An Integrated MCDA Framework for Prioritising Emerging Technologies in the Transition from Industry 4.0 to Industry 5.0

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The proposed framework supports companies in prioritizing technologies during the transition from Industry 4.0 to 5.0, aiding strategic decisions from economic, organizational, and technological perspectives.

Abstract

As industrial companies transition from the Industry 4.0 stage to the more human-centric and resilient Industry 5.0 paradigm, there is a growing need for structured assessment tools to prioritize modern technologies. This paper presents an integrated multi-criteria decision analysis (MCDA) approach to support the strategic assessment of technologies from three complementary perspectives: economic, organizational, and technological. The proposed model encompasses six key transformation areas and 22 technologies representing both the Industry 4.0 and 5.0 paradigms. A hybrid approach combining the DEMATEL (Decision-Making Trial and Evaluation Laboratory) and PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation) methods is used to identify cause–effect relationships between the transformation areas and to construct technology rankings in each of the assessed perspectives. The results indicate that technologies such as the Internet of Things (IoT), cybersecurity, and supporting IT systems play a central role in the transition process. Among the Industry 5.0 technologies, hyper-personalized manufacturing, smart grids and new materials stand out. Moreover, the economic perspective emerges as the dominant assessment dimension for most technologies. The proposed analytical framework offers both theoretical input and practical decision-making support for companies planning their transformation towards Industry 5.0, enabling a stronger alignment between implemented technologies and long-term strategic goals.

1. Introduction

In the past decade, the dynamic transformation of the industry towards digitalization, automation, and sustainable development, known as Industry 4.0, has significantly influenced the operations of manufacturing and logistics companies. The implementation of technologies such as the Internet of Things (IoT), artificial intelligence (AI), Big Data analysis, and advanced robotization has led to increased operational efficiency, cost optimization, and increased process flexibility. At the same time, increasing social and environmental challenges, as well as the need for greater human involvement in industrial processes, have led to the emergence of a new paradigm—Industry 5.0—which emphasizes human-centric values, system resilience, and the pursuit of sustainable development. The literature in this field indicates the need for a comprehensive and systematic approach to assessing the readiness of enterprises to transition from Industry 4.0 to 5.0. Trojahn et al. [1] emphasize the importance of integrating decision-support tools and knowledge generation strategies in order to overcome digital barriers, taking into account both technological and social aspects. Nguyen et al. [2] propose a readiness assessment model that takes into account employee competencies, organizational culture, and adaptability as key factors for the effective implementation of Industry 5.0 assumptions. In turn, Madhavan et al. [3] show that a higher level of readiness for transformation translates into sustainable growth of small- and medium-sized enterprises, especially in the context of implementing human- and environment-friendly technologies.

Despite the growing number of conceptual and application studies, the literature still does not offer sufficiently integrated models enabling a comprehensive assessment of technologies supporting the transformation from Industry 4.0 to 5.0. There is a lack of analytical tools that simultaneously consider (i) the cause-and-effect relationships between technologies and transformation areas, (ii) the ranking of technologies in terms of their effectiveness across three key dimensions (economic, organizational, and technological), and (iii) their role in the full transformation cycle from digitalization to human-centric resilience.

The aim of this article is to develop a framework for assessing modern technologies characteristic of Industry 4.0 and 5.0, addressing this gap through the integrated MCDM (multi-criteria decision-making) methodology, combining the DEMATEL method (for the analysis of cause–effect relationships) and PROMETHEE II (for the assessment of preferences and decision priorities). The developed model includes 22 technologies, assigned to six areas of transformation, assessed from three perspectives: economic, organizational, and technological.

The following research questions were formulated in this study:

Q1: What technologies should be prioritized for implementation by companies seeking to transform towards Industry 5.0?

Q2: What are the relationships between technology assessment criteria in the context of the transformation from Industry 4.0 to 5.0?

Q3: How does the adopted assessment perspective (economic, organizational, or technological) influence differences in the classification and prioritization of assessed technologies?

The formulated research questions were embedded in the analytical structure of this study: Q1—the identification of technologies with the highest implementation priority; Q2—the relationships between the evaluation criteria; Q3—differences in the assessment of technologies from three perspectives, economic, organizational, and technological.

The structure of this paper is as follows: Section 2 outlines a review of the literature on Industry 4.0/5.0 and the application of MCDA methods. Section 3 describes the adopted methodology in detail. Section 4 presents the implementation process of the hybrid model. Section 5 discusses the results, and Section 6 provides conclusions, highlights limitations, and offers recommendations for further research.

2. Literature Review

In recent years, there has been dynamic growth in research on the digital transformation of enterprises in the context of the transition from the Industry 4.0 paradigm to Industry 5.0. This evolution is associated not only with the adaptation of new technologies but also with a shift in the approach to the role of humans, sustainable development, and organizational resilience. This chapter presents a literature review, focusing on the concepts of enterprises’ readiness to implement the principles of Industry 5.0, the identification of technologies supporting this process, and multi-criteria assessment methods used in the analysis of industrial transformation.

2.1. Industry 4.0 Technologies

In the scientific literature, there is a growing effort to classify and assess the impact of advanced digital technologies that shape the foundations of Industry 4.0. One of the main directions of research is to explore the potential of solutions such as blockchain, the IoT, Big Data, and cyber–physical systems in the context of industrial transformation and enhanced production system efficiency.

Liu, Li, and Huang [4] analyse the potential of integrating blockchain technology with energy management platforms in sustainable business models for the energy sector. Rad et al. [5] systematically review the impact of eleven key Industry 4.0 technologies on supply chain performance, identifying AI, the IoT, and Big Data as critical success factors.

Munirathinam [6] offers a detailed discussion the IIoT architecture as a foundation for industrial digitalization, paying attention to security and interoperability issues. More broadly, Liao et al. [7] propose a research agenda for advancing of the Industry 4.0 concept, defining its main technologies and application areas.

Kamble et al. [8] and Lee, Kao, and Yang [9] emphasize the role of data-driven solutions, such as predictive analytics, in agriculture and intelligent manufacturing systems. In a similar vein, Li et al. [10] analyse the potential of resource virtualization and service selection in the context of cloud-based logistics, and Cannavacciuolo et al. [11] underline the necessity of integrating technological innovations with strategic and organizational processes.

Soori et al. [12]’s focus on the role of the IoT in creating smart factories aligns with the Industry 4.0 paradigm, while Amiri et al. [13] present methods for organizing technologies in a multi-criteria approach. Asrol [14] provides a cross-sectional analysis of the adoption of Industry 4.0 solutions in supply chain operations.

The advancement of virtual technologies is the subject of the work of Soori et al. [15], who investigate the potential of simulation environments and digital twins. Molinaro and Orzes [16] examine the impact of digital technologies on the efficiency of the wood sector, especially in terms of raw material tracking and process optimization.

Ma et al. [17] discuss the application of Industry 4.0 technologies in energy-intensive industries, emphasizing their significance in the context of clean production and emission reduction. Zhou et al. [18] analyse the conceptual foundations of Industry 4.0, pointing out the challenges and future opportunities of technological transformation.

In summary, the literature affirms the strategic importance of Industry 4.0 technologies in modern industry. However, as numerous analyses have shown, there is still no unified approach to their prioritization and assessment, taking into account different decision-making perspectives, which justifies the application of multi-criteria methods, such as DEMATEL and PROMETHEE II, in the subsequent section of this study.

2.2. Industry 5.0 Technologies

Industry 5.0 represents a qualitative leap towards more sustainable, resilient, and human-centric production systems. Unlike the Industry 4.0 paradigm, which focuses mainly on efficiency and automation, Industry 5.0 assumes cooperation between humans and advanced technologies, emphasizing well-being, ethics, and social responsibility.

The literature emphasizes the growing importance of designing production systems that take into account both technological potential and human creativity. Valette et al. [19] conducted a systematic literature review, identifying the most important components of the IoT and cyber–physical systems (CPSs) in the context of Industry 5.0 and the role of humans as decision-makers in smart industrial environments. Verma [20] also points to the need to humanize industrial processes and move away from technocratic production models towards systems that support well-being.

Ethical issues and compliance with Trustworthy AI principles are discussed in detail by Vyhmeister and Castane [21], who analyse the conditions for safe AI implementation in accordance with social and environmental values. From a technological and application perspective, Maddikunta et al. [22] emphasize the role of edge computing, digital twins and blockchain as key tools of Industry 5.0.

The transition to a new paradigm requires not only technology but also a change in mentality. Crnobrnja et al. [23] in their work systematize the main trends and barriers of digital transformation, emphasizing the importance of responsibility and value co-creation. In a similar vein, Briken et al. [24] focus on working conditions and a participatory approach to digitalization.

Digital twins and AI are at the core of innovative implementation concepts. Tyagi et al. [25] describe the potential of such technologies in intelligent prediction and process optimization, while Raj et al. [26] extend this perspective to include edge AI applications in industry and healthcare.

The societal perspective is also included by Sun et al. [27] who describe the role of IoT and Big Data in creating connected, resilient communities—an approach that can also be adapted in the context of Industry 5.0. A strategic framework for sustainability management is presented by van Erp et al. [28] who propose new models integrating social and environmental goals.

The official vision of Industry 5.0 is outlined by the European Commission in the report by Breque et al. [29], which establishes the pillars of the new model: sustainability, resilience, and human-centricity. The sectoral approach is represented by the article by Fuqaha and Nursetiawan [30], who investigate AI and IoT in smart waste management.

A comprehensive approach to Industry 5.0 in organizational management was proposed by Piccarozzi et al. [31], pointing out the need for a holistic approach in research and implementation. In turn, Kyriakopoulos [32] draws attention to the tension between the technological efficiency of Industry 4.0 and the social dimension of Society 5.0, calling for a transformation focused on human well-being.

Finally, Turner and Oyekan [33] analyse the evolution of manufacturing systems in the light of Industry 5.0 assumptions, showing that holonic, reconfigurable, and flexible systems can enable the integration of human and technological elements in a sustainable industrial environment.

Collectively, studies emphasize that Industry 5.0 is not just a continuation of previous industrial revolutions but a qualitative leap towards more sustainable, resilient, and human-centred production systems. The integration of advanced technologies with human creativity and well-being is becoming a key factor for success in modern industry.

2.3. Transition from Industry 4.0 to 5.0—Evolutionary Perspective and Transformational Challenges

The transformation from Industry 4.0 to Industry 5.0 is gaining momentum in response to the growing demand for industrial systems that are not only efficient but also resilient, sustainable, and human-centric. The need for a strategic reorientation that considers social and ethical values as well as technological innovation is increasingly emphasized in the scientific and technical literature. The transformation from Industry 4.0 to Industry 5.0 is not merely a continuation of the previous paradigm but rather a qualitative change in which technologies support humans, rather than replace them. This evolution brings with it significant shifts in values, goals, and human–technology relationships.

Verma [34] emphasizes that Industry 5.0 represents a shift from efficiency and automation towards human-centricity, sustainable development, and cooperation between humans and intelligent systems. Xu et al. [35] provide a concise comparison of Industry 4.0 and 5.0, highlighting different assumptions regarding the role of humans, approaches to innovation, and expectations of implemented technologies. Samuels and Pelser [36] focus on the transformation within supply chain management and human resources, emphasizing the need to integrate social values in organizational strategies.

Adel and Alani [37] propose a framework of Industry 5.0 and Society 5.0, aimed at the implementation of the UN Sustainable Development Goals. Their approach demonstrates the potential of synergy between technology and social good in building resilient, inclusive cities and communities. Meanwhile, Dmitrieva et al. [38] analyse the evolution of artificial intelligence—from automation to systems that incorporate ethics and human well-being.

Rijvani et al. [39] review technological trends and implementation challenges, highlighting the key role of edge computing, blockchain, 6G, and cobots in shaping Industry 5.0 environments. Rahman and Chandan [40] argue that Industry 5.0 is a response to the shortcomings of the purely technocratic approach of Industry 4.0—through strategic innovations that enhance organizational resilience.

Crnobrnja et al. [41] provide an overview of digital transformation in VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) environments, emphasizing the growing importance of the human-centric approach. A practical example of such systems is presented by Fraga-Lamas et al. [42], who design low-cost thermal imaging systems using mist and edge computing aligning with the principle of Industry 5.0.

Shabur et al. [43] highlight that Industry 5.0 smart factories integrate advanced technologies with human creativity, fostering personalization and sustainable development.

Movahed and Nozari [43] emphasize the development of smart factories, analysing human–machine relations in the context of emerging technologies and sustainability. Similarly, Nahavandi [44] introduces Industry 5.0 as a human-centric solution supporting personalization and well-being in industrial processes.

Among the key technologies enabling the transition to Industry 5.0 is 3D printing—Pizoń and Gola [45] examine the mechanisms of human–machine interaction in the context of additive manufacturing. Grybauskas and Cárdenas-Rubio [46] demonstrate how large language models (LLMs) can support the analysis of organizational attitudes related to human-centric management.

Islam et al. [47] and Zizic et al. [48] systematize the challenges and opportunities of the transformation, emphasizing the necessity of parallel technological, organizational, and social development. Their analyses provide a roadmap for the integration of Industry 5.0 into modern industrial structures.

The presented research confirms that the transition from Industry 4.0 to 5.0 is not merely a technological shift but above all a transformation of the management paradigm, in which humans play a central role as co-creators of intelligent, sustainable, and resilient industrial systems. A proper understanding of these relationships is essential for the effective implementation of the Industry 5.0 strategies in business practice.

2.4. Application of DEMATEL and PROMETHEE II Methods in Technology Assessment

In the scientific literature, increasing attention has been devoted to multi-criteria decision analysis methods as tools supporting the assessment and selection of technologies in the context of industrial transformation. Specifically, DEMATEL and PROMETHEE II have found wide application in the analysis of complex technological systems. DEMATEL enables the identification of causal relationships among evaluation criteria, thereby revealing the internal structure of interdependencies. PROMETHEE II, on the other hand, facilitates the ranking of alternatives based on expert preferences and multiple evaluation perspectives. Together, these methods support a structured and interpretable decision-making process, which is particularly valuable for prioritizing emerging technologies within Industry 4.0 and 5.0.

In the study by Nguyen and Chu [49], a hybrid DEMATEL–ANP (Analytic Network Process)–Fuzzy PROMETHEE II approach was presented and applied to the evaluation and classification of startups. The authors demonstrated the usefulness of this combination of methods under uncertainty, indicating its potential in the analysis of modern technologies. Similarly, Zhu et al. [50] used DEMATEL to model the interdependencies among risks in construction projects, which allowed for a better understanding of the structure of risk factors in highly complex environments.

PROMETHEE II in the work of Jia and Wang [51] was used to develop a probabilistic decision model aimed at increasing precision in uncertain environments. In the study by Bertoncini et al. [52], this method was applied to compare energy requalification strategies in the design of post-coal cities, demonstrating its usefulness in assessing sustainable development scenarios.

In infrastructure applications, Multikanga et al. [53] compared water loss management strategies while Mahad et al. [54] combined PROMETHEE II with DEA (Data Envelopment Analysis) to evaluate the performance of financial institutions. Other studies, such as that of Sahraei et al. [55], demonstrated the application of hybrid GIS (Geographic Information System)-MCDA methods in flood hazard assessment.

Integrated methods such as GRA (Grey Relational Analysis)–DEMATEL–PROMETHEE II have been successfully applied to the multi-criteria evaluation of renewable energy alternatives (Li et al. [56]), and Wątróbski [57] proposed a time-based extension of PROMETHEE II for managing alternative fuels. The innovativeness of MCDA methods is further supported by the research of Bongo et al. [58], which demonstrated their effectiveness in assessing the mental workload of flight controllers, and the work of Chatzifoti et al. [59], where DEMATEL was used to analyse the success factors in knowledge management implementation.

The selection of DEMATEL and PROMETHEE II for a hybrid methodology leverages their complementary strengths. DEMATEL effectively uncovers causal relationships and feedback loops among transformation areas or evaluation criteria—an aspect that classical methods such as AHP or TOPSIS do not capture. PROMETHEE II, on the other hand, provides a robust ranking mechanism based on preference functions and pairwise comparisons, without requiring consistency checks or hierarchical structures. Compared to ANP, the combined DEMATEL–PROMETHEE II approach is more interpretable and less data-intensive, making it well-suited for expert-based strategic evaluations involving interdependencies and multiple perspectives. Furthermore, both methods have strong theoretical foundations and have been applied successfully in technology prioritization and industrial transformation domains.

Although the literature shows a wide range of applications of the discussed methods, there remains a lack of models that combine the hierarchical assessment of technologies with the analysis of their impact from multiple perspectives: economic, organizational, and technological.

This study addresses the identified gap by offering an integrated approach to prioritizing and classifying technologies with respect to their impact in three decision-making perspectives, which forms the basis for answering questions Q1–Q3.

3. Areas and Modern Technologies of Industry 4.0 and 5.0

The proposed technology assessment model considers the affiliation of technologies with two complementary paradigms: Industry 4.0 and Industry 5.0.

Table 1. Industry 4.0 and 5.0 areas and technologies.

Industrial Era	Industry Area	Industry Area Notation	Modern Technology	Modern Technology Notation	Reference
4.0	Automation and Connectivity	K1	Internet of Things (IoT)	L11	[60]
			Cybersecurity	L12	[61]
			Wireless Communication/5G	L13	[62]
	Data-Driven Optimization	K2	Artificial Intelligence (AI) and Machine Learning (ML)	L21	[63]
			Big Data and Data Analytics	L22	[64]
			IT Support Systems	L23	[65]
	Agility and Flexibility	K3	Cloud Computing	L31	[66]
			3D Printing (Additive Manufacturing)	L32	[67]
			Robotics and Automation/Autonomous Production Systems	L33	[68]
			Augmented and Virtual Reality (AR/VR)	L34	[69]
5.0	Human-Centricity	K4	Human-Centered Robotics (New-Generation Cobots)	L41	[70]
			Hyper-Personalized Production	L42	[71]
			Neuromorphic Computers and Brain–Machine Interfaces	L43	[72]
			Ethical AI and Social Responsibility	L44	[73]
			AI Combined with Human Intelligence	L45	[74]
	Sustainability	K5	Sustainable Production and Circular Economy	L51	[75]
			Renewable Energy and Smart Grids	L52	[76]
			Autonomous Vehicles and Drones	L53	[77]
	Resilience	K6	Biotechnology and Nanotechnology	L61	[78]
			Quantum Computing	L62	[79]
			New Materials	L63	[80]
			Digital Twin	L64	[81]

presents six key areas of industrial transformation (K1–K6) and the 22 modern technologies (L11–L64) assigned to them. Areas K1–K3 refer to typical features of Industry 4.0, such as automation, digitalization, and system integration, while K4–K6 reflect the assumptions of Industry 5.0, including human-centricity, sustainable development, and system resilience. The selection of technologies was based on a literature review and expert consultations. This classification serves as the foundation for the subsequent cause–effect and ranking analyses in this study.

To facilitate analysis and ensure interpretative clarity, a marking system is used that distinguishes the affiliation of technologies to individual areas. Transformation areas are marked with the symbol K (K1–K6), with K1–K3 representing Industry 4.0 and K4–K6 corresponding to Industry 5.0. In turn, modern technologies are coded as L11–L34 (technologies assigned to Industry 4.0) and L41–L64 (technologies characteristic of Industry 5.0). This structured labelling approach allows for consistent identification of components in the further part of the model, including in the DEMATEL influence matrices and in rankings generated using the PROMETHEE II method.

Automation and Connectivity (K1). The Automation and Connectivity area focuses on the integration of physical and digital systems to enhance production processes and ensure seamless data exchange improvement. Key technologies in this area enable the automation of operational tasks and remote control, fostering the development of intelligent, flexible, and connected industrial environments. The foundation here is reliable connectivity and securing the digital infrastructure.

Data-Driven Optimization (K2). This area focuses on using data to continuously improve processes, make decisions and increase operational efficiency. Through technologies such as artificial intelligence, data analytics, or advanced IT systems, companies can identify patterns, predict events, and optimize resources in real time. This approach is the basis for intelligent management in Industry 4.0.

Agility and Flexibility (K3). This area refers to the ability of companies to quickly adapt to changing market conditions, individual customer needs and operational disruptions. Thanks to flexible technologies such as cloud computing, 3D printing, or automated production systems, companies can produce differentiated products in a short time and in a more personalized manner, without sacrificing efficiency.

Human-Centricity (K4). This area of Industry 5.0 emphasizes the role of humans in an automated work environment. It is about creating technologies that support employee well-being, promote human–machine collaboration (e.g., through cobots), and enhance personalization and user comfort. Human-Centricity promotes an ethical approach to innovation and a balance between productivity and social values.

Sustainability (K5). This area focuses on the integration of sustainable practices in industrial processes. The aim is to minimize the negative impact of production activities on the environment by using technologies that support the circular economy, renewable energy usage, and the optimization of resource consumption. Sustainability in Industry 5.0 combines technological innovation with environmental responsibility.

Resilience (K6). Resilience refers to the ability of industrial systems to adapt, recover, and continue to operate despite disruptions—such as economic crises, climate change, or supply chain disruptions. In Industry 5.0, this means designing resilient technological and organizational structures that are able to respond swiftly, and learn from experience, while maintaining production continuity and quality.

The use of technologies characteristic of Industry 4.0 and 5.0 plays a crucial role in assessing the readiness of enterprises for digital transformation. These technologies not only determine the level of technical advancement of organizations but also reflect their capacity to adapt, integrate humans in production processes, and respond to changes in the environment.

Automation and Connectivity solutions, such as the IoT or 5G, enable the assessment of the level of automation and connectivity of systems that are the foundation of digital operations. Data-Driven Optimization technologies, such as AI and Big Data, are used to analyse processes and identify optimization potential, which reflects organizations’ maturity in knowledge and data management.

In turn, technologies from the Agility and Flexibility area, such as 3D printing or AR/VR, demonstrate a company’s ability to quickly respond to market needs and personalize production. The Human-Centricity area enables the assessment of whether the company considers the role of humans as active participants in processes, which is a key principle of the Industry 5.0 paradigm.

The Sustainability and Resilience dimensions enable the assessment of sustainable development and the ability of an organization to endure and adapt under crisis conditions. Such a comprehensive technological approach is the foundation for creating a framework for assessing the readiness of enterprises and indicates in which direction further transformation should be planned.

Internet of Things (IoT) (L11). The Internet of Things enables connectivity between machines, sensors, and systems in real time, which allows the collection and analysis of data from the entire production chain. The IoT increases operational transparency, automates processes, and supports data-driven decision-making, providing the foundation for smart factories.

Cybersecurity (L12). Cybersecurity is a set of strategies and tools designed to protect industrial systems from digital threats. In the context of Industry 4.0, it plays a key role in ensuring the data integrity and the stability of connected networks, minimizing the risk of attacks, operational disruptions, or loss of trust.

Wireless Communication/5G (L13). Wireless communication based on 5G technology provides ultra-fast data transmission, low latency and connection stability. This enables the implementation of advanced industrial applications such as autonomous robots, remote control, and augmented reality, improving communication between devices in real time.

Artificial Intelligence (AI) and Machine Learning (ML) (L21). Artificial intelligence and machine learning enable automatic pattern recognition in data and decision-making based on data—without the need to explicitly program every rule. In industry, they contribute to process optimization, predictive maintenance, and intelligent production control.

Big Data and Data Analytics (L22). Big Data and advanced data analytics allow for processing huge amounts of information from various sources. This allows companies to discover hidden patterns, identify inefficiencies, and better plan and predict operational activities.

IT Support Systems (L23). IT support systems include tools for resource management, production planning (ERP, MES), and performance and quality monitoring. They enable an integrated approach to enterprise management, supporting automation, and data-driven decision-making.

Cloud Computing (L31). Cloud computing enables flexible access to computing power and IT resources, without the need for on-premises infrastructure. In the context of industry, it increases scalability, facilitates real-time collaboration, and supports the rapid implementation of innovations.

3D Printing (Additive Manufacturing) (L32). Three-dimensional printing enables fast and flexible production of elements without the use of traditional tools and moulds. It facilitates product customization, shortens time to market, and allows for small-scale production with minimal material losses.

Robotics and Automation/Autonomous Production Systems (L33). Robotic and autonomous production systems facilitate independent execution of complex tasks without human intervention. They increase efficiency, reduce errors, and enable quick adaptation to dynamic production conditions.

Augmented and Virtual Reality (AR/VR) (L34). Augmented and virtual reality technologies support design, training, and remote management. They enable interactive process visualization and simulation of operations in secure, digital environments, increasing the flexibility and precision of operations.

Human-Centered Robotics (New-Generation Cobots) (L41). New-generation cobots are designed to work safely with humans in the production space. They facilitate the integration of human and machine competencies, supporting workers physically and cognitively, while increasing operational efficiency and safety.

Hyper-Personalized Production (L42). Hyper-personalized production enables the creation of highly personalized products tailored to the individual customer needs. It is based on data, flexible technologies, and advanced automation, responding to increasing customer expectations and supporting the on-demand production model.

Neuromorphic Computers and Brain–Machine Interfaces (L43). Neuromorphic computing systems and brain–machine interfaces facilitate direct communication between digital systems and the human nervous system. They support the development of intelligent decision support systems by combining technology with human biological functioning.

Ethical AI and Social Responsibility (L44). Ethical AI refers to an approach grounded in transparency, accountability, and adherence to societal norms in automation and decision-making processes. It promotes technologies that respect human rights, privacy, and principles of social justice.

AI Combined with Human Intelligence (L45). The integration of artificial intelligence with human cognitive capabilities leads to decision support systems where a machine augments human cognitive abilities. This synergy allows for more accurate, balanced, and contextually embedded decisions in complex industrial environments.

Sustainable Production and Circular Economy (L51). Sustainable Production and Circular Economy emphasizes minimizing resource consumption and reducing waste through reusing, recycling, and regenerating materials. These technologies support business models oriented towards long-term value, ecology, and energy efficiency.

Renewable Energy and Smart Grids (L52). The integration of renewable energy sources with smart grids enables dynamic management of energy production and distribution. These technologies increase energy independence, reduce greenhouse gas emissions, and integrate prosumers into the energy system.

Autonomous Vehicles and Drones (L53). Autonomous vehicles and drones support sustainable transport and logistics by optimizing routes, reducing fuel consumption and emissions. In the industrial sector, they are employed for infrastructure inspections, autonomous deliveries, and environmental monitoring in hard-to-reach places.

Biotechnology and Nanotechnology (L61). Biotechnology and nanotechnology play a pivotal role in enhancing the resilience of industrial, healthcare, and environmental systems. They enable the creation of advanced materials and intelligent pharmaceuticals, as well as solutions supporting the preservation of natural resources and public health.

Quantum Computing (L62). Quantum computing offers transformative computational capabilities, accelerating solutions to computationally complex problems. Their use can significantly strengthen an organization’s resilience to disruptions in the area of security, logistics, or risk modelling.

New Materials (L63). Advanced materials such as composites, self-healing materials, or ultra-strong alloys increase the durability of products and infrastructures. They support the design of systems resistant to extreme environmental conditions and dynamic changes in the market environment.

Digital Twin (L64). Digital twin technology enables the creation of virtual replicas of physical objects, enabling their ongoing monitoring, analysis, and optimization. It increases operational resilience by failure prediction, scenario simulation, and real-time decision support.

Table 2. Perspectives for assessing industrial transformation.

Perspectives for Assessing Industrial Transformation	Designations of Perspectives for Assessing Industrial Transformation	Description
Economic	U1	The assessment of economic aspects centres on the analysis of the costs and benefits associated with implementing specific technologies in an enterprise, taking into account both investment outlays and long-term financial effects. In the context of the transformation to Industry 5.0, it is crucial not only to estimate the expenses related to technology deployment, maintenance, and integration technologies (e.g., IoT infrastructure, AI licenses or AR/VR systems) but also their impact on process efficiency, waste reduction, resource consumption optimization, or reductions in operating costs. Factors such as return on investment (ROI), total cost of ownership (TCO) and the potential of a given technology to generate new revenue streams are also analysed, e.g., by personalizing products, entry into new markets, or modelling digital services. From the perspective of Industry 5.0, the economic value of sustainable development is also playing an increasingly important role, e.g., through technologies that enable efficient production while reducing the impact on the environment and improving employee well-being. The economic aspect therefore considers not only immediate financial outcomes and long-term transformational potential.
Organizational (Process-Oriented)	U2	Organizational aspects refer to the influence of emerging technologies on the structure, culture, and operational processes of enterprises. In the context of Industry 4.0 and 5.0, an organization’s capacity to adapt and integrate new technologies into existing work systems is of particular importance. Assessment includes, among other factors, the readiness of managerial staff to implement changes, the level of employee competence, the maturity of processes, and the degree of formalization of implementation procedures. Also key are the mechanisms facilitating interdepartmental collaboration (e.g., IT, production, logistics), flexibility in designing information flows, and opportunities for continuous improvement. Technologies such as AI, AR/VR, or digital twins often necessitate the reorganization of roles, the training of teams, and the implementation of new communication standards. From the perspective of Industry 5.0, the human-centric approach becomes increasingly relevant, i.e., creating work environments that support the creativity, autonomy, and safety of employees in interaction with technology. Finally, an organization’s readiness to manage change and its process resistance to disruptions resulting from the integration of innovative solutions are also assessed.
Technological	U3	The assessment of technological aspects in the context of the transformation from Industry 4.0 to 5.0 focuses on the maturity, interoperability, and the integrability of specific technologies with existing production and IT systems. The key here is to determine the level of technological advancement (Technology Readiness Level—TRL), infrastructure requirements, technical feasibility, and the ability to scale implementation. Technologies are assessed in terms of their compliance with industrial standards and ease of deployment in various production environments (including cyber–physical systems), as well as their potential for further development towards sustainable, autonomous, and intelligent solutions. Compatibility with other technologies is also a critical factor—for example, the use of artificial intelligence in the analysis of data from IoT or the use of digital twins to monitor performance of advanced materials. In the context of Industry 5.0, the humanization of technologies also plays an important role, i.e., their adaptation to safe and intuitive cooperation with humans (e.g., cobots or brain–computer interfaces). Technology assessment allows us to identify not only the current capabilities of a given technology but also its potential for adaptation to the increasingly dynamic and complex production ecosystem of the future.

proposes three perspectives for evaluating industrial transformation in which Industry 4.0 and Industry 5.0 technologies will be assessed: economic, process, and technological.

4. Solution Methodology

The integration of Industry 4.0 and 5.0 technologies into transformation readiness assessment process becomes particularly effective when combined with multi-criteria decision-making (MCDM) methods such as DEMATEL and PROMETHEE II.

DEMATEL [82] enables the identification of cause–effect relationships between technologies within specific domains (e.g., IoT, AI and robotics), allowing for the classification of technologies that act as key drivers in the transformation process. This facilitates determining which technologies have the greatest impact on others and should be implemented first.

In turn, PROMETHEE II [83] facilitates the ranking of technologies or areas of enterprise readiness based on specific criteria (e.g., implementation costs, level of integration with existing infrastructure, impact on the environment or employee competences). This method provides a comprehensive and structured assessment of alternatives, supporting strategic transformation decisions.

Combining these methods allows not only for assessing the current state but also for indicating optimal paths for technology implementation and enterprise development planning in line with the assumptions of Industry 5.0—considering technological, economic, and organizational perspectives.

To enhance clarity and transparency, Figure 1 presents a conceptual overview of the integrated DEMATEL–PROMETHEE II framework. The diagram maps the logical sequence of analytical steps, beginning from the construction of influence matrices and the computation of centrality and causality indices via the DEMATEL method. Based on these outputs, two separate rankings are derived: (1) the six transformation areas (K1–K6) and (2) the 22 modern technologies (L11–L64) representing the Industry 4.0 and 5.0 paradigms.

These results are then integrated into the PROMETHEE II method, where preference function pairwise comparisons evaluate technologies across economic (U1), organizational (U2), and technological (U3) decision-making perspectives. This process yields a third ranking, reflecting the relative importance of the assessment perspectives in the context of industrial transformation.

4.1. DEMATEL Method

The DEMATEL method begins with n experts evaluating the direct impact of each of the k criteria on the others, using a scale from 0 (no impact) to 4 (very strong impact). This produces n preliminary matrices:

$$Z^{\left(m\right)}=\left[x_{ij}^{\left(m\right)} \right], m=1,2, \dots , n$$

(1)

where $z_{ij}^{\left(m\right)}$ denotes expert m’s assessment of the influence of factor i on j. Then, the aggregated influence matrix is determined:

$$A={\displaystyle \frac{1}{n}}\sum _{m=1}^n{Z}^{\left(m\right)}$$

(2)

After normalizing the matrix A, the matrix N is obtained:

$$N={\displaystyle \frac{A}{max({\sum }_i{a}_{ij}, {\sum }_j{a}_{ij})}}$$

(3)

On this basis, the total impact matrix is determined:

$$T={N(I-N)}^{-1}$$

(4)

where $I$ is the identity matrix. From the matrix $T$, the total influence $D_i$ and received influence $R_i$ are calculated:

$$D_i={\sum }_{j=1}^k{t}_{ij} , R_i={\sum }_{j=1}^k{t}_{ji} ,$$

(5)

We also have centrality ${(D}_i+R_i)$ and causality indices ${(D}_i-R_i)$. The obtained values serve as the input data to the PROMETHEE II method. The weights of the criteria are calculated:

$$W_i={\displaystyle \frac{D_i+R_i}{{\sum }_{i=1}^k(D_i+R_i)}}$$

(6)

4.2. PROMETHEE II Method

Based on the weights obtained from DEMATEL, the PROMETHEE II method is used to rank the alternatives. For each pair of alternatives $(a,b)$, the difference in preferences as $d_{a,b}^{\left(c\right)}$ with respect to the criterion $c$ is calculated, which is then transformed by the preference function $P_c\left(d\right)$. A preference index with weights is determined:

$$\pi \left(a,b\right)=\sum _{c=1}^k{w}_c{P}_c\left(d_{a,b}^{\left(c\right)}\right)$$

(7)

The flows are calculated. Positive flow:

$${\varphi }^{+}\left(a\right)={\displaystyle \frac{1}{k-1}}\sum _{b\ne a}\pi (a,b)$$

(8)

Negative flow:

$${\varphi }^{-}\left(a\right)={\displaystyle \frac{1}{k-1}}\sum _{b\ne a}\pi (b,a)$$

(9)

Net flow:

$$\varphi \left(a\right)={\varphi }^{+}\left(a\right)- {\varphi }^{-}\left(a\right)$$

(10)

The alternatives are ranked in descending order of value.

5. Results and Discussion

The application of the proposed multi-criteria approach to the assessment of technologies situated at the interface of two paradigms, Industry 4.0 and Industry 5.0, is presented below. The analysis covers six transformation areas (K1–K6), identified through a comprehensive literature review and expert consultations, and 22 modern technologies (L11–L64) related to them. The first three areas (K1–K3) correspond to the key features of Industry 4.0, such as digital automation, data-driven optimization, and intelligent system connections. The remaining areas (K4–K6) reflect the fundamental principles of Industry 5.0—human-centricity, sustainable development, and system resilience. The analysis directly addresses Q1 by identifying the technologies considered by experts to be the most strategic in the context of the transformation towards Industry 5.0. The assessment process involved eleven domain experts, invited from manufacturing firms, digital technology consultancies, and academic institutions in industrial innovation. The panel consisted of five experts specializing in production systems and six in digital transformation and Industry 4.0/5.0 strategies. All participants had a minimum of five years of relevant professional experience and held at least a doctoral degree (PhD). Each expert self-declared a high level of subject-matter familiarity. To minimize bias, all assessments were collected individually and anonymously. The experts assessed the strength of direct influence between individual areas of transformation, using a scale from 0 to 4. To ensure the reliability of the assessments, the Kendall coefficient of concordance $\left(W\right)$ was calculated, which is a frequently used measure of the degree of agreement between independent assessments [84].

$$W={\displaystyle \frac{12S}{n^2(k^3-k)}}$$

(11)

The value $S$ represents the sum of the squares of the deviations of the sums of the item ratings from their mean value. An analysis of inter-expert consistency, conducted on the basis of data obtained from 11 experts assessing 22 technologies, yielded a coefficient value of $W=0.60$, at $S=\mathrm{76,507}$. The obtained result indicates a satisfactory level of compliance in the assessment of cause–effect relationships between the analysed technologies. In accordance with the DEMATEL methodology, individual initial impact matrices (1) were developed, which were then transformed (2) into an aggregated direct impact matrix, A (Appendix A,

Table A1. Aggregated direct impact matrix A.

	L11	L12	L13	L21	L22	L23	L31	L32	L33	L34	L41	L42	L43	L44	L45	L51	L52	L53	L61	L62	L63	L64
L11	0	3.750	1.250	1.083	2.917	3.167	1.917	2.083	2.833	1.333	1.250	3.750	1.250	1.917	2.083	2.083	3.667	1.167	2.833	1.250	3.667	2.833
L12	3.917	0	1.083	1.333	3.833	3.750	2.083	2.000	3.917	1.083	1.333	3.917	1.083	1.667	2.083	2.167	3.833	1.333	3.417	1.333	3.917	2.917
L13	1.083	1.333	0	2.583	1.917	1.667	1.917	2.667	1.917	0.917	2.250	1.417	2.250	1.500	1.417	1.833	1.583	0.917	1.250	0.750	0.833	1.583
L21	1.250	1.250	2.500	0	1.667	2.083	2.250	1.583	1.417	2.250	2.333	0.917	2.417	1.833	1.417	2.250	1.417	1.583	2.083	2.250	1.417	1.750
L22	2.833	3.750	1.167	0.917	0	3.083	1.750	2.083	2.333	1.250	1.250	1.917	1.083	1.750	2.000	2.167	2.500	1.083	3.083	1.000	2.250	3.000
L23	3.167	3.417	1.083	1.083	2.917	0	2.083	1.583	2.250	0.833	1.167	3.250	0.833	2.333	2.417	2.083	1.667	1.167	2.333	1.167	2.667	2.833
L31	1.750	1.917	1.167	1.917	2.000	2.167	0	2.167	1.750	1.167	1.833	1.833	1.750	2.000	1.667	1.917	1.833	1.667	2.083	1.250	2.083	2.083
L32	1.667	1.417	1.500	1.583	2.167	1.917	1.667	0	1.833	1.583	1.750	1.583	1.583	1.750	1.667	2.000	2.083	2.000	1.667	1.667	1.667	1.833
L33	2.917	3.917	1.083	0.833	2.500	2.583	1.583	1.750	0	0.833	1.083	1.750	1.167	1.417	2.167	1.750	1.667	1.000	2.583	1.250	2.417	2.500
L34	1.250	1.333	1.583	2.250	1.417	1.167	2.250	2.583	0.917	0	2.250	0.833	2.583	1.833	1.583	1.667	1.167	2.583	1.250	1.083	0.833	1.500
L41	1.083	1.250	1.500	2.333	2.583	1.667	1.833	1.583	1.167	2.667	0	1.167	2.083	1.583	2.333	2.583	1.083	1.500	0.917	2.667	1.583	1.417
L42	3.917	3.667	1.250	1.250	2.667	2.833	1.750	1.417	3.250	1.167	1.167	0	1.083	1.750	1.417	1.750	3.667	1.250	2.500	1.250	3.583	2.417
L43	1.250	1.083	1.583	2.500	1.583	1.583	2.500	1.583	0.917	1.417	1.083	0.917	0	0.917	1.500	1.083	1.083	1.083	2.417	0.917	1.167	1.583
L44	1.417	1.917	0.917	1.500	2.000	1.667	2.167	1.833	2.000	1.083	1.417	2.167	1.083	0	1.833	2.167	1.667	1.667	1.917	1.083	2.167	2.083
L45	1.667	1.750	1.833	1.917	1.750	2.083	1.500	2.000	1.917	1.417	1.833	1.750	1.833	1.833	0	1.583	2.000	1.583	1.667	1.583	1.583	1.667
L51	1.500	1.583	1.500	1.417	1.583	1.750	1.583	1.917	1.250	2.417	2.583	1.500	1.083	1.583	2.167	0	1.500	1.667	1.917	1.667	1.833	2.083
L52	3.333	3.167	1.250	1.083	3.000	2.833	1.667	1.750	2.417	1.167	1.250	3.250	1.250	1.667	1.417	2.167	0	1.167	2.333	1.000	3.583	3.000
L53	1.083	1.083	0.917	2.083	2.250	2.583	2.083	1.917	2.333	2.667	2.583	1.583	2.500	1.167	2.583	1.417	1.583	0	2.083	2.583	1.167	1.750
L61	2.333	3.750	1.083	1.167	2.917	2.583	1.583	2.167	2.667	0.917	1.083	2.500	1.083	2.167	1.917	1.917	1.417	1.167	0	1.250	2.333	2.333
L62	1.333	1.083	2.583	2.667	1.667	1.917	1.500	1.833	1.167	2.250	1.583	1.167	1.417	1.917	1.833	1.500	1.083	2.333	1.250	0	1.167	1.750
L63	3.583	3.250	1.250	1.083	3.250	3.167	1.750	1.583	2.333	1.167	1.250	3.417	1.250	1.417	2.167	1.667	3.333	1.083	2.250	1.250	0	3.583
L64	2.750	3.083	1.167	0.917	2.417	2.583	2.000	1.667	2.333	1.083	1.167	3.250	1.167	0.833	1.917	2.000	2.917	1.083	2.667	1.167	1.417	0

), normalized (3) to the N matrix (Appendix A,

Table A2. Normalized impact matrix $N$.

	L11	L12	L13	L21	L22	L23	L31	L32	L33	L34	L41	L42	L43	L44	L45	L51	L52	L53	L61	L62	L63	L64
L11	0	7.212	2.404	2.083	5.61	6.091	3.687	4.006	5.448	2.564	2.404	7.212	2.404	3.687	4.006	4.006	7.052	2.244	5.448	2.404	7.052	5.448
L12	7.533	0	2.083	2.564	7.371	7.212	4.006	3.846	7.533	2.083	2.564	7.533	2.083	3.206	4.006	4.167	7.371	2.564	6.571	2.564	7.533	5.61
L13	2.083	2.564	0	4.967	3.687	3.206	3.687	5.129	3.687	1.763	4.327	2.725	4.327	2.885	2.725	3.525	3.044	1.763	2.404	1.442	1.602	3.044
L21	2.404	2.404	4.808	0	3.206	4.006	4.327	3.044	2.725	4.327	4.487	1.763	4.648	3.525	2.725	4.327	2.725	3.044	4.006	4.327	2.725	3.365
L22	5.448	7.212	2.244	1.763	0	5.929	3.365	4.006	4.487	2.404	2.404	3.687	2.083	3.365	3.846	4.167	4.808	2.083	5.929	1.923	4.327	5.769
L23	6.091	6.571	2.083	2.083	5.61	0	4.006	3.044	4.327	1.602	2.244	6.25	1.602	4.487	4.648	4.006	3.206	2.244	4.487	2.244	5.129	5.448
L31	3.365	3.687	2.244	3.687	3.846	4.167	0	4.167	3.365	2.244	3.525	3.525	3.365	3.846	3.206	3.687	3.525	3.206	4.006	2.404	4.006	4.006
L32	3.206	2.725	2.885	3.044	4.167	3.687	3.206	0	3.525	3.044	3.365	3.044	3.044	3.365	3.206	3.846	4.006	3.846	3.206	3.206	3.206	3.525
L33	5.61	7.533	2.083	1.602	4.808	4.967	3.044	3.365	0	1.602	2.083	3.365	2.244	2.725	4.167	3.365	3.206	1.923	4.967	2.404	4.648	4.808
L34	2.404	2.564	3.044	4.327	2.725	2.244	4.327	4.967	1.763	0	4.327	1.602	4.967	3.525	3.044	3.206	2.244	4.967	2.404	2.083	1.602	2.885
L41	2.083	2.404	2.885	4.487	4.967	3.206	3.525	3.044	2.244	5.129	0	2.244	4.006	3.044	4.487	4.967	2.083	2.885	1.763	5.129	3.044	2.725
L42	7.533	7.052	2.404	2.404	5.129	5.448	3.365	2.725	6.25	2.244	2.244	0	2.083	3.365	2.725	3.365	7.052	2.404	4.808	2.404	6.891	4.648
L43	2.404	2.083	3.044	4.808	3.044	3.044	4.808	3.044	1.763	2.725	2.083	1.763	0	1.763	2.885	2.083	2.083	2.083	4.648	1.763	2.244	3.044
L44	2.725	3.687	1.763	2.885	3.846	3.206	4.167	3.525	3.846	2.083	2.725	4.167	2.083	0	3.525	4.167	3.206	3.206	3.687	2.083	4.167	4.006
L45	3.206	3.365	3.525	3.687	3.365	4.006	2.885	3.846	3.687	2.725	3.525	3.365	3.525	3.525	0	3.044	3.846	3.044	3.206	3.044	3.044	3.206
L51	2.885	3.044	2.885	2.725	3.044	3.365	3.044	3.687	2.404	4.648	4.967	2.885	2.083	3.044	4.167	0	2.885	3.206	3.687	3.206	3.525	4.006
L52	6.41	6.091	2.404	2.083	5.769	5.448	3.206	3.365	4.648	2.244	2.404	6.25	2.404	3.206	2.725	4.167	0	2.244	4.487	1.923	6.891	5.769
L53	2.083	2.083	1.763	4.006	4.327	4.967	4.006	3.687	4.487	5.129	4.967	3.044	4.808	2.244	4.967	2.725	3.044	0	4.006	4.967	2.244	3.365
L61	4.487	7.212	2.083	2.244	5.61	4.967	3.044	4.167	5.129	1.763	2.083	4.808	2.083	4.167	3.687	3.687	2.725	2.244	0	2.404	4.487	4.487
L62	2.564	2.083	4.967	5.129	3.206	3.687	2.885	3.525	2.244	4.327	3.044	2.244	2.725	3.687	3.525	2.885	2.083	4.487	2.404	0	2.244	3.365
L63	6.891	6.25	2.404	2.083	6.25	6.091	3.365	3.044	4.487	2.244	2.404	6.571	2.404	2.725	4.167	3.206	6.41	2.083	4.327	2.404	0	6.891
L64	5.289	5.929	2.244	1.763	4.648	4.967	3.846	3.206	4.487	2.083	2.244	6.25	2.244	1.602	3.687	3.846	5.61	2.083	5.129	2.244	2.725	0

), and then transformed (4) to the total impact matrix T (Appendix A,

Table A3. Total impact matrix $T$.

	L11	L12	L13	L21	L22	L23	L31	L32	L33	L34	L41	L42	L43	L44	L45	L51	L52	L53	L61	L62	L63	L64
L11	1.800	2.611	1.267	1.337	2.391	2.434	1.775	1.816	2.182	1.310	1.400	2.417	1.324	1.640	1.839	1.850	2.338	1.284	2.216	1.281	2.375	2.295
L12	2.650	2.106	1.319	1.463	2.699	2.687	1.914	1.914	2.503	1.349	1.504	2.585	1.378	1.701	1.958	1.982	2.499	1.394	2.457	1.379	2.557	2.456
L13	1.361	1.498	0.711	1.265	1.577	1.530	1.320	1.458	1.437	0.912	1.221	1.382	1.175	1.144	1.241	1.330	1.379	0.902	1.358	0.865	1.269	1.460
L21	1.509	1.613	1.256	0.893	1.666	1.735	1.490	1.381	1.463	1.240	1.333	1.416	1.297	1.304	1.356	1.512	1.460	1.113	1.623	1.216	1.484	1.618
L22	2.086	2.371	1.128	1.173	1.633	2.192	1.576	1.649	1.890	1.170	1.264	1.885	1.165	1.458	1.653	1.691	1.923	1.143	2.055	1.113	1.912	2.104
L23	2.155	2.324	1.118	1.207	2.170	1.641	1.640	1.564	1.886	1.101	1.254	2.124	1.125	1.566	1.732	1.681	1.795	1.162	1.932	1.148	1.996	2.083
L31	1.675	1.811	1.030	1.252	1.788	1.816	1.105	1.512	1.587	1.060	1.258	1.650	1.187	1.364	1.437	1.490	1.605	1.144	1.683	1.059	1.674	1.739
L32	1.593	1.652	1.058	1.159	1.750	1.706	1.368	1.067	1.541	1.104	1.209	1.540	1.125	1.276	1.389	1.454	1.587	1.172	1.548	1.100	1.537	1.631
L33	1.984	2.273	1.046	1.083	1.969	1.985	1.451	1.493	1.353	1.024	1.154	1.740	1.107	1.312	1.587	1.517	1.671	1.056	1.857	1.088	1.829	1.900
L34	1.365	1.468	1.007	1.219	1.477	1.430	1.382	1.446	1.248	0.748	1.231	1.263	1.247	1.200	1.272	1.293	1.292	1.211	1.347	0.932	1.249	1.432
L41	1.436	1.561	1.056	1.294	1.775	1.615	1.378	1.343	1.373	1.295	0.877	1.410	1.210	1.228	1.483	1.533	1.364	1.077	1.375	1.267	1.469	1.515
L42	2.406	2.496	1.209	1.300	2.248	2.278	1.667	1.620	2.165	1.221	1.319	1.651	1.234	1.539	1.643	1.711	2.249	1.236	2.068	1.222	2.271	2.128
L43	1.268	1.326	0.931	1.167	1.388	1.388	1.323	1.166	1.147	0.919	0.925	1.180	0.681	0.952	1.152	1.087	1.175	0.856	1.448	0.815	1.208	1.339
L44	1.553	1.742	0.940	1.125	1.716	1.658	1.445	1.394	1.571	0.999	1.135	1.645	1.019	0.942	1.409	1.475	1.516	1.100	1.589	0.985	1.627	1.672
L45	1.599	1.716	1.123	1.224	1.684	1.740	1.344	1.441	1.563	1.074	1.226	1.576	1.174	1.296	1.081	1.385	1.578	1.098	1.553	1.087	1.529	1.606
L51	1.533	1.647	1.047	1.121	1.622	1.646	1.335	1.406	1.411	1.245	1.348	1.499	1.027	1.231	1.461	1.067	1.459	1.104	1.561	1.090	1.536	1.647
L52	2.247	2.346	1.179	1.239	2.246	2.218	1.611	1.635	1.965	1.192	1.302	2.183	1.231	1.484	1.599	1.740	1.536	1.191	1.986	1.148	2.212	2.174
L53	1.565	1.676	1.018	1.325	1.851	1.908	1.521	1.499	1.699	1.364	1.424	1.605	1.360	1.243	1.630	1.424	1.562	0.867	1.700	1.327	1.517	1.696
L61	1.927	2.291	1.074	1.175	2.088	2.031	1.490	1.605	1.884	1.070	1.189	1.909	1.123	1.480	1.581	1.587	1.668	1.117	1.428	1.118	1.857	1.916
L62	1.430	1.481	1.216	1.319	1.566	1.610	1.281	1.349	1.337	1.184	1.140	1.370	1.063	1.251	1.352	1.299	1.320	1.192	1.386	0.748	1.348	1.523
L63	2.341	2.416	1.208	1.270	2.340	2.328	1.664	1.645	1.998	1.219	1.331	2.261	1.261	1.476	1.769	1.693	2.187	1.205	2.019	1.219	1.612	2.322
L64	1.983	2.157	1.077	1.116	1.978	2.008	1.544	1.498	1.805	1.086	1.188	2.022	1.125	1.228	1.558	1.583	1.912	1.087	1.894	1.088	1.683	1.463

). As part of the analysis of the resulting interaction structure, a quantitative evaluation of the roles of individual components within the industrial transformation system was performed. Based on the total impact matrix, two indicators (5) were determined for each area (K1–K6) and each technology (L11–L64): centrality ${(D}_i+R_i)$, reflecting the hierarchy of key factors, and causality ${(D}_i-R_i)$, indicating the direction of the dominant influence. These values are presented in

Table 3. Indicators ${(D}_i+R_i)$ and ${(D}_i-R_i)$.

Industry Area Notation	${\mathit{D}}_{\mathit{i}}$	${\mathit{R}}_{\mathit{i}}$	${\mathit{D}}_{\mathit{i}}+{\mathit{R}}_{\mathit{i}}$	${\mathit{D}}_{\mathit{i}}-{\mathit{R}}_{\mathit{i}}$	Modern Technology Notation	${\mathit{D}}_{\mathit{i}}$	${\mathit{R}}_{\mathit{i}}$	${\mathit{D}}_{\mathit{i}}+{\mathit{R}}_{\mathit{i}}$	${\mathit{D}}_{\mathit{i}}-{\mathit{R}}_{\mathit{i}}$
K1	1.038	0.972	2.010	0.066	L11	4.118	3.947	8.065	0.172
					L12	4.445	4.258	8.704	0.187
					L13	2.779	2.402	5.181	0.378
K2	0.947	1.007	1.954	−0.060	L21	3.098	2.673	5.770	0.425
					L22	3.624	4.162	7.786	−0.539
					L23	3.640	4.158	7.799	−0.518
K3	0.851	0.878	1.729	−0.028	L31	3.193	3.262	6.455	−0.070
					L32	3.056	3.290	6.347	−0.234
					L33	3.348	3.701	7.048	−0.353
					L34	2.776	2.489	5.265	0.287
K4	0.853	0.850	1.703	0.003	L41	2.993	2.723	5.717	0.270
					L42	3.888	3.831	7.719	0.057
					L43	2.484	2.564	5.048	−0.079
					L44	3.026	2.932	5.957	0.094
					L45	3.070	3.318	6.388	−0.249
K5	0.916	0.871	1.788	0.045	L51	3.004	3.338	6.343	−0.334
					L52	3.766	3.708	7.474	0.059
					L53	3.278	2.471	5.749	0.807
K6	0.939	0.966	1.905	−0.027	L61	3.461	3.808	7.269	−0.347
					L62	2.877	2.430	5.306	0.447
					L63	3.878	3.775	7.653	0.103
					L64	3.408	3.972	7.380	−0.564

. The obtained values enable the identification of both the key drivers of transformation and more reactive components that are influenced by other factors and that depend on the system context.

With regard to the centrality index ${(D}_i+R_i)$, which reflects the degree of involvement of individual technologies in cause-and-effect relationships, it is possible to identify the most active and therefore the most important components in both paradigms.

Among the Industry 4.0 areas, the highest value of the centrality index (2.010) was obtained by K1 (Automation and Connectivity), followed by K2 (Data-Driven Optimization) and K3 (Agility and Flexibility). In turn, among the Industry 5.0 areas, the highest value (1.905) was observed for K6 (Resilience), followed by K5 (Sustainability) and K4 (Human-Centricity). The highest value observed within Industry 4.0 exceeds its counterpart for the Industry 5.0 area, and therefore the Industry 4.0 area is more important.

Among the technologies assigned to Industry 4.0, the most prominent are L12 (Cybersecurity), L11 (Internet of Things (IoT)), and L23 (IT Support Systems). These technologies should be regarded as key drivers in the context of designing a strategy for implementing digital transformation. The remaining technologies in this group, including L22, L33, and L31, demonstrate lower centrality levels and are likely to serve supportive or complementary roles.

A corresponding analysis for the set of technologies assigned to Industry 5.0 reveals the dominant roles of L42 (Hyper-Personalized Production), L63 (New Materials), and L52 (Renewable Energy and Smart Grids). These technologies occupy central positions within the network of interdependencies and should be serve as key reference points when developing strategies consistent with the assumptions of Industry 5.0. Other technologies, such as L64, L61, or L45, despite lower centrality indices, may nonetheless play an important role in the context of supporting long-term transformation processes.

The top-ranked technologies—L12 (Cybersecurity) and L11 (Internet of Things)—can be viewed as foundational enablers for both Industry 4.0 and 5.0. Their prioritization reflects both their relative maturity and the urgency of their deployment across sectors to support connectivity, data acquisition, and security in increasingly digitalized environments. In contrast, technologies such as L42 (Hyper-Personalized Production), while central to the Industry 5.0 vision, are perceived as more emergent and reliant on foundational layers such as IoT infrastructure, secure networks, and integrated data platforms. Therefore, the higher ranking of L12 and L11 does not diminish the relevance of Industry 5.0 solutions but rather highlights the staged nature of transformation—where enabling technologies are implemented first to create the conditions for downstream innovations.

Figure 2 provides a graphical representation of cause-and-effect relationships for Industry 4.0/5.0 areas.

The causality indicator ${(D}_i-R_i)$ enables the determination of the direction of interactions within the analysed system. A positive value of the indicator means that a given parameter exerts an influence on other elements of the network, while a negative value indicates a receiving role. In the analysis of transformation areas, the highest positive causality values were recorded for K1 (Automation and Connectivity), K5 (Sustainability), and K4 (Human-Centricity), suggesting that these areas act as primary drivers within the interaction structure. On the contrary, K2 (Data-Driven Optimization), K3 (Agility and Flexibility), and K6 (Resilience) exhibited negative values, which suggests their dependence on other elements of the system.

These findings reflect the typical progression of transformation: connectivity, automation, and data infrastructure (K1) serve as foundational enablers, paving the way for the development of more adaptive and resilient systems (K6). Strategically, K1 underpins the functioning of all other areas, particularly those characteristic of Industry 5.0. In Figure 2, K1—due to its dominant centrality and causality values—is additionally highlighted using bold formatting to emphasize its systemic importance.

The position of individual technologies from these areas is visible in Figure 3, which presents the system of connections in relation to both indicators. Figure 2 and Figure 3 employ bold formatting to draw attention to the most influential technologies. Notably, L12 has the highest centrality index, and L53 has the highest causality index within the DEMATEL-based structure.

In the set of Industry 4.0 technologies, the most pronounced influencing components are the positive values ${(D}_i-R_i)$ for L21 (Artificial Intelligence (AI) and Machine Learning (ML)), L13 (Wireless Communication/5G), and L34 (Augmented and Virtual Reality-AR/VR).

For Industry 5.0, strong potential impact is observed in the cases of L53 (Autonomous Vehicles and Drones), L62 (Quantum Computing), and L41 (Human-Centered Robotics, New-Generation Cobots).

Simultaneously, it is important to highlight technologies that function primarily as ‘influence receivers’—these include L64 (Digital Twin), L22 (Big Data and Data Analytics), and L23 (IT Support Systems). These technologies, although characterized by lower causality values, may serve as critical support components, reacting to changes initiated by other, more influential components.

Using the centrality index ${(D}_i+R_i)$ and (6), a ranking of Industry 4.0 and 5.0 areas (

Table 4. Industry 4.0/5.0 technology ranking.

Modern Technology Notation	${\mathit{W}}_{\mathit{i}}$	Ranking
L12	59.444	1
L11	55.080	2
L23	53.263	3
L22	53.175	4
L42	52.717	5
L63	52.266	6
L52	51.044	7
L64	50.402	8
L61	49.644	9
L33	48.135	10
L31	44.085	11
L45	43.627	12
L32	43.347	13
L51	43.320	14
L44	40.683	15
L21	39.406	16
L53	39.263	17
L41	39.044	18
L62	36.237	19
L34	35.957	20
L13	35.384	21
L43	34.475	22

) and technologies (

Table 5. Ranking of Industry 4.0/5.0 areas.

Industry Area Notation	${\mathit{W}}_{\mathit{i}}$	Ranking
K1	18.126	1
K2	17.621	2
K6	17.179	3
K5	16.124	4
K3	15.592	5
K4	15.358	6

) was developed. This distinction enables a separate assessment of the strategic significance of individual technologies and the domains in which they are implemented, offering a more comprehensive perspective on their transformational potential.

The ranking analysis shows that the highest-rated technology, based on the global preference flow, is L12, ahead of L11, L23, and L22. Technologies characteristic of Industry 5.0, including L42 and L63, also ranked high, confirming their growing role in the directions of transformation. Technologies at the bottom of the list, such as L43, L13, L34, L62, or L41, despite their lower position, may still serve complementary or specialized roles depending on the context of implementation.

In the ranking of transformation spheres, the top positions are occupied by K1 and K2, related to automation, digitalization, and process optimization in the spirit of Industry 4.0. A surprisingly high position is also achieved by K6, which represents the dimension of systemic resilience—a key component of Industry 5.0. The differentiation of values in Table 5 indicates that not all transformation areas have an equal impact according to expert evaluations, which can help companies define investment and technology priorities.

In the next stage of the PROMETHEE II study, experts evaluated the extent to which each of the 22 technologies studied (L11–L64) contributes to the achievement of objectives within three key perspectives in assessing industrial transformation: U1—economic; U2—organizational (process-oriented); U3—technological. The evaluations were conducted using a five-point scale (where 5 meant very strong impact). The average values are presented in

Table 6. The impact of Industry 4.0 and Industry 5.0 technologies on the perspectives in industrial transformation assessment.

Modern Technology Notation	U1	U2	U3
L11	4.917	3.917	4.583
L12	4.917	4.083	4.417
L13	1.750	2.250	2.167
L21	3.833	3.083	2.333
L22	4.083	3.667	3.833
L23	3.833	3.750	3.917
L31	3.417	2.917	3.083
L32	3.417	2.417	2.500
L33	3.333	3.167	3.250
L34	1.417	1.667	3.500
L41	1.583	1.417	1.167
L42	4.750	4.000	4.333
L43	1.417	1.333	1.417
L44	2.750	1.583	2.333
L45	2.667	2.250	3.500
L51	2.583	2.750	2.167
L52	4.583	3.917	4.250
L53	1.583	1.917	1.833
L61	4.083	3.583	3.917
L62	0.917	1.583	1.083
L63	4.667	3.833	4.167
L64	2.083	1.833	1.917

, allowing the identification of those technologies considered the most universal and strategic for the development of industrial enterprises.

The consistency of expert assessments in this stage was further analysed using the Kendall coefficient of concordance $\left(W\right)$. For the same group of 11 experts and 22 technologies, the coefficient value was obtained as $W=0.79$, with $S=\mathrm{100,734}$, indicating a high level of agreement regarding the impact of the technology on the implementation of new solutions.

The resulting values were $W=0.60$ for DEMATEL and $W=0.79$ for PROMETHEE II. According to commonly accepted interpretative thresholds in MCDM research, a value of $W>0.70$ typically indicates strong agreement, while values between 0.50 and 0.70 reflect moderate agreement, considered sufficiently reliable for strategic assessments. In this context, the obtained values confirm that the expert judgments were sufficiently consistent to ensure the credibility of the aggregated results.

It should be emphasized that these evaluations were collected using a separate assessment form, independent of the form used in the DEMATEL analysis. Maintaining methodological separation between data sources enables the triangulation of results, thereby enhancing the credibility and transparency of the applied multi-criteria decision-making approach [85]. Such a procedure is of significant importance when making strategic technology-related decisions in the context of long-term transformation.

The results presented in Table 6 indicate that technologies L12 (Cybersecurity), L11 (Internet of Things (IoT)), L42 (Hyper-Personalized Production), and L52 (Renewable Energy and Smart Grids) obtained the highest average scores in all three perspectives (marked in bold in Table 6). This suggests that these technologies are perceived as strategically critical from the points of view of both economic efficiency (U1) and the improvement of organizational processes (U2), as well as the development of technological potential (U3). The obtained results directly address research question Q1, indicating the technologies with the highest implementation priority in the context of the transition to Industry 5.0. The evaluation of technologies representing Industry 5.0, such as L42 or L53, as high-impact indicates the growing importance of the human-centric approach and sustainability in modern industrial models. On the other hand, technologies such as L62, L43, L41, or L53 received relatively lower scores, which may indicate their more specialized or complementary nature in the transformation structure. It is worth emphasizing that lower scores do not imply a lack of relevance in specific industry contexts—they may rather indicate the need for targeted implementation in specific applications.

The final analysis involved a comparative evaluation of all 22 technologies across the three separate transformation perspectives (U1—Economic; U2—Organizational; U3—Technological). The comparison of differences presented in

Table 7. Differences between perspectives in assessing industrial transformation.

Modern Technology Notation	U1–U2	U1–U3	U2–U1	U2–U3	U3–U1	U3–U2
L11	1.000	0.334	−1.000	−0.666	−0.334	0.666
L12	0.834	0.500	−0.834	−0.334	−0.500	0.334
L13	−0.500	−0.417	0.500	0.083	0.417	−0.083
L21	0.750	1.500	−0.750	0.750	−1.500	−0.750
L22	0.416	0.250	−0.416	−0.166	−0.250	0.166
L23	0.083	−0.084	−0.083	−0.167	0.084	0.167
L31	0.500	0.334	−0.500	−0.166	−0.334	0.166
L32	1.000	0.917	−1.000	−0.083	−0.917	0.083
L33	0.166	0.083	−0.166	−0.083	−0.083	0.083
L34	−0.250	−2.083	0.250	−1.833	2.083	1.833
L41	0.166	0.416	−0.166	0.250	−0.416	−0.250
L42	0.750	0.417	−0.750	−0.333	−0.417	0.333
L43	0.084	0.000	−0.084	−0.084	0.000	0.084
L44	1.167	0.417	−1.167	−0.750	−0.417	0.750
L45	0.417	−0.833	−0.417	−1.250	0.833	1.250
L51	−0.167	0.416	0.167	0.583	−0.416	−0.583
L52	0.666	0.333	−0.666	−0.333	−0.333	0.333
L53	−0.334	−0.250	0.334	0.084	0.250	−0.084
L61	0.500	0.166	−0.500	−0.334	−0.166	0.334
L62	−0.666	−0.166	0.666	0.500	0.166	−0.500
L63	0.834	0.500	−0.834	−0.334	−0.500	0.334
L64	0.250	0.166	−0.250	−0.084	−0.166	0.084

between technology assessments in individual perspectives provides an answer to research question Q2, revealing the dependencies and relationships between decision-making criteria.

For each technology, the differences between the ratings assigned to the different perspectives were calculated. This comparative approach makes it possible to determine in which areas a given technology is perceived as more useful or more dominant. Table 7 presents a summary of these differences in the form of comparative values (e.g., U1−U2, U1−U3, etc.) for each of the 22 technologies. The interpretation of the results is based on the sign and magnitude of the difference: a positive value suggests that a given technology received a higher score in the first of the two compared perspectives, and a negative value suggests a higher score in the second perspective.

The data presented in Table 7 indicate that technologies such as L11, L12, L21, L32, L44, and L63 received significantly higher scores in the economic perspective (U1) compared to the organizational (U2) and technological (U3) perspectives. This suggests their strategic value in terms of financial efficiency and cost optimization. In contrast turn, technologies such as L34, L45, and L53 exhibit strongly negative values in the U1–U3 comparison, indicating that they are rated much higher from a technological perspective than from an economic one. Additionally, technologies L13, L53, and L51 display minimal differences between perspectives, suggesting their relatively balanced usefulness across all dimensions of assessment. The use of such a comparison makes it possible to identify not only the most universally applicable technologies but also those that have advantages in specific aspects of industrial transformation. This provides practical guidance for prioritizing implementations. In the final stage of the evaluation, within the PROMETHEE II model, each of the 22 technologies (L11–L64) was assigned an appropriate type and class of preference function. Depending on the characteristics of the technology, either quantitative functions (e.g., based on measurable units) or qualitative functions (based on expert assessments) were applied. For the quantitative functions, parameters and reference units were also defined to facilitate managerial interpretation and practical implementation. All assignments are presented in

Table 8. Industry 4.0 and 5.0 technologies and perspectives for industrial transformation assessment.

Modern Technology	Modern Technology Notation	Type of Preference Function	Class of Preference Function	Parameter Value	Unit of Preference Function	Perspective for Assessment of Industrial Transformation
Internet of Things (IoT)	L11	Quantitative	1	0	Number of beacons	U3
Cybersecurity	L12	Quantitative	3	1	Number of security system elements in terms of hardware, software, and processes	U2
Wireless Communication/5G	L13	Quantitative	3	2	Percentage of communication share	U3
Artificial Intelligence (AI) and Machine Learning (ML)	L21	Qualitative	2	1	-	U2
Big Data and Data Analytics	L22	Qualitative	2	1	-	U2
IT Support Systems	L23	Quantitative	1	0	Number of IT systems	U3
Cloud Computing	L31	Qualitative	2	1	-	U1
3D Printing (Additive Manufacturing)	L32	Quantitative	3	2	Share in total production	U1
Robotics and Automation/Autonomous Production Systems	L33	Quantitative	3	2	Share in total production	U1
Augmented and Virtual Reality (AR/VR)	L34	Qualitative	2	1	-	U2
Human-Centered Robotics (New-Generation Cobots)	L41	Quantitative	1	0	Number of cobots	U1
Hyper-Personalized Production	L42	Quantitative	3	2	Share in total production	U1
Neuromorphic Computers and Brain–Machine Interfaces	L43	Qualitative	2	1	-	U3
Ethical AI and Social Responsibility	L44	Qualitative	2	1	-	U2
AI Combined with Human Intelligence	L45	Qualitative	2	1	-	U2
Sustainable Production and Circular Economy	L51	Qualitative	2	1	-	U1
Renewable Energy and Smart Grids	L52	Quantitative	3	2	Percentage of energy demand coverage	U1
Autonomous Vehicles and Drones	L53	Quantitative	1	0	Number of devices	U3
Biotechnology and Nanotechnology	L61	Qualitative	2	1		U3
Quantum Computing	L62	Qualitative	2	1		U3
New Materials	L63	Quantitative	3	2	Share in total production	U3
Digital Twin	L64	Quantitative	3	2	Share of production processes covered by the model	U1

. Additionally, each technology was assigned a dominant transformation perspective (U1–U3), in which its implementation is particularly impactful. This categorization was based on the results of the previous ranking analysis and validated through expert consultation.

In the literature, several classes of preference functions are distinguished [86]. The most commonly used ones include the following: class 1—a function without a discrimination threshold; class 2—a function with a fixed threshold value; class 3—a linear preference function. The selection of the appropriate type of preference function—qualitative or quantitative—determines the assignment of a specific parameter to each function (

Table 9. Differences between perspectives for industrial transformation assessment considering the value of the preference function parameter.

Modern Technology Notation	Parameter Value	U1–U2	U1–U3	U2–U1	U2–U3	U3–U1	U3–U2
L11	0	1	1	0	0	0	1
L12	1	0.834	0.500	0	0	0	0.334
L13	2	0	0	0.250	0.042	0.209	0
L21	1	0	1	0	0	0	0
L22	1	0	0	0	0	0	0
L23	0	1	0	0	0	1	1
L31	1	0	0	0	0	0	0
L32	2	0.500	0.459	0	0	0	0.042
L33	2	0.083	0.042	0	0	0	0.042
L34	1	0	0	0	0	1	1
L41	0	1	1	0	1	0	0
L42	2	0.375	0.209	0	0	0	0.167
L43	1	0	0	0	0	0	0
L44	1	1	0	0	0	0	0
L45	1	0	0	0	0	0	1
L51	1	0	0	0	0	0	0
L52	2	0.333	0.167	0	0	0	0.167
L53	0	0	0	1	1	1	0
L61	1	0	0	0	0	0	0
L62	1	0	0	0	0	0	0
L63	2	0.417	0.250	0	0	0	0.167
L64	2	0.125	0.083	0	0	0	0.042

). This parameter can take one of three values: 0, 1, or 2. The value 2 indicates a strong association between the given technology and a specific perspective, while the value 0 signals a low likelihood that the given perspective is relevant for the assessed technology.

Considering the values from Table 9 and the weighting coefficients from Table 4, the final form of the differences (

Table 10. Differences between perspectives in industrial transformation assessment considering the values of weighting factors.

Modern Technology Notation	${\mathit{W}}_{\mathit{i}}$	U1–U2	U1–U3	U2–U1	U2–U3	U3–U1	U3–U2
L11	59.444	59.444	59.444	0	0	0	59.444
L12	55.080	45.937	27.540	0	0	0	18.397
L13	53.263	0	0	13.316	2.237	11.132	0
L21	53.175	0	53.175	0	0	0	0
L22	52.717	0	0	0	0	0	0
L23	52.266	52.266	0	0	0	52.266	52.266
L31	51.044	0	0	0	0	0	0
L32	50.402	25.201	23.135	0	0	0	2.117
L33	49.644	4.120	2.085	0	0	0	2.085
L34	48.135	0	0	0	0	48.135	48.135
L41	44.085	44.085	44.085	0	44.085	0	0
L42	43.627	16.360	9.118	0	0	0	7.286
L43	43.347	0	0	0	0	0	0
L44	43.320	43.320	0	0	0	0	0
L45	40.683	0	0	0	0	0	40.683
L51	39.406	0	0	0	0	0	0
L52	39.263	13.075	6.557	0	0	0	6.557
L53	39.044	0	0	39.044	39.044	39.044	0
L61	36.237	0	0	0	0	0	0
L62	35.957	0	0	0	0	0	0
L63	35.384	14.755	8.846	0	0	0	5.909
L64	34.475	4.309	2.861	0	0	0	1.448

) between the evaluation perspectives for each technology is determined based on Equation (7).

Based on the PROMETHEE II method, the top-ranked technologies within each evaluation perspective can be summarized as follows:

• Economic perspective (U1): L42 (Hyper-Personalized Production), L52 (Renewable Energy and Smart Grids), L64 (Digital Twin);
• Organizational perspective (U2): L12 (Cybersecurity), L22 (Big Data and Data Analytics), L45 (AI Combined with Human Intelligence);
• Technological perspective (U3): L11 (Internet of Things—IoT), L23 (IT Support Systems), L63 (New Materials).

This summary enables easier cross-perspective comparison and supports decision-makers in identifying the most strategically relevant technologies in line with their organizational priorities.

In the final stage of the PROMETHEE II method’s application, three key parameters were calculated for each of the three analysed perspectives: leaving flow (8), entering flow (9), and net flow (10). These parameters form the basis for the final ranking of the transformation assessment perspectives, reflecting their overall preference level in the context of Industry 4.0 and 5.0. The values are presented in

Table 11. Leaving flow, entering flow, and net flow for perspectives in the assessment of industrial transformation.

Perspectives for Assessing Industrial Transformation	Markers of Perspectives for Assessing Industrial Transformation	Leaving Flow ${\mathit{\Phi }}^{+}\left(\mathit{i}\right)$	Entering Flow ${\mathit{\Phi }}^{-}\left(\mathit{i}\right)$	Net Flow $\mathit{\Phi }\left(\mathit{i}\right)$	Ranking
Economic	U1	559.718334	202.936717	356.781617	1
Organizational	U2	137.725796	567.198739	−429.472943	3
Technological	U3	394.903327	322.212001	72.691326	2

.

Based on the obtained results, it can be concluded that the economic perspective (U1) obtained the highest net flow value (356.78), indicating its dominant role in the overall assessment of the analysed technologies. This suggests that the technologies included in this study are most closely aligned with this perspective.

Based on the technology ranking in Table 4 and the association of the U1 perspective with the technologies presented in Table 8, the technologies identified as most important for U1 can be ranked as follows: Hyper-Personalized Production (L42), Renewable Energy and Smart Grids (L52), Digital Twin (L64), Robotics and Automation/Autonomous Production Systems (L33), Cloud Computing (L31), 3D Printing-Additive Manufacturing (L32), Sustainable Production and Circular Economy (L51), Human-Centered Robotics, and New-Generation Cobots (L41).

The technological perspective (U3) ranked second with a positive net score (72.69), indicating its growing importance in the context of long-term infrastructure development, innovation, and the advancement of production systems. It also suggests a significant impact of this perspective on transformation processes.

The technologies most strongly associated with U3 include the Internet of Things (IoT) (L11), IT Support Systems (L23), New Materials (L63), Biotechnology and Nanotechnology (L61), Autonomous Vehicles and Drones (L53), Quantum Computing (L62), Wireless Communication/5G (L13), and Neuromorphic Computers and Brain–Machine Interfaces (L43).

In contrast, the organizational perspective (U2) recorded a negative value (429.47), indicating its relatively lower importance within the analysed set of technologies—or its need for additional support in the context of implementing industrial solutions.

This result suggests that the assessed technologies are less aligned with organizational and process-oriented transformation goals. This may stem from implementation challenges at the operational level or from the fact that organizational outcomes tend to be more dispersed and harder to evaluate objectively.

The ranking of the most important technologies for U2 is as follows: Cybersecurity (L12), Big Data and Data Analytics (L22), AI Combined with Human Intelligence (L45), Ethical AI and Social Responsibility (L44), Artificial Intelligence AI and Machine Learning ML (L21), and Augmented and Virtual Reality AR/VR (L34).

This hierarchy of perspectives and corresponding technologies provide practical guidance for decision-makers in planning implementation priorities and identifying areas requiring further technological or organizational support.

The observed precedence of the economic perspective (U1) over technological (U3) and organizational (U2) factors stems from expert evaluations rather than the model’s inherent structure. Experts assessed the relevance of each perspective in the context of industrial transformation, resulting in the ordering U1 > U3 > U2, as reflected in the PROMETHEE II ranking shown in Table 11. This indicates that economic feasibility and return on investment remain dominant considerations in strategic decision-making, even within the broader Industry 5.0 context. The finding aligns with common managerial practice, where resource allocation for new technologies and organizational change is primarily driven by profit–loss assessments. This indicates a potential disconnection between the aspirational dimensions of sustainability and human-centricity and the economic priorities of decision-makers. From a managerial perspective, this underlines the need to integrate financial rationale with innovation and sustainability to facilitate the practical adoption of future-oriented technologies.

The comparison of rankings across the three perspectives U1, U2, and U3 provides a direct answer to research question Q3. The results clearly demonstrate that the adopted assessment perspective has a significant influence on the technology hierarchy. Some technologies are highly rated in technological terms and may have lower value in economic or organizational terms and vice versa.

The results of this study are consistent with previous analyses on technology prioritization in the context of Industry 4.0 and 5.0.

For example, the research conducted by Albayrak and Erkayman [87] showed that vertical integration is a key technology influencing success factors in Industry 4.0 implementation. A hybrid F-BWM and CoCoSo approach was applied to identify and evaluate these factors.

In the context of small- and medium-sized enterprises (SMEs), Ozdemir [88] developed a performance evaluation model based on a hybrid SF-AHP–WSM approach, incorporating criteria such as software, manufacturing, and external partners. This model emphasizes the role of data integration and automation of information flows, which aligns with our findings regarding technologies L22–L23.

Regarding AI applications in manufacturing, the literature review conducted by del Real Torres et al. [89] highlights the potential of Deep Reinforcement Learning (DRL) to improve the resilience and adaptability of manufacturing processes, outperforming traditional methods.

This confirms the importance of AI-based technologies, such as L21–L22, identified as important in our study. The consistency of the results with the literature confirms the validity of the applied MCDA approach and its usefulness in the analysis of technology priorities and strategic planning of industrial transformation.

Across industries, technologies related to AI, the IoT, data integration, and cybersecurity consistently emerge as core components of industrial development strategies of the industrial sector, reinforcing their central role in long-term competitiveness and innovation.

6. Conclusions

In the era of the parallel development of the Industry 4.0 and Industry 5.0 concepts, industrial enterprises face the challenge of selecting technologies that not only support automation and digitalization but also respond to human needs, flexibility, and organizational resilience.

The article presents a methodical framework for assessing 22 selected technologies in terms of their alignment with six strategic transformation areas and three evaluation perspectives: economic, organizational, and technological. The proposed structure is designed to assist decision-makers in identifying the most valuable technologies according to their adopted development strategy.

The presented DEMATEL–PROMETHEE II model goes beyond the assessment of technological advancement—it also enables the identification of critical leverage points for the implementation of future-ready technologies.

The scientific contribution of this study lies in the development of an integrated technology assessment approach tailored to two complementary industrial paradigms—Industry 4.0 and 5.0.

The use of a hybrid MCDM methodology (DEMATEL + PROMETHEE II) made it possible to analyse cause–effect relationships, centrality, and technology preference rankings. A further innovation of this work is the analytical structure, based on six transformation areas (K1–K6), technologies assigned to them (L11–L64), and a three-dimensional evaluation model reflecting perspectives U1–U3.

The analysis considered both the directional impact of each technology and its practical relevance within different organizational contexts. The use of a dual-questionnaire structure allowed for data triangulation, increasing the reliability and credibility of the findings. This study therefore provides a robust foundation for strategic planning, particularly in environments seeking to balance technological innovation with human- and sustainability-oriented values.

Based on the conducted analysis, several key recommendations have been formulated:

• Given that U1 received the highest global net flow value, it should serve as the primary reference point when defining technology deployment strategies.
• Technologies L12 (Cybersecurity), L11 (Internet of Things (IoT)), L23 (IT Support Systems), and L22 (Big Data and Data Analytics) exhibit the highest centrality and ranking positions, suggesting their importance as strategic elements. These technologies should be treated as core enablers that facilitate broader transformation processes across all perspectives.
• Technologies L42 (Hyper-Personalized Production), L63 (New Materials), and L52 (Renewable Energy and Smart Grids), all of which are aligned with the Industry 5.0 paradigm, also achieve high centrality and causality indices, highlighting their increasing systemic importance in the industrial system.
• The transformation areas K1 (Automation and Connectivity) and K2 (Data-Driven Optimization) should be treated as top priorities in the context of further implementation efforts within Industry 4.0. In contrast, within Industry 5.0, the leading position is occupied by the area K6 (Resilience) which reflects the growing need for robust, adaptable systems that can withstand disruption and support sustainable development.

Beyond organizational strategy, the proposed framework can also serve as a decision-support tool for public authorities and policymakers. By pinpointing technologies with significant transformational impact—such as cybersecurity, the IoT, and renewable energy systems—its findings may guide national or regional innovation policies, funding programmes, and infrastructure investments aligned with the core objectives of Industry 5.0, including resilience, sustainability, and human-centricity. Moreover, the human-centric and sustainability-oriented evaluation framework provides a valuable foundation for formulating policies that balance competitiveness with societal and environmental objectives.

Further research could proceed in several key directions:

• Validation of the proposed model in real conditions within industrial, service, and commercial enterprises. This would provide empirical evidence of its applicability and robustness in diverse operational environments.
• The assessment framework could be extended to include emerging Industry 5.0 technologies, such as neurotechnologies, immersive technologies, and advanced tools for human–machine collaboration.
• Integration of the model with ERP/MES tools and AI-based decision-making systems. Future work could explore the integration of the model with ERP, MES, and AI-based decision support systems, enhancing its utility in real-time planning and operations.
• The application of alternative MCDM methods (e.g., fuzzy AHP, ANP, TOPSIS) and exploration of machine learning models for dynamic weight adaptation. Further analysis could employ other multi-criteria decision-making techniques—such as fuzzy AHP, ANP, or TOPSIS—and investigate the use of machine learning algorithms for adaptive weighting based on evolving priorities or data streams.
• The development of user interfaces in the form of dashboards and tools for quick interpretation of scenario results. Designing interactive dashboards and scenario-based visualization tools could facilitate faster interpretation of results and support real-time strategic decision-making by managers and stakeholders.
• Conducting a dedicated sensitivity analysis to evaluate the robustness of the ranking results in relation to possible variations in the criteria weights. Methods such as weight perturbation analysis or Monte Carlo simulation could provide additional validation of the stability and reliability of the prioritization outcomes.

The results showed moderate to strong agreement among expert judgments ($W=0.60$ for DEMATEL; $W=0.79$ for PROMETHEE II), which supports the reliability of the findings. However, several limitations should be acknowledged:

• This study is based on a limited expert panel and single-round evaluation. Expanding the panel and introducing iterative or Delphi-based procedures could enhance the robustness and stability of the findings.
• Although the methods applied provide a balance between rigor and managerial usefulness, more advanced statistical methods (e.g., structural equation modelling or Bayesian analysis) could provide greater validation precision and deepen theoretical insight.
• There is a lack of a fully developed visual layer of results—developing intuitive dashboards, interactive maps, or graphical summaries could significantly increase the interpretability and practical utility of the findings for decision-makers.
• The set of 22 technologies assessed in this study represents a static selection based on their current strategic relevance; however, it does not encompass rapidly emerging domains such as generative AI or neurotechnologies. While this constitutes a limitation of the present version, the framework is intentionally designed to be extensible. Future assessments may expand the technology pool and incorporate new developments by reapplying the same MCDM procedure with updated expert input.
• The expert evaluation offers a static snapshot of judgments captured at a single point in time. Although the method ensures consistency within the panel, it does not account for evolving perspectives or dynamic shifts in strategic priorities. To enhance the temporal robustness of expert input, future studies could adopt iterative approaches, such as Delphi panels or multiple rounds of feedback.
• Although the expert panel was diverse in terms of industry background and domain expertise, it did not include representatives from small- and medium-sized enterprises (SMEs), whose priorities and constraints may differ significantly from those of larger organizations. To ensure broader applicability and relevance, future research should consider stratified sampling or targeted inclusion of SME stakeholders.