (2023). AI-Enhanced Predictive Maintenance for Industrial Machinery Using IoT Data. International Transactions in Artificial Intelligence, 7(7).
Boppiniti, S. T. (2022). Ethical Implications Of Artificial Intelligence: A Review Of Early Research And Perspectives. Available at SSRN 5062191.
Boppiniti, S. T. (2021). Evolution of Reinforcement Learning: From Q-Learning to Deep. Available at SSRN 5061696.
Boppiniti, S. T. (2021). Artificial Intelligence In Financial Markets: Algorithms And Applications. Available at SSRN 5061691.
Boppiniti, S. T. (2020). AI for Remote Patient Monitoring: Bridging the Gap in Chronic Disease Management. International Machine learning journal and Computer Engineering, 3(3).
Boppiniti, S. T. (2020). A Survey On Explainable Ai: Techniques And Challenges. Available at SSRN 5060811.
Boppiniti, S. T. (2021). Real-time data analytics with ai: Leveraging stream processing for dynamic decision support. International Journal of Management Education for Sustainable Development, 4(4).
Davuluri, M. (2018). AI in Preventive Healthcare: From Risk Assessment to Lifestyle Interventions. International Numeric Journal of Machine Learning and Robots, 2(2).
Davuluri, M. (2017). AI-Enhanced Telemedicine: Bridging the Gap in Global Healthcare Access. International Numeric Journal of Machine Learning and Robots, 1(1).
Vattikuti, M. C. (2024). Transfer Learning for Early Diagnosis of Rare Diseases Using Medical Imaging. Transactions on Recent Developments in Artificial Intelligence and Machine Learning, 16(16).
Vattikuti, M. C. (2024). Natural Language Processing for Automated Legal Document Analysis and Contract Review. International Journal of Sustainable Devlopment in field of IT, 16(16).
Vattikuti, M. C. (2023). Real-Time Anomaly Detection in Industrial IoT Systems Using Hybrid AI Models. International Scientific Journal for Research, 5(5).
Vattikuti, M. C. (2023). Ethical AI Framework for Bias Mitigation in Machine Learning Algorithms. International Scientific Journal for Research, 5(5).
Vattikuti, M. C. (2022). Federated Learning for Privacy-Preserving AI in Healthcare Applications. International Transactions in Artificial Intelligence, 6(6).
Vattikuti, M. C. (2022). Generative Adversarial Networks for Data Augmentation in Medical Imaging. International Journal of Sustainable Development in Computing Science, 4(3).
Vattikuti, M. C. (2024). Improving Drug Discovery and Development Using AI: Opportunities and Challenges. Research-gate journal, 10(10).
Vattikuti, M. C. (2022). Comparative Analysis of Deep Learning Models for Tumor Detection in Medical Imaging. Research-gate journal, 8(8).
Vattikuti, M. C. (2020). A Comprehensive Review of AI-Based Diagnostic Tools for Early Disease Detection in Healthcare. Research-gate journal, 6(6).
Vattikuti, M. C. (2018). Leveraging Edge Computing for Real-Time Analytics in Smart City Healthcare Systems. International Transactions in Artificial Intelligence, 2(2).
Vattikuti, M. C. (2018). Leveraging AI for Sustainable Growth in AgTech: Business Models in the Digital Age. Transactions on Latest Trends in IoT, 1(1), 100-105.
Kolla, V. R. K. (2020). Forecasting the Future of Crypto currency: A Machine Learning Approach for Price Prediction. International Research Journal of Mathematics, Engineering and IT, 7(12).
Kolla, V. R. K. (2018). Forecasting the Future: A Deep Learning Approach for Accurate Weather Prediction. International Journal in IT & Engineering (IJITE).
Kolla, V. R. K. (2016). Analyzing the Pulse of Twitter: Sentiment Analysis using Natural Language Processing Techniques. International Journal of Creative Research Thoughts.
Kolla, V. R. K. (2015). Heart Disease Diagnosis Using Machine Learning Techniques In Python: A Comparative Study of Classification Algorithms For Predictive Modeling. International Journal of Electronics and Communication Engineering & Technology.
Kolla, V. R. K. (2020). Paws And Reflect: A Comparative Study of Deep Learning Techniques For Cat Vs Dog Image Classification. International Journal of Computer Engineering and Technology.
Velaga, S. P. (2014). DESIGNING SCALABLE AND MAINTAINABLE APPLICATION PROGRAMS. IEJRD-International Multidisciplinary Journal, 1(2), 10.
Velaga, S. P. (2016). LOW-CODE AND NO-CODE PLATFORMS: DEMOCRATIZING APPLICATION DEVELOPMENT AND EMPOWERING NON-TECHNICAL USERS. IEJRD-International Multidisciplinary Journal, 2(4), 10.
Velaga, S. P. (2017). “ROBOTIC PROCESS AUTOMATION (RPA) IN IT: AUTOMATING REPETITIVE TASKS AND IMPROVING EFFICIENCY. IEJRD-International Multidisciplinary Journal, 2(6), 9.
Velaga, S. P. (2018). AUTOMATED TESTING FRAMEWORKS: ENSURING SOFTWARE QUALITY AND REDUCING MANUAL TESTING EFFORTS. International Journal of Innovations in Engineering Research and Technology, 5(2), 78-85.
Velaga, S. P. (2020). AIASSISTED CODE GENERATION AND OPTIMIZATION: LEVERAGING MACHINE LEARNING TO ENHANCE SOFTWARE DEVELOPMENT PROCESSES. International Journal of Innovations in Engineering Research and Technology, 7(09), 177-186.
Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).
Deekshith, A. (2021). Data engineering for AI: Optimizing data quality and accessibility for machine learning models. International Journal of Management Education for Sustainable Development, 4(4), 1-33.
Deekshith, A. (2022). Cross-Disciplinary Approaches: The Role of Data Science in Developing AI-Driven Solutions for Business Intelligence. International Machine learning journal and Computer Engineering, 5(5).
Deekshith, A. (2023). Scalable Machine Learning: Techniques for Managing Data Volume and Velocity in AI Applications. International Scientific Journal for Research, 5(5).
DEEKSHITH, A. (2018). Seeding the Future: Exploring Innovation and Absorptive Capacity in Healthcare 4.0 and HealthTech. Transactions on Latest Trends in IoT, 1(1), 90-99.
DEEKSHITH, A. (2017). Evaluating the Impact of Wearable Health Devices on Lifestyle Modifications. International Transactions in Artificial Intelligence, 1(1).
DEEKSHITH, A. (2016). Revolutionizing Business Operations with Artificial Intelligence, Machine Learning, and Cybersecurity. International Journal of Sustainable Development in computer Science Engineering, 2(2).