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.

Rapid urban growth in Middle Eastern cities has intensified congestion-related challenges, yet traffic data-based decision making remains limited. This study leverages crowd-sourced travel time data from the Google Maps API to evaluate temporal and spatial patterns of congestion across multiple strategic routes in Jeddah, Saudi Arabia, a coastal metropolis with a complex road network characterized by narrow, high-traffic corridors and limited public transit. A real-time Congestion Index quantifies traffic flow, incorporating free-flow speed benchmarking, dynamic profiling, and temporal classification to pinpoint congestion hotspots. The analysis identifies consistent peak congestion windows and route-specific delays that are critical for travel behavior modeling. In addition to congestion monitoring, the framework contributes to urban sustainability by supporting reductions in traffic-related emissions, enhancing mobility equity, and improving economic efficiency through data-driven transport management. To our knowledge, this is the first study to systematically use the validated, real-time Google Maps API to quantify route-specific congestion in a Middle Eastern urban context. The approach provides a scalable and replicable framework for evaluating urban mobility in other data-sparse cities, especially in contexts where traditional traffic sensors or GPS tracking are unavailable. The findings support evidence-based transport policy and demonstrate the utility of publicly accessible traffic data for smart city integration, real-time traffic monitoring, and assisting transport authorities in enhancing urban mobility.

In pharmaceutical manufacturing, the reliability of machines and utility assets is critical to ensuring product quality, regulatory compliance, and uninterrupted operations. Traditional Risk-Based Maintenance (RBM) models quantify asset criticality using the Risk Priority Number (RPN), calculated from the probability and impact of failure alongside detectability. However, these models often neglect the practical challenges involved in diagnosing and resolving equipment issues, particularly in GMP-regulated environments. This study proposes an enhanced RPN framework that replaces the conventional detectability component with Maintainability Complexity (MC), quantified through two practical indicators: Ease of Diagnosis (ED) and Ease of Resolution (ER). Thirteen Key Performance Indicators (KPIs) were developed to assess Probability, Impact, and MC across 185 pharmaceutical utility assets. To enable objective risk stratification, Jenks Natural Breaks Optimization was applied to group assets into Low, Medium, and High risk tiers. Both multiplicative and normalized averaging methods were tested for score aggregation, allowing comparative analysis of their impact on prioritization outcomes. The enhanced model produced stronger alignment with operational realities, enabling more accurate asset classification and maintenance scheduling. A 3D risk matrix was introduced to translate scores into proactive strategies, offering traceability and digital compatibility with Computerized Maintenance Management Systems (CMMS). This framework provides a practical, auditable, and scalable approach to maintenance planning, supporting Industry 4.0 readiness in pharmaceutical operations.

Maintaining a state of compliance with Current Good Manufacturing Practices (CGMP) remains a challenge for manufacturing automated processes within pharmaceuticals industries. The challenge lies in dealing with integrated control systems and easy, user friendly and reliable monitoring approach that reduces the problem of manufacturing faults, which can lead to cost the organization a lot. This paper presents a mechatronics automated system for upgrading a tablet coating machine in pharmaceutical plant, in order to achieve a powerful control processing system, process monitoring and reporting while concerning in increasing process productivity and quality. The approach used in the mechatronic automated system, to Upgrade the tablets coating machine, based on investing in the power of two, relatively novel, technologies; Programmable Logic Controllers (PLC) and Supervisory Control And Data Acquisition (SCADA). The integration of both technologies provided a real powerful upgraded design that increases the efficiency, reliability and robustness of the control and monitoring process, and reduces time, cost and rework.

This paper presents a systematic literature review investigating the key applications of Artificial Intelligence (AI) and Blockchain technologies for enhancing the resilience of pharmaceutical supply chains (PSCs). Using the PRISMA methodology, a total of 139 relevant articles published between 2000 and 2025 were identified from Web of Science, ScienceDirect, and IEEE databases. The analysis reveals that AI is primarily applied in PSCs for demand forecasting, inventory optimization, anomaly detection, logistics planning, and drug development, enabling improved decision-making and operational efficiency. Blockchain applications, on the other hand, focus on cold chain monitoring, data sharing and transparency, smart contracts, and traceability, supporting secure, tamper-proof, and real-time information flow across the supply chain. By synthesizing these applications, this study highlights how the integration of AI and Blockchain can address key resilience challenges in PSCs, such as supply disruptions, counterfeit drugs, and inventory imbalances. The findings provide researchers and practitioners with a comprehensive overview of current technological applications, serving as a foundation for future development of resilient and technology-enabled pharmaceutical supply chains.

This study presents the first systematic literature review on the Physical Internet (PI) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A significant gap exists in the current literature regarding how PI can transform traditional logistics systems into sustainable models, which serves as the core motivation for this research. The review addresses two primary research questions: (a) identifying current trends in PI-related research, and (b) uncovering the key barriers hindering PI adoption. A total of 275 articles were initially retrieved from Web of Science and ScienceDirect, covering the period from 2019 to 2025. After applying inclusion criteria, 50 studies were selected for analysis. Findings reveal a growing academic interest in PI. Major barriers to its adoption include lack of standardization, insufficient government policy support, high initial investment costs, and concerns over data security. This review offers a foundational synthesis to guide future academic inquiry and practical strategies for overcoming challenges in PI implementation. The scope of this study is limited to the defined time