Intelligent Logistics and Supply Chain Systems Based on Industry 4.0/5.0

1. Introduction

The rapid evolution of global markets, customer expectations, and technological capabilities has fundamentally transformed the way logistics and supply chain systems are designed and managed. In recent years, the emergence of Industry 4.0 and 5.0 has introduced new paradigms that emphasize digital integration, intelligence, resilience, and human-centricity across industrial ecosystems. Within this context, intelligent logistics and supply chain systems have become a critical area of research and practice, as organizations look to improve decision-making, responsiveness, sustainability, and operational efficiency in settings that are becoming more complicated and unpredictable.

The incorporation of modern digital technologies into industrial processes, including automation, cloud computing, big data analytics, artificial intelligence, cyber–physical systems, and the Internet of Things, is known as Industry 4.0. In supply chain management and logistics, these technologies enable real-time visibility, seamless information exchange, and autonomous or semi-autonomous decision-making across interconnected networks of customers, distributors, manufacturers, and suppliers. Intelligent logistics systems based on Industry 4.0 principles leverage data-driven insights to optimize transportation, warehousing, inventory management, and demand forecasting, thereby reducing costs, minimizing delays, and improving service levels. The transition from linear and fragmented supply chains to digitally connected and adaptive networks marks a fundamental shift in how value is created and distributed.

As supply chains become more digitalized and interconnected, intelligence plays a central role in managing complexity and uncertainty. Advanced analytics and algorithms machine learning enable businesses to handle enormous volumes of diverse data produced by sensors, enterprise systems, and external sources, enabling predictive and prescriptive capabilities that were previously unattainable. Intelligent supply chain systems can anticipate disruptions, dynamically reconfigure logistics flows, and support proactive risk management strategies. These capabilities have gained particular importance in light of recent global challenges, such as pandemics, geopolitical instability, and climate-related disruptions, which have exposed the vulnerability of traditional supply chain models and underscored the need for greater resilience and adaptability. While Industry 4.0 has primarily focused on technological efficiency, automation, and system optimization, the emerging concept of Industry 5.0 extends this vision by re-centering human, societal, and environmental values within industrial development. Industry 5.0 promotes a more balanced interaction between humans and intelligent machines, emphasizing collaboration rather than replacement, as well as sustainability, ethical responsibility, and social well-being. In the domain of logistics and supply chain management, this shift translates into intelligent systems that not only optimize performance metrics but also support human decision-makers, enhance worker safety and skills, and contribute to sustainable and responsible operations. Human-in-the-loop systems, explainable artificial intelligence, and collaborative robotics are increasingly viewed as essential components of next-generation logistics infrastructures.

Intelligent logistics and supply chain systems based on Industry 4.0 and 5.0 principles therefore represent a convergence of technological innovation and strategic transformation. These systems are designed to operate across multiple layers, integrating physical flows, information flows, and decision processes in a cohesive and adaptive manner. Real-time data acquisition and digital twins enable the continuous monitoring and simulation of logistics operations, while intelligent control mechanisms allow systems to respond autonomously to changing conditions. At the same time, Industry 5.0-oriented approaches ensure that technological advancement remains aligned with human expertise, organizational learning, and long-term sustainability goals.

Despite their significant potential, the implementation of intelligent logistics and supply chain systems also presents substantial challenges. Issues related to data interoperability, cybersecurity, system integration, workforce readiness, and ethical governance must be carefully addressed to ensure successful adoption. Moreover, the transition from traditional to intelligent supply chain models requires not only technological investment but also organizational change, cross-functional collaboration, and the development of new competencies. From a research perspective, there is a growing need for interdisciplinary frameworks that combine engineering, information systems, operations management, and social sciences to fully capture the complexity of intelligent supply chains in theera of Industry 4.0 and 5.0.

Thus, Industry 4.0 and the new ideas of Industry 5.0 are driving modern industrial transformation, and intelligent logistics and supply chain systems are key components of this change. Global supply networks can be made more efficient, resilient, and socially impactful by combining cutting-edge digital technologies with human-centered and sustainable values. Understanding their conceptual foundations, technological enablers, and organizational implications is essential for both researchers and practitioners seeking to navigate the future supply chain management and logistics and in an increasingly intelligent and interconnected world.

2. Overview of the Contributions

In the evolving landscape of supply chain and logistics research, the convergence of digital technologies has driven the rapid accumulation of scholarly work focused on intelligent systems rooted in the paradigms of Industry 4.0 and the emerging transition toward Industry 5.0 [1]. The body of research produced in recent years reflects a growing consensus that digitalization, data-driven intelligence, and human-centric innovation are central to the redesign of modern supply chains and logistics operations. At the core of these contributions lies the recognition that traditional linear supply chains are insufficient to address the demands of dynamic markets, complex global networks, and sustainability objectives. Industry 4.0 technologies, consisting of machine learning, artificial intelligence (AI), big data analytics, the Internet of Things (IoT), and cyber–physical systems, have been widely studied as foundational enablers that increase visibility, agility, and responsiveness across logistics processes [2]. The scholarly literature has therefore placed significant emphasis on systematically synthesizing these technological contributions to better understand their impacts and limitations.

A number of recent comprehensive reviews have cataloged the proliferation and effects of AI and related technologies within supply chain management. Teixeira and colleagues provide an updated systematic literature review highlighting AI applications across strategic and operational phases of supply chains, noting substantial contributions to resilience, optimization, and sustainability domains through advanced machine learning, deep learning, and emerging AI paradigms such as explainable AI and federated learning. Their analysis underscores the significance of AI, not only in enhancing predictive capabilities and decision support but also in addressing complex challenges related to supply disruptions, uncertainty, and performance trade-offs that characterize today’s interconnected supply networks [3,4]. In parallel, contributions focusing on logistics specifically have documented the transformative effects of digital technologies on logistics structures and practices. Recent research emphasizes the increased adoption of Industry 4.0 technologies in transportation, warehousing, and inventory management. For instance, investigations into logistics 4.0 research trends reveal that interconnectivity, data analytics, intelligent decision-making, and operational efficiency have become dominant themes guiding scholarly exploration. These studies often use scientometric methods to map the evolution of the field, showing the acceleration of the process of releasing publications that integrate digital and automation technologies with logistics processes [5,6]. Such bibliometric insights are critical for identifying both where research has concentrated and which technological clusters (such as robotics, IoT, and big data analytics) remain most influential in driving innovation [7].

With academics explaining how human-centric and sustainable principles enhance the technological underpinnings laid by Industry 4.0, the shift from Industry 4.0 to Industry 5.0 has become a prominent topic. Industry 5.0, according to the authors, places more emphasis on human collaboration, personalization, and environmental responsibility within supply chains than Industry 4.0, which at first concentrated on efficiency, automation, and connection. Research examining the evolution from Industry 4.0 to Industry 5.0 underscores this human-technology synergy, suggesting that future intelligent logistics systems will balance advanced automation with human judgment and creativity to achieve more resilient and inclusive supply networks [8,9,10]. Moreover, literature reviews in the field of sustainability-oriented logistics confirm that while digital technologies significantly enhance performance, they must be aligned with broader environmental and social goals to fully realize their potential in modern supply chains [11,12]. Another substantial area of contribution concerns supply chain visibility and risk management. AI-driven approaches have been investigated extensively in the context of risk assessment, offering predictive models that anticipate disruptions and support real-time decision making. Systematic reviews highlight the transformative impact of AI and machine learning in risk quantification, enabling supply chains to dynamically adapt to external shocks and to embed resilience into their operational frameworks. These contributions illustrate how intelligent algorithms enhance traditional supply chain risk assessment, pushing beyond static models toward real-time, data-informed adaptive strategies [13,14].

Furthermore, academics have explored the specific subdomains within logistics and supply chains where intelligent technologies are most actively applied. Transportation 4.0, for example, has been analyzed as a critical subsystem whose digital transformation unlocks new efficiencies in freight movement, shipment coordination, and multimodal integration. Recent work expands this perspective by identifying gaps in the literature and outlining pathways toward seamless integration with a broader Industry 5.0 vision that incorporates sustainability, human roles, and equitable accessibility across transportation contexts [15].

Collectively, the scholarly contributions over the last five years have not only documented the integration of digital technologies into logistics and supply chain systems but have also critically examined the implications of this integration for resilience, sustainability, and organizational capacity. The research trend reveals an expanding interest in interdisciplinary frameworks that join technical innovation with strategic, human, and socio-environmental considerations [16]. These contributions enrich our understanding by situating intelligent logistics within broader socioeconomic transformations, foregrounding the need for collaborative intelligence that marries computational optimization with human expertise and ethical governance.

In summary, the literature on intelligent logistics and supply chain systems grounded in Industry 4.0/5.0 reflects a vibrant and rapidly evolving field of study. It synthesizes technological development with systemic insights, articulating a trajectory from efficiency-driven automation toward human-centric, sustainable, and adaptive supply networks. As researchers continue to refine theoretical frameworks and empirical evidence, these contributions collectively establish a foundation for future inquiry and practical innovation in intelligent logistics and digital supply chain management.

3. Conclusions and Future Perspectives

The analysis of intelligent logistics and supply chain systems grounded in the paradigms of Industry 4.0 and Industry 5.0 highlights a profound transformation in how supply networks are designed, managed, and evaluated. The ability of logistics and supply chain systems to operate with increased visibility, flexibility, and efficiency has been greatly improved by the integration of modern digital technologies (i.e., artificial intelligence, the Internet of Things, cyber–physical systems, and data analytics).Industry 4.0 has paved the way for this transition by enabling automation, real-time data interchange, and intelligent decision support among interconnected supply chain participants. These capabilities have proven critical in addressing the growing complexity and uncertainty of global logistics environments.

At the same time, the emerging Industry 5.0 perspective expands this technological focus by emphasizing human-centricity, sustainability, and resilience as core design principles. Intelligent logistics systems are no longer evaluated solely on efficiency and cost reduction, but increasingly on their capacity to support human decision-making, enhance workforce well-being, and contribute to broader societal and environmental objectives. The shift toward collaborative human–machine systems and explainable intelligence reflects a growing recognition that sustainable competitiveness in supply chains depends on balancing technological autonomy with human expertise and ethical responsibility.

Despite notable progress, several challenges remain that define important directions for future research and practice. Issues related to data interoperability, cybersecurity, governance of intelligent systems, and organizational readiness continue to limit large-scale implementation. Moreover, the transition toward Industry 5.0 raises new questions regarding the design of inclusive and transparent intelligent systems that align with regulatory frameworks and social expectations. Future research should therefore focus on interdisciplinary approaches that integrate technical innovation with organizational, behavioral, and policy perspectives.

In conclusion, intelligent logistics and supply chain systems based on Industry 4.0 and 5.0 represent a strategic pathway toward more adaptive, resilient, and sustainable supply networks. Continued research and practical experimentation will be essential to fully realize their potential and to guide the responsible evolution of intelligent supply chains in an increasingly interconnected global economy.