Analyzing the driving forces defining Logistics 5.0: human-centricity, resilience and sustainability in the new industrial era

For centuries, the production of basic goods and services has relied on manual labor and human power. However, with the onset of the Industrial Revolution 1.0 in the early 18th century, this changed, leading to a radical transformation in production processes (Yavari and Pilevari, 2020). This transformation reflects a broader historical pattern, as industrial revolutions have consistently served as major turning points in civilization. Industry 1.0 refers to mechanized production systems powered by steam, which began in the 18th century. This industrial phase focused on industries such as steam power, textiles, cement, iron and transportation. Industry 2.0 was a transformation that increased efficiency with the advent of electrical energy and internal combustion engines in the 19th century. It continued with the development of railroads, electrification, automobiles and communication technologies. Industry 3.0, which occurred in the 20th century, introduced automation using electronic and digital technologies. The semiconductor industry has been highlighted by telecommunications, automation and robotics. Today, Industry 4.0 refers to intelligent systems in various industries that incorporate artificial intelligence (AI) and machine learning (ML). Although each industrial revolution has achieved significant success, it has also introduced certain challenges. Industry 5.0, on the other hand, envisions a future where humans and robots collaborate, with greater emphasis on sustainability (Akundi et al., 2022; Nahavandi, 2019). The evolutionary history of the Industrial Revolution is shown in Figure 1 (Leng et al., 2022; Paschek et al., 2022).

With a human-centric paradigm, Industry 5.0 represents the transformation of production processes and their positive impact on the innovation ecosystem. This approach is based on key elements such as human-centricity, resilience and sustainability. To achieve true prosperity, industry must focus on a purpose that encompasses social, environmental and community aspects (Carayannis and Morawska-Jancelewicz, 2022). Industry 4.0 is defined as the integration of the internet and new technologies, resulting in a fundamental paradigm shift in industrial production. During this period, production processes have made significant advances in efficiency and automation through the adoption of technologies such as smart factories, data analysis and AI. Industry 5.0 builds on Industry 4.0 by emphasizing research and innovation to create a sustainable, human-centered and resilient industry (Broo et al., 2022). In this era, humans collaborate with technology and robots. Production processes become increasingly adaptable and customized. The focus of Industry 5.0 is on increasing prosperity and creating a sustainable future while addressing social and environmental needs. This approach shapes future industrial development by making technology more valuable and useful for people.

In parallel with industrial development, the logistics sector has evolved in five distinct phases. Logistics 1.0, the era of mechanization, introduced machinery for the transportation and storage of goods. Logistics 2.0, the era of electrification, enhances operational efficiency through the adoption of electric-powered systems. Logistics 3.0, corresponding to the information age, optimizes logistics processes through computing technologies and automation. Logistics 4.0, the era of networking, established interconnected logistics networks integrated with the internet and digital data systems. Logistics 5.0, the era of parallel intelligence, aims to create an intelligent and sustainable logistics ecosystem by integrating human expertise with advanced information systems (Li et al., 2023). The implementation of Logistics 5.0 aligns closely with the approach adopted in Industry 5.0. To achieve the objective of establishing a sustainable and environmentally responsible system, Logistics 5.0 must prioritize the role of humans and their interactions with both humans and machines. Within this transformation, human and technological systems collaborate to develop a logistics ecosystem that is more efficient, resilient and human-centric (Trstenjak et al., 2022).

Industry 5.0 and Logistics 5.0 are emerging paradigms for researchers and organizations, characterized by their inherent structural complexity. Therefore, these entities will require time to navigate this complexity and fully leverage the strategic benefits offered by these frameworks. To facilitate this transition, a set of criteria has been established to help logistics enterprises recognize the opportunities of Logistics 5.0 and secure competitive advantages. These criteria function as essential driving forces for successful implementation, enabling firms to develop targeted and strategic frameworks. By integrating these drivers into their operational structures, organizations can significantly streamline the adoption and execution of Logistics 5.0.

The concepts of Industry 5.0 and Logistics 5.0 have emerged in recent years as frameworks describing transformative shifts within manufacturing and supply chain operations. While these paradigms are gaining scholarly and practical traction, they have not yet achieved the maturity required for widespread organizational adoption. Transitioning to these models necessitates a tailored approach that aligns with specific organizational requirements and existing technological infrastructure. Adoption rates are expected to accelerate as technological capabilities mature and strategic awareness expands. To contribute to this evolution and provide actionable guidance for practitioners, this study investigates Logistics 5.0 through the lens of Industry 5.0's core pillars. Given the nascent stage of this domain, research examining Logistics 5.0 from an Industry 5.0 perspective remains notably scarce. Considering the strategic centrality of logistics management in contemporary competitive environments, evaluating this domain against the core pillars of Industry 5.0 human-centricity, sustainability and resilience is imperative. Accordingly, this study evaluates the driving forces of Logistics 5.0 aligned with these dimensions utilizing Multi-Criteria Decision-Making (MCDM) methodologies. Drawing on the extant literature, 18 key criteria were identified and systematically categorized under the three foundational dimensions. Following the criterion definition, their relative weights were determined via the Fuzzy DEMATEL (F-DEMATEL) method, leveraging structured expert judgments. Expert assessments are inherently subjective and context-dependent, making them more effectively captured through linguistic variables rather than precise numerical values (Mohapatra et al., 2023). Consequently, a fuzzy-based methodology was adopted to model and mitigate the uncertainties inherent in expert evaluations, a widely validated approach in MCDM applications. Furthermore, the DEMATEL framework enables robust analysis of causal relationships and inter-criteria influences (Du and Shen, 2023). The integrated F-DEMATEL approach was selected for its demonstrated efficacy in handling conflicting criteria and mapping complex interdependencies. Ultimately, the derived outputs facilitate a structured prioritization of the identified criteria.

Unlike the Industry 4.0 paradigm, which prioritizes full automation, data optimization and cost minimization, Logistics 5.0 represents a conceptual shift. Logistics 5.0 is a socio-technical paradigm where technological capabilities and human/organizational factors co-evolve and jointly shape systemic outcomes. Whereas Logistics 4.0 centers on technology and minimizes human intervention, Logistics 5.0 emphasizes the synergy between human expertise and digital tools. It prioritizes environmental balance, operational resilience and stakeholder value creation over linear efficiency metrics. The primary aim of this study is to move beyond a narrow perspective. This perspective treats Logistics 5.0 merely as a set of technical and operational driving forces. Instead, this study examines it as a holistic management paradigm. Accordingly, the research evaluates these driving forces not as isolated success factors. It analyzes them through three conceptual pillars. The first is human-technology integration, which aligns technical infrastructure with human dynamics. The second is operational flexibility and dynamic adaptation, which ensure process resilience amid crises and uncertainty. The third is environmental sustainability, which directs logistics flows toward resource efficiency and ecosystem health. This theoretical framework moves Logistics 5.0 beyond a simple technological upgrade or a checklist of criteria. Ultimately, this study aims to contribute to the literature by establishing a holistic management approach.

The most important contributions of this study are listed below:

1. Conceptual Integration: Aligned with the Industry 5.0 paradigm, this study organizes the driving forces of Logistics 5.0 into a hierarchical structure. These factors have been previously addressed under fragmented headings in the literature. The framework is built on three core pillars: human-centricity, resilience and sustainability. This synthesis transforms an abstract transformation into a structured and manageable model.

2. Contribution to the Logistics 5.0 Literature: This study represents a pioneering effort to examine Industry 5.0 in the logistics context systematically. It integrates human dynamics, operational resilience and environmental responsibility into a unified framework. This study explicitly maps the hierarchical structure and interaction dynamics among the driving forces. This approach shifts Logistics 5.0 from an abstract vision to an actionable strategy. Consequently, this study makes a distinct conceptual and practical contribution to the field.

3. Strategic Preparation Roadmap: This study provides a concrete implementation roadmap for logistics enterprises, addressing investment prioritization, competency development and operational planning during the transition to Logistics 5.0. The stepwise activation structure among the driving forces enables firms to allocate their transformation budgets efficiently. It also supports phased change management processes. This structured approach minimizes operational and financial risks during adoption.

4. Methodological Validation and Cross-Comparison: The priority structure and causal mechanisms identified via F-DEMATEL were cross-validated using Fuzzy AHP (F-AHP) and Fuzzy FUCOM (F-FUCOM) techniques. This multi-method analysis utilized identical expert panel data. The cross-validation process significantly enhanced the methodological robustness of the findings.

This paper is organized into six sections. Section 1 introduces the conceptual foundations of Industry 5.0 and Logistics 5.0, outlining the study's purpose, motivation and theoretical contributions. Section 2 establishes the theoretical background, comprising a comprehensive literature review on Logistics 5.0 and Industry 5.0 perspectives, a keyword-based analysis of recent developments indexed in the WoS and a clear delineation of existing research gaps. Section 3 details the methodological framework, focusing on fuzzy set theory and the DEMATEL technique. Section 4 presents the empirical application of the model and validates the outcomes through a comparative analysis with alternative MCDM methods. Section 5 discusses the findings, translating the priority rankings into strategic implications and context-specific recommendations. Finally, Section 6 concludes the study by summarizing the core results, outlining the theoretical and practical implications, extracting key lessons, acknowledging the limitations and proposing directions for future research.

This section comprises four subsections. The first reviews existing studies on Industry 5.0 and Logistics 5.0. The second section defines the main and subcriteria applied in this study. The third analyzes recent developments using the VOSviewer software. The final subsection identifies the current research gaps.

This section presents a comprehensive overview of the academic evolution of Logistics 5.0 and Industry 5.0 paradigms. The literature review is organized thematically, progressing from foundational concepts and strategic drivers to implementation challenges, technological enablers, human-centric transformations and operational frameworks.

The theoretical conceptualization of Logistics 5.0 has evolved to position human-centricity, sustainability and resilience as core pillars of industrial transformation. The existing literature defines Logistics 5.0 not merely as a technological advancement, but as a profound paradigm shift built on the pillars of human-centricity, sustainability and resilience (Andres et al., 2024; Mehta et al., 2026). Bibliometric analyses by Moyano-Londoño et al. (2025) reveal that post-2020 research has shifted its focus from pure digitalization toward more holistic clusters, including circular economy, traceability and governance. Similarly, Monferdini et al. (2025) emphasize that the automation-centric approach of Industry 4.0 has evolved in the Industry 5.0 era, with logistics systems now prioritizing human-centricity and human-machine integration. These findings confirm that the theoretical axis of industrial transformation is shifting from technical efficiency toward socio-technical integration. To examine the transformative role of Industry 5.0 in smart logistics, Hsu et al. (2024a) analyzed 13 key driving forces identified through a literature review using Fuzzy ISM and MICMAC methods. Their results identified “ Active support from the government ” and “human-centric manufacturing and logistics” as the most critical factors for transformation (Hsu et al., 2024a). Torbacki (2025) developed a strategic roadmap for logistics, supply chain management and IT investments to support Industry 5.0 adoption, employing a hybrid DEMATEL-PROMETHEE II methodology. The evaluation determined that transformation efforts should prioritize resilience solutions, AI and machine learning in IT and demand planning and forecasting in logistics (Torbacki, 2025). Finally, Sindhwani et al. (2022) assessed the impact of Industry 5.0 driving forces on sustainability using Pythagorean Fuzzy Delphi and Pythagorean Fuzzy AHP-COCOSO methods. The results indicated that personal customization and human–machine collaboration are the most critical criteria for achieving sustainability (Sindhwani et al., 2022).

Despite these conceptual advancements, the scholarly literature consistently identifies systemic barriers that complicate the translation of theoretical principles into organizational practice. These impediments are particularly pronounced in resource-constrained contexts and sector-specific operational environments. Deivanayagampillai et al. (2023) identified the most critical barriers as expenses and the funding system, capacity scalability and workforce upskilling and reskilling. Similarly, Mohamed and Gamal (2023) emphasized cost and fund, scalability, lack of socio-technological planning, security and privacy as primary challenges. Mukherjee et al. (2023) confirmed that financing constraints, scalability limitations and skill transformation gaps remain the most prominent barriers and proposed mitigation strategies based on the Green, Resilient and Inclusive Development (GRID) framework. In specialized sectors such as pharmaceutical logistics, the principal obstacle to Industry 5.0 adoption is identified as the disconnect between virtual reality applications and physical operations (Sharma et al., 2022). For the transition toward circular supply chains, however, the most significant barriers are financial and organizational requirements, compounded by a shortage of qualified personnel (Werner-Lewandowska et al., 2025). Additionally, Trstenjak et al. (2022) observed that while organizational awareness of digital paradigms such as Industry 4.0 and 5.0 remains limited, firms demonstrate greater openness to adopting green and sustainable practices.

Within the Logistics 5.0 paradigm, digital infrastructure and intelligent systems are theoretically framed as essential enablers of adaptive and resilient supply networks. These technologies are conceptualized not as ends in themselves, but as means to strengthen sustainability, operational resilience and human–machine symbiosis. Andres et al. (2024) identify the Internet of Things (IoT) as the most fundamental and mature technology driving Logistics 5.0 development. This technological infrastructure enhances operational efficiency while reducing the carbon footprint (Andres et al., 2024). From a resilience perspective, Ahmed et al. (2023) demonstrated that AI-based imperatives reduce human error and strengthen supply chain survivability. Nicoletti and Appolloni examine, through a theoretical innovation framework, the integration of foundation models into route optimization, waste management and warehouse organization to build a sustainable structure within Logistics 5.0, along with their contributions to environmental and social objectives (Nicoletti and Appolloni, 2024). Similarly, Lin focuses on the dynamic role of Generative AI (GAI) in decision support processes. Through the UNISONE model, the study presents empirical pathways to enhance delivery flexibility, carbon efficiency and resource stability in supply chains, providing firms with both operational agility and long-term sustainability orientation (Lin, 2025). Hsu et al. (2024c) addressed Industry 5.0 dynamics to enhance risk resilience in sustainable supply chains. Using an integrated ANP-VIKOR model, they demonstrated the critical role of key driving forces, particularly responsible consumption, organizational trust and energy efficiency, in minimizing logistical, financial and technological risks (Hsu et al., 2024c). In sustainability-focused transportation models, Qahtan et al. (2022) found that environmental criteria carry the highest weight and priority, whereas demand, supply and technical factors rank lower. In high-sensitivity domains such as healthcare logistics, Nayeri et al. (2023) position the adoption of advanced technologies as a fundamental strategy for minimizing operational risks.

Central to this theoretical evolution is the redefinition of human capital, shifting from a peripheral operational element to a core socio-technical driver of systemic performance. Contemporary scholarship positions workforce well-being and cognitive ergonomics as foundational to sustained logistical excellence. The most distinctive contribution of the Logistics 5.0 literature lies in the shift in the human role within systems from a cost-centric to a value-centric paradigm. It is posited that employee well-being and job satisfaction directly enhance operational performance through innovation (Wang et al., 2025). The concepts of “Warehousing 5.0” and “Operator 5.0” characterize future warehouses not merely as automated facilities, but as socio-technical ecosystems where smart robotics integrate with neuroscience-based cognitive load assessments, ergonomic design and human-machine symbiosis (Ekren et al., 2026; Panter et al., 2024). Within this new paradigm, Modgil et al. (2023) emphasized through the AHP that logistics professionals must transcend routine operational tasks to develop advanced managerial and technical competencies, including strategic thinking, crisis management and AI governance. Furthermore, this digitized work environment and emerging technologies are highlighted as strategic levers capable of dismantling traditional gender barriers, fostering an inclusive workforce and strengthening women's participation in the logistics sector (Narkhede et al., 2025). At a theoretical and holistic level, Chan and Choi (2024) present a comprehensive strategic roadmap (“IJLRA”) that merges innovative technologies with sustainability pillars, while Fox et al. (2025) define an expansive “bio-socio-technical” interaction framework that extends logistics processes beyond the cyber-physical domain to incorporate planetary factors such as biodiversity and zoonotic risks.

To operationalize these core pillars into practice, contemporary scholarship develops strategic frameworks for embedding them into logistical planning. Such conceptual models aim to reconcile adaptive capacity with long-term resilience within the Logistics 5.0 paradigm. The operational implementation of Logistics 5.0 paradigms necessitates the development of dynamic, sustainability-oriented decision support systems (DSS). In this context, Monferdini et al. (2025) and Hsu et al. (2024b) demonstrate that Industry 5.0 complements the technological competencies of Industry 4.0 by infusing human-centric dimensions and that integrating these two paradigms is essential for achieving operational excellence. At the strategic modeling level, Trstenjak et al. (2022) prioritize transformation elements such as green warehousing and human resources based on investment cost and complexity criteria, thereby providing managers with concrete implementation roadmaps. In his study, Lo identifies digital transformation and corporate cultural change as fundamental prerequisites for establishing a network aligned with Industry 5.0 and Logistics 5.0 during supplier evaluation processes. The author develops a sustainable model that integrates MCDM with data-driven rule-based approaches, explicitly incorporating human-centricity and resilience Dynamics (Lo, 2023). Furthermore, Gamal et al. (2023) analyze the complex interrelationships among the driving forces enabling the transition to Industry 5.0 in logistics operations using the Grey-DEMATEL method. The research presents a strategic roadmap for decision-makers that facilitates efficient resource management while ensuring resilience, human-centricity and alignment with sustainability objectives (Gamal et al., 2023).

To evaluate the driving forces of Logistics 5.0 from the perspective of Industry 5.0, a comprehensive literature review was conducted, and various criteria were determined. 5.0 from an Industry 5.0 perspective. This initial identification process established a preliminary pool of candidate drivers, grounded in current academic research. The candidate list was then presented to the decision-making group for validation and refinement purposes. Overlapping criteria were merged, and those deemed non-essential for the scope of the study were excluded. The experts then reviewed each criterion, assessing its relevance, clarity and applicability to the Logistics 5.0 context. As a result of the evaluation, 18 criteria were identified as presented in Table 1. The following subsections explain each criterion.

Industry 5.0 is an industrial paradigm that combines digital transformation with a human-centric focus, emphasizing sustainability and efficiency in production. This concept is characterized by three interlinked core pillars: human-centricity, resilience and sustainability (Figure 2). The Industry 5.0 framework strengthens the agility and resilience of production systems by integrating flexible and adaptable technologies (Huang et al., 2022).

Human-centricity: Industry 5.0 positions fundamental human needs and societal interests at the core of production, shifting from a purely technology-driven paradigm to a human- and society-centric model. This framework redefines industrial workers as strategic investments rather than operational costs, recognizing their value as essential human capital. It promotes the integration of technology as a collaborative tool alongside human expertise, enhancing both the usability and effectiveness of business processes. Manufacturing technologies must therefore serve human and societal needs, remaining adaptable to the diverse requirements of the workforce. Furthermore, Industry 5.0 ensures that digitalization extends beyond mere efficiency and speed, prioritizing human well-being and broader societal benefits. Ultimately, this transition enables workers to assume more creative and meaningful roles while fostering production systems that align with societal demands (Xu et al., 2021).

Resilience: Corporate flexibility and agility demonstrate an organization's capacity to respond effectively to dynamic market conditions (Jafari et al., 2022). Fluctuating customer demand further drives the need for mass customization, underscoring the critical role of operational flexibility. Furthermore, the resilience pillar emphasizes enhancing the robustness of production and supply networks, enabling them to withstand disruptions and safeguard critical infrastructure during crises (Nikolić et al., 2023). As a core pillar of Industry 5.0, resilience extends beyond organizational risk management. It encompasses the capacity to navigate uncertainties originating from market volatility, supply chain disruptions and evolving customer expectations while strengthening the adaptive flexibility of national or regional industrial ecosystems (Leng et al., 2022). Ultimately, this approach integrates advanced technologies, human expertise and risk mitigation strategies into a cohesive industrial ecosystem that can proactively manage disruptions and sustain operational continuity.

Sustainability: Sustainability refers to meeting present needs without compromising the capacity of future generations to meet their own. The transition to Industry 5.0 has intensified expectations for businesses to achieve higher sustainability standards (Aheleroff et al., 2022). Organizations are increasingly integrating sustainable business models into their operations to address growing environmental and social concerns. When industries recognize the associated cost savings, they tend to view sustainability as a strategic imperative rather than an optional consideration (Breque et al., 2021). This approach emphasizes concepts such as the circular economy, reverse logistics and sustainable value chains. Sustainable development in business seeks to minimize environmental impact through eco-friendly products and logistics systems, advancing toward waste reduction goals. Furthermore, production processes can enhance environmental performance by adopting renewable resources and green computing practices (Jafari et al., 2022).

The 18 criteria identified as the driving forces of Logistics 5.0, presented in Table 1, are as follows.

Human–Machine Collaboration: In the Industry 5.0 era, establishing an optimal balance between humans and machines has become a critical imperative in operations management. Rather than displacing the workforce through automation, this paradigm enhances productivity and innovation by synergizing human expertise with technology. Industry 5.0 necessitates a focus on holistic, human-centric practices and corporate social responsibility to maximize human potential and generate substantive value. Consequently, the framework prioritizes not only technological advancement and operational efficiency but also employee well-being and broader societal impact (Choi et al., 2022).

Employee Safety and Management: Industry 5.0 plays a pivotal role in safeguarding employee well-being, reducing workplace accidents, ensuring business continuity and fulfilling organizations' legal and ethical obligations. Healthy and safe employees demonstrate higher performance, motivation and operational efficiency. Although intelligent machines and robotics drive innovation and automation, they are designed to augment, rather than replace, human labor. By leveraging autonomous systems for high-risk tasks, organizations enable employees to work in safer, more flexible and productive environments (Javaid and Haleem, 2020).

Employee Training: Industry 5.0 demands both intelligent technologies and a highly skilled workforce to enhance productivity and operational efficiency. Therefore, continuous training is essential to prepare employees for the evolving tasks associated with this transition. Effective collaboration between humans and intelligent systems will shape the management of future production environments. Consequently, developing versatile human competencies alongside digital literacy is imperative. Ongoing training and professional development are critical for securing a skilled workforce required for sustainable competitiveness (Adel, 2022; Alves et al., 2023).

Lean Management Philosophy: Lean management within the Industry 5.0 framework emphasizes enhancing production and business processes through efficiency, effectiveness and human-centric design. This philosophy addresses the needs of both employees and customers, minimizes waste to optimize resource utilization and prioritizes the creation of customer value. By integrating these principles, lean management strengthens organizational competitiveness and facilitates adaptation to future production paradigms (Rahardjo and Wang, 2022).

Collaboration and Partnership: Industry 5.0 is characterized by accelerated technological advancement and digital transformation. This evolution fosters cross-sectoral collaboration by connecting companies with diverse stakeholders. Digital platforms and networks, in particular, enable data sharing, streamline communication and catalyze innovation through enhanced connectivity. This approach promotes collaboration across internal departments and among external partners, including suppliers, customers and broader communities (Ghobakhloo et al., 2023).

Customer Orientation: By prioritizing personalization and innovation, Industry 5.0 addresses the limitations inherent in the Industry 4.0 paradigm. This new model emphasizes the delivery of genuinely customized products and services, moving beyond standardized mass customization to meet individual customer preferences. As a result, consumers are increasingly willing to pay a premium for offerings aligned with their specific needs. Advanced technologies such as AI, IoT and big data analytics play a pivotal role in enabling the personalization process (Patnaik et al., 2023).

Digital Technologies: The innovative technologies of Industry 5.0 (edge computing, IoT, big data analytics, 6G and blockchain, digital twin, cobots, etc.) create competitive advantages by making processes more efficient, transparent and customer-oriented (Maddikunta et al., 2022). By leveraging these technologies, supply chain data can be managed more effectively, real-time tracking and monitoring become feasible and human-machine collaboration in warehouses and distribution centers significantly enhances logistics operations.

Data Orientation: Data enables organizations to operate intelligently, adaptively and efficiently within the Industry 5.0 paradigm. In this context, data-driven management of production and logistics processes supported by advanced analytics facilitates more accurate and timely decision-making. This further enhances process visibility, traceability and transparency (Ghobakhloo et al., 2022). The data-centric approach of Industry 5.0 employs advanced digital communication tools to establish intelligent production systems. The cutting-edge technologies introduced through this transition aim to mitigate operational disruptions and optimize system performance. Data infrastructure supports the storage and analysis of large-scale information, enabling more comprehensive strategic insights (Javaid and Haleem, 2020). In logistics, this ensures efficient process execution through the collection and real-time analysis of the operational data.

Competitiveness and Efficiency: Industry 5.0 provides organizations with opportunities to enhance their competitiveness and operational efficiency. The strategic adoption of digital transformation and advanced technologies enables businesses to build a more sustainable and competitive future. Within the transportation sector, Industry 5.0 facilitates the development of highly efficient and eco-friendly transport systems by minimizing congestion and reducing emissions (George and George, 2023).

Innovation: Maintaining competitiveness in dynamic markets requires the continuous development of products and services that generate added value for customers and organizations. An innovation-driven culture, combined with creatively minded employees, enables firms to capitalize on emerging opportunities and sustain their operational agility. However, innovation must remain practically applicable to the real world. It requires technical and financial feasibility, alignment with human needs and measurable benefits for stakeholders (Aslam et al., 2020). Consequently, businesses must prioritize strategic investments in novel technologies, processes and service models to secure long-term market position (George and George, 2023).

Collaborative and Flexible Platforms: These platforms function as technological ecosystems that facilitate seamless interactions between humans, machines and advanced digital systems. Integrating human expertise with technological capabilities remains a critical priority. The primary challenge is ensuring interoperability across diverse technologies and legacy infrastructures. When deploying AL, ML, robotics, cloud computing and IoT within these environments, organizations must guarantee coordinated workflows and frictionless data exchange (George and George, 2023).

Industry 4.0 Experiences: Industry 5.0 builds upon the digital transformation and automation foundations established during Industry 4.0, while explicitly integrating human-centric pillars to foster sustainable production environments (Aheleroff et al., 2022). Rather than replacing previous paradigms, Industry 5.0 extends them by emphasizing the strategic role of human oversight. Consequently, the operational insights and technological infrastructure developed during Industry 4.0 are essential for successfully aligning digital systems with human factors in this new era.

Green Logistics: The core principle of green logistics is to advance sustainability by minimizing or eliminating waste across supply chains (Trstenjak et al., 2022). These practices encompass activities that reduce the environmental and social impacts of logistics operations while preserving natural ecosystems. Key interventions include sustainable transportation, transparent information sharing, eco-friendly packaging and distribution, energy conservation, waste management and green performance monitoring (Agyabeng-Mensah and Tang, 2021).

Circular Economy: The circular economy represents an industrial model that optimizes resource utilization and extends product lifecycles through reuse, remanufacturing and recycling. This approach aims to minimize material and energy waste while reducing the environmental footprint (Möller et al., 2022). In Industry 5.0, respecting planetary boundaries is a societal imperative. Consequently, business models and technologies must evolve to advance circular economy principles while integrating environmental considerations (Müller, 2020).

Value Chains: Industry 5.0 fosters cross-sectoral collaboration to establish circular and sustainable value chains. These chains illustrate value creation across the entire lifecycle from production to end-consumer delivery and encompass all associated business processes. This vision addresses future industrial needs by enhancing reuse, improving production flexibility and strengthening resilience to disruptions. Through coordinated collaboration, value chains promote long-term sustainability and enable the efficient and effective management of manufacturing operations (Patera et al., 2021).

Green Computing: Industry 5.0 aims to create sustainable and environmentally friendly production/service systems. Green computing refers to the development, use and management of computer systems and digital technologies in an environmentally friendly manner. The aim is to increase energy efficiency, use resources efficiently, reduce waste and minimize environmental impact (Raja, 2021; Sindhwani et al., 2022).

Energy Efficiency: Industry 5.0 promotes the development of energy-efficient solutions for innovative products and services. Through sensor integration and data analytics, intelligent systems enable more effective operational planning and optimization processes. In logistics, real-time data support efficient route planning and reduce energy consumption across transportation activities. Intelligent logistics systems further enhance energy efficiency by optimizing warehouse management and internal processes. Improved warehouse utilization ensures more effective space allocation and lower energy demand for logistics operations.

Renewable Resources: The utilization of renewable resources is essential for achieving sustainability, a foundational objective of Industry 5.0. By displacing fossil fuel-based energy systems, renewable sources significantly mitigate environmental impacts. These include solar, wind, hydropower, geothermal and biomass energy. Within Industry 5.0, applications such as solar-powered production lines, wind-driven machinery and hydropower-integrated facilities are increasingly prioritized.

Keyword analysis was performed using VOSviewer software to visualize the effects of the study. This software makes information more meaningful and accessible by creating word clouds and network diagrams based on the word frequencies in scientific articles. A total of 818 relevant articles with the keywords (“Logistics 5.0” OR “Industry 5.0” OR “Fifth Industrial Revolution” OR “Fifth Logistics Revolution” OR “Multi-criteria Decision-Making” OR “MCDM”) AND (“Logistics”) were found. A total of 15,589 keywords were retrieved from these articles. By setting the threshold value in the VOSviewer software to five, 111 relevant words from 738 keywords were considered for the analysis. The analysis of the keyword network is presented in Figure 3.

In keyword analysis, similar or closely related words are displayed in the same color. This color coding is used to highlight relationships between words and to visualize the proximity of similar topics or terms to each other. The analysis results show that the most frequently focused topics or terms are “barrier” (cluster 1), “Industry” (Cluster 2), “fuzzy set” (Cluster 3) and “logistics service provider” (Cluster 4). Cluster 1 is represented by purple. Some keywords in this group are introduction, critical factor, critical success and significant obstacle. Cluster 2 is represented by the color pink and contains words such as resilience, smart logistics, automation and information systems. Cluster 3 is represented by the color green and consists of terms such as multi-criteria decision-making problem (MCDM problem), fuzzy set, fuzzy DEMATEL. Cluster 4 is yellow and consists of terms such as fuzzy decision, key factor, fuzzy MCDM and logistics outsourcing.

The current literature on Logistics 5.0 predominantly focuses on technological adoption and operational metrics, often treating human-centricity, resilience and sustainability as isolated constructs rather than interdependent theoretical pillars. While MCDM techniques have been increasingly applied to evaluate transformation drivers, these studies frequently lack a theoretically grounded criteria architecture, resulting in methodological applications that are not fully anchored in the socio-technical logic of the Logistics 5.0 paradigm. Consequently, a critical theoretical gap remains in understanding how these drivers interact conceptually, hierarchically structure and collectively shape strategic transformation pathways. Methodologically, existing approaches rarely demonstrate how MCDM frameworks can be explicitly derived from and validated against a coherent Logistics 5.0 theoretical model. To address this dual gap, the present study develops a theoretically structured framework of Logistics 5.0 drivers and employs a rigorous MCDM methodology to quantify their relative importance and interdependencies. This integration contributes to the literature by bridging conceptual fragmentation with methodological robustness, thereby advancing both the theoretical understanding of Logistics 5.0 as a socio-technical paradigm and the practical application of decision-support models in strategic logistics planning.

This section consists of two subsections. The first section explains the fuzzy set theory. The second section presents the F-DEMATEL method used in the study. The proposed methodology is illustrated in Figure 4.

Zadeh's fuzzy set theory was developed to model the uncertainty and ambiguity in human reasoning. The basic concept of this theory is that elements can have different degrees of membership in a fuzzy set. A fuzzy set is defined by a membership function that maps the degrees of membership of the elements to a specific range of values, which is usually [0,1]. Different types of fuzzy membership functions are used in fuzzy logic. The triangular fuzzy number (TFN) is frequently used because of its simple calculation (Li et al., 2020).

A TFN can be defined as (l, m, u), and the membership functions are found as in Equation (1) (Bakır and Atalık, 2021):

In a TFN, l, m and u represent the lower, middle and upper values, respectively. Let $A~1$ = ( $l1$ , $m1$⁠, $u1$ ) and $A~2$ = ( $l2$ , $m2$⁠, $u2$ ) be two TFNs. The basic arithmetic operations – addition, subtraction, multiplication, division and reciprocal – are applied to these numbers, as shown in Equations (2)–(6) (Bakır and Atalık, 2021; Stanković et al., 2020).

The F-DEMATEL method converts linguistic expressions provided by experts into fuzzy triangular numbers to cope with difficult decision-making situations. In this way, the relationships between the criteria are clearly expressed, and the effect diagram helps to better explain the problem (Çelik and Arslankaya, 2023; Mahdinia et al., 2022). The steps of the F-DEMATEL method are as follows (Albayrak and Erkayman, 2018; Çelik and Çağıl, 2021).

• Step 1. Determining the criteria and creating a fuzzy evaluation scale

In this step, all the criteria to be used in the application are defined. Pairwise comparisons are then made between the defined criteria to determine the extent to which one criterion influences the other. The interaction between these factors is difficult to express quantitatively. Therefore, linguistic expressions are used. The fuzzy scale, which corresponds to linguistic terms, is expressed using triangular fuzzy numbers. The linguistic expressions for fuzzy numbers are presented in Table 2 (Zhang et al., 2023).

• Step 2. Creating the direct fuzzy relationship matrix

To determine the levels of relationships between the criteria $C={C1,C2….Cn}$⁠, the expert group creates a pairwise comparison matrix using the linguistic scale given in Table 2. The result of the comparisons is the Fuzzy Direct Relationship Matrix (“$Z~$”). Accordingly, the direct relationship matrix, which consists of triangular fuzzy numbers $Z~$_ij = (⁠$lij$⁠, $mij$⁠, $uij$⁠) belonging to expert k and indicates the degree to which criterion i influences criterion j, is shown in Equation (7).

• Step 3. Creating the normalized fuzzy direct relationship matrix

Equations (8) and (9) are used to create a normalized matrix of direct relations.

The “u” columns in the direct relationship matrix are collected, and the maximum value “r” was determined. All numbers in the fuzzy direct relationship matrix are divided by “r” values and thus normalized. The normalized fuzzy direct relationship matrix is created using Equation (10).

• Step 4. Creating the total fuzzy direct relationship matrix

Applying Equation (11) to the normalized fuzzy direct relationship matrix, the total fuzzy direct relationship matrix is obtained.

Because it is difficult to apply Equation (11) to the normalized fuzzy direct relationship matrix, which consists of triangular numbers, separate matrices are created from the numbers l, m and u. Identical mathematical operations are applied to each component. The difference between the identity and normalized matrices was computed. This result is inverted and multiplied by the original normalized matrix. The procedure was repeated for all three matrices. Finally, the outputs are recombined to construct the fuzzy total relation matrix, as defined in Equation (12).

• Step 5. Determining the sender and receiver groups

Once the total relationship matrix $(T~)$ has been obtained, the sum of the column elements in the matrix gives the $D~i$ value and the sum of the row elements in the matrix gives the $R~i$ value. The values $D~i+R~~i$ and $D~i−R~~i$ are calculated using Equations (13) and (14), respectively. Because these values consist of triangular fuzzy numbers, defuzzification is performed by applying Equations (15) and (16) to these values.

• Step 6. Obtaining the weights

The weighting of each criterion is calculated using Equations (17) and (18).

This study applies an MCDM approach to identify and evaluate the driving forces of Logistics 5.0. These forces are structured around the core pillars of Industry 5.0. Three core pillars – human-centricity (C1), resilience (C2) and sustainability (C3) – were established as the main criteria. An extensive literature review was conducted to identify 18 specific driving forces relevant to this paradigm. These subcriteria were systematically categorized under the three main dimensions. The resulting hierarchical framework is shown in Figure 5.

The weights of the main and subcriteria were calculated using the F-DEMATEL method. When applying the F-DEMATEL method, the decision-making process was completed by forming a group of four experts. When the MCDM literature is examined, it is observed that studies across various fields utilize different numbers of experts (Albayrak Ünal et al., 2025; Çodur et al., 2024; Öztaş, 2025; Wei et al., 2023). In multi-criteria analyses, the number of participants is often intentionally limited to manage computational complexity and ensure high consistency (Ivanović et al., 2022). This study emphasizes the depth of expertise rather than the statistical quantity. Given the emerging and advanced nature of Logistics 5.0, a group of four individuals with academic experience and expertise in this field was formed to conduct this study. This approach was chosen to ensure that the insights remained expert-validated and focused, avoiding the potential inconsistencies of a larger, less specialized group.

Experts were selected using purposive sampling to include professionals with specialized knowledge in logistics, manufacturing systems and Industry 4.0/5.0. Each expert was selected based on holding a Ph.D. degree, and a minimum of five years of academic experience was required to ensure that the participants had reached a level of professional maturity and research expertise in their respective fields. Additionally, familiarity with MCDM methodologies was a key factor in the selection process to ensure the technical reliability of the evaluations. As detailed in Table 3, the experts' backgrounds cover a diverse yet complementary range of areas, including Logistics and Inventory Management, Manufacturing Systems and MCDM. This composition was intended to ensure that the evaluation of Logistics 5.0 drivers was grounded in theoretical knowledge and methodological expertise.

This study employed a consensus-driven approach for data collection. This methodology leverages the collective intelligence of an expert panel within the emerging Logistics 5.0 domain. To maintain objectivity, each expert first conducted an independently assessed the criteria. This preliminary phase ensured that professional judgments were made without peer influence. A structured deliberation process reconciled these independent inputs into unified decisions for each criterion. During this phase, the experts jointly reviewed and debated the criteria. Each evaluation was validated by the panel before finalization. This approach was preferred over simple mathematical averaging to better capture the socio-technical complexities inherent in Logistics 5.0 and mitigate individual biases. Furthermore, potential inconsistencies were identified and resolved during the consensus-building phase. This eliminates the need for a subsequent consistency check. The final data thus reflect expert-endorsed agreements with high reliability.

Instead of including all calculations performed with the DEMATEL method, example calculations for the subcriteria of the main criterion of human-centricity are shown below. The results of the other calculations are presented in summarizing tables in the following sections. First, the main and subcriteria were defined. The expert group used the linguistic expressions in Table 2 for pairwise comparisons to determine the extent to which one criterion influences the other criterion. The linguistic expressions were converted into triangular fuzzy numbers according to their fuzzy equivalents, and a fuzzy matrix of direct relationships was created (Table 4).

Using the created direct fuzzy relationship matrix Equations (8) and (9), the normalized direct fuzzy relationship matrix is presented in Table 5.

By applying Equation (11) to the normalized fuzzy direct relationship matrix, the total fuzzy direct relationship matrix in Table 6 was calculated.

Once the total relationship matrix was obtained, the values of the sender and receiver groups were calculated using Equations (13) and (14), respectively. The defuzzification process was then performed using Equations (15) and (16). The weighting of each criterion was calculated using Equations (17) and (18). The criteria weights and priorities are presented in Table 7.

The steps of the F-DEMATEL method were applied separately to the main and subcriteria. The results are presented in Table 8.

Based on the F-DEMATEL results, the main criteria were ranked as follows: C1 > C3 > C2. For the human-centricity dimension (C1), the subcriteria ranking was C12 > C14 > C11 > C15 > C13 > C16. Within the resilience dimension (C2), the order was C21 > C22 > C24 > C26 > C25 > C23. For sustainability (C3), the subcriteria were prioritized as follows: C31 > C32 > C33 > C35 > C36 > C34.

This section compares the F-DEMATEL results with those of two alternative weighting methods: F-AHP and F-FUCOM. The criteria weights were calculated using all three methods. The resulting rankings for each approach were determined. The steps of these methods were applied separately, and the results are shown in Tables 9 and 10. Detailed information can be found in the relevant articles on the F-AHP method (Xu et al., 2023) and the F-FUCOM method (Kılıç and Erkayman, 2023).

The results of this comparison are presented in Table 11. According to the experts' assessments, the criterion with the highest weighting among the main criteria is human-centricity (C1), the second-highest is sustainability (C3) and the lowest is resilience (C2). Notably, the ranking of the main criteria was the same for all the methods used. When examining the ranking of the subcriteria in the “human-centricity” category, the top ranks remain unchanged across all methods. In the ranking of the subcriteria within the “resilience” category, the first ranks were also identical for all methods. In the sustainability category, the criterion that is in first place for the F-DEMATEL and F-AHP methods is in second place for the F-FUCOM method. There were no significant changes between the first two ranks. Simultaneously, the variations in the last three rankings across the applied methods do not significantly impact practitioners' decision-making processes. The extent to which the top three rankings vary is crucial. According to the results of the comparative analysis, the desired confidence in the decision-making process was achieved, as the three methods produced highly consistent results.

Human-centricity, resilience and sustainability, the core pillars of Industry 5.0, are of great importance for balanced economic, environmental and social sustainability in logistics. The transformation of logistics in the age of Industry 4.0 is primarily focused on reducing the role of human operators and adapting to new technologies to increase efficiency. However, with Industry 5.0, the focus is on humans and the environment. New technologies are not simply replacing human employees but are now being used to support operations more effectively and provide better access to highly personalized products and services. In this context, three features that are the basic features of Industry 5.0 were selected as the main criteria. Under these main criteria, the weights of 18 subcriteria that may be relevant to Logistics 5.0 were calculated. Because Industry 5.0 and Logistics 5.0 are still new concepts, the opinions of the academic group working on this topic were considered in the evaluation. In this context, the F-DEMATEL method was used to calculate the criteria's weights. The results of the F-DEMATEL method were compared with those of the F-AHP and F-FUCOM methods. According to the results, the pillar of human-centricity (C1) was rated as the most important criterion, the pillar of sustainability (C3) as the second most important criterion and the pillar of resilience (C2) as the third most important criterion. The results of the weighting of the main and subcriteria according to the methods used are presented in Figure 6.

When examining the results for the subcriteria under the human-centricity main criterion, employee safety and management (C12) and lean management philosophy (C14) emerged as priorities. This finding demonstrates that operational stability fundamentally relies on human factors, despite the increasing technological intensity in smart logistics centers. In facilities integrated with high-speed Automated Guided Vehicles (AGVs) and collaborative robots, physical safety protocols (LiDAR sensors, virtual perimeters and emergency stop zones) and ergonomic workstation designs serve as critical factors. These measures effectively prevent system downtime and accident-related halts. Concurrent with this, lean management eliminates logistical waste, such as unnecessary motion, waiting, over-processing and transport. It operates on a secure infrastructure while standardizing human-machine synchronization. For instance, e-commerce distribution centers utilize pick-to-light systems and voice-directed workflows. These technologies reduce the cognitive load of operators, thereby lowering error rates. Consequently, this approach directly enhances employee satisfaction and process efficiency. These two subcriteria function as enablers of digital technologies and sustainability objectives within the resilience dimension. Ultimately, technological investments deliver continuous and measurable benefits only when they are integrated with human-centered work design.

Human-machine collaboration (C11), employee training (C13), collaboration and partnership (C15) and customer orientation (C16) represent the dynamic capabilities built on this foundation. The operational integration of AGVs and cobots necessitates a shift in the roles of human operators. These roles now prioritise exception handling, quality control and process improvement. This transition is enabled by continuous digital literacy training and mastery of Warehouse Management Systems (WMS) and Transportation Management Systems (TMS) interfaces. Multiparty data-sharing platforms within logistics networks enhance transparency among suppliers, carriers and end-users. Simultaneously, customer-centricity transforms this operational flow into personalized delivery windows and streamlined reverse logistics. For example, in cold chain logistics, IoT-based temperature monitoring data determine operators' intervention priorities. This real-time visibility simultaneously reinforces the trust of the customer. These subcriteria demonstrate that human-centricity is not a passive element in Logistics 5.0. Instead, it functions as an active synthesizer. This mechanism enhances technological resilience in operational settings and integrates sustainability goals into the customer experience.

The prominent role of digital technologies (C21) and data orientation (C22) within the resilience dimension reflects a fundamental transformation. This transformation concerns operational coordination mechanisms under the Logistics 5.0 paradigm. This finding demonstrates that human-centered post-disruption response, traditionally activated after disruptions, has been replaced by a proactive automated coordination system. This system integrates real-time data processing, AI-supported scenario simulations and autonomous decision-making processes. It anticipates disruptions in advance and proactively reallocates resources. Digital twins, cloud-based TMS and edge computing generate real-time replicas of distribution networks. These systems render route and load optimization dynamic. For example, big data analytics and machine learning algorithms instantly assess traffic congestion, weather conditions and fuel costs to reconfigure multi-stop delivery routes. Simultaneously, three-dimensional load optimization software maximizes pallet and container utilization. This reduces the number of trips and empty runs. These technologies define resilience not merely as operational speed, but as resource efficiency and continuity. Data-driven approaches function as the decision-making engine of this infrastructure. The processing of raw sensor data enables collision-free management of in-warehouse AGV traffic. It also ensures the proactive resolution of supply chain bottlenecks. Consequently, C21 and C22 transform operational resilience beyond a static technical capability. They establish a dynamic nervous system that enables the supply network to autonomously reorganize in response to external shocks.

Innovation (C24), collaborative and flexible platforms (C25) and Industry 4.0 experiences (C26) represent the strategic layers that elevate the technical capacity provided by digital technologies (C21) and data orientation (C22) to an organizational level. The relatively lower priority of competitiveness and efficiency (C23) indicates that competitive advantage in Logistics 5.0 is no longer measured solely by cost minimization or isolated process optimization. Instead, it is defined by adaptation speed, multi-party ecosystem integration and value-driven service quality. In this context, C25 and C26 function as strategic amplifiers that activate once the core resilience infrastructure matures. Cloud-based load-matching platforms and open Application Programming Interface (API) architectures provide capacity flexibility by scaling carrier pools in real time during demand peaks or supply disruptions. Meanwhile, the legacy inherited from Industry 4.0 extends beyond hardware such as AGVs or autonomous forklifts. Standardized data protocols, a culture of system integration and predictive maintenance infrastructure enable the implementation of next-generation human-machine collaboration models with lower transition risk and cost. As this infrastructural maturity is internalized, Innovation (C24) becomes a catalyst for logistics service innovation. Adapting existing digital tools to micro-fulfillment networks, autonomous last-mile solutions or multi-modal transition scenarios transforms flexibility from an operational feature into a strategic differentiator. For instance, in port hinterland logistics, the integration of container tracking, customs and road transport platforms enables rapid switching to alternative rail or inland distribution routes during vessel delays or road restrictions, thereby absorbing supply chain disruptions within minutes. These subcriteria elevate technical flexibility to the level of business model design and supply network governance, thereby completing the socio-technical resilience of Logistics 5.0. The critical mechanism intersecting with human-centricity lies in platform transparency, enabling operators' active participation in decision-making processes, while the innovation cycle is continuously fed by field feedback. Thus, resilience evolves into a dynamic, self-reconfiguring organizational capacity that balances human competencies, data-driven automation and ecosystem collaboration.

The sustainability dimension secures the environmental, economic and social legitimacy of logistics operations from a long-term perspective. Within this dimension, the prioritized position of green logistics (C31) and the circular economy (C32) reveals that environmental objectives are positioned not as additional costs but as creators of strategic value and resilience. Green logistics directly reduces carbon footprints through route consolidation, electric vehicle fleets and packaging optimization. Simultaneously, the circular economy transforms these flows into a closed loop by integrating return, repair and recycling processes into the supply chain. For example, integrating reverse logistics channels with automated sorting systems in smart warehouses enables damaged products to be rapidly routed into repair and recycling cycles. This reduces waste management costs and raw material dependency, thereby enhancing supply flexibility. These two subcriteria establish a strong causal link with route and load optimization in the resilience dimension, as well as with worker safety in the human-centricity dimension. Reduced trip frequency and optimized load distribution lower emissions while minimizing driver fatigue and accident risks. Consequently, sustainability has evolved from a mere environmental constraint into a systemic lever that simultaneously strengthens operational efficiency and supply chain resilience.

Value chains (C33), energy efficiency (C35), renewable resources (C36) and green computing (C34) complete the infrastructural and long-term dimensions of sustainability. C33 requires the diffusion of sustainability pillars across multi-layered logistics networks and transparent coordination among carriers, customs brokers, warehouses and end customers. This criterion functions as a risk-sharing and standard-diffusion mechanism that can be extended to supplier and customer ecosystems once internal green and circular logistics practices mature. The lower-ranked subcriteria, C35, C36 and C34, serve as complementary elements at the technical and infrastructural level. Technical improvement areas such as warehouse lighting, cold chain energy optimization and renewable energy integration gain momentum in parallel with the implementation of green logistics and circular economy strategies. Green computing represents an advanced sustainability dimension that becomes increasingly critical as logistics digitalization scales. Managing the environmental costs of route optimization algorithms, cloud-based TMS servers and digital traceability platforms serves as a strategic complement to the long-term balance of Logistics 5.0 and addresses the digital-environmental paradox. These subcriteria, combined with data-driven optimization capabilities within the resilience dimension, support the principles of a just transition that are consistent with the social dimension of a human-centric approach, whilst dynamically managing resource consumption. Ultimately, the C3 subcriteria position Logistics 5.0 not merely as an eco-friendly model, but as a balanced, self-renewing and resilient logistics ecosystem that harmonizes human, technological and natural systems.

The interaction of the three dimensions demonstrates that the Logistics 5.0 ecosystem inherently exhibits a gradual, feedback-driven and mutually reinforcing structure. Human-centric logistics infrastructure establishes the psychological trust, change management capacity and field-based learning foundation required for adopting digital fleet and warehouse automation systems alongside green operational protocols. Digital technologies and data orientation resilience mechanisms enable the traceability, measurability and simulation-based scenario management of sustainability objectives, including carbon tracking, reverse logistics flow optimization and green route planning. Sustainability practices, in turn, reinforce the human-centric logistics culture by strengthening employee motivation, corporate reputation and stakeholder confidence. A “foundation-amplifier” and “progressive activation” relationship exists among the criteria. High-priority factors form the backbone and field acceptability of logistics transformation. Other factors function as strategic components that activate as this backbone strengthens, extending their impact to the endpoints of the logistics network and scaling the transformation. This structure provides logistics enterprises with a sequential yet holistic prioritization framework for resource allocation, competency development and transformation roadmap design. All criteria are positioned as indispensable components of the Logistics 5.0 value chain. High-priority criteria represent the operational construction and adoption phase, while others represent the maturation, ecosystem integration and value maximization phases of the logistics network.

Strategic and Managerial Implications: The ranking structure indicates that logistics firms must adopt a fundamental paradigm shift in their investment and resource-allocation models. Capital-intensive automation and physical infrastructure investments, traditionally prioritized in logistics management, cannot ensure sustainable operational resilience without first establishing human-machine interface safety, streamlined process architectures and data governance infrastructures. Therefore, directing transformation budgets toward employee well-being protocols, waste-eliminating process designs and cross-functional data integration, rather than direct technology procurement, represents a critical strategic implication for long-term competitive advantage and recovery capacity during crises. Furthermore, the progressive activation structure of high-priority criteria enables managers to adopt a “foundation first, then scale” principle. This approach helps prevent resource waste and operational bottlenecks that are commonly encountered in early-stage transformation projects.

Operational and Technological Implications: At the operational level, the findings indicate that logistics process redesign and digital tool integration should be addressed sequentially rather than concurrently. When WMS/TMS systems, AGV/cobot fleets or blockchain-based traceability solutions are directly integrated into non-standardized and variable processes, they can increase operational complexity and error margins of the processes. Establishing lean management principles and safety protocols directly enhances the field adoption, usage effectiveness and maintenance lifecycle of digital tools. The high prioritization of data-driven approaches reveals a necessary evolution in logistics decision-making mechanisms from intuitive experience to analytical foresight. This transformation is meaningful only when supported by data quality standards, real-time traceability infrastructure and end-to-end integration. Within this framework, technology investments should not be designed solely around operational targets. Instead, they should address how operational objectives can be made more secure, agile and measurable using technology.

Theoretical and Conceptual Implications: At the theoretical level, the findings support a paradigm shift. This transition moves from the “cost minimisation and speed optimisation” framework dominant in Logistics 4.0 literature toward a new theoretical framework grounded in Logistics 5.0's triangle of “human well-being, environmental balance and operational resilience.” The prioritization of sustainability and human-centricity dimensions over technological flexibility indicates that environmental and social dimensions in supply chain theory should no longer be modeled as external constraints or compliance costs. Instead, they must be conceptualized as “internal competitive factors” and “value-creating mechanisms.” This situation provides a theoretical foundation for reinterpreting dynamic capability theory and the socio-technical systems approach within the logistics context. It facilitates the integration of resilience, adaptive capacity and stakeholder trust into the literature as quantitative strategic assets.

Policy and Sectoral Implications: At the policy and sectoral levels, the findings indicate that regulatory authorities, logistics associations and financial institutions must restructure standardization, incentive and collaboration mechanisms. The high prioritization of green logistics and circular economy criteria demonstrates the critical importance of harmonizing carbon reporting standards, investing in reverse logistics infrastructure and developing public-private partnership models for sustainable transportation modes. Furthermore, integrating human-machine collaboration protocols, psychological safety measurement tools and lean-digital hybrid competency frameworks into national logistics standards and vocational qualification systems will accelerate the social acceptance, legal compliance and workforce readiness of the transformation. At the sectoral scale, the commonality of high-priority criteria makes ecosystem collaboration strategically necessary. Rather than competition among rival firms, this collaboration should focus on infrastructure standards, data-sharing protocols and green transport corridors.

Implications of Low-Ranked Criteria: The implications of lower-ranked criteria should not be interpreted as insignificance but rather as conditional activation and indicators of a maturity phase. Factors such as customer focus, competitiveness, green computing or renewable resource utilization cannot independently generate measurable impact or sustainable competitive advantage when foundational human, data and sustainability infrastructures are not yet established. However, once core criteria are implemented, these factors become strategic amplifiers that directly determine transformation scalability, market penetration, ecosystem integration and long-term value maximization. This distinction provides logistics managers with a phased roadmap for resource allocation. It establishes a holistic framework that prevents early-stage frustrations, technological bloat and measurement complexity during transformation processes.

In summary, the ranking results demonstrate that the Logistics 5.0 journey is not a one-dimensional digitalization project. Instead, it constitutes a multi-layered transformation. This process is based on human safety and lean culture, builds on digital technologies and data orientation resilience and consolidates long-term ecosystem integration through green and circular practices. These implications expand the theoretical boundaries and measurement parameters of Logistics 5.0 in the academic literature. Simultaneously, they provide industry stakeholders with a strategic framework that minimizes risk, enhances resource efficiency and progressively maximizes value.

Cold Chain and Pharmaceutical Logistics: Product integrity, time sensitivity and regulatory compliance are critical in these sectors. Employee safety and management (C12) is prioritized to minimize physical risks, particularly in cold storage environments characterized by intensive human-machine interactions. The green logistics (C31) criterion is directly linked to energy efficiency and low-emission transport modes, given the high energy consumption inherent in cold-chain operations. Data orientation (C22) is critical for temperature traceability, expiry date management and regulatory reporting. In these sectors, digital technologies (C21) should be designed as validation systems that minimize human error and provide real-time traceability infrastructure.

E-commerce and Retail Logistics: Speed, personalization and last-mile flexibility determine the competitive dynamics of this sector. Human-machine collaboration (C11) can enhance operational speed while reducing worker fatigue in order-picking and packaging processes through cobots and augmented reality-supported systems. Digital technologies (C21) and data orientation (C22) play central roles in demand forecasting, dynamic route optimization and customer experience personalization. Customer orientation (C16) and collaborative and flexible platforms (C25) should be operationalized through alternative delivery points, time slot selection and platform-based courier networks in the last-mile delivery. The circular economy (C32) has the potential to create value in packaging recovery and returns management processes.

Automotive and Heavy Industry Logistics: Supply chain depth, just-in-time production integration and high capital intensity characterize this sector. The lean management philosophy (C14) serves as a strategic tool for eliminating waste and enhancing flow efficiency across the supply chain. The value chain (C33) criterion is critical for diffusing sustainability standards and ensuring transparent coordination from suppliers to assembly lines. Digital technologies (C21) should be prioritized as investment areas for synchronizing production planning with logistics flow and testing risk scenarios. Human–machine collaboration (C11) must be designed to enhance worker ergonomics and operational safety during heavy load handling and quality control processes.

3PL/4PL Logistics Service Providers: Multi-client portfolios, economies of scale and platform-based coordination determine the competitive advantages of these actors. Collaborative and flexible platforms (C25) play a central role in capacity sharing, load matching and cross-documentation integration. Data orientation (C22) enables cross-organizational learning and industry benchmarking by anonymizing operational data from diverse clients. Green logistics (C31) and the circular economy (C32) should be positioned as tools for service differentiation and alignment with corporate clients' Environmental, Social and Governance (ESG) objectives. Employee training (C13) represents a strategic investment area for enhancing the digital literacy and adaptive capacity of operational teams working in multi-client and multi-system environments.

SMEs (Small and Medium-sized Enterprises): Resource constraints and agile structures require SMEs to follow a distinct roadmap in their Logistics 5.0 transformation. The lean management philosophy (C14) can deliver quick wins through process improvement tools that do not demand high capital investment. Employee training (C13) represents a low-cost yet high-return investment area for the gradual adoption of digital tools and development of multi-competency capabilities. Collaboration and partnerships (C15) can enable SMEs to benefit from economies of scale by accelerating ecosystem integration with technology providers, logistics platforms and industry associations. Digital technologies (C21) should initially be adopted as cloud-based subscription-model Software as a Service (SaaS) solutions to ensure accessibility. Data orientation (C22) should be developed progressively, starting with basic KPI tracking and simple analytical dashboards.

Large-Scale and Multinational Logistics Enterprises: Resource access, system integration and standardization capacity constitute the competitive advantages of these organizations. Digital technologies (C21) and data orientation (C22) should be positioned for the end-to-end visibility of global operations, balancing central and local decision-making and enabling cross-functional optimization. Employee safety and management (C12) must be elevated to a global management priority through standardized safety protocols and psychological safety measurement tools for operational teams across diverse geographies and cultures. Green logistics (C31) and value chains (C33) should form the operational arm of the global sustainability strategy, which should be implemented through supplier codes of conduct, carrier certifications and customer reporting standards.

Family Businesses and Professionally Managed Institutions: Decision-making speed, resistance to change and differences in organizational culture influence the transformation approach. In family businesses, employee safety and management (C12) and collaboration and partnerships (C15) can enhance change acceptance when they are structured around organizational trust and long-term relationships. In professionally managed institutions, data orientation (C22) and innovation (C24) should be institutionalized through performance management systems and R&D budgets to transform them into sustainable competitive advantages.

Warehouse and Distribution Center-Focused Operations: Internal process optimization, inventory accuracy and order agility are prioritized. Human–machine collaboration (C11) should be designed with a focus on ergonomics and efficiency in order-picking, packaging and shipment preparation processes. The lean management philosophy (C14) should be operationalized through the continuous improvement of intra-warehouse flows, layout planning and handling standards. Digital technologies (C21) should enhance operational visibility through WMS integration, RFID/QR code-based traceability and autonomous transportation vehicles.

Transportation and Fleet Management-Focused Operations: Route optimization, fuel efficiency and driver management are critical success factors. Data orientation (C22) should support dynamic route planning through telemetry data, traffic analytics and demand forecasting. Green logistics (C31) should simultaneously optimize operational costs and environmental impact through low-emission fleet renewal, modal shift strategies and eco-driving training programs. Employee safety and management (C12) should ensure proactive risk management through driver fatigue management, safety training and fleet tracking systems.

B2B and B2C Operational Differences: In B2B (business-to-business) operations, order size, contractual service level agreements and integrated planning are prioritized. Therefore, value chains (C33) and collaborative and flexible platforms (C25) are critical for supplier-customer integration. In B2C (business-to-consumer) operations, order frequency, delivery flexibility and individual customer experience take precedence. In this context, customer orientation (C16), digital technologies (C21) and innovation (C24) should be operationalized through the use of last-mile solutions and personalized service designs.

In light of these contextual differences, the Logistics 5.0 transformation should adopt a strategy of “universal principles, context-specific implementation” rather than a “one-size-fits-all” approach. While the pillars of human-centricity, resilience and sustainability apply universally across all sectors and organizations, their operational pathways, investment priorities and measurement parameters must be tailored to contextual dynamics. This approach not only enhances resource efficiency but also maximizes the social acceptance, operational sustainability and strategic impact of transformation.

This study examined the strategic drivers of Logistics 5.0 by integrating its core theoretical dimensions, human-centricity, resilience and sustainability, into a structured MCDM framework. By moving beyond technology-centric evaluations, this study conceptualizes Logistics 5.0 as a socio-technical paradigm in which operational transformation is driven by the interplay of human, digital and ecological factors.

The findings revealed a distinct hierarchical structure across these dimensions. Within human-centricity, employee safety and management and lean management philosophy emerge as foundational prerequisites for organizational readiness. In the resilience dimension, digital technologies and data orientation function as core enablers, facilitating adaptive capacity and proactive disruption management. Simultaneously, the sustainability dimension is anchored by green logistics and circular economy practices, which reframe environmental objectives from compliance costs into long-term value creation mechanisms. Methodologically, the applied MCDM framework validated the interdependence and phased activation of these drivers, offering a theoretically grounded decision architecture that bridges conceptual fragmentation and strategic prioritization.

The results underscore that Logistics 5.0 adoption is not a linear technological upgrade but a context-sensitive, phased evolution that requires balanced strategic alignment. The following sections outline the theoretical and practical implications of these findings, distill key lessons for organizational transformation, acknowledge the methodological and contextual limitations and propose pathways for future scholarly inquiry.

The prioritization framework provides logistics managers with a structured, resource-efficient pathway for Logistics 5.0 adoption. Implementation should follow a sequential progression: organizations are advised to initially allocate resources toward foundational drivers, specifically C12 (Employee Safety and Management), C14 (Lean Management Philosophy) and baseline C21 (Digital Technologies) to establish operational and cultural readiness. Upon stabilizing these core elements, firms can integrate interdependent capabilities such as C31 (Green Logistics), C32 (Circular Economy), C11 (Human-Machine Collaboration) and C22 (Data Orientation) to enhance systemic sustainability and resilience. The final phase involves scaling outcome-oriented initiatives, including C25 (Collaborative and Flexible Platforms) and C33 (Value Chain Integration), to optimize ecosystem alignment. To operationalize this roadmap, practitioners should conduct targeted maturity assessments against the highest-weighted criteria, identify performance gaps and anchor each priority driver to specific, measurable KPIs for continuous monitoring. Furthermore, implementation strategies require contextual calibration; small and medium-sized enterprises may commence with cost-efficient lean and digital tools, whereas large-scale operators can pursue integrated data architectures and sustainability infrastructure from the outset. This calibrated, evidence-based approach enables experts to translate analytical insights into actionable transformation strategies while optimizing resource allocation and mitigating transition risks.

During the implementation process, data orientation integration should be addressed as a strategic prerequisite, rather than a technical detail. Inconsistencies across disparate sources (WMS, TMS, ERP and IoT) hinder end-to-end visibility; therefore, API-based middleware solutions should be included in the initial phase of the project. Human resistance and transformation fatigue can delay the operational realization of investments. Consequently, role-based training and transparent communication strategies should be implemented concurrently with the rollout of new processes. Change management metrics must be monitored in parallel with the technical KPIs. Financial constraints and regulatory compliance are particularly decisive for SMEs. Therefore, phased adoption models should be adopted to manage high initial costs and compliance processes should be integrated into operational workflows using automation tools.

Strategic trade-offs require managers to consciously balance short-term performance with long-term sustainability. In the trade-off between human-centricity and operational speed, safety protocols and well-being metrics may initially reduce short-term agility. However, this approach ensures sustainable efficiency over the long term by minimizing accident-related downtime, employee turnover and error costs. Regarding the balance between sustainability and short-term costs, the initial capital burden and increased unit costs of green logistics and circular economy models are offset in the medium to long term by energy savings, reduced waste disposal expenses and enhanced brand reputation. In the tension between resilience and standardization, high customization and dynamic route management can increase operational complexity. This complexity must be managed through modular system design, standardized data protocols and scalable cloud architectures to preserve both cost control and customized service quality. Finally, in balancing data sharing with cybersecurity and trade secret protection, supply chain transparency introduces risks of data leakage and competitive disadvantage. These risks must be mitigated within a secure transparency framework by simultaneously implementing privacy-preserving mechanisms such as tokenization, federated learning and dynamic access layers.

Although this study provides a comprehensive analysis, it has some limitations. First, the framework exhibits a degree of temporal staticity that may contrast with dynamic market conditions. The identified drivers are based on a literature review and expert opinion, which may evolve over time as Logistics 5.0 matures; technological breakthroughs, supply disruptions or regulatory shifts could dynamically alter the relative weights and prioritization sequence of criteria. Second, translating qualitative constructs, such as human well-being, psychological safety, circular value and ecosystem trust, into standardized KPIs remains methodologically complex. Although field practices often rely on proxy indicators, the absence of direct, comparable metrics complicates the tracking of longitudinal progress. Third, the weighting and ranking of factors relied on the consensus of a small group of experts (n = 4). Although these experts were selected for their high level of domain expertise to ensure data quality, the small sample size implies that the results may reflect specific perspectives rather than a broad industry consensus, limiting the generalizability of the findings. Although the framework offers universal principles, sectoral regulatory intensity, organizational maturity and geographical infrastructure disparities necessitate the localized calibration of implementation roadmaps. Additionally, the identified criteria and their interrelationships may not fully capture all the complexities of real-world logistics applications, making the outcomes inherently sensitive to context-specific dynamics.

Future research should extend beyond the identification and prioritization of Logistics 5.0 drivers to systematic empirical validation and theoretical testing. First, the proposed framework can be validated through large-scale, performance-linked survey studies that examine the relationship between the adoption of high-priority drivers identified in this study, such as C12 (Employee Safety and Management), C21 (Digital Technologies) and C31 (Green Logistics) and measurable operational, financial and sustainability outcomes.

Building on the current prioritization, future studies could test illustrative hypotheses derived directly from the study's criteria structure, such as:

1. H1: The implementation of C12 (Employee Safety and Management) and C14 (Lean Management Philosophy) positively mediates the relationship between C21 (Digital Technologies) adoption and operational resilience.

2. H2: C22 (Data Orientation) capabilities strengthen the impact of C32 (Circular Economy) practices on long-term environmental and economic performance.

Second, researchers are encouraged to employ structural equation modeling (e.g. PLS-SEM) or longitudinal panel designs to empirically examine the causal pathways and moderating effects among the three main dimensions (C1: Human-Centricity, C2: Resilience and C3: Sustainability) and their 18 subcriteria. Third, cross-industry and cross-regional comparative studies would help assess the contextual boundaries of the framework, particularly in emerging economies and advanced logistics hubs. Finally, methodological advancements, such as hybrid MCDM-AI frameworks or machine learning-based dynamic weighting, could further enhance the objectivity and adaptability of driver prioritization in evolving Logistics 5.0 ecosystems.

AI tools were used only for language editing and to improve readability. All conceptual, methodological, analytical and interpretative components of this study were developed by the authors. We also thank the reviewers who contributed constructive comments and feedback to improve this paper.