Moving from Industry 4.0 to Industry 5.0: What Are the Implications for Smart Logistics?
<p>Nine pillars of Industry 4.0.</p> "> Figure 2
<p>Flow diagrams of literature search: (<b>a</b>) smart logistics in Industry 4.0; (<b>b</b>) smart logistics in Industry 5.0.</p> "> Figure 3
<p>Publication trends.</p> "> Figure 4
<p>Co-citation analysis network of smart logistics in Industry 4.0.</p> "> Figure 5
<p>Co-citation analysis network of smart logistics in Industry 5.0.</p> "> Figure 6
<p>Keyword co-occurrence analysis of smart logistics in Industry 4.0.</p> "> Figure 7
<p>Keyword co-occurrence analysis of smart logistics in Industry 5.0.</p> "> Figure 8
<p>Supporting technologies in smart logistics of Industry 5.0.</p> ">
Abstract
:1. Introduction
2. Literature Review
2.1. Industry 4.0
2.2. Industry 5.0
2.3. Literature Gaps and Contributions of This Research
3. Research Method
- Problem Formulation: Entails the identification of the research goals and scopes by defining relevant research questions. It is worthy to note that, for studies including more than one reviewer, there should be a consensus over the questions to avoid evaluation bias.
- Literature Search and Screening: This stage commences with a precise search within the selected databases according to the identified keywords for each research question. The resulted papers are to be filtered out through the inclusion and exclusion of relevant criteria, which are further narrowed down by the screening procedure.
- Bibliometric Analysis: According to the meta-data associated with the extracted papers, a quantitative analysis is conducted to reveal the relations between various characteristics of the research articles, i.e., publication trend, keywords focus, involved journals, etc.
- Content Analysis: Qualitative analysis that aims at a thorough evaluation of the selected papers to explore the current status of the research area and to highlight the future research agenda.
4. Problem Formulation and Literature Search
- Research Question 1 (RQ1): What are the connection and differences in smart logistics between Industry 4.0 and Industry 5.0?
- Research Question 2 (RQ2): What are the main characteristics and enabling technologies of smart logistics in Industry 5.0?
- Research Question 3 (RQ3): What is the research agenda of smart logistics in Industry 5.0?
- Keyword Search. This step employs two search techniques: (1) using a double quotation for an exact match with regard to phrase search; and (2) taking advantage of Boolean operators (OR/AND) to combine various taxonomies of keywords. To thoroughly reveal the connection and differences in smart logistics between Industry 4.0 and Industry 5.0, we searched the respective literature in two groups. The first group emphasized the connection between Industry 4.0 and smart logistics, which primarily yielded two contextual categories connected with “OR”, as shown in Table 1. The second group was to explore the literature that discussed the characteristics, implications, driving factors, and definitions of Industry 5.0-enabled smart logistics. The primary database for the literature search was Web of Science (WoS), which is the most extensively used platform [52]. However, due to the limited number of papers related to smart logistics and Industry 5.0 in WoS, Scopus was also used to yield a reasonable sample for analysis. The literature search was conducted in late December 2021, and the initial search for the first group yielded 288 papers, while it resulted in 247 for the second group (91 and 156 in WoS and Scopus, respectively).
- 2.
- Inclusion/Exclusion of Search Criteria. This procedure attempts to narrow down the collected papers from the previous step by either including or excluding particular criteria. Primarily, the language of the research items was selected as ‘English’ to emphasize the international contributions. To ensure the quality of analysis, the papers were restricted to journal articles and conference proceedings. As also outlined, the introduction of Industry 4.0 was traced back to 2011 [22,23], while the literature had recorded 2017 for Industry 5.0 despite its initial introduction being in 2015 [2,53]. Thus, the next criterion was to set the publication years of the two groups of papers to be after 2011 and 2017, respectively. Another key filter that remarkably impacts the search results is the publication categories, which seeks to eliminate articles with the least correspondence in terms of their scientific fields. Based on the applied filters, there were 114 and 146 in the two groups. Ultimately, a duplicate check for the second group was essential due to the use of two databases, which, in turn, decreased the results to 110.
- 3.
- First Screening (investigation of titles, abstracts, and keywords). The initial consideration in this stage was to exclude review articles, which were respectively recorded as 6 and 9 papers for the two groups. This was followed by a thematic investigation that aimed at filtering out the papers with weak conceptual relevance associated with the research questions. Throughout this process, the titles, abstracts, and keywords of the articles were investigated. This process led to the exclusion of 49 and 59 papers in the two groups.
- 4.
- Second Screening (full-text investigation). During this process, the selected papers from the previous step were entirely read to filter out the ones that were incapable of addressing the research questions directly. After the full-text investigation, 12 papers were eliminated from the first group, and 10 papers were eliminated from the second group.
5. Comparative Bibliometric Analysis
5.1. Publication Trend
5.2. Sources Contributions, Interactions, and Co-Citation Analysis
5.2.1. Source Contributions
5.2.2. Interaction and Co-Citation Analysis
5.3. Keyword Co-Occurrence Analysis
6. Content Analysis and Discussion
6.1. The Three Key Elements of Industry 5.0
- Human-Centricity. Conveys the fact the production and logistics system must be improved with solid attention to human benefits and needs, by which the human is transformed from ‘cost’ to ‘investment’ [2]. From the operational aspect, this urges the promotion of hybrid alternatives in response to the industrial challenges, where the human power and human brain are involved not only in maintaining the surveillance but also in incorporating more intelligence and innovation and, to some extent, making decisions [3,35]. Industry 5.0 emphasizes research and development (R&D) activities to translate information into knowledge and meet sustainable social goals by upskilling humans through formal education or training schemes [2,6,36,56,57]. From the social and economic point of view, Industry 5.0 shapes the ground to not only prevent the elimination of human labor engaged in the manufacturing industry but also create more job opportunities in the supportive industries, which provide technological solutions, i.e., robot manufacturing, sensor manufacturing, etc. [3,34,36]. Hence, based on these objectives, Industry 5.0 is a human-centric paradigm that transfers the human back to the center of production cycles.
- Resilience. Represents the flexibility and agility that a production plant needs to maintain in response to market change [36,58]. Today, customers are strikingly bombarded with high-tech innovations and products, and according to the constant changing in the market, personalized demands are one of the most significant challenges to the manufacturing industry [35]. To a larger extent, manufacturing systems are expected to transform from mass customization to mass personalization [36]. From a tactical perspective, this is realized by incorporating the customers into the design phase to build up the personalized product from scratch [34,59]. To improve the operational flexibility in this regard, human–robot collaboration has significant potential, which conducts versatility of fabrication in a more efficient time [36,37]. It is worthwhile to highlight that while the main task is accomplished by the robot, human collaboration facilitates the problem solving of the work and process flows, and improves intelligence and innovation [35,37].
- Sustainability. The concept of sustainable development was initially introduced by Brundtland in 1987 and defined as the “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [60]. While the social- and human-related issues are an integral part of this concept, they are merely discussed within human-centricity in the context of Industry 5.0. This approach emphasizes reverse logistics [61,62], circular economy [2], value chains, and so forth [63]. Sustainable development seeks the protection of the environment through sustainable products and logistics systems to approach the zero waste objective [34]. In addition to waste prevention, the manufacturing processes must be environmentally friendly—for example, by using renewable resources and green computing [37].
6.2. Smart Logistics in Industry 5.0
6.2.1. Intelligent Automation
6.2.2. Intelligent Devices
6.2.3. Intelligent Systems
6.2.4. Intelligent Materials
6.3. Discussions and Research Agenda
6.3.1. Analysis of Enabling Technologies
6.3.2. Similarities and Differences between Industry 4.0 and Industry 5.0 for Smart Logistics
6.3.3. Research Agenda
- Smart and sustainable logistics network design. Logistics network design is one of the most important strategic decisions. The human-centric and technology-driven paradigm shift will largely affect the smart logistics operations in Industry 5.0; however, this leads to more challenges in strategic logistics network design to accommodate these configurational and operational changes within the whole planning horizon. Thus, research focus needs to be given to smart and sustainable logistics network design considering both human and technological factors in Industry 5.0.
- Mobile transportation. Intralogistics operations and material handling systems are some of the most significant challenges related to manufacturing logistics, which significantly impact the system’s flexibility and agility. In this regard, smart mobile transportation means, i.e., UAVs, AGVs, have shown significant capabilities with intelligence and connectivity utilities. These pave the way for a smart collaboration with the operator, which not only satisfies the resilience goal but also takes human centricity into account. Given the least attention from the literature, it is of significance to devote more effort in this direction.
- Additive manufacturing. Due to its high adaptability and flexibility, additive manufacturing would significantly influence the sustainable supply chain and logistics operations compared with other techniques in Industry 5.0. Various logistics operations and supply chain activities can benefit. For example, in warehouse management, digital inventories of a large variety of products with low and irregular demands can be held with the help of additive manufacturing, which reduces both costs and environmental impacts. Thus, research attention needs to be given to AM in smart logistics of Industry 5.0 to improve both economic and environmental performance while maintaining a high service level.
- Intelligent materials and supply chain. Biotechnologies and intelligent materials are among the primary technologies for Industry 5.0. Given its low rate of attention from scholars, it is of significance to invest more research effort in this direction. In addition, it is highly beneficial to study the impact of intelligent material on smart and sustainable logistics systems, i.e., green logistics, reverse logistics, circular economy, etc.
- Warehouse and inventory operations. Although plenty of technological discussions exist within the context of manufacturing industries, some other logistics activities are neglected in the agenda. Warehouse and inventory operations could be investigated from various aspects considering both new technologies and human-centric operations—for instance, the use of virtual technologies to improve the information transparency and cognitive skills of warehousing or inventory activities. In addition, innovative human–robot solutions along with advances in sensing technologies potentially serve as valuable topics to be studied further in this context.
- Human-centric manufacturing and logistics. On the one hand, the human operator, supported by technologies, is the most important element in an Industry 5.0-enabled manufacturing and logistics system. On the other hand, the diversified human demands drive the way of technological breakthroughs and paradigm changes in manufacturing and logistics. Hence, it is substantially important to understand the interplay between humans and technologies in the transition by, for example, studying the impact of cobots and other human-centric technologies on manufacturing and logistics.
- Smart logistics solutions for unexpected events and disasters. Recently, the world witnessed several catastrophic events and humanitarian disasters, e.g., the COVID-19 pandemic, the war between Russia and Ukraine, etc., which require more smart and responsive logistics solutions. For example, satisfying the rapidly increasing demand for personal protective equipment (PPE) [129] and properly dealing with infectious medical waste are among the most critical logistics challenges during the pandemic [130]. In this regard, Industry 5.0 may play an important role by providing innovative solutions through autonomous logistics solutions, human–robot collaboration, etc. Thus, future research in this direction is suggested.
7. Conclusions
- RQ1: We conduct a comparative bibliometric analysis to thoroughly present the connection and differences in smart logistics between industry 4.0 and Industry 5.0.
- RQ2: We thoroughly evaluate the characteristics and key enabling technologies of smart logistics in Industry 5.0.
- RQ3: We propose a research agenda with seven directions to inspire future research on smart logistics in Industry 5.0.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Author [Ref. No] | AI a | Cobot | Sim. and DT b | Sensor Tech | Cloud. Comp c | Big Data | ML/DL d | VR/AR e | UAV/AGV f | Bio-Tech. | IoT | AM g | Block. h |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Callaghan [6] | √ | ||||||||||||
Nahavandi [3] | √ | √ | √ | √ | √ | √ | |||||||
Xu, Lu [2] | √ | √ | √ | √ | |||||||||
Patera, Garbugli [63] | √ | √ | √ | √ | |||||||||
Pathak, Pal [37] | √ | √ | √ | ||||||||||
Gaiardelli, Spellini [35] | √ | √ | |||||||||||
Duggal, Malik [131] | √ | √ | √ | √ | √ | ||||||||
Kumar, Gupta [56] | √ | √ | √ | √ | √ | ||||||||
Javaid and Haleem [36] | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||
Saptaningtyas and Rahayu [34] | √ | √ | √ | √ | √ | √ | |||||||
Demir, Döven [38] | √ | √ | √ | √ | √ | ||||||||
Doyle-Kent and Kopacek [75] | √ | ||||||||||||
Gürdür Broo, Kaynak [132] | √ | √ | |||||||||||
Rega, Di Marino [76] | √ | √ | √ | ||||||||||
Brunzini, Peruzzini [91] | √ | √ | √ | √ | |||||||||
Thakur and Kumar Sehgal [110] | √ | ||||||||||||
Fraga-Lamas, Lopes [113] | √ | √ | √ | ||||||||||
Zhang, Hu [133] | √ | √ | |||||||||||
Golov, Palamarchuk [111] | √ | √ | √ | ||||||||||
Resende, Cerqueira [71] | √ | ||||||||||||
Ávila-Gutiérrez, Aguayo-González [92] | √ | √ | √ | √ | |||||||||
Doyle-Kent and Kopacek [80] | √ | ||||||||||||
Bathla, Singh [119] | √ | √ | √ | ||||||||||
Romero and Stahre [70] | √ | √ | √ | √ | √ | √ | |||||||
Jabrane and Bousmah [81] | √ | √ | √ | √ | |||||||||
Fraga-Lamas, Varela-Barbeito [100] | √ | √ | √ | ||||||||||
Fornasiero and Zangiacomi [78] | √ | √ | √ | ||||||||||
Carayannis, Dezi [59] | √ | √ | |||||||||||
Carayannis, Christodoulou [117] | √ | √ | √ | ||||||||||
Hol [73] | √ | √ | √ | √ | √ | ||||||||
Doyle Kent and Kopacek [79] | |||||||||||||
Longo, Padovano [93] | √ | √ | |||||||||||
Doyle-Kent and Kopacek [31] | √ | √ | |||||||||||
Martynov, Shiryaev [57] | √ | ||||||||||||
Martynov, Shavaleeva [53] | √ | √ | √ | ||||||||||
Mihardjo, Sasmoko [58] | √ | ||||||||||||
Welfare, Hallowell [72] | √ | ||||||||||||
Rahman, Muda [118] | √ | √ | √ | √ | √ |
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Main Category (‘AND’ Boolean Operator) | Sub-Keywords (‘OR’ Boolean Operator) |
---|---|
Smart Logistics | smart logistics; logistics 4.0; smart supply chain; supply chain 4.0; operator 4.0 |
Industry 4.0 | industry 4.0; i4.0; fourth industrial revolution; cyber-physical system; internet of things; cloud computing; augmented reality; big data analytics; artificial intelligence; virtual technology; simulation; additive manufacturing; autonomous robots; cyber security; digital twin |
Technological Enabler of Smart Logistics | Source Title | No. Items |
---|---|---|
Industry 4.0 | IFIP Advances in Information and Communication Technology | 7 |
Computers & Industrial Engineering | 5 | |
IFAC-PapersOnline | 5 | |
Procedia Manufacturing | 3 | |
Industry 5.0 | Lecture Notes in Mechanical Engineering | 4 |
Applied Sciences Switzerland | 3 | |
Sensors | 3 | |
Journal of The Knowledge Economy | 2 |
Cluster | Source Title | TLS | Features |
---|---|---|---|
Cluster 1 | International Journal of Production Research | 1176 | The application of computerized technologies in manufacturing and operation research |
Computers in Industry | 641 | ||
International Journal of Production Economics | 625 | ||
Cluster 2 | Computers & Industrial Engineering | 541 | Role of technology in manufacturing and logistics |
International Journal of Advanced Manufacturing Technology | 390 | ||
Cluster 3 | Procedia Manufacturing | 780 | Manufacturing engineering, processes, and automation |
Procedia CIRP | 671 | ||
IFAC-PapersOnline | 544 |
Cluster | Source Title | TLS | Features |
---|---|---|---|
Cluster 1 | Assembly Automation | 241 | An inter-disciplinary combination of manufacturing technologies and information management |
Journal of Industrial Information Integration | 224 | ||
Journal of Industrial Integration and Management | 217 | ||
Industrial Robot | 192 | ||
Cluster 2 | Sensors | 195 | An inter-disciplinary readership with a focus on engineering, social, human, economic, and environmental aspects |
IEEE Access | 184 | ||
Sustainability (Switzerland) | 171 | ||
Cluster 3 | Applied Sciences Switzerland | 102 | Manufacturing engineering and technology management |
Procedia CIRP | 69 | ||
Computers & Industrial Engineering | 66 |
No. | Industry 4.0 | Industry 5.0 | ||||
---|---|---|---|---|---|---|
Keyword | Occur. | TLS | Keyword | Occur. | TLS | |
1 | Industry 4.0 | 32 | 123 | Industry 5.0 | 33 | 116 |
2 | Internet | 13 | 58 | Industry 4.0 | 20 | 84 |
3 | Operator 4.0 | 13 | 42 | Industrial Revolutions | 6 | 30 |
4 | Big Data | 5 | 31 | Robotics | 5 | 29 |
5 | Future | 4 | 30 | Artificial Intelligence | 6 | 25 |
6 | Design | 5 | 27 | Manufacturing | 4 | 25 |
7 | Industry | 4 | 27 | Smart Manufacturing | 4 | 23 |
8 | Logistics 4.0 | 10 | 26 | Internet of Things | 5 | 22 |
9 | Internet of things | 6 | 24 | Human–Robot Collaboration | 4 | 21 |
10 | Things | 6 | 24 | Industrial Research | 4 | 18 |
11 | Logistics | 6 | 23 | Collaborative Robots | 3 | 16 |
12 | Framework | 3 | 21 | Design and Development | 3 | 16 |
13 | Performance | 4 | 21 | Man–Machine Systems | 3 | 16 |
14 | Smart Logistics | 6 | 19 | Manufacture | 2 | 16 |
15 | Augmented Reality | 3 | 17 | Technology | 3 | 16 |
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Jefroy, N.; Azarian, M.; Yu, H. Moving from Industry 4.0 to Industry 5.0: What Are the Implications for Smart Logistics? Logistics 2022, 6, 26. https://doi.org/10.3390/logistics6020026
Jefroy N, Azarian M, Yu H. Moving from Industry 4.0 to Industry 5.0: What Are the Implications for Smart Logistics? Logistics. 2022; 6(2):26. https://doi.org/10.3390/logistics6020026
Chicago/Turabian StyleJefroy, Niloofar, Mathew Azarian, and Hao Yu. 2022. "Moving from Industry 4.0 to Industry 5.0: What Are the Implications for Smart Logistics?" Logistics 6, no. 2: 26. https://doi.org/10.3390/logistics6020026
APA StyleJefroy, N., Azarian, M., & Yu, H. (2022). Moving from Industry 4.0 to Industry 5.0: What Are the Implications for Smart Logistics? Logistics, 6(2), 26. https://doi.org/10.3390/logistics6020026