Smart Logistics Facing Industry 5.0: Research on Key Enablers and Strategic Roadmap

Abstract

While Industry 4.0 has played a significant role in advancing smart logistics, it has yet to provide adequate solutions for widespread concerns such as human centricity, sustainability, and resilience. The emergence of Industry 5.0 addresses and complements these shortcomings of Industry 4.0. However, there is currently a notable gap in the research regarding how Industry 5.0 can drive the transformation of smart logistics. To address this gap, this study develops a strategic roadmap that offers a solution to this issue. The research is initiated by conducting a comprehensive literature review with a focus on content, identifying 13 key enablers crucial for realizing smart logistics in Industry 5.0. Subsequently, this study establishes the hierarchical relationship among these key enablers through the application of the Fuzzy Interpretative Structural Model (FISM). Following this, the study employs the Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) to compute the driving force and dependence of each enabler. The results underscore the significant roles of “Active support from the government” and “Human-centric manufacturing and logistics” as the most critical enablers for Industry 5.0. The strategic roadmap, informed by expert opinions, provides valuable insights for policymakers and implementers while explaining the methods and strategies needed to drive Industry 5.0 transformation in smart logistics. Furthermore, it determines the impact relationship between enablers and the optimal development order, facilitating their synergistic alignment. Ultimately, this strategic roadmap serves as an actionable guide for the logistics industry, steering it toward achieving smart logistics and fortifying competitiveness in the industry 5.0 era.

1. Introduction

The advent of Industry 4.0, while groundbreaking in its technological advancements, has been criticized for its insufficient consideration of human and sustainability aspects. This oversight has contributed to exacerbating social problems, including environmental degradation and imbalanced development [1]. Moreover, traditional logistics faces persisting challenges such as inflexibility and resource wastage [2]. Recognizing these shortcomings, Industry 5.0 emerges as a transformative paradigm in industrial development, serving as an extended and evolved version of Industry 4.0. Industry 5.0 introduces a heightened emphasis on the following three key dimensions: human centricity, sustainability, and resilience [3]. Departing from the predominant focus on automation in smart factories within Industry 4.0, Industry 5.0 advocates for collaborative and symbiotic work models between humans and machines [4]. The objective of this is to achieve synergy and complementarity between these entities, with a greater focus on employee involvement and the creation of value in work scenarios [5]. In stark contrast to its predecessor, Industry 5.0 places paramount importance on environmental sustainability issues, aligning with the growing global demand for personalized products and services [6]. It champions a paradigm shift toward holistic environmentally responsible industrial practices. Simultaneously, smart logistics refers to the optimized and automated management of logistics systems through advanced technologies such as the Internet of Things (IoT), big data analytics, and other information technology (IT), with the aim of enhancing the efficiency, transparency, and continuity of logistics operations [7]. The application of advanced technologies, particularly IoT and cloud computing, has provided robust support and tools for the development of smart logistics [8]. Consequently, there is an increasing global interest in understanding how Industry 5.0 can significantly contribute to the evolution and enhancement of smart logistics, ensuring a more sustainable, efficient, and adaptable logistics landscape [9].

The synergy between Industry 5.0 and smart logistics is closely intertwined. In the vision of Industry 5.0, technology can optimize workplace and worker performance, facilitating interaction between humans and machines [10]. The strategic use of IoT technology facilitates smart logistics, enabling real-time adjustments and optimizations in complex supply chain scenarios [11]. Smart logistics, which rely on advanced IT, robotics, automated equipment, and unmanned aerial vehicle technology, streamline logistics management and operational processes. This convergence results in an enhanced efficiency, intelligence, and sustainability in logistics operations [12]. Despite these advancements, Industry 5.0 is a new concept, and exploration is in its early stages [13]. The existing research on Industry 5.0 is limited and lacks systematic development [14]. While we recognize the technologies in Industry 5.0 that promote smart logistics, challenges persist in understanding precisely how Industry 5.0 drives this development. Issues such as the complexity of technology integration, data security, and privacy protection present ongoing challenges in realizing the full potential of Industry 5.0’s impact on smart logistics.

In summary, the existing body of literature on how Industry 5.0 can promote smart logistics, while substantial, remains fragmented, lacking a comprehensive exploration of how Industry 5.0 truly propels the advancement of smart logistics. Furthermore, a notable gap exists in systematic research on the factors influencing Industry 5.0’s efficacy in driving smart logistics development. Without a well-established understanding of the role of Industry 5.0 in shaping smart logistics within its contextual framework, the potential of Industry 5.0 to address pervasive issues in smart logistics transformation, such as environmental pollution, technological utilization, social conflicts, and economic resource consumption, may be compromised.

To address this critical knowledge gap, this study addresses the following research questions:

(i) What are the key enablers of Industry 5.0 in driving smart logistics?

(ii) What is the strategic roadmap for Industry 5.0 in driving smart logistics?

(iii) How does Industry 5.0 facilitate transformation changes in driving smart logistics?

To address these questions, this study first undertook the collection, analysis, and categorization of the existing literature, aiming to pinpoint the key enablers for achieving smart logistics in Industry 5.0. Subsequently, the Fuzzy Interpretation Structure Model (FISM) and Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) model were used to analyze an expert group’s opinions on the relationships among these key enablers. This analysis resulted in a hierarchical division, leading to the establishment of an FISM that visually represents the driving forces and dependence of each factor. Lastly, combining the results of the FISM and MICMAC multiplication, the expert group’s judgment elucidated the contextual relationships between the enablers, culminating in the creation of a strategic roadmap. This roadmap facilitates the visualization of the strategic actions and methods necessary for the transformation and development of Industry-5.0-driven smart logistics. To the best of our knowledge, this research is the first comprehensive and systematic study in this field, providing critical insights into the enablers and strategic roadmap for Industry 5.0 in advancing the transformation and development of smart logistics. This study represents a pioneering effort, with its findings on the key enablers and strategic roadmap for Industry 5.0 carrying significant implications for enterprise managers and government decision makers, offering essential guidance and a scientific foundation for their management and decision-making processes. Therefore, this study identifies the factors propelling the development of smart logistics within the framework of Industry 5.0 and charts a strategic roadmap. By doing so, stakeholders in Industry 5.0 can effectively leverage advanced information and technology to address various enablers, thereby facilitating the progress of smart logistics with limited resources.

The subsequent content of this study is structured as follows: the Section 2 primarily introduces the concepts of Industry 5.0 and smart logistics while providing a detailed exposition of the key enablers for realizing smart logistics within the context of Industry 5.0. The Section 3 describes the detailed steps for a comprehensive analysis of the selected enablers by using the FISM and MICMAC. The Section 4 outlines the process of drawing the roadmap and analyzes the relationships between the enablers in detail. Finally, the Section 5 discusses the significance of the research and provides directions for future studies.

2. Literature Review

In this section, we review related works to identify research gaps and demonstrate the position of the present study. In this regard, we searched key words such as smart logistics, Industry 5.0, and enablers in authoritative journals. Then, among them, we selected the relevant articles from 2018 to 2024 and analyzed them in this section.

2.1. Industry 5.0 and Smart Logistics

Industry 5.0, in its incipient phase, signifies the evolution beyond Industry 4.0 and is currently undergoing preliminary exploration, lacking systematic research outcomes [14]. The concept of Industry 5.0 was officially defined by the European Commission (EC) in January 2021. Despite its recent emergence, several scholars have put forth diverse perspectives regarding its understanding. Longo et al. [15] contend that Industry 5.0, distinct from its predecessor, Industry 4.0, emphasizes a paradigm shift toward smart and automated factories, placing human-centric transformation at its core. Xu et al. [16] contend Industry 5.0 is an open and evolving concept aimed at realizing an industrial system that fosters human–machine collaboration, value-driven approaches, and sustainability. Sindhwani et al. [17] add to the discourse by suggesting that Industry 5.0 places a greater emphasis on delivering products or services that contribute to enhancing the quality of life in society. Ivanov [18] proposes that Industry 5.0, as a conceptual framework, aligns societal needs, values, and responsibilities as its objectives. Dedicated to achieving sustainability and resilience, Industry 5.0 emerges as a driving force for social and economic progress.

On the other hand, logistics plays a crucial role in driving economic growth and is a key enabler of national and corporate competitiveness [19]. Song et al. [20] believe that the introduction of advanced Industry 5.0 technologies, such as IoT, big data, and artificial intelligence (AI), can promote the development of the logistics industry. According to Jiang [21], the establishment of an information platform based on IoT and cloud computing represents a strategic goal and direction for advancing smart logistics. Tang [22] highlights that smart logistics uses IoT and IT to optimize distribution networks, thereby enhancing the efficiency of logistics delivery. Zhan et al. [23] contend that smart logistics, through the application of technologies such as big data analytics and AI, can optimize transportation routes and the real-time monitoring of cargo status. Therefore, by integrating advanced Industry 5.0 technologies at various stages, smart logistics can enhance logistics efficiency, thereby promoting progress in logistics and socio-economic development [24].

While Industry 4.0 has also been acknowledged for its role in promoting the development of smart logistics, it tends to overlook critical issues such as human centricity, sustainability, and resilience. This gap in consideration has promoted the Industry 5.0 agenda. However, the current landscape lacks sufficient research addressing the transformation of smart logistics driven by Industry 5.0. The existing literature exploring the relationship between Industry 5.0 and smart logistics mainly consists of theoretical framework reviews, with few specific measures proposed. For instance, Jafari, Azarian, and Yu [9] identify a gap in the research landscape, noting a lack of comprehensive exploration of the impact of Industry 5.0 on smart logistics through a literature review. They use comparative bibliometric analysis methods to show the links and differences between Industry 4.0, Industry 5.0, and smart logistics.

In summary, the existing research institutions lack clear guidance on how enterprises can effectively use Industry 5.0 to promote the transformation of smart logistics, and have not systematically studied the enablers of the development of smart logistics driven by Industry 5.0. Recognizing this gap, this study addresses it by presenting a specific strategic roadmap grounded in the enablers of Industry 5.0. This roadmap aims to provide clarity on development strategies for driving the transformation of smart logistics within the context of Industry 5.0, ultimately promoting the progress of the logistics industry and achieving sustainable development goals.

2.2. Enablers of Industry 5.0 Driving Smart Logistics

Industry 5.0 is not positioned as a replacement for Industry 4.0, but rather as a complementary framework. As noted by Alvarez-Aros and Bernal-Torres [25], many technologies from Industry 4.0 persist in Industry 5.0. Consequently, in exploring the enablers of Industry 5.0 for advancing smart logistics, this study draws upon research related to smart logistics within the context of Industry 4.0. A comprehensive review of 51 articles was conducted, systematically categorizing them based on the following three focal aspects: human centricity, sustainability, and resilience. Throughout the synthesis of these articles, a total of 34 factors promoting smart logistics within Industry 5.0 were identified and consolidated. Subsequently, factors sharing similar implications and themes were distilled and integrated, leading to the identification of 13 key enablers for the realization of Industry 5.0 in the field of smart logistics. The following section introduces these 13 pivotal enablers.

2.2.1. Flexibility of Production Systems (FPS)

FPS, an evolution stemming from Industry 4.0, emphasizes the imperative need for heightened capabilities within logistics and production systems [16]. Achieving this requires improvements in logistics intelligence and automation [26], coupled with high monitoring and control capabilities of physical and network connections [11]. Real-time systems, facilitated by automatic identification and sensor technology, play a crucial role in promptly recording events and states [27]. These measures result in adaptive production capabilities, fostering enhanced production agility and resilience. Consequently, the overall effect is an improvement in product durability and adaptability. Notably, these efforts align with the goals of waste reduction and cost efficiency, ultimately contributing to the augmentation of both production and logistics systems [9].

2.2.2. Sustainable Corporate Governance (SCG)

SCG, involving a strategic framework for overseeing and developing corporate objectives and directions, is indispensable for realizing smart logistics [28]. Within the framework of Industry 5.0, companies are encouraged to emphasize environmental protection and enhance their commitment to societal development. Concurrently, the integration of sustainable goals such as environmental protection, human centricity, and resilience into corporate and business strategies is imperative. This integration involves the implementation of effective and measurable indicators to comprehend the industry’s developmental landscape, harmonize the interests of stakeholders, society, and the environment, and advance toward an SCG model, thus achieving the overall goal of Industry 5.0 [29].

2.2.3. Active Support from Government (ACG)

Based on the transformation impact of Industry 4.0, the emergence of Industry 5.0 necessitates a fresh approach to government support. Governments play a significant role in cultivating an industrial ecosystem and a new economic model centered on the core values of Industry 5.0 [30]. This includes implementing strategic initiatives, such as reducing labor taxes, providing guidance for the adoption of optimal regulatory frameworks, and fostering a culture of innovation through incentivized funding [31]. It also encompasses policy directives linked to national economic agencies, societal activities, and environmental improvement efforts [32]. Simultaneously, to promote the utilization and advancement of advanced technologies in enterprises, such as AI and robotics, the government needs to establish comprehensive legislation and a series of relevant regulations to ensure that technology is developed and used within reasonable boundaries [33].

2.2.4. Eco-Innovation and Resource Planning (ERP)

ERP allows value networks to reduce and mitigate the environmental impacts associated with production, manufacturing, and consumption patterns. Furthermore, they contribute to the social values of Industry 5.0. These values include, but are not limited to, enhancing customer satisfaction, intelligently optimizing the workplace, and fostering more inclusive business models across the entire value chain [5,30]. Within the framework of Industry 5.0, governments and enterprises are compelled to engage in more targeted research and innovation in energy efficiency, prioritize resources such as time, labor, and IT, and increase investments in the development of low-carbon, circular, and regenerative solutions for logistics in the industrial sector [31]. Ultimately, ERP emphasizes the need for a proactive approach to resource allocation and energy efficiency innovations, aiming to create a sustainable operational framework that benefits both the environment and society.

2.2.5. Combining Digital Technology with Green (CDG)

One of the core visions of Industry 5.0 is to achieve a better resource efficiency and establish an efficient circular economy through the recycling of natural resources, reducing waste, and mitigating environmental impacts [16]. Consequently, technological transformation must align with socioeconomic development goals and be pursued sustainably. Historical research on Industry 4.0 has demonstrated the significant impacts of new digital technologies on sustainability and environmental conservation [34]. In Industry 5.0, the use of IoT technology contributes to reductions in greenhouse effects [35]. The incorporation of AI, collaborative robots, and sensor technologies, in conjunction with the use of renewable resources, aims to achieve sustainable technology governance, including green computing and renewable integration [36]. In contrast, CDG focuses on the integration of advanced digital technologies to optimize resource usage, highlighting the necessity for innovation that not only enhances operational efficiency, but also promotes environmental stewardship through smart technology applications.

2.2.6. Stakeholder Collaboration and Integration (SCI)

The scope covered by Industry 5.0 extends beyond the digital transformation of individual companies [16]. Stakeholders include government agencies, businesses, customers, distributors, and suppliers [37]. Stakeholders involved in Industry 5.0 should align with its transformation [31]. This necessitates real-time information exchange and the timely communication of relevant needs, expectations, and conflicting interests. It calls for stakeholders to collaboratively design and implement essential management frameworks, entrepreneurship initiatives, job opportunities, and skill enhancement plans [38].

2.2.7. Human-Centric Manufacturing and Logistics (HML)

Industry 5.0 places humans at its core, prioritizing human needs and interests in the production process [31]. Unlike the technology-driven Industry 4.0, Industry 5.0 is value-driven, focusing on societal needs, values, and responsibilities. This shift in core positioning implies that workers will transform their value from a cost to an investment [16]. Consequently, when enterprises engage in strategic planning, they must fully consider human interests and needs. This includes using technology to adapt the production process to meet workers’ requirements, designing and optimizing workplace layouts [39], and making workplaces more inclusive and secure to safeguard workers’ fundamental rights [36].

2.2.8. Customer-Oriented Individual Manufacturing (CIM)

Customers are autonomous entities, yet enterprises rely on them, because their existence and operation are influenced by the inherent needs of customers [32]. In Industry 5.0, through recommendation systems within blockchain technology, social media, and various identification analytics technologies, customer preferences can be captured to facilitate tailored and personalized customer recommendations [40]. Enterprises can also leverage advanced technologies such as big data analysis and physical network systems to meet consumer expectations for customer-centric, personalized products and business outlooks [30]. They can establish advanced manufacturing ecosystems and logistics systems based on rapid reconfigurable production processes and personalized consumer preferences [31].

2.2.9. Value Chain Integration (VCI)

The COVID-19 pandemic necessitates a reconsideration of the fragility of global supply chains. Industry 5.0 emphasizes the integration of customers and small- to medium-sized enterprises across the entire value chain [16]. In this context, businesses should establish value chains that are more resilient, circular, and sustainable. They should actively engage in the integration of global supply chains, selecting the most competitive partners worldwide and simultaneously developing interconnected dynamic supply networks to create new business models [30]. Ultimately, this will lead to the promotion of supply chain agility, renewable integration, and product adaptability [29].

2.2.10. Paying Attention to the Well-Being of Employees (PWE)

In the era of Industry 5.0, humans will play a pivotal role with the support of various advanced technological solutions. Industry 5.0 emphasizes the human aspect of the manufacturing process, focusing on collaboration between people and intelligent manufacturing systems, with a primary emphasis on employee well-being [32]. It also highlights the importance of employees’ knowledge and expertise in resource management, enhancing their technical skills through formal education or training programs [5]. Furthermore, it offers high-quality, inclusive, and accessible education and training programs suitable for the digital age’s smart logistics transformation [41].

2.2.11. Information Security Maintenance (ISM)

In past research on Industry 4.0, information security has been regarded as one of the primary domains for the application of logistic technology. Enterprises should have solutions that can safeguard their infrastructure and information assets [42]. When implementing emerging technologies such as big data analysis, cloud computing, and the IoT for the transformation of smart logistics, it is crucial to emphasize the security maintenance of various technologies and information [26]. ISM is a vital means for ensuring the security of enterprises and is conducive to standardized operations and management [43].

2.2.12. Resilience and Sustainability Performance Indicators (RSIs)

From a strategic perspective, the performance of an enterprise’s supply chain network is crucial for its success [9]. In Industry 5.0, companies should reconsider the design of their value chains and economic activities based on resilience principles. They should introduce sustainability metrics that are suitable for the company’s industrial ecosystem. This approach places a greater emphasis on the measurement of material and tangible economic activities, such as “energy return on investment” and “human capital value”, rather than relying solely on financial indicators [30,31].

2.2.13. Risk Prevention to Improve Resilience (RPR)

Frequent disruptions within an enterprise may simultaneously impact procurement, production, and demand management [44]. Resilience within the supply chain ensures its performance and sustainability [45]. Enterprises can use machine algorithms and data-driven intelligent analytics to provide predictive solutions and warnings for the supply chain [46]. This approach enables more flexible and risk-inclusive strategic deployment, with a focus on predicting potential risks and customizing solutions.

3. Methods

This study systematically employs the FISM to model the relationships between factors in complex systems. The FISM is an extension of ISM (Interpretative Structural Modeling), which was initially proposed by the American scholar Warfield. While ISM is an effective method for identifying the presence or absence of relationships between factors, it cannot quantify the strength of these relationships and is not suitable for representing fuzzy or uncertain relationships in the real world. To overcome these limitations, the FISM integrates a fuzzy approach to better capture the complexity and uncertainty inherent in smart logistics systems. Subsequently, the FISM–MICMAC method is used for a comprehensive analysis of the selected enablers, ultimately leading to the development of a strategic roadmap for promoting smart logistics. The specific research process of this study is illustrated in Figure 1.

3.1. Collecting Expert Opinions

The FISM leverages expert opinions to unveil the contextual relationships among system factors. Therefore, this study collected the opinions of experts with rich experience and an educational background in the field of Industry 4.0/5.0, in order to determine the mutual influences among the factors that drive the development of smart logistics in Industry 5.0. According to the expert selection agreement, 15 experts who may know and pay attention to Industry 4.0/5.0 were identified. We contacted these experts and asked them to fill out a simple self-assessment to measure their familiarity with the phenomenon of Industry 5.0. According to this assessment, four experts were identified as unqualified, one of whom showed a lack of familiarity with the concept of Industry 5.0, one emphasized a lack of familiarity with the concepts related to smart logistics, and two claimed that they could not fully participate in all the expert group meetings. Finally, we screened 11 experts, including 3 women and 8 men, who were professors with extensive practical and teaching experience in the fields of Industry 4.0/5.0 and smart logistics. In terms of academic background, the expert team consisted of three professors in the field of smart logistics, two professors in logistics and operation management, one professor in sustainable development, one associate professor in production engineering, one associate professor in the advanced research process of innovation and the digital economy, one associate professor in resilience, and one professor in the digital economy. These 11 experts actively participated in meetings to discuss and determine the interrelationships among the enablers.

3.2. Constructing a Fuzzy Matrix

This study utilized triangular fuzzy numbers to construct a fuzzy direct relationship matrix and establish a triangular fuzzy relationship matrix to assess the strength of the relationships between the Industry 5.0 enablers. The questionnaire results were subjected to fuzzification, as shown in

Table 1. Fuzzy matrix.

Fuzzification	FPS	SCG	ACG	ERP	CDG	SCI	HML	CIM	VCI	PWE	ISM	RSIs	RPR
Flexibility of production systems (FPS)	0.000	3.000	4.000	3.000	3.000	2.000	3.000	4.000	2.000	4.000	2.000	4.000	3.000
	0.000	5.889	6.111	5.667	7.111	5.111	5.444	6.889	6.556	7.000	6.222	6.667	5.556
	0.000	7.000	9.000	7.000	8.000	8.000	8.000	9.000	9.000	8.000	9.000	8.000	9.000
Sustainable corporate governance (SCG)	5.000	0.000	4.000	4.000	4.000	3.000	3.000	2.000	4.000	3.000	3.000	3.000	2.000
	6.778	0.000	6.667	6.000	6.444	6.444	6.444	6.556	5.889	6.333	6.222	6.222	5.667
	9.000	0.000	8.000	9.000	9.000	9.000	9.000	9.000	9.000	9.000	8.000	9.000	8.000
Active support from the government (ACG)	3.000	1.000	0.000	1.000	1.000	1.000	0.000	0.000	0.000	1.000	1.000	1.000	0.000
	5.222	5.222	0.000	6.000	5.000	4.333	5.111	4.778	5.444	4.778	5.000	5.333	4.667
	7.000	9.000	0.000	8.000	8.000	6.000	9.000	7.000	8.000	7.000	7.000	9.000	7.000
Eco-innovation and resource planning (ERP)	3.000	3.000	4.000	0.000	3.000	1.000	4.000	2.000	1.000	4.000	2.000	3.000	2.000
	6.000	6.333	7.333	0.000	6.778	5.000	6.444	5.889	6.000	5.333	5.667	6.333	5.222
	8.000	9.000	9.000	0.000	9.000	8.000	9.000	9.000	9.000	8.000	8.000	9.000	8.000
Combining digital technology with green (CDG)	3.000	5.000	3.000	0.000	0.000	1.000	3.000	2.000	1.000	3.000	2.000	3.000	3.000
	5.556	6.556	7.444	6.444	0.000	6.000	6.222	5.778	5.222	6.333	5.778	6.556	6.222
	8.000	9.000	9.000	8.000	8.000	9.000	9.000	9.000	9.000	8.000	8.000	9.000	8.000
Stakeholder collaboration and integration (SCI)	2.000	4.000	3.000	2.000	0.000	0.000	3.000	3.000	2.000	1.000	1.000	1.000	1.000
	4.667	6.111	6.111	5.444	5.222	0.000	4.556	4.889	5.222	4.667	5.778	4.667	5.222
	8.000	8.000	9.000	9.000	8.000	0.000	9.000	8.000	9.000	8.000	9.000	8.000	8.000
Human-centric manufacturing and logistics (HML)	3.000	4.000	3.000	1.000	2.000	0.000	0.000	1.000	1.000	3.000	1.000	3.000	1.000
	5.667	6.111	6.333	6.111	6.222	4.556	0.778	5.889	5.222	6.222	5.333	4.889	5.000
	9.000	8.000	9.000	8.000	8.000	7.000	0.000	8.000	8.000	9.000	9.000	8.000	7.000
Customer-oriented individual manufacturing (CIM)	4.000	4.000	2.000	2.000	2.000	2.000	1.000	0.000	2.000	3.000	3.000	1.000	1.000
	7.222	6.556	5.111	5.444	6.778	5.778	6.000	0.000	6.000	5.778	6.222	5.111	4.333
	9.000	9.000	9.000	9.000	9.000	9.000	9.000	0.000	9.000	8.000	9.000	9.000	6.000
Value chain integration (VCI)	5.000	4.000	3.000	1.000	4.000	3.000	1.000	4.000	0.000	1.000	2.000	2.000	2.000
	6.889	6.111	5.778	5.667	6.000	5.889	5.111	6.333	0.000	5.444	5.111	4.778	5.444
	8.000	9.000	8.000	8.000	8.000	9.000	7.000	8.000	0.000	8.000	9.000	9.000	8.000
Paying attention to the well-being of employees (PWE)	2.000	4.000	2.000	4.000	3.000	2.000	6.000	1.000	1.000	0.000	2.000	2.000	3.000
	4.889	6.111	5.222	5.667	5.444	4.556	7.333	4.889	4.667	0.000	5.111	5.778	4.111
	8.000	8.000	7.000	7.000	8.000	7.000	9.000	7.000	7.000	0.000	9.000	8.000	7.000
Information security maintenance (ISM)	1.000	3.000	3.000	2.000	4.000	5.000	1.000	3.000	2.000	1.000	0.000	2.000	3.000
	4.889	5.667	5.889	5.000	7.000	7.222	5.556	5.556	5.444	6.333	0.000	4.444	5.778
	9.000	7.000	8.000	7.000	8.000	9.000	7.000	7.000	7.000	9.000	0.000	7.000	8.000
Resilience and sustainability performance indicators (RSIs)	4.000	3.000	2.000	4.000	3.000	3.000	3.000	1.000	1.000	1.000	2.000	0.000	2.000
	6.111	6.556	6.667	6.778	6.333	6.222	5.778	5.444	5.111	5.667	4.444	0.000	5.778
	8.000	8.000	9.000	9.000	7.000	7.000	8.000	8.000	9.000	8.000	7.000	0.000	8.000
Risk prevention to improve resilience (RPR)	2.000	1.000	1.000	3.000	3.000	3.000	3.000	2.000	2.000	3.000	5.000	3.000	0.000
	5.444	5.333	5.556	5.778	5.667	5.222	5.778	4.556	6.000	5.778	6.778	5.778	0.000
	8.000	7.000	7.000	7.000	7.000	8.000	9.000	7.000	8.000	8.000	8.000	7.000	0.000

.

3.3. Developing the Initial Reachability Matrix

Following the definition of fuzzy numbers, the initial reachable matrix obtained after the defuzzification of the fuzzy data is presented in

Table 2. Initial reachability matrix ${\mathrm{Y}}_{\mathrm{i}}$.

Defuzzification	FPS	SCG	ACG	ERP	CDG	SCI	HML	CIM	VCI	PWE	ISM	RSIs	RPR
Flexibility of production systems (FPS)	0.00	6.93	5.07	5.67	5.52	4.89	5.89	6.74	6.63	4.96	4.96	6.04	5.15
Sustainable corporate governance (SCG)	5.30	0.00	5.07	6.11	6.85	6.04	6.04	6.52	6.37	6.04	5.22	5.85	4.44
Active support from the government (ACG)	6.37	6.22	0.00	6.78	6.48	6.04	6.11	5.37	5.59	4.74	5.63	5.89	4.52
Eco-innovation and resource planning (ERP)	5.22	6.33	5.00	0.00	4.81	5.48	5.04	5.48	4.89	5.56	4.67	6.59	5.26
Combining digital technology with green (CDG)	6.04	6.48	4.67	6.26	2.67	4.41	5.41	5.93	6.00	5.48	6.33	5.44	5.22
Stakeholder collaboration and integration (SCI)	5.04	6.15	3.78	4.67	5.33	0.00	3.85	5.59	5.96	4.52	7.07	5.41	5.41
Human-centric manufacturing and logistics (HML)	5.48	6.15	4.70	6.48	6.07	5.52	0.26	5.33	4.37	7.44	4.52	5.59	5.93
Customer-oriented individual manufacturing (CIM)	6.63	5.85	3.93	5.63	5.59	5.30	4.96	0.00	6.11	4.30	5.19	4.81	4.52
Value chain integration (VCI)	5.85	6.30	4.48	5.33	5.07	5.41	4.74	5.67	0.00	4.22	4.81	5.04	5.33
Paying attention to the well-being of employees (PWE)	6.33	6.11	4.26	5.78	5.78	4.56	6.07	5.59	4.81	0.00	5.44	4.89	5.59
Information security maintenance (ISM)	5.74	5.74	4.33	5.22	5.26	5.26	5.11	6.07	5.37	5.37	0.00	4.48	6.59
Resilience and sustainability performance indicators (RSIs)	6.22	6.07	5.11	6.11	6.19	4.56	5.30	5.04	5.26	5.26	4.48	0.00	5.26
Risk prevention to improve resilience (RPR)	5.85	5.22	3.89	5.07	5.74	4.74	4.33	3.78	5.15	4.70	5.59	5.26	0.00

.

In the FISM model, an excessive or insufficient level of mutual influence among factors can impact the hierarchy of the resulting hierarchical structure model. Therefore, to establish a reasonable hierarchical division, discussions were held with the expert group to determine the optimal solution of the λ value. The goal was to ensure that the number of “1” entries in the reachable matrix fell within the range of 15–30. The threshold value was determined by calculating the values in Table 2. Ultimately, in this study, the threshold value λ = 6.3 was set based on the discussion with the expert group, and the reference results of the λ value calculation are shown in

Table 3. Optimal solutions of $\mathsf{\lambda }$ value.

$\mathsf{\lambda }$ Value	FPS	SCG	ACG	ERP	CDG	SCI	HML	CIM	VCI	PWE	ISM	RSIs	RPR	Total
6.40	1	2	0	2	2	0	0	2	1	1	1	1	1	14
6.30	3	3	0	2	2	0	0	2	2	1	2	1	1	19
6.20	4	5	0	3	2	0	0	2	2	1	2	1	1	23
6.10	4	8	0	5	3	0	1	2	3	1	2	1	1	31
6.00	5	9	0	5	4	2	3	3	4	2	2	2	1	42
5.90	5	9	0	5	4	2	3	4	5	2	2	2	2	45
5.80	7	10	0	5	4	2	4	4	5	2	2	4	2	51
5.70	8	11	0	6	6	2	4	4	5	2	2	4	2	56

.

The resulting adjacency matrix is shown in

Table 4. Adjacency matrix A.

	FPS	SCG	ACG	ERP	CDG	SCI	HML	CIM	VCI	PWE	ISM	RSIs	RPR
Flexibility of production systems (FPS)	0	1	0	0	0	0	0	1	1	0	0	0	0
Sustainable corporate governance (SCG)	0	0	0	0	1	0	0	1	1	0	0	0	0
Active support from the government (ACG)	1	0	0	1	1	0	0	0	0	0	0	0	0
Eco-innovation and resource planning (ERP)	0	1	0	0	0	0	0	0	0	0	0	1	0
Combining digital technology with green (CDG)	0	1	0	0	0	0	0	0	0	0	1	0	0
Stakeholder collaboration and integration (SCI)	0	0	0	0	0	0	0	0	0	0	1	0	0
Human-centric manufacturing and logistics (HML)	0	0	0	1	0	0	0	0	0	1	0	0	0
Customer-oriented individual manufacturing (CIM)	1	0	0	0	0	0	0	0	0	0	0	0	0
Value chain integration (VCI)	0	0	0	0	0	0	0	0	0	0	0	0	0
Paying attention to the well-being of employees (PWE)	1	0	0	0	0	0	0	0	0	0	0	0	0
Information security maintenance (ISM)	0	0	0	0	0	0	0	0	0	0	0	0	1
Resilience and sustainability performance indicators (RSIs)	0	0	0	0	0	0	0	0	0	0	0	0	0
Risk prevention to improve resilience (RPR)	0	0	0	0	0	0	0	0	0	0	0	0	0

.

3.4. Developing Final Reachability Matrix

The final reachable matrix, denoted as M and obtained through iterative calculations of the matrix (A + I) multiplications, is presented in

Table 5. Final reachable matrix M.

	FPS	SCG	ACG	ERP	CDG	SCI	HML	CIM	VCI	PWE	ISM	RSIs	RPR	Driving Power
Flexibility of production systems (FPS)	1	1	0	0	1	0	0	1	1	0	1	0	1	7
Sustainable corporate governance (SCG)	1	1	0	0	1	0	0	1	1	0	1	0	1	7
Active support from the government (ACG)	1	1	1	1	1	0	0	1	1	0	1	1	1	10
Eco-innovation and resource planning (ERP)	1	1	0	1	1	0	0	1	1	0	1	1	1	9
Combining digital technology with green (CDG)	0	0	0	0	1	0	0	1	1	0	1	0	1	5
Stakeholder collaboration and integration (SCI)	0	0	0	0	0	1	0	0	0	0	1	0	1	3
Human-centric manufacturing and logistics (HML)	1	1	0	1	1	0	1	1	1	1	1	1	1	11
Customer-oriented individual manufacturing (CIM)	1	1	0	0	1	0	0	1	1	0	1	0	1	7
Value chain integration (VCI)	0	0	0	0	0	0	0	0	1	0	0	0	0	1
Paying attention to the well-being of employees (PWE)	1	1	0	0	1	0	0	1	1	1	1	0	1	8
Information security maintenance (ISM)	0	0	0	0	0	0	0	0	0	0	1	0	1	2
Resilience and sustainability performance indicators (RSIs)	0	0	0	0	0	0	0	0	0	0	0	1	0	1
Risk prevention to improve resilience (RPR)	0	0	0	0	0	0	0	0	0	0	0	0	1	1
Dependence power	7	7	1	2	8	1	1	8	9	2	10	4	11	\

.

3.5. Establishing the Hierarchy Level

This study adopts a result-first hierarchical extraction rule and divides the level of the reachable matrix of the enablers of Industry 5.0 to realize smart logistics. The iterative extraction process of the hierarchy is shown in

Table 6. Precede Set, Reachable Set, and Intersection Result.

Si	R (Si)	Q (Si)	T (Si)
1. Flexibility of production systems (FPS)	[1,2,5,8,9,11,13]	[1–4,7,8,10]	[1,2,8]
2. Sustainable corporate governance (SCG)	[1,2,5,8,9,11,13]	[1–4,7,8,10]	[1,2,8]
3. Active support from the government (ACG)	[1–5,8,9,11–13]	[3]	[3]
4. Eco-innovation and resource planning (ERP)	[1,2,4,5,8,9,11–13]	[3,4,7]	[4]
5. Combining digital technology with green (CDG)	[5,8,9,11,13]	[1–5,7,8,10]	[5,8]
6. Stakeholder collaboration and integration (SCI)	[6,11,13]	[6]	[6]
7. Human-centric manufacturing and logistics (HML)	[1,2,4,5,7–13]	[7]	[7]
8. Customer-oriented individual manufacturing (CIM)	[1,2,5,8,9,11,13]	[1–5,7,8,10]	[1,2,5,8]
9. Value chain integration (VCI)	[9]	[1–5,7–10]	[9]
10. Paying attention to the well-being of employees (PWE)	[1,2,5,8–11,13]	[7,10]	[10]
11. Information security maintenance (ISM)	[11,13]	[1–8,10,11]	[11]
12. Resilience and sustainability performance indicators (RSIs)	[12]	[3,4,7,12]	[12]
13. Risk prevention to improve resilience (RPR)	[13]	[1–8,10,11,13]	[13]

. The final hierarchical extraction result is as follows:

L1 = {3,7}; L2 = {4,10}; L3 = {1,2}; L4 = {5,6,8}; L5 = {11}; L6 = {9,12,13}

3.6. Building the Structural Model

The relationships between the enablers are combined with the divided levels to construct a multi-level hierarchical structure model of the enablers of smart logistics in Industry 5.0. The relationships between the enablers are visually reflected in a directed graph, as shown in Figure 2.

3.7. Driving and Dependence Analysis

The MICMAC technique is employed to depict the driving and dependency relationships among the various enablers within a system, represented in the form of a coordinate axis. Based on the determinations of the final reachable matrix, the driving dependency quadrant coordinates were calculated to analyze the relationships between the driving and dependency enablers for the implementation of Industry-5.0-driven smart logistics. The construction of the MICMAC model divided the enablers into the following four major clusters: independent, linkage, autonomous, and dependent. Figure 3 represents the MICMAC analysis of the Industry 5.0 enablers, where ACG, ERP, HML, and PWE are the enablers. These four enablers are positioned in the independent cluster, indicating strong driving capabilities and weak dependencies, making them a priority for implementation. Enablers in the linkage cluster exhibit both high driving capabilities and dependencies, including FPS, SCG, and CIM, serving as the foundation for transferring the value contained in enablers to dependent enablers. The autonomous cluster comprises enablers with relatively weak driving and dependency characteristics, represented by SCI and RSIs, with lower strategic priority. Enablers in the dependent cluster demonstrate weak driving capabilities and strong dependencies, such as CDG, VCI, ISM, and RPR, which are complex and challenging to implement and rely on the driving force of other enablers.

4. A Strategy Roadmap

4.1. Drawing the Strategic Roadmap

The main objective of this study was to develop a strategic roadmap that describes how Industry 5.0 drives smart logistics, with a specific focus on human centricity, sustainability, and resilience. We conducted a comprehensive review of the literature using a content-centric approach to identify the enablers of Industry 5.0 for smart logistics. Figure 2 illustrates the sequential relationships among these enablers. However, the explanations of the connections between the enablers for the implementation of Industry-5.0-driven smart logistics lack sufficient detail. To address this gap, we designed an expert questionnaire based on the FISM hierarchy and gathered expert opinions. We integrated the questionnaire results with the FISM hierarchy to create a strategic roadmap for implementing Industry-5.0-driven smart logistics.

The relationships between the Industry 5.0 enablers in this roadmap are established based on the hierarchical relationships determined within the FISM. Each connection in the roadmap is based on the expert opinions collected through the questionnaire. For ease of presentation, we select the full names of the selection factors of each enabler to draw the strategic roadmap. Detailed explanations of the specific enablers can be found in Section 2.2, Enablers of Industry 5.0 Driving Smart Logistics.

The direct relationships between the enablers in the roadmap correspond to the relationships identified in the adjacency matrix and the expert opinion table. The expert panel consisted of 11 experts. To ensure a high level of consistency in their recommendations for creating the strategic roadmap, we analyzed the opinions compiled after the expert group meetings. We observed that the expert opinions tended to stabilize when using a threshold of 75% to 80%. Therefore, we selected 80% as the threshold for this study’s enabler. This threshold implies that, when the number of experts recognizing a relationship between enablers exceeds 80% of the total, the relationship between enablers is designated as 1; otherwise, it is designated as 0. The matrix of mutual relationships between the enablers, as derived in this study, is presented in

Table 7. Correlation matrix between driving enablers.

	ACG	HML	ERP	PVE	SCG	FPS	CIM	CDG	SCI	ISM	VCI	RSIs	RPR
Active support from the government (ACG)		1	1	1	1	0	0	1	0	1	1	0	0
Human-centric manufacturing and logistics (HML)			1	1	1	0	1	0	0	0	0	0	1
Eco-innovation and resource planning (ERP)				0	1	1	1	1	0	0	1	1	0
Paying attention to the well-being of employees (PWE)					1	0	1	1	0	0	0	1	0
Sustainable corporate governance (SCG)						1	1	1	1	0	1	1	1
Flexibility of production systems (FPS)							1	0	1	0	1	1	1
Customer-oriented individual manufacturing (CIM)								1	1	0	1	0	0
Combining digital technology with green (CDG)									1	1	1	1	1
Stakeholder collaboration and integration (SCI)										0	1	1	0
Information security maintenance (ISM)											1	0	1
Value chain integration (VCI)												1	1
Resilience and sustainability performance indicators (RSIs)													1
Risk prevention to improve resilience (RPR)

. Based on this relationship matrix and using FISM and MICMAC analyses, we created the final strategic roadmap.

4.2. Results and Discussion

In this study, we identified the enablers of smart logistics within the human-centric, sustainable, and resilient Industry 5.0. The FISM model, as illustrated in Figure 2, categorizes these enablers into six levels. However, Figure 2 is not regarded as a strategic roadmap because the FISM–MICMAC analysis model only depicts direct causal relationships between these enablers and does not fully capture the contextual relationships between each pair of enablers. Therefore, a more intricate relationship between these 13 enablers needs to be represented through a strategically drawn roadmap, as shown in Figure 4.

The strategic roadmap reveals that ACG at the first level can drive technological innovation, digital transformation, and infrastructure development in the logistics industry. ACG is the most crucial driving force for realizing smart logistics in the Industry 5.0 era, as it has the highest driving force and can directly promote other enablers. For example, ACG can revise tax policies, reduce labor taxes to promote HML, enhance corporate performance, and increase profitability. It can ensure a safe working environment by implementing health and safety standards and providing supportive policies for training and education through PWE. Additionally, research by Ghobakhloo et al. [47] suggests that Industry 5.0 can promote a human-centric approach through workforce retraining, customized technology, and improved safety in the working environment. ACG can also promote the integration of digital technologies and green concepts by promoting environmentally friendly packaging, developing targeted energy policies, and digital transformation programs (CDG). This helps to reduce resource wastage (ERP) in the production process, promotes HML, and encourages companies to embrace a sustainable development path. Research by Mahmoudi et al. [48] also supports the idea that government policy support can encourage logistics companies to adopt more sustainable operating methods to minimize adverse environmental impacts through the development of communication technology, the implementation of data protection regulations, network security law, and other measures to help enterprises strengthen ISM. This, in turn, promotes enterprises to integrate value networks (VCI), thus promoting industrial transformation and upgrading, which provides strong support for the sustainable development of smart logistics.

The enabler at the first level is HML, which emphasizes employee satisfaction, production efficiency, and safety. HML helps enterprises to reduce logistic costs and develop employees’ innovative thinking and teamwork, promoting innovation and improvement in smart logistics. This aligns with the research of Hitka et al. [49], emphasizing that attaching importance to employee enthusiasm and satisfaction promotes the sustainable development of logistic enterprises. By paying attention to human resources and costs (ERP), re-planning the production work area, and adapting digital technology to people’s production needs, employees’ work efficiency, physical and mental health, and safety can be guaranteed (PWE). Logistics enterprises can also reduce human failures and mistakes through training, quality control, standardized processes, risk management, and other measures, enhancing RPR, as supported by the research of Wang et al. [50]. CIM focuses on providing customized products and services for customers, while HML brings the customer’s personalized needs into the product formulation stage to meet customer demands. Moreover, HML helps to achieve SCG by paying attention to employee welfare, social responsibility, and corporate culture, which supports the previous research of Ghobakhloo, Iranmanesh, Morales, Nilashi, and Amran [30].

ERP and PWE are positioned at the second level of the strategic roadmap. As available resources become increasingly scarce and the use of polluted resources increases, companies should take measures to address these issues. In the era of Industry 5.0, smart logistics has become the key to realizing sustainable development, and ERP plays an important role in this process. ERP can realize the visual management of logistics links. By integrating data and information from different links, enterprises can grasp the dynamics of the logistics process more efficiently and adjust and optimize their operations in time. Research by Berraies et al. [51] also suggests that improving employee well-being plays a crucial role in enhancing cohesion and loyalty between employees and companies in the logistics industry. Moreover, the combination of ERP and PWE can better promote SCG. This will help to achieve sustainability by reducing resource waste, improving employee loyalty, and minimizing environmental impacts, which creates a solid foundation for the development of the logistics industry to better meet the customization needs of customers (CIM). Furthermore, by combining digital technology with green practices, smart logistics can better optimize processes, save costs, and achieve sustainability goals. Establishing RSIs can help the logistics industry adapt to changes, reduce risks, and enable companies to obtain a more accurate understanding of each step in the logistics process. This, in turn, promotes the development of smart logistics. This finding aligns with the views of Hu et al. [52], emphasizing that companies can greatly promote the intelligent development of logistics by visually understanding the dynamics of logistics processes.

The enablers of the third level include FPS and SCG. FPS plays a crucial role in achieving smart logistics in the context of Industry 5.0. To improve FPS, companies can use CDG to efficiently use resources in the production process to better respond to market demand changes. Additionally, focusing on social sustainability brings many benefits to companies in terms of improving their business image and long-term performance [53]. Therefore, driven by the strategic goal of sustainable development, companies can establish bridges for communication and collaboration among stakeholders, strengthen transparency in business by using technologies such as physical network systems and big data analysis, and foster cooperation among stakeholders such as companies, customers, and suppliers (SCI). This, in turn, helps to improve the value network between partners and customers (VCI), supporting the findings of Antoldi and Cerrato [54]. SCG emphasizes social responsibility and employee well-being, contributing to the establishment of a more robust corporate structure and reducing the occurrence of accidents, thus improving FPS and better preventing and responding to unknown risks (RPR). Research by Li et al. [55] also indicates that SCG enhances risk prevention and resilience. Smart logistics, with its higher resilience, can assist companies in adapting quickly, ensuring supply chain stability, enhancing competitiveness, and minimizing losses.

CIM, CDG, and SCI are placed at the fourth level. As enterprises rely on customers for survival and development, CIM allows businesses to understand and collect customer needs for personalized products and services through various analytical and identification techniques. ISM is also critical during the information collection process. With CDG, customer needs are integrated into production processes through adjustments in product design, manufacturing processes, and quality control, ensuring consistency between products or services and customer needs. These efforts enhance trust between enterprises and consumers, ultimately promoting SCI and driving the development of human-centric smart logistics. This finding supports the research of Zhang et al. [56], emphasizing the importance of transitioning from an inventory-based manufacturing model to a demand-driven, mass customization model that better meets customer needs.

Notably, ISM is placed as a separate fifth level in the strategic roadmap and is essential for achieving the transformation to Industry 5.0. It reduces errors and improves the responsiveness of the value chain by enhancing data security, clarity, and accuracy. It promotes VCI and enables quick responses to and the resolution of potential risks in business processes. Additionally, it facilitates the flexible handling of process interruptions (RPR). By protecting user information, ISM enhances consumer trust and increases the credibility of smart logistics systems, thereby effectively promoting the continuous development and application of smart logistics. This finding is consistent with recent research, such as that of Zhan, Dong, and Hu [23], which emphasizes the important role of information security in the implementation of smart logistics transformation.

VCI, RSIs, and RPR are located at the sixth level of the model and are characterized by a relatively low driving force. This means that, while the VCI, RSIs, and RPR enablers do not drive other enablers in smart logistics implementation for Industry 5.0, they cannot be ignored. VCI is essential because it promotes the establishment of a value chain that is more resilient, circular, sustainable, and transparent. This includes selecting the most competitive partners around the world, integrating dynamic supply networks, and integrating global supply chains to ultimately enhance supply chain agility. This perspective aligns seamlessly with the research viewpoints of Zhang, Chen, Lin, and Chen [29]. Logistics companies introduce RSIs to evaluate and monitor the impacts of logistics operations on the environment, society, and economy, continuously improving optimization to achieve sustainable development goals. In smart logistics, the real-time monitoring, analysis, and optimization of each link in the supply chain process require attention to RPR to ensure the stable operation of the smart logistics system. This helps to increase the competitiveness of companies in uncertain market environments. This finding supports the research of Liu et al. [57], which identifies different risks in the smart logistics ecosystem to ensure that companies effectively prevent these risks in order to promote the sustained and stable development of smart logistics.

Although the 13 identified enablers in this study make significant contributions to the development of smart logistics in various aspects, they also play significant promoting roles in the three core goals of Industry 5.0, which can be summarized as follows:

Human-centric goals—these mainly include promoting human-centric manufacturing and logistics (HML), paying attention to the well-being of employees (PWE), and customer-oriented individual manufacturing (CIM).

Sustainable goals—these are mainly supported by active support from the government (ACG), sustainable corporate governance (SCG), combining digital technology with green practices (CDG), eco-innovation and resource planning (ERP), the flexibility of production systems (FPS), value chain integration (VCI), resilience and sustainability performance indicators (RSIs), and stakeholder collaboration and integration (SCI).

Resilient goals—these include risk prevention to improve resilience (RPR), resilience and sustainability performance indicators (RSIs), information security maintenance (ISM), the flexibility of production systems (FPS), and active support from the government (ACG).

5. Conclusions

In conclusion, the emergence of Industry 5.0 serves as a valuable complement to Industry 4.0, providing essential technological support for the advancement of smart logistics. Despite this promising alliance, the specific mechanisms through which Industry 5.0 drives the transformation toward smart logistics remain unclear. To address this knowledge gap, this study initially focused on Industry 5.0 and smart logistics. Employing a content-centric literature review, we identified 13 enablers for Industry-5.0-driven smart logistics grounded in the principles of human centricity, sustainability, and resilience. Subsequently, FISM and MICMAC analyses were conducted to determine the developmental sequence of these enablers. Finally, our strategic roadmap elucidated the contextual relationships of each enabler and delineated their distinctive roles. These findings contribute valuable insights that are anticipated to benefit entrepreneurs, policy institutions, and scholars.

5.1. Theoretical Implications

The transformative potential of Industry 5.0’s advanced technologies, including IoT, AI, big data, and other related innovations, has a demonstrably positive impact on the evolution and enhancement of smart logistics. These technologies are instrumental in optimizing the logistics system and improving its efficiency. To achieve the goal of driving the transformation of smart logistics with Industry 5.0, a set of enablers must act as the driving force. However, there is currently a lack of research on how Industry 5.0 can realize the transformation of smart logistics. Therefore, this study aimed to fill this void by using the identified enablers of Industry 5.0 to facilitate the transformation of smart logistics. The methodology employed involved the creation of a strategic roadmap that is anticipated to contribute to the advancement of the logistics industry. To the best of our knowledge, this study represents the first pioneering effort in this field of study, addressing both Industry 5.0 and smart logistics.

By providing a structured approach to understanding and implementing the enablers of Industry 5.0 and smart logistics transformation, this study identified 13 essential enablers for achieving smart logistics through Industry 5.0. The results indicate that these enablers are interrelated and sequential, so should be developed in a specific order for the optimal effectiveness. We believe that no enabler should be overlooked, as the synergistic benefits arising from the complementarity among these enablers are crucial for the transformation of smart logistics through Industry 5.0.

The multi-level hierarchical structure model, as determined by the FISM, establishes a clear sequence that signifies the priority of development for these enablers. Enablers positioned at the lowest level exhibit a unique ability to actively drive other enablers, making them the most critical enablers for Industry 5.0. These include ACG and HML. In the MICMAC analysis, ACG, HML, ERP, and PWE form an independent cluster, highlighting their significant roles in strategic deployment. Therefore, enterprises should pay special attention to these enablers when implementing their strategic initiatives. However, it is essential not to overlook the enablers at the top level. For example, VCI, RSIs, and RPR, although positioned at the top level, possess the weakest driving ability. Both VCI and RPR are part of the dependent cluster in the MICMAC analysis, signifying their complexity and the challenges associated with their development as driving forces. Despite variations in their driving and dependency abilities, each identified enabler remains indispensable for the success of Industry 5.0. Notably, some determined enablers extend beyond the scope of Industry 5.0. For example, ISM and CDG have long been associated with the breakthrough development of technology since the Fourth Industrial Revolution. However, in the era of Industry 5.0, the progress of technology and management redefines the depth and breadth of these enablers, allowing for their integrated development to support the transformation of smart logistics driven by Industry 5.0.

5.2. Practical Significance

In the practical realm, as enterprises harness Industry 5.0 to propel the transformation of smart logistics, the strategic roadmap developed in this study offers a valuable guide for systematic development. The following steps outline a specific order for enterprises to consider:

First, ACG is crucial. The study’s findings indicate that favorable policy support and effective regulation play crucial roles in driving the transformation of smart logistics in Industry 5.0. Enterprises are encouraged to collaborate with government departments and other relevant stakeholders, using the funding obtained to support advanced research and the development of new logistic solutions and technologies. This collaborative approach will enhance the competitiveness of their operations.

Second, enterprises themselves need to prioritize a human-centric approach, emphasizing the safety and well-being of employees. Simultaneously adopting a customer-centric mindset is crucial to ensure that the developed solutions align with customer needs and expectations. Additionally, enterprises can augment their digital transformation capabilities by incorporating advanced technologies such as AI, collaborative robots, and sensing technologies. Integrating these technologies with the use of renewable resources will facilitate sustainable technology governance, including practices such as green computing and renewable energy integration. Building upon a solid foundation, enterprises should continuously strengthen their awareness of information security protection. Finally, a focus on risk prevention becomes paramount to enhancing resilience. By adopting measures that proactively identify and address risks, enterprises can not only mitigate potential disruptions, but also promote economic growth, reduce waste, and improve efficiency, thereby further facilitating the transformation of smart logistics.

In conclusion, this study has yielded noteworthy and valuable insights, with the key finding summarized as follows:

(1) Industry 5.0 can facilitate the transformation toward smart logistics through 13 interconnected enablers. These enablers include a spectrum of micro-level technological aspects, including the integration of digital technologies and green concepts with macro-level managerial enablers such as corporate governance transformation.

(2) Each identified enabler independently contributes to the goals of Industry 5.0 in driving smart logistics. These interconnections underscore their indispensability, with each enabler being crucial to the transformation toward smart logistics.

(3) The strategic roadmap developed in this study emphasizes the importance of prioritizing these enablers. Prioritization enables the maximization of their effectiveness, guiding enterprises in undertaking more impactful measures to advance the development of the logistics industry within the Industry 5.0 framework.

5.3. Limitations and Future Research Directions

While this study offers valuable insights into the enablers of smart logistics in the context of Industry 5.0, several limitations should be acknowledged. First, the study heavily relies on expert opinions for constructing the FISM and performing the MICMAC analysis. Although a fuzzy approach was employed to mitigate subjectivity, there remains a risk of bias due to potential limitations in the geographical and industry diversity of the expert panel. Additionally, the research was conducted within a specific timeframe, and given the rapid and dynamic development of smart logistics and Industry 5.0 technologies, future advancements may introduce new enablers or alter the priority of existing ones. Lastly, the literature reviewed in this study, while comprehensive, was based on an emerging field, and some key sources may not yet be widely cited or fully developed. As more research is conducted in this area, the theoretical foundations and findings presented here may require further updating or refinement.

In summary, this study identified the key enablers crucial for developing smart logistics within Industry 5.0. The sequence of these enablers, as established by the FISM–MICMAC model, highlights the potential interactions among them. Given that Industry 5.0 is a relatively new research area with an evolving body of literature, the relationships among these enablers are subject to continuous changes influenced by time, policy, technology, and other dynamic enablers. Consequently, there are several intriguing and valuable avenues for future research. To deepen our understanding, future studies should conduct more extensive literature reviews, building upon the foundational insights laid out in this research. Furthermore, there is an opportunity to enhance the analysis of sample reliability, fostering a multi-disciplinary and holistic examination of the enablers within Industry 5.0.

It is essential to emphasize that Industry 5.0, as an extension of Industry 4.0, should not be perceived as a replacement, but rather as a long-term vision. Its primary objective is to achieve circular and sustainable value by promoting close collaboration and coordination among stakeholders in various processes. In essence, Industry 5.0 acts as a complement, underscoring the significance of environmental sustainability and facilitating a balance between the development of smart logistics and environmental considerations. Looking ahead, as Industry 5.0 continues to mature, it will serve as a bridge between stakeholders and industry, transcending the limitations of Industry 4.0 and propelling us toward a future characterized by human centricity, sustainability, and resilience.