With the current sources of US supply chains being more diversified than before, China’s share in US goods imports is declining while Mexico becomes the largest exporter to the US market in 2023. However, can Mexico use this trade diversion to successfully outweigh China in US supply chains? This paper thus investigates whether the Mexico manufacturing sector is competitive enough to completely replace its Chinese counterparts and rise to a strategically vital supplier for the US economy. Based on multiple empirical evidence, we find that although US supply chain sources are shifting from China to Mexico, the major part of the value added of Mexican exports to the US market is generated in China. Moreover, our evidence shows that Mexico’s exports to the US concentrate on low-skill sectors, while China’s mainly consists of high-skill goods. Further discussion shows that the current US trade shift is highly likely due to China’s FDI inflows to Mexico’s traditionally strong export sector, motor vehicles. However, this shift is not significant enough for Mexico to become a capable substitute for China in the US supply chains. We conclude that the "trade diversion" strategy alone cannot support Mexico’s role in reducing the US supply chain dependence on China. Therefore, the US should better consider how to establish a sustainable trade framework that fosters stable cooperation with China.

This review aimed to analyze some problems and challenges that emerged from the COVID-19 pandemic since it has affected all global business sectors. During the pandemic, remote work was increased in response to social distance requirements to decrease the transmission of disease. In addition, increased delegation, teamwork, supply chains, sales activities, and business operations have all been disrupted. Many challenges have emerged in the management of organizations due to the pandemic including the lack of direct contact as the lockdown made many people work from home and placed restrictions on movement and travel and uncertainty about the future. The healthcare system was also affected by the COVID-19 pandemic and faced several difficulties including increased demand for medical supplies and personal protective equipment, a greater need for nurses and other skilled healthcare professionals, and increased pressure on healthcare facilities, emergency services, and critical care departments. These challenges have created some opportunities in the management systems of healthcare organizations and other sectors. It is recommended for leaders prepare and continuously work proactively to be ready for unexpected future crises. Risk management and planning for any unexpected situation are among the very important aspects of organizational management.

Pharmaceutical supply chains are critical to ensuring the availability of safe and effective medications, yet they face numerous challenges that can jeopardize public health. This article provides a comprehensive analysis of the key issues impacting pharmaceutical supply chains, including regulatory compliance, demand forecasting, supply chain visibility, quality assurance, and geopolitical risks. Regulatory compliance remains a significant concern due to the stringent guidelines imposed by authorities such as the FDA and EMA, which can lead to increased operational costs and time delays. Additionally, traditional demand forecasting methods often fail to accurately predict fluctuations in drug demand, resulting in stockouts or excess inventory. Limited supply chain visibility further complicates these challenges, hindering timely decision-making and operational efficiency. Quality assurance is paramount, as maintaining the integrity of pharmaceutical products throughout the supply chain is crucial to preventing costly recalls and ensuring patient safety. Moreover, the globalization of supply chains introduces vulnerabilities to geopolitical risks, trade disputes, and natural disasters. In response to these issues, this article outlines strategic recommendations for optimizing pharmaceutical supply chains. These include leveraging advanced analytics and IoT technologies to enhance demand forecasting and visibility, strengthening compliance through automated systems and training, fostering collaboration among stakeholders, implementing robust risk management frameworks, and investing in quality management systems. By adopting these strategies, pharmaceutical companies can enhance the efficiency and resilience of their supply chains, ultimately ensuring the continuous availability of essential medications for patients worldwide. This analysis serves as a critical resource for industry professionals seeking to navigate the complexities of pharmaceutical supply chains in an increasingly dynamic global environment.

The operational performance of Active Pharmaceutical Ingredients (API) supply chains often suffers from significant disruptions attributed to inherent vulnerabilities. Despite theoretical discussions, empirical evidence validating these vulnerabilities remains sparse. This study endeavours to empirically substantiate the vulnerabilities arising from dynamic disruptions within the pharmaceutical supply chain. Its primary goal is to discern actionable insights that can inform the development of robust resilience strategies capable of effectively mitigating such disruptions. This study investigates vulnerabilities within the active pharmaceutical ingredient (API) supply chain in response to disruptions. Despite theoretical insights, empirical evidence validating these vulnerabilities remains limited. Through empirical analysis, this research aims to identify and elucidate the specific vulnerabilities exacerbated by dynamic disruptions in the API supply chain. The findings are intended to inform the development of resilient strategies capable of mitigating the impact of disruptions on pharmaceutical supply chains.

An increasing number of adverse events are raising concern in the pharmaceutical supply chain due to contaminated active pharmaceutical ingredients (APIs). Most of the active pharmaceutical ingredients are not currently under the scope of environmental regulations, despite their negative impact on human health and the environment. API's life cycle plays a significant role in identifying potential supply chain sources and determining their impact on the environment. The Covid-19 pandemic's intermittent manufacturing interruptions and the increase in the frequency of drug shortages over the past ten years have sparked worries about how resilient the world's drug supply chains are. Many clinical trials were conducted on patients with COVID-19 during the SARS-CoV-2 pandemic and resulted in millions of deaths globally by 2022.

The pharmaceutical supply chain environment has undergone tremendous change in recent decades due to technology, and this shift is intensifying. One of the main concerns of business practitioners is how to cost-effectively integrate, implement, and manage technologies across the supply chain of an organization. Pharmaceutical organizations that produce, ship, and supply goods have trouble tracking their goods, which makes it easier for counterfeiters to get fake medications into the system. The creation and implementation of a stringent technological system might be a significant step in the arduous battle against the prevalence of fake medications and other healthcare items. In supply chain management, digital technologies have a number of potential advantages. The usage of the Internet of Things in supply chains can make every component visible and create a visible supply chain, making it possible to identify the position and specifications of all the components and materials in the supply chain at any given time.

The growing complexity of the operating environment urges pharmaceutical innovation. This essay addresses the need for the integration of advanced technologies in the pharmaceutical supply chain. It justifies the value proposition and presents a concrete use case for the integration of deep learning insights to make data-driven decisions. The supply chain has always been a priority for the pharmaceutical industry; research and development recognizes companies' increasing investment in big data strategies, with plans for a CAGR in big data tool adoption. The work presented herein has a preliminary explorative character to recuperate and integrate evidence from partly overlooked practical experience and know-how. The practical relevance of the essay is directed toward practitioners in pharmaceutical production, supply chain management, logistics, and regulatory agencies. The literature has shown a long-term concern for enhanced performance in the pharmaceutical supply chain network. This essay demonstrates the application of deep learning-driven insights to reveal non-evident flow dependencies. The main aim is to present a comprehensive insight into deep learning-driven decision support. The supply chain is portrayed in a holistic manner, seeking end-to-end visibility. Implications for public policy are discussed, such as data equity: many countries are protecting their populations and economic growth by building resilience and efficiency to ensure the capacity to move goods across supply chains. The implementation strategy is covered. The combined reduction of variability, efficiency as matured richness, reliability (on stochastic flows and their understanding through deep learning and data), and system noise (increased dampening through the inclusiveness of all stakeholders) results in increased responsiveness of supply chains for pharmaceutical products. Future work involves the integration of external data, closing the loop between planning and its application in reality.

Managing supply chains efficiently has become a major concern for organizations. One of the important factors to optimize in supply chain management is logistics. The advent of technology and the increase in data availability allow for the enhancement of the efficiency of logistics in a supply chain. This discussion focuses on the blending of analytics with innovation in logistics to improve the operations of a supply chain. An approach is presented on how predictive analytics can be used to improve logistics operations. In order to analyze big data in logistics effectively, an artificial intelligence computational technique, specifically deep learning, is employed. Two case studies are illustrated to demonstrate the practical employability of the proposed technique. This reveals the power and potential of using predictive analytics in logistics to project various KPI values ahead in the future based on the contemporary data from the logistics operations; sheds light on the innovative technique of employing deep learning through deep learning-based predictive analytics in logistics; suggests incorporating innovative techniques like deep learning with predictive analytics to develop an accurate forecasting technique in logistics and optimize operations and prevent disruption in the supply chain. The network of supply chains has become more complex, necessitating the need for the latest technological advancements. The sectors that have gained a fair amount of attention for the application of technology to optimize their operations are manufacturing, healthcare, aerospace, and the automotive industry. A little attention has been diverted to the logistics sector; many describe how analytics and artificial intelligence can be used in the logistics sector to achieve higher optimization. Currently, significant research has been done in optimizing logistics operations. Nevertheless, with the explosive volume of historical data being produced by the logistics operations of an organization, there is a great opportunity to learn valuable insights from the data accumulated over time for more long-term strategic planning. To develop the logistics operations in an organization, the use of historical data is essential to understand the trends in the operations. For example, regular maintenance planning and resource allocation based on trends are long-term activities that will not affect logistics operations immediately but can affect the business’s strategic planning in the long run. A predictive analysis technique employed on historical data of logistics can narrow down conclusions based on the future trends of logistics operations. Thus, the technique can be used to prevent the disruption of the supply chain.

The management of supply chains is increasingly complex. This study provides a comparative analysis of the cost-benefit analysis for managing various risks. It identifies the financial implications of leveraging artificial intelligence in supply chains to better address risk. Empirical results show a business case for managing some sources of risk more proactively facilitated through predictive modeling techniques offered by AI. Across investigation streams, the use of AI results in an average total cost saving ranging from 41,254 to 4,099,617. Findings from our research can be used to inform managers and theorists about the implications of integrating AI technologies to manage risks in the supply chain. Our work also highlights areas for future research. Given the growing interest in studying sub-second forecasting, our research could be a point of departure for future investigations aimed at considering the impact of forecasting horizons such as an intra-day basis. We formulate a conceptual framework that considers how and to what extent performance evaluation metrics vary according to differences in the fidelity of predictive models and factor importance for identifying risks. We also utilize a mixed-method approach to demonstrate the applicability of our ideas in practice. Our results illustrate the financial implications of integrating AI predictive tools with business processes. Results suggest that real-world companies can circumvent inefficiencies associated with trying to manage many classes of risk via the use of AI-enhanced predictive analytics. As managers need to justify investment to top management, our work supports decision-making by providing a means of conducting a trade-off analysis at the tactical level.

The pharmaceutical industry is fundamental to global healthcare, providing essential medicines that improve health outcomes and quality of life. However, the demand and supply dynamics within this sector are highly complex, shaped by various factors including demographic changes, evolving disease burdens, technological advancements, regulatory challenges, and economic pressures. This manuscript explores the intricate relationship between pharmaceutical medicine demand and supply, focusing on key strategies that can help companies effectively navigate these challenges. The demand for pharmaceutical products is driven by several factors, such as population growth, the aging population, the rise of chronic diseases, and the emergence of new health threats. Additionally, healthcare accessibility, affordability, and policy changes significantly impact the consumption of medicines, while innovations in medical technologies and therapies create new treatment needs. On the supply side, pharmaceutical companies face challenges related to manufacturing capacity, raw material availability, distribution logistics, and compliance with ever-evolving global regulatory frameworks. To address these challenges, the manuscript discusses strategic approaches to managing both demand and supply in the pharmaceutical sector. Key strategies include advanced demand forecasting through data analytics, optimizing supply chains for efficiency and resilience, implementing just-in-time inventory models, and investing in flexible manufacturing systems. Furthermore, global collaboration and partnerships, as well as effective risk management practices, are highlighted as essential to ensuring the availability of medicines, particularly in times of crisis or global health emergencies. This manuscript also delves into the role of policy advocacy and regulatory harmonization in stabilizing the pharmaceutical market, ensuring that medicines are accessible to all populations. In conclusion, the pharmaceutical industry must continually adapt to meet the evolving challenges of demand and supply, embracing innovation and collaboration while maintaining a focus on patient access and global healthcare equity. Through strategic planning and adaptive solutions, the pharmaceutical sector can ensure the continuous availability of critical medicines worldwide, meeting both current and future health needs.

The global pharmaceutical supply chain plays a crucial role in ensuring the timely and safe delivery of medicines to patients worldwide. However, the increasing presence of counterfeit drugs within this supply chain poses a significant and growing risk to public health, patient safety, and the integrity of the pharmaceutical industry. Counterfeit drugs—medications that are fraudulently manufactured, mislabeled, or contain incorrect or harmful ingredients—are a major concern as they can lead to ineffective treatments, adverse health effects, and even death. Despite stringent regulatory frameworks and advanced technological solutions, counterfeit drugs continue to infiltrate legitimate supply chains due to factors such as the complexity of the distribution system, global trade practices, and inadequate enforcement in certain regions. This manuscript explores the primary causes behind the proliferation of counterfeit drugs in pharmaceutical distribution, the associated risks, and the multifaceted approaches required to address this growing threat. It discusses the importance of regulatory measures, including international cooperation and stronger compliance frameworks, as well as the role of emerging technologies like serialization, blockchain, and RFID in ensuring traceability and product authenticity. By focusing on the integration of these technologies, the paper also highlights the potential of innovative solutions to enhance transparency, reduce vulnerabilities, and protect the integrity of pharmaceutical supply chains. Additionally, it emphasizes the importance of public awareness campaigns and collaboration between key stakeholders, including pharmaceutical manufacturers, distributors, regulators, and healthcare providers, in creating a more secure and trustworthy pharmaceutical distribution ecosystem. Through a comprehensive exploration of these strategies, this manuscript aims to provide a roadmap for mitigating the risks posed by counterfeit drugs and ensuring the safety and efficacy of medicines for consumers worldwide.

The evolution of supply chains (SC) from a linear to a network structure created an opportunity for new processes, product/service offerings, and provider-business. Rising customer service expectations have led to the need for innovative SC designs to develop and sustain competitive performance globally. Firms are forced to respond and adapt accordingly, thereby leading to design, network, operational, and performance dynamics. Traditionally, SCs are treated as static structures, focusing solely on design and/or operational optimization. Such perspectives are not viable options for SC domains, as they address only a portion of the dynamic problem space, use a deterministic assumption of dominant design variables, capitalize on past data to predict future decisions, and offer pre-classified forecasting options complemented with a limited comprehension of systemic SC elasticity. Novel self-learning agentic systems are proposed that blend the sciencematics of SC decisions and dynamics. The designs guide firms seeking to build adaptive SCs using operational decision processes. The designs address the agentic nature of SC, embedding computational interaction models of firm SC networks. The designs contrast the stochastic action-taking and thereby the performance outcomes, discovering opportunities for adaptive operational designs of SC tasks. Fine-tuning and meta-learning are new design capabilities that adapt to evolving dynamic environments. Frameworks for behavioral customization and systematic exploration of the design space are provided as user guides. Exemplar designs are also provided to serve as a translation template for users to express operational models of their own contexts. To account for the dynamics of supply chains (SC), agent-based models are increasingly adopted. Such models exhibit SC structure and/or formulation dynamics. Though existing efforts commence adjacent-only structural changes, dynamism with respect to tasks is crucial for SC design and operational strategy development. Proposed is a process modeling library and workflow for discovering intricate designs of adaptive agentic systems. The library revises Dataflow and Structure, concealing sequencing and context designs of processes. Prompted specifications describe and enact designs. Applications in SC formulation discovery are provided.

The supply chain ecosystem plays a very important role in the success or failure of organizations, markets, and economies. Supply chain ecosystems are broadly defined as supply chain organizations and their collaborators. Today's combined challenges of pandemic shutdowns, rising internet usage, and skyrocketing climate change concerns demand that the supply chain ecosystem better connect with customers, when and how they want, to provide products and services with high levels of availability and zero defects, yet collaboratively do this to reduce transportation and production risks, often at the same time reducing operational costs and carbon footprints. Addressing these challenges, this work explores the cloud delivery capabilities of cloud-native architectures to enable the big data integrations and analytics that are needed to grow smarter supply chain ecosystems. This work describes what smart supply chain ecosystems are and how they are planning to grow their technology and integration capabilities. Discussing the industry-leading advanced and manufacturing technology producer ecosystems, it is explained how their technology collaboration and investment plans are driven by climate change and job creation goals. With these background models, the work examines the new digital reality of customer-driven experiences and economies that are demanding cloud-native and intelligent technology partnerships to deliver climate objectives, operational responsiveness, and compatibility to avoid trading economies of scale for economies of integration. The final objectives of this paper are to share key ideas about the need to balance the growing customer service direct-to-consumer business models with those for collaborative investment by market and industry. In doing this, it hopes to promote an intelligent supply chain ecosystem foundation for helping its different participating countries survive and thrive in the digital economy.