Submitted Successfully!
Thank you for your contribution! You can also upload a video entry related to this topic through the link below: https://encyclopedia.pub/user/video_add?id=22013
Check Note
2000/2000
Ver. Summary Created by Modification Content Size Created at Operation
1 -- 2450 2022-04-20 13:16:15 |
2 format correct + 50 word(s) 2500 2022-04-21 05:50:27 |
Smart Logistics in Industry 5.0
Edit
Upload a video

Given the importance of human centricity, resilience, and sustainability, the emerging concept of Industry 5.0 has pushed forward the research frontier of the technology-focused Industry 4.0 to a smart and harmonious socio-economic transition driven by both humans and technologies, where the role of the human in the technological transformation is predominantly focused on. The core elements of Industry 5.0 show that following the technology-centric transition of Industry 4.0, the societal, environmental, and human perspectives require more attention, which will yield significant impacts on logistics operations and management. For instance, the personalization of demands implies a personalized delivery system. Incorporating customers into the design requires highly intelligent CPS and system integration. Human–machine interaction triggers the interaction of various topics such as safety, human behavior, etc. Thus, there exist various challenges and approaches to addressing smart logistics issues in Industry 5.0. 

Industry 5.0 Industry 4.0 smart logistics sustainable logistics bibliometric analysis literature review
Information
View Times: 791
Revisions: 2 times (View History)
Update Date: 21 Apr 2022
Table of Contents

    1. Introduction

    Industrial revolutions, throughout history, are primarily driven by disruptive technological breakthroughs that change the manufacturing paradigms and the way of customer demand satisfaction. With the increasing adoption of advanced manufacturing technologies, digitalization, and information and communication technology (ICT), Industry 4.0, also known as the fourth industrial revolution, aims at achieving a higher level of automation and intelligence [1]. Through leveraging the effectiveness and efficiency of manufacturing processes, Industry 4.0 predominantly emphasizes the paradigm shift led by new technologies, but less attention has been paid to the human aspects [2][3][4]. This is, however, argued as a threat to the sustainable development of humans and society [5], which requires more attention and effort from both industrial practitioners and academia [6]. Although this concern can be partially addressed by incorporating Industry 4.0 within the context of sustainability [7], circular economy [8], green supply chain [9], and so forth, it is still important to have a systematic conceptual development to fill the missing points of Industry 4.0. Thus, given the importance of human centricity, resilience, and sustainability [10], the concept of Industry 5.0 is proposed to complement the existing Industry 4.0 [11] in order to better meet the industrial and technological goals without compromising the socio-economic and environmental performance [2][3]. Among others, personalization, human–machine collaboration, bioeconomy, and sustainability are the most important pillars in Industry 5.0 [12]. As argued by Di Nardo and Yu [13], the increasing adoption of Industry 5.0 technologies will not hinder human value, but rather promote a dual integration between human intelligence and machine intelligence in a collaborative environment [14].
    Logistics, as a key function of a company or a supply chain, has been significantly affected by recent technological advancements and innovation [15]. Smart logistics operations are enabled by the increasing use of new technological solutions, which lead to the emergence of intelligent warehouse management [16], smart transportation [17], digital twin [18], and so forth. By comparing the development of logistics operations with the four industrial revolutions in history, Wang [19] proposed the concept of Logistics 4.0, which integrates Industry 4.0 technologies into various logistics operations to improve smartness and automation. This concept is further developed to adapt to the characteristics of specific industries, e.g., food logistics [20] and forest supply chain [21].
    Even though significant research effort has been given to understand the impacts of new technologies on smart logistics operations and management, no effort has been directed to the human and environmental aspects brought by Industry 5.0. A recent literature review has put forward the concept of supply chain 4.0 to supply chain 5.0 [4], but no research has been done to provide a comprehensive understanding of the implications of Industry 5.0 for smart logistics. To fill this gap, this entry presents a comparative bibliometric analysis to show the connection and differences between Industry 4.0 and Industry 5.0 and smart logistics. A thorough content analysis is then given to illustrate the features of smart logistics in Industry 5.0 concerning four areas, namely intelligent automation, intelligent devices, intelligent systems, and intelligent materials. Finally, a research agenda is proposed for identifying future research directions of smart logistics in the era of Industry 5.0.

    2. The Three Key Elements of Industry 5.0

    As rooted from Industry 4.0, Industry 5.0 embraces similar technologies and a clear distinction between these two industrial revolutions is thus of significance. The official introduction of Industry 5.0 underpins the evolution of this novel paradigm with respect to a trinary concept to pinpoint its corresponding core values [22]: human-centricity, resilience, sustainability.
    • 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][23]. 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][24][25][26]. 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][24][27]. 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 [24][28]. 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 [23]. To a larger extent, manufacturing systems are expected to transform from mass customization to mass personalization [24]. From a tactical perspective, this is realized by incorporating the customers into the design phase to build up the personalized product from scratch [27][29]. To improve the operational flexibility in this regard, human–robot collaboration has significant potential, which conducts versatility of fabrication in a more efficient time [24][30]. 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 [23][30].
    • 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” [31]. 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 [32][33], circular economy [2], value chains, and so forth [34]. Sustainable development seeks the protection of the environment through sustainable products and logistics systems to approach the zero waste objective [27]. In addition to waste prevention, the manufacturing processes must be environmentally friendly—for example, by using renewable resources and green computing [30].

    3. Smart Logistics in Industry 5.0

    The core elements of Industry 5.0 show that following the technology-centric transition of Industry 4.0, the societal, environmental, and human perspectives require more attention, which will yield significant impacts on logistics operations and management. For instance, the personalization of demands implies a personalized delivery system [25]. Incorporating customers into the design requires highly intelligent CPS and system integration [30]. Human–machine interaction triggers the interaction of various topics such as safety, human behavior, etc. [23]. Thus, there exist various challenges and approaches to addressing smart logistics issues in Industry 5.0. With a focus on the interaction between technology and humans in smart logistics, researchers present discussions through a quadripartite intelligence framework [3][24], namely intelligent automation, intelligent devices, intelligent systems, and intelligent material.

    3.1. Intelligent Automation

    The major focus of Industry 5.0 is human-centricity, which, from a pragmatic aspect, puts forward the presence and high importance of the human in a system. However, there is a trade-off between human integration and automation to satisfy the goals of Industry 5.0, and this concern resides in the context of intelligent automation [23][24], e.g., human–robot collaboration. It impacts the resilience of a logistics system and thus requires special attention and intelligence to achieve a lean collaboration [35][36][37]. The human’s role in a logistics system was initially investigated in 2016 under the concept of ‘Operator 4.0′, which aims, by taking advantage of technological advancements, at maximizing the human’s contribution from three functional aspects [38][39], namely assisted work, collaborative work, and augmented work. The first function highlights the tasks that are mainly completed by human operators with the help of assisting technologies. The second requires collaboration between machine/robot and human. The last relies on technologies that could extend the human’s physical and visional capabilities. Considering logistics operations at different stages, e.g., production, warehousing, etc., material handling and information flow are two operational categories that significantly benefit from these applications [40].
    Industry 5.0 paves the way to extending this framework by considering both resilience and human-centricity. Romero and Stahre [41] introduce the concept of ‘Operator 5.0′ as “a smart and skilled operator that uses human creativity, ingenuity, and innovation empowered by information and technology as a way of overcoming obstacles in the path to create new, frugal solutions for guaranteeing manufacturing operations sustainable continuity and workforce wellbeing in light of difficult and/or unexpected conditions”. In the context of Industry 5.0, this paradigm encourages technological development in two main directions: self-resilience and system resilience. Self-resilience emphasizes human sustainability from biological, physical, cognitive, and psychological dimensions and focuses on human-centricity in the technological transition, i.e., work ethics, social impacts, legal issues, etc. [42][43][44][45]. System resilience, however, signifies the functional collaboration between humans and machines in terms of sharing and trading control [46].
    Human–robot collaboration in Industry 5.0 also plays a vital role in reacting to highly unexpected events, e.g., the COVID-19 pandemic, which requires high production agility and flexibility to fulfill the rapidly increasing demands of medical supplies [41][47][48]. In this regard, collaborative robots (cobots) are one of the most discussed enabling technologies in Industry 5.0. However, two important issues, namely the human skills and the behavior of cobots, need to be taken into account when cobots are integrated into a production or logistics system. As the main lever of Industry 5.0, through proper training, humans must be capable of working together with cobots [45][49][50][51][52]. For this purpose, the use of several supportive technologies, i.e., virtual reality, augmented reality, and simulation, has been extensively investigated [3][48][53]. For instance, operators can learn and understand the cobot motions under specific conditions without compromising safety measures and productivity [3][48]. On the other hand, cobots can be programmed or trained to establish a lean collaboration with the operators, which may lead to an increase in the productivity and efficiency of the workflow [54]. Human–robot collaboration not only requires hardware capabilities, i.e., sensors, etc., but also implies the essence of cognitive and intelligent behaviors of the cobot [54]. In this regard, the latest computation methodologies, i.e., machine learning (ML), deep learning (DL), clustering, regression, etc., have become increasingly important for the development of versatile applications [3][48][55][56][57][58][59].

    3.2. Intelligent Devices

    Machines, robots, and other facilities that are used in the production and logistics systems must be improved and equipped with smart technologies to maximize functionality and performance through physical and cyber connections with high monitoring and controlling capacities [60][61][62][63]. Considering the scopes of Industry 5.0, this objective signifies the interaction between humans and robots/machines. On the one hand, these intelligent devices, e.g., intelligent machines, smart robots, cobots, etc., require cognitive capabilities for decision-making by themselves to not only perform operations alongside the humans but also actively prevent undesired incidents. On the other hand, due to the operators’ inherent physical and intellectual limitations, the shortcomings for accessing the information flow and augmented functional abilities can be resolved by intelligent devices [50]. The collaboration between robot and operator raises concerns about human constraints as opposed to machines, which requires extra effort to resolve their integration issues. In this regard, operators’ conditions need to be constantly traced with capture motion and eye-tracking devices, wearable biometric equipment, etc., under various workload conditions from both physical and cognitive perspectives [64][65][66]. This helps to facilitate a resilient workplace in which the environment adaptability can be improved in varied conditions [65].
    In addition, Industry 5.0 emphasizes human-centricity through the use of technologies and hardware to improve and support the operators’ performance in logistics systems and supply chain operations. In this regard, human wearable devices that boost cognitive and operational capacities are increasingly being utilized and improved in manufacturing industries [66]. Exoskeleton refers to augmenter equipment that gives extra strength and physical capabilities to protect the operator from the adverse effects of heavy workloads [67][68][69][70]. Benefiting from virtual technologies, i.e., smart AR glass, spatial AR projector, etc., are viable and novel gadgets that facilitate flexible operations and technical guidance through information transmission and virtualization [41].
    Moreover, the latest improvements in unmanned aerial vehicles (UAVs) have radically altered the intralogistics and material handling systems in a highly novel manner, and this additionally represents significant potential for personalized delivery systems [25][71][72]. Furthermore, Auto Identification (Auto-ID) and RFID have been extensively investigated in smart logistics and supply chains, which support traceability, warehouse operations, and inventory management [51][73].

    3.3. Intelligent Systems

    The systematic approach of Industry 5.0 requires information transmission for individualized and case-based tasks in the production system and enhanced interaction with better decision-making processes throughout the whole supply chain [74][75][76][77][78]. This characteristic urges improved data and information exchange among different stakeholders, which largely affects the agility and intelligence of a smart logistics system. This aim can be realized by a network of data interoperability, where sensors exchange and process information in a big data environment [3][25][79][80][81][82]. In the context of Industry 5.0, a Smart Cyber–Physical System (SCPS) can be established for promoting data transmission and the sustainability of production and logistics systems [83][84]. This digital transformation, however, must be energy-efficient by taking into account green procedures, i.e., green production, green recycling/disposal, green IoT (G-IoT), etc., to facilitate a lean circular economy (CE) [85][86].
    A digital transition to Industry 5.0 and Society 5.0 triggers the development of blockchain computing [27][87][88][89][90][91]. In addition, it benefits the supply chain by enabling demand customization and personalization through recommender systems, which capture customers’ preferences using social networks, text recognition, and analytical techniques [92]. Benefiting from internet-based connectivity, the transparency of information and manufacturing traceability can be drastically enhanced [25][51]. Real-time decision-making and high-quality visualization form the foundation of a virtual smart logistics system in Industry 5.0 [93], which facilitates the emergence of the smart digital twin for logistics systems [3][81][94][95][96].

    3.4. Intelligent Materials

    One of the revolutionary improvements in Industry 5.0 is the development of smart materials. The characteristics of these new materials may significantly impact the supply chain activities by serving multiple functionalities and capabilities under certain conditions. For example, manipulating the shape and properties of the material and/or product according to varying physical conditions, e.g., temperature, light, stress, etc. [96][97][98][99]. The primary implication is related to additive manufacturing, where the 4D printing method strongly benefits from smart materials [24]. Compared with traditional 3D printing, 4D printing employs similar technology that fabricates parts and components through the layer-wise adhesion of a corresponding material. However, the major difference lies in the material type [97][98][100][101]. By using smart materials, the products can maintain various shapes and functionalities according to the environmental condition to improve the durability, adaptability, and reliability of the product. Various examples exist in medical science, aerospace, semiconductors, etc.

    References

    1. Qin, J.; Liu, Y.; Grosvenor, R. A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP 2016, 52, 173–178.
    2. Xu, X.; Lu, Y.; Vogel-Heuser, B.; Wang, L. Industry 4.0 and Industry 5.0-Inception, conception and perception. J. Manuf. Syst. 2021, 61, 530–535.
    3. Nahavandi, S. Industry 5.0-A Human-Centric Solution. Sustainability 2019, 11, 4371.
    4. Frederico, G.F. From supply chain 4.0 to supply chain 5.0: Findings from a systematic literature review and research directions. Logistics 2021, 5, 49.
    5. Alexa, L.; Pîslaru, M.; Avasilcăi, S. From Industry 4.0 to Industry 5.0—An Overview of European Union Enterprises. In Sustainability and Innovation in Manufacturing Enterprises; Springer Nature: Cham, Switzerland, 2022; pp. 221–231.
    6. Callaghan, C.W. Transcending the threshold limitation: A fifth industrial revolution? Manag. Res. Rev. 2019, 43, 447–461.
    7. Ghobakhloo, M. Industry 4.0, digitization, and opportunities for sustainability. J. Clean. Prod. 2020, 252, 119869.
    8. Romero, C.A.T.; Castro, D.F.; Ortiz, J.H.; Khalaf, O.I.; Vargas, M.A. Synergy between circular economy and industry 4.0: A literature review. Sustainability 2021, 13, 4331.
    9. Sun, X.; Yu, H.; Solvang, W.D. Industry 4.0 and Sustainable Supply Chain Management. In International Workshop of Advanced Manufacturing and Automation; Lecture Notes in Electrical Engineering; Springer: Singapore, 2021; Volume 737.
    10. Saniuk, S.; Grabowska, S.; Straka, M. Identification of Social and Economic Expectations: Contextual Reasons for the Transformation Process of Industry 4.0 into the Industry 5.0 Concept. Sustainability 2022, 14, 1391.
    11. Madsen, D.Ø.; Berg, T. An exploratory bibliometric analysis of the birth and emergence of industry 5.0. Appl. Syst. Innov. 2021, 4, 87.
    12. Sindhwani, R.; Afridi, S.; Kumar, A.; Banaitis, A.; Luthra, S.; Singh, P.L. Can industry 5.0 revolutionize the wave of resilience and social value creation? A multi-criteria framework to analyze enablers. Technol. Soc. 2022, 68, 101887.
    13. Di Nardo, M.; Yu, H. Special issue “industry 5.0: The prelude to the sixth industrial revolution”. Appl. Syst. Innov. 2021, 4, 45.
    14. Elangovan, U. Industry 5.0: The Future of the Industrial Economy; CRC Press: Boca Raton, FL, USA, 2022.
    15. Sun, X.; Yu, H.; Solvang, W.; Wang, Y.; Wang, K. The application of Industry 4.0 technologies in sustainable logistics: A systematic literature review (2012–2020) to explore future research opportunities. Environ. Sci. Pollut. Res. 2021, 29, 9560–9591.
    16. Ali, I.; Phan, H.M. Industry 4.0 technologies and sustainable warehousing: A systematic literature review and future research agenda. Int. J. Logist. Manag. 2022.
    17. Efthymiou, O.K.; Ponis, S.T. Industry 4.0 Technologies and Their Impact in Contemporary Logistics: A Systematic Literature Review. Sustainability 2021, 13, 11643.
    18. Ivanov, D.; Dolgui, A. A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Prod. Plan. Control 2020, 32, 775–788.
    19. Wang, K. Logistics 4.0 Solution-New Challenges and Opportunities. In 6th International Workshop of Advanced Manufacturing and Automation; Atlantis Press: Manchester, UK, 2016.
    20. Jagtap, S.; Bader, F.; Garcia-Garcia, G.; Trollman, H.; Fadiji, T.; Salonitis, K. Food logistics 4.0: Opportunities and challenges. Logistics 2021, 5, 2.
    21. He, Z.; Turner, P. A Systematic Review on Technologies and Industry 4.0 in the Forest Supply Chain: A Framework Identifying Challenges and Opportunities. Logistics 2021, 5, 88.
    22. European Commission. Industry 5.0: Towards A Sustainable, Human-Centric and Resilient European Industry; Publications Office: Luxembourg, 2021.
    23. Gaiardelli, S.; Spellini, S.; Lora, M.; Fummi, F. Modeling in Industry 5.0. In 2021 Forum on Specification & Design Languages (FDL); IEEE: Piscataway, NJ, USA, 2021.
    24. Javaid, M.; Haleem, A. Critical components of industry 5.0 towards a successful adoption in the field of manufacturing. J. Ind. Integr. Manag. 2020, 5, 327–348.
    25. Kumar, R.; Gupta, P.; Singh, S.; Jain, D. Human Empowerment by Industry 5.0 in Digital Era: Analysis of Enablers. In Lecture Notes in Mechanical Engineering; Springer: Singapore, 2021; pp. 401–410.
    26. Martynov, V.; Shiryaev, O.; Zaytseva, A.; Filosova, E.; Baikov, R. The Use of Artificial Intelligence in Modern Educational Technologies in the Transition to a Smart Society. In Proceedings of the 2019 21st International Conference “Complex Systems: Control and Modeling Problems”, CSCMP 2019, Samara, Russia, 3–6 September 2019.
    27. Saptaningtyas, W.W.E.; Rahayu, D.K. A proposed model for food manufacturing in smes: Facing industry 5.0. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Dubai, UAE, 10–12 March 2020.
    28. Mihardjo, L.W.W.; Sasmoko, S.; Alamsjah, F.; Djap, E. Boosting the firm transformation in industry 5.0: Experience-agility innovation model. Int. J. Recent Technol. Eng. 2019, 8, 735–742.
    29. Carayannis, E.G.; Dezi, L.; Gregori, G.; Calò, E. Smart Environments and Techno-centric and Human-Centric Innovations for Industry and Society 5.0: A Quintuple Helix Innovation System View Towards Smart, Sustainable, and Inclusive Solutions. J. Knowl. Econ. 2021.
    30. Pathak, P.; Pal, P.R.; Shrivastava, M.; Ora, M.S. Fifth revolution: Applied AI and human intelligence with cyber physical systems. Int. J. Eng. Adv. Technol. 2019, 8, 23–27.
    31. Imperatives, S. Report of the World Commission on Environment and Development: Our Common Future. 1987. Available online: https://sustainabledevelopment.un.org/content/documents/5987our-common-future.pdf (accessed on 25 February 2022).
    32. Yu, H.; Solvang, W.D. A general reverse logistics network design model for product reuse and recycling with environmental considerations. Int. J. Adv. Manuf. Technol. 2016, 87, 2693–2711.
    33. Yu, H.; Solvang, W. A Stochastic Programming Approach with Improved Multi-Criteria Scenario-Based Solution Method for Sustainable Reverse Logistics Design of Waste Electrical and Electronic Equipment (WEEE). Sustainability 2016, 8, 1331.
    34. Patera, L.; Garbugli, A.; Bujari, A.; Scotece, D.; Corradi, A. A layered middleware for ot/it convergence to empower industry 5.0 applications. Sensors 2022, 22, 190.
    35. Butner, K.; Ho, G. How the human-machine interchange will transform business operations. Strategy Leadersh. 2019, 47, 25–33.
    36. Mekid, S.; Schlegel, T.; Aspragathos, N.; Teti, R. Foresight formulation in innovative production, automation and control systems. Foresight 2007, 9, 35–47.
    37. Pagliosa, M.; Tortorella, G.; Ferreira, J.C.E. Industry 4.0 and Lean Manufacturing: A systematic literature review and future research directions. J. Manuf. Technol. Manag. 2019, 32, 543–569.
    38. Romero, D.; Bernus, P.; Noran, O.; Stahre, J.; Fast-Berglund, Å. The operator 4.0: Human cyber-physical systems. In IFIP International Conference on Advances in Production Management Systems; Springer: Berlin/Heidelberg, Germany, 2016.
    39. David, R.; Stahre, J.; Wuest, T.; Noran, O.; Bernus, P.; Berglund, Å.F.; Gorecky, D. Towards an operator 4.0 typology: A human-centric perspective on the fourth industrial revolution technologies. In Proceedings of the international conference on computers and industrial engineering (CIE46), Tianjin, China, 29–31 October 2016.
    40. Cimini, C.; Lagorio, A.; Romero, D.; Cavalieri, S.; Stahre, J. Smart Logistics and The Logistics Operator 4.0. IFAC PapersOnLine 2020, 53, 10615–10620.
    41. Romero, D.; Stahre, J. Towards the Resilient Operator 5.0: The Future of Work in Smart Resilient Manufacturing Systems. Procedia CIRP 2021, 104, 1089–1094.
    42. Demir, K.A.; Döven, G.; Sezen, B. Industry 5.0 and Human-Robot Co-working. Procedia Comput. Sci. 2019, 158, 688–695.
    43. Resende, A.; Cerqueira, S.; Barbosa, J.; Damásio, E.; Pombeiro, A.; Silva, A.; Santos, C. Ergowear: An ambulatory, non-intrusive, and interoperable system towards a Human-Aware Human-robot Collaborative framework. In Proceedings of the 2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC, Santa Maria da Feira, Portugal, 28–29 April 2021.
    44. Welfare, K.S.; Hallowell, M.R.; Shah, J.A.; Riek, L.D. Consider the human work experience when integrating robotics in the workplace. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI); IEEE: Piscataway, NJ, USA, 2019.
    45. Hol, A. Business Transformations Within Intelligent Eco-Systems. Lect. Notes Netw. Syst. 2021, 149, 275–284.
    46. Inagaki, T. Adaptive automation: Sharing and trading of control. Handb. Cogn. Task Des. 2003, 8, 147–169.
    47. Doyle-Kent, M.; Kopacek, P. Collaborative Robotics Making a Difference in the Global Pandemic. Lect. Notes Mech. Eng. 2022, 161–169.
    48. Rega, A.; Di Marino, C.; Pasquariello, A.; Vitolo, F.; Patalano, S.; Zanella, A.; Lanzotti, A. Collaborative workplace design: A knowledge-based approach to promote human–robot collaboration and multi-objective layout optimization. Appl. Sci. 2021, 11, 12147.
    49. Doyle-Kent, M.; Kopacek, P. Industry 5.0: Is the manufacturing industry on the cusp of a new revolution? In Proceedings of the International Symposium for Production Research; Springer: Berlin/Heidelberg, Germany, 2019.
    50. Nagyova, A.; Kotianova, Z.; Glatz, J.; Sinay, J. Human Failures on Production Line as a Source of Risk of Non-conformity Occurrence. In Advances in Intelligent Systems and Computing; IEEE: Piscataway, NJ, USA, 2020; pp. 97–103.
    51. Fornasiero, R.; Zangiacomi, A. Reshaping the Supply Chain for Society 5.0. In IFIP Advances in Information and Communication Technology; Springer International Publishing: Cham, Switzerland, 2021; pp. 663–670.
    52. Doyle Kent, M.; Kopacek, P. Do We Need Synchronization of the Human and Robotics to Make Industry 5.0 a Success Story. In Digital Conversion on the Way to Industry 4.0; Springer International Publishing: Cham, Switzerland, 2021.
    53. Doyle-Kent, M.; Kopacek, P. Adoption of collaborative robotics in industry 5.0. An Irish industry case study. IFAC-PapersOnLine 2021, 54, 413–418.
    54. Jabrane, K.; Bousmah, M. A New Approach for Training Cobots from Small Amount of Data in Industry 5.0. Int. J. Adv. Comput. Sci. Appl. 2021, 12, 634–646.
    55. Kavousi-Fard, A.; Khosravi, A.; Nahavandi, S. A new fuzzy-based combined prediction interval for wind power forecasting. IEEE Trans. Power Syst. 2015, 31, 18–26.
    56. Khosravi, A.; Nahavandi, S.; Creighton, D. Prediction interval construction and optimization for adaptive neurofuzzy inference systems. IEEE Trans. Fuzzy Syst. 2011, 19, 983–988.
    57. Khosravi, A.; Nahavandi, S.; Creighton, D. Prediction intervals for short-term wind farm power generation forecasts. IEEE Trans. Sustain. Energy 2013, 4, 602–610.
    58. Nguyen, T.; Khosravi, A.; Creighton, D.; Nahavandi, S. Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering. J. Neurosci. Methods 2014, 238, 43–53.
    59. Zhou, H.; Kong, H.; Wei, L.; Creighton, D.; Nahavandi, S. Efficient road detection and tracking for unmanned aerial vehicle. IEEE Trans. Intell. Transp. Syst. 2014, 16, 297–309.
    60. Crutzen, C.K. Intelligent Ambience between Heaven and Hell: A Salvation? J. Inf. Commun. Ethics Soc. 2005, 3, 219–232.
    61. Matindoust, S.; Nejad, M.B.; Zou, Z.; Zheng, L.R. Food quality and safety monitoring using gas sensor array in intelligent packaging. Sens. Rev. 2016, 36, 169–183.
    62. Shammar, E.A.; Zahary, A.T. The Internet of Things (IoT): A survey of techniques, operating systems, and trends. Library Hi Tech 2019, 38, 5–66.
    63. Sreekumar, M.; Nagarajan, T.; Singaperumal, M.; Zoppi, M.; Molfino, R. Critical review of current trends in shape memory alloy actuators for intelligent robots. Ind. Robot. Int. J. 2007, 34, 285–294.
    64. Brunzini, A.; Brunzini, A.; Grandi, F.; Khamaisi, R.K.; Pellicciari, M. A preliminary experimental study on the workers’ workload assessment to design industrial products and processes. Appl. Sci. 2021, 11, 12066.
    65. Ávila-Gutiérrez, M.J.; Aguayo-González, F.; Lama-Ruiz, J.R. Framework for the development of affective and smart manufacturing systems using sensorised surrogate models. Sensors 2021, 21, 2274.
    66. Longo, F.; Padovano, A.; Umbrello, S. Value-oriented and ethical technology engineering in industry 5.0: A human-centric perspective for the design of the factory of the future. Appl. Sci. 2020, 10, 4182.
    67. Puvvada, Y.S.; Vankayalapati, S.; Sukhavasi, S. Extraction of chitin from chitosan from exoskeleton of shrimp for application in the pharmaceutical industry. Int. Curr. Pharm. J. 2012, 1, 258–263.
    68. Spada, S.; Ghibaudo, L.; Gilotta, S.; Gastaldi, L.; Cavatorta, M. Analysis of exoskeleton introduction in industrial reality: Main issues and EAWS risk assessment. In International Conference on Applied Human Factors and Ergonomics; Springer: Berlin/Heidelberg, Germany, 2017.
    69. Sung, T.K. Industry 4.0: A Korea perspective. Technol. Forecast. Soc. Change 2018, 132, 40–45.
    70. Sylla, N.; Bonnet, V.; Colledani, F.; Fraisse, P. Ergonomic contribution of ABLE exoskeleton in automotive industry. Int. J. Ind. Ergon. 2014, 44, 475–481.
    71. Coelho, J.F.; Ferreira, P.C.; Alves, P.; Cordeiro, R.; Fonseca, A.C.; Góis, J.R.; Gil, M.H. Drug delivery systems: Advanced technologies potentially applicable in personalized treatments. EPMA J. 2010, 1, 164–209.
    72. Goole, J.; Amighi, K. 3D printing in pharmaceutics: A new tool for designing customized drug delivery systems. Int. J. Pharm. 2016, 499, 376–394.
    73. Fraga-Lamas, P.; Varela-Barbeito, J.; Fernandez-Carames, T.M. Next Generation Auto-Identification and Traceability Technologies for Industry 5.0: A Methodology and Practical Use Case for the Shipbuilding Industry. IEEE Access 2021, 9, 140700–140730.
    74. Cao, Y.; You, J.; Shi, Y.; Hu, W. The obstacles of China’s intelligent automobile manufacturing industry development: A structural equation modeling study. Chin. Manag. Stud. 2020, 14, 159–183.
    75. Rogale, S.F.; Rogale, D.; Dragčević, Z.; Nikolić, G.; Bartoš, M. Technical systems in intelligent clothing with active thermal protection. Int. J. Cloth. Sci. Technol. 2007, 19, 222–233.
    76. Sakamoto, S.; Barolli, A.; Barolli, L.; Okamoto, S. Implementation of a Web interface for hybrid intelligent systems: A comparison study of two hybrid intelligent systems. Int. J. Web Inf. Syst. 2019, 15, 420–431.
    77. Sykora, M. Engineering social media driven intelligent systems through crowdsourcing: Insights from a financial news summarisation system. J. Syst. Inf. Technol. 2016, 18, 255–276.
    78. Xie, K.; Liu, Z.; Fu, L.; Liang, B. Internet of Things-based intelligent evacuation protocol in libraries. Library Hi Tech 2019, 38, 145–163.
    79. Kumar, R. Sustainable supply chain management in the era of digitialization: Issues and challenges. In Handbook of Research on Social and Organizational Dynamics in the Digital Era; IGI Global: Hershey, PA, USA, 2020; pp. 446–460.
    80. Kumar, R. Espousal of Industry 4.0 in Indian manufacturing organizations: Analysis of enablers. In Research Anthology on Cross-Industry Challenges of Industry 4.0; IGI Global: Hershey, PA, USA, 2021; pp. 1244–1251.
    81. Paschek, D.; Mocan, A.; Draghici, A. Industry 5.0—The expected impact of next industrial revolution. In Proceedings of the Thriving on Future Education, Industry, Business, and Society, Proceedings of the MakeLearn and TIIM International Conference, Piran, Slovenia, 15–17 May 2019.
    82. Skobelev, P.; Borovik, S.Y. On the way from Industry 4.0 to Industry 5.0: From digital manufacturing to digital society. Industry 4.0 2017, 2, 307–311.
    83. Thakur, P.; Kumar Sehgal, V. Emerging architecture for heterogeneous smart cyber-physical systems for industry 5.0. Comput. Ind. Eng. 2021, 162, 107750.
    84. Golov, R.S.; Palamarchuk, A.G.; Anisimov, K.V.; Andrianov, A.M. Cluster Policy in a Digital Economy. Russ. Eng. Res. 2021, 41, 631–633.
    85. Zhu, C.; Leung, V.C.M.; Shu, L.; Ngai, E.C.-H. Green Internet of Things for Smart World. IEEE Access 2015, 3, 2151–2162.
    86. Fraga-Lamas, P.; Lopes, S.I.; Fernández-Caramés, T.M. Green iot and edge AI as key technological enablers for a sustainable digital transition towards a smart circular economy: An industry 5.0 use case. Sensors 2021, 21, 5745.
    87. Pramanik, P.K.D.; Mukherjee, B.; Pal, S.; Upadhyaya, B.K.; Dutta, S. Ubiquitous manufacturing in the age of industry 4.0: A state-of-the-art primer. In A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development; Springer: Berlin/Heidelberg, Germany, 2020; pp. 73–112.
    88. Puthal, D.; Malik, N.; Mohanty, S.P.; Kougianos, E.; Das, G. Everything you wanted to know about the blockchain: Its promise, components, processes, and problems. IEEE Consum. Electron. Mag. 2018, 7, 6–14.
    89. Samaniego, M.; Deters, R. Virtual Resources & Blockchain for Configuration Management in IoT. J. Ubiquitous Syst. Pervasive Netw. 2018, 9, 1–13.
    90. Carayannis, E.G.; Christodoulou, K.; Christodoulou, P.; Chatzichristofis, S.A.; Zinonos, Z. Known Unknowns in an Era of Technological and Viral Disruptions—Implications for Theory, Policy, and Practice. J. Knowl. Econ. 2021, 2021, 1–24.
    91. Rahman, N.A.A.; Muda, J.; Mohammad, M.F.; Ahmad, M.F.; Rahim, S.A.; Mayor-Vitoria, F. Digitalization and leap frogging strategy among the supply chain member: Facing GIG economy and why should logistics players care? Int. J. Supply Chain. Manag. 2019, 8, 1042–1048.
    92. Bathla, G.; Singh, P.; Kumar, S.; Verma, M.; Garg, D.; Kotecha, K. Recop: Fine-grained opinions and sentiments-based recommender system for industry 5.0. Soft Comput. 2021.
    93. Matsuda, M.; Nishi, T.; Hasegawa, M.; Matsumoto, S. Virtualization of a supply chain from the manufacturing enterprise view using e-catalogues. Procedia CIRP 2019, 81, 932–937.
    94. Nahavandi, S.; Preece, C. A virtual manufacturing environment with an element of reality. Proceedings of Fourth International Conference on Factory 2000—Advanced Factory Automation, York, UK, 3–5 October 1994.
    95. Sulema, Y. ASAMPL: Programming language for mulsemedia data processing based on algebraic system of aggregates. In Interactive Mobile Communication, Technologies and Learning; Springer: Berlin/Heidelberg, Germany, 2017.
    96. Hakanen, E.; Rajala, R. Material intelligence as a driver for value creation in IoT-enabled business ecosystems. J. Bus. Ind. Mark. 2018, 33, 857–867.
    97. Javaid, M.; Haleem, A. Industry 4.0 applications in medical field: A brief review. Curr. Med. Res. Pract. 2019, 9, 102–109.
    98. Li, X.; Shang, J.; Wang, Z. Intelligent materials: A review of applications in 4D printing. Assem. Autom. 2017, 37, 170–185.
    99. Yang, X.; Ma, C.; Zhu, C.; Qi, B.; Pan, F.; Zhu, C. Design of hazardous materials transportation safety management system under the vehicle-infrastructure connected environment. J. Intell. Connect. Veh. 2019, 2, 14–24.
    100. Pei, E. 4D Printing: Dawn of an emerging technology cycle. Assem. Autom. 2014, 34, 310–314.
    101. Pei, E.; Loh, G.H.; Harrison, D.; De Almeida, H.; Verona, M.D.M.; Paz, R. A study of 4D printing and functionally graded additive manufacturing. Assem. Autom. 2017, 37, 147–153.
    More
    Information
    Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , ,
    View Times: 791
    Revisions: 2 times (View History)
    Update Date: 21 Apr 2022
    Table of Contents
      1000/1000

      Confirm

      Are you sure you want to delete?

      Video Upload Options

      Do you have a full video?
      Cite
      If you have any further questions, please contact Encyclopedia Editorial Office.
      Azarian, M.; Jafari, N.; Yu, H. Smart Logistics in Industry 5.0. Encyclopedia. Available online: https://encyclopedia.pub/entry/22013 (accessed on 07 February 2023).
      Azarian M, Jafari N, Yu H. Smart Logistics in Industry 5.0. Encyclopedia. Available at: https://encyclopedia.pub/entry/22013. Accessed February 07, 2023.
      Azarian, Mohammad, Niloofar Jafari, Hao Yu. "Smart Logistics in Industry 5.0," Encyclopedia, https://encyclopedia.pub/entry/22013 (accessed February 07, 2023).
      Azarian, M., Jafari, N., & Yu, H. (2022, April 20). Smart Logistics in Industry 5.0. In Encyclopedia. https://encyclopedia.pub/entry/22013
      Azarian, Mohammad, et al. ''Smart Logistics in Industry 5.0.'' Encyclopedia. Web. 20 April, 2022.
      Top
      Feedback