crossrefSustainability2025-07-19Cited by 22

A Systematic Review of Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaceutical Supply Chain (PSC) Resilience: Current Trends and Future Directions

The resilience of the pharmaceutical supply chain (PSC) is crucial to ensuring the availability of medical products. However, increasing complexity and logistical bottlenecks have exposed weaknesses within PSC frameworks. These challenges underscore the urgent need for more resilient and intelligent supply chain solutions. Recently, Artificial Intelligence and machine learning (AI/ML) have emerged as transformative technologies to enhance PSC resilience. This study presents a systematic review evaluating the role of AI/ML in advancing PSC resilience and their applications across PSC functions. A comprehensive search of five academic databases (Scopus, the Web of Science, IEEE Xplore, PubMed, and EMBASE) identified 89 peer-reviewed studies published between 2019 and 2025. PRISMA 2020 guidelines were implemented, resulting in a final dataset of 32 studies. In addition to analyzing applications, this study identifies the AI/ML grouped into five main categories, providing a clearer understanding of their impact on PSC resilience. The findings reveal that despite AI/ML’s promise, significant research gaps persist. Particularly, AI/ML-driven regulatory compliance and real-time supplier collaboration remain underexplored. Over 59.3% of studies fail to address regulatory frameworks and ethical considerations. In addition, major challenges emerge such as the limited real-world deployment of AI/ML-driven solutions and the lack of managerial impacts on PSC resilience. This study emphasizes the need for stronger regulatory frameworks, broader empirical validation, and AI/ML-driven predictive modeling. This study proposes recommendations for future research to foster more efficient, transparent and ethical PSCs capable of navigating the complexities of global healthcare.

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AI-Driven Sustainable Transformation of the Educational Supply Chain: Comparative Evaluation of Machine Learning Models for an Early Warning System and Design-Level Frameworks for Institutionalization and Impact Assessment

Higher education institutions face the persistent challenge of student attrition, a critical risk node within the educational supply chain (ESC). This study adopts a supply chain management (SCM) perspective to apply artificial intelligence (AI) for sustainable transformation of…

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment a…

Artificial Neural Networks (ANNs) and Machine Learning (ML) Modeling Employee Behavior with Management Towards the Economic Advancement of Workers

The role of employee behavior in organizations and their interaction with management is crucial in advancing the economic progress of workers. This study examines the impact of employee behavior and management practices on organizational performance and economic progress, using a…

The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends

Machine learning (ML) and deep learning (DL) have become very popular in the research community for addressing complex issues in intelligent transportation. This has resulted in many scientific papers being published across various transportation topics over the past decade. This…

Human–AI Collaboration: Students’ Changing Perceptions of Generative Artificial Intelligence and Active Learning Strategies

This paper explores ways to use AI for active learning strategies so that students in higher education may perceive generative artificial intelligence (generative AI) as a collaborative partner in their learning experience. This study proposes AI can help advance educational sust…