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Journal articles on the topic 'Resilient Manufacturing Systems'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Lee, Jay, Shahin Siahpour, Xiaodong Jia, and Patrick Brown. "Introduction to resilient manufacturing systems." Manufacturing Letters 32 (April 2022): 24–27. http://dx.doi.org/10.1016/j.mfglet.2022.02.002.

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2

Zhang, Jiawen, and Cheng Wang. "Supply–Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model." Applied Sciences 15, no. 4 (2025): 1747. https://doi.org/10.3390/app15041747.

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In response to the escalating demand for personalisation and customisation, the manufacturing industry is increasingly driven towards digital and intelligent transformation. The fluctuating market environment and variable consumer demands impose significant challenges on the resilience of manufacturing systems. With the advent of Industry 5.0, developing a resilient manufacturing system has become even more critical. While existing studies have extensively explored participant satisfaction in cloud manufacturing, there remains a notable gap in understanding how network resilience impacts suppl

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3

Lounis, Stavros, Anastasios Koukopoulos, Timoleon Farmakis, and Maria Aryblia. "Evaluation Methodology for Circular and Resilient Information Systems." Applied Sciences 14, no. 17 (2024): 8089. http://dx.doi.org/10.3390/app14178089.

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Digital technologies nowadays provide essential support for companies, making them a priority for businesses and a prominent area of study for researchers. In response to the increasing emphasis on sustainability and resilience, new information systems are developing to meet evolving business needs, namely circular and resilient information systems (CRISs). These systems integrate with traditional ones to optimise key performance indicators (KPIs) related to circularity and resiliency. Despite extensive methodologies for evaluating traditional information systems, systems designed for circular

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4

Jin, Xiaoning, and Xi Gu. "Option-Based Design for Resilient Manufacturing Systems." IFAC-PapersOnLine 49, no. 12 (2016): 1602–7. http://dx.doi.org/10.1016/j.ifacol.2016.07.809.

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5

Lu, Jinzhi, Xiaochen Zheng, and Dimitris Kiritsis. "Special Issue: Smart Resilient Manufacturing." Applied Sciences 13, no. 1 (2022): 464. http://dx.doi.org/10.3390/app13010464.

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During the past decades, the global manufacturing industries have been reshaped by the rapid development of advanced technologies, such as cyber-physical systems, Internet of Things, artificial intelligence (AI), machine learning, cloud/edge computing, smart sensing, advanced robotics, blockchain/distributed ledger technology, etc [...]

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6

Fjodorovs, Nikita, Salih Panz, and Philipp Harder. "Soziotechnisches Informationssystem für resiliente Produktion/Socio-technical information system for resilient production." wt Werkstattstechnik online 115, no. 01-02 (2025): 58–65. https://doi.org/10.37544/1436-4980-2025-01-02-62.

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Unternehmensresilienz gewinnt angesichts globaler Unsicherheiten zunehmend an Bedeutung. Um auf externe Veränderungen und Krisen schnell reagieren zu können, müssen Unternehmen ihre Prozesse und Strukturen flexibel anpassen – und damit auch die IT-Systeme, auf die sie angewiesen sind, wie etwa Enterprise-Resource-Planning und Manufacturing-Execution-Systems. Die steigende Abhängigkeit der Unternehmen von diesen IT-Systemen macht ihre Anpassungsfähigkeit umso wichtiger. Viele IT-Systeme bieten zwar eine gewisse technische Flexibilität, notwendige Veränderungen werden jedoch häufig durch organis

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Mukherjee, Avik, Moritz Glatt, Waleed Mustafa, Marius Kloft, and Jan C. Aurich. "Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience." Procedia CIRP 115 (2022): 83–88. http://dx.doi.org/10.1016/j.procir.2022.10.054.

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8

Malyshev, Evgeniy A., and Ilya A. Serov. "Analyzing of the Lean Production Mechanism Sustainability in the Design of a Production System under Conditions of Uncertainty." Transbaikal State University Journal 30, no. 4 (2024): 120–27. https://doi.org/10.21209/2227-9245-2024-30-4-120-127.

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In conditions of high uncertainty in international markets and complex supply chains, manufacturing companies face the need to integrate resilience into their systems to counter disruptions caused by external and internal factors such as pandemics and climate crisis. The object of the study is manufacturing systems vulnerable to these factors, and the aim is to develop an approach to designing systems that combines the principles of lean manufacturing and resilience. The main tasks include analyzing existing approaches to designing production systems, identifying key principles of lean manufac

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9

Mourtzis, Dimitris, John Angelopoulos, and Nikos Panopoulos. "Robust Engineering for the Design of Resilient Manufacturing Systems." Applied Sciences 11, no. 7 (2021): 3067. http://dx.doi.org/10.3390/app11073067.

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As the industrial requirements change rapidly due to the drastic evolution of technology, the necessity of quickly investigating potential system alternatives towards a more efficient manufacturing system design arises more intensely than ever. Production system simulation has proven to be a powerful tool for designing and evaluating a manufacturing system due to its low cost, quick analysis, low risk and meaningful insight that it may provide, improving the understanding of the influence of each component. In this research work, the design and evaluation of a real manufacturing system using D

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Uhlmann, Eckart, Julian Polte, and Christopher Mühlich. "Distributed real-time production control for resilient manufacturing systems." Procedia CIRP 120 (2023): 1274–79. http://dx.doi.org/10.1016/j.procir.2023.09.162.

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11

Bauer, Dennis, Can Kaymakci, Thomas Bauernhansl, and Alexander Sauer. "Intelligent Energy Systems as Enabler for Increased Resilience of Manufacturing Systems." Procedia CIRP 104 (November 26, 2021): 217–22. https://doi.org/10.1016/j.procir.2021.11.037.

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The discussion on manufacturing systems’ resilience is currently dominated by logistic targets. However, with the ongoing energy transition and the respective increasing volatility of power supply, the focus is broadened to include power supply as a considerable source of disturbances. Therefore, an efficient adaptive control of manufacturing companies' power consumption dependent on the power supply is necessary without counteracting the logistic targets. This paper elaborates the benefits of information systems supporting manufacturing companies in automating the synchronization of

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12

Romero, David, and Johan Stahre. "Towards The Resilient Operator 5.0: The Future of Work in Smart Resilient Manufacturing Systems." Procedia CIRP 104 (2021): 1089–94. http://dx.doi.org/10.1016/j.procir.2021.11.183.

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Sanchez, Dr Alejandro Rodriguez, and Prof Maria Lopez Garcia. "Cybersecurity for Industry 4.0: Safeguarding Manufacturing Systems for the Future." International journal of networks and security 5, no. 3 (2025): 13–21. https://doi.org/10.55640/ijns-05-01-02.

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The manufacturing industry is undergoing a significant transformation, with digitalization, automation, and the Internet of Things (IoT) paving the way for more intelligent, connected, and efficient production systems. However, as manufacturing systems become increasingly digitalized, they are also becoming more vulnerable to cyber threats. This article examines the key challenges and solutions in ensuring that cybersecurity strategies are fit for the future of manufacturing, focusing on the integration of emerging technologies, proactive security measures, and the importance of collaboration

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Sbaragli, Andrea, Federica Tomelleri, Francesco Picariello, Enrico Picariello, and Francesco Pilati. "Safe Operator 5.0 digital architecture: towards resilient human-centric manufacturing systems." IFAC-PapersOnLine 58, no. 19 (2024): 265–70. http://dx.doi.org/10.1016/j.ifacol.2024.09.187.

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15

Olaitan, Ololade Funke, Ogheneruemu Nathaniel Akatakpo, Chukwujama Osinachi Victor, et al. "Secure and Resilient Industrial IoT Architectures for Smart Manufacturing: A Comprehensive Review." Journal of Engineering Research and Reports 27, no. 6 (2025): 331–44. https://doi.org/10.9734/jerr/2025/v27i61548.

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Smart manufacturing, driven by the Industrial Internet of Things (IIoT), is transforming real-time data sharing, predictive maintenance, and process automation. However, the increasing complexity and connectivity of IIoT environments present significant security and resilience challenges that threaten operational safety, data integrity, and business continuity. This paper presents a comprehensive review of secure and resilient architectures for IIoT in smart manufacturing, highlighting the interaction between enabling technologies (e.g., edge computing, AI/ML, blockchain) and emerging threats

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16

Tomiyama, Tetsuo, and Florian Moyen. "Resilient architecture for cyber-physical production systems." CIRP Annals 67, no. 1 (2018): 161–64. http://dx.doi.org/10.1016/j.cirp.2018.04.021.

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17

Taimun, MD Toukir Yeasir, S. M. Mobasshir Islam Sharan, Md Ashraful Azad, and Md Mofakhkharul Islam Joarder. "Smart Maintenance and Reliability Engineering in Manufacturing." Saudi Journal of Engineering and Technology 10, no. 04 (2025): 189–99. https://doi.org/10.36348/sjet.2025.v10i04.009.

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Smart Maintenance and Reliability Engineering (SMRE) in manufacturing leverages advanced technologies such as Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and Machine Learning (ML) to enhance asset performance, reduce downtime, and optimize maintenance strategies. By integrating predictive maintenance, condition monitoring, and real-time data analytics, SMRE improves operational efficiency and extends equipment lifespan. This paper explores the role of digital twins, cloud computing, and cyber-physical systems in revolutionizing maintenance practices. The study also disc

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18

Łada, Magdalena Alicja, Adam Wieczorek, and Katarzyna Łukiewska. "Selected management concepts in supply chains." Olsztyn Economic Journal 16, no. 1 (2021): 29–44. http://dx.doi.org/10.31648/oej.6789.

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Supply chain management is considered a complex process that can lead to errors, conflicts, or a breakdown of cooperation. In chain creation, it is important to organize and rationalize the network of interconnections to which the assumptions of concepts, such as lean manufacturing, agile manufacturing, and resilient supply chains, can be applied. For this reason, the aim of the research presented in the study was to attempt to identify how selected management systems in supply chains can influence the improved functioning of enterprises in a vulnerable condition and in the contemporary challe

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19

Paraschos, Panagiotis D., Alexandros S. Xanthopoulos, Georgios K. Koulinas, and Dimitrios E. Koulouriotis. "Machine learning integrated design and operation management for resilient circular manufacturing systems." Computers & Industrial Engineering 167 (May 2022): 107971. http://dx.doi.org/10.1016/j.cie.2022.107971.

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20

Kaasinen, Eija, Anu-Hanna Anttila, Päivi Heikkilä, Jari Laarni, Hanna Koskinen, and Antti Väätänen. "Smooth and Resilient Human–Machine Teamwork as an Industry 5.0 Design Challenge." Sustainability 14, no. 5 (2022): 2773. http://dx.doi.org/10.3390/su14052773.

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Smart machine companions such as artificial intelligence (AI) assistants and collaborative robots are rapidly populating the factory floor. Future factory floor workers will work in teams that include both human co-workers and smart machine actors. The visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators. What kinds of approaches can help design these kinds of resilient human–machine teams and collaborations within them? In this paper, w

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21

Leong, Wan Yee, Kuan Yew Wong, and Wai Peng Wong. "A New Integrated Multi-Criteria Decision-Making Model for Resilient Supplier Selection." Applied System Innovation 5, no. 1 (2022): 8. http://dx.doi.org/10.3390/asi5010008.

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Unexpected worldwide disruptions brought various challenges to supply chain management thus manipulating the research direction towards resilience. Since the supplier is one of the important supply chain elements, the challenges can be overcome through resilient supplier selection. Supplier selection is a multi-criteria decision-making problem where several criteria are involved. In this study, GRA-BWM-TOPSIS was proposed to evaluate resilient suppliers. Seven resilience criteria which were Quality, Lead Time, Cost, Flexibility, Visibility, Responsiveness and Financial Stability have been prop

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22

Nugraha, Yurid E., Tomohisa Hayakawa, Hideaki Ishii, Ahmet Cetinkaya, and Quanyan Zhu. "Two-Player Incomplete Games of Resilient Multiagent Systems." IFAC-PapersOnLine 56, no. 2 (2023): 258–63. http://dx.doi.org/10.1016/j.ifacol.2023.10.1578.

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23

Keskin, M. Hakan, Murat Koray, Ercan Kaya, Mehmet Maşuk Fidan, and Mehmet Ziya Söğüt. "Additive Manufacturing for Remedying Supply Chain Disruptions and Building Resilient and Sustainable Logistics Support Systems." Sustainability 17, no. 6 (2025): 2783. https://doi.org/10.3390/su17062783.

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Leading industries have implemented various strategic initiatives to enhance the resilience and sustainability of their logistics support systems in response to series of unforeseen disruptions that have significantly impacted supply chains (SCs) and incurred substantial costs over the past few decades. It is essential to assess whether incorporating additive manufacturing (AM) technologies into logistics support processes—either as a complementary solution or in conjunction with existing strategies—can effectively reduce vulnerabilities to disruptions in modern, complex SCs. AM technologies t

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24

Gardner, Liam, and James Colwill. "A Framework for the Resilient use of Critical Materials in Sustainable Manufacturing Systems." Procedia CIRP 41 (2016): 282–88. http://dx.doi.org/10.1016/j.procir.2016.01.003.

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25

Monteleone, Gabriella. "Revolutionising Industry 5.0: unlocking AI-at-the-Edge for sustainable manufacturing transformation." Project Repository Journal 22, no. 1 (2025): 102–5. https://doi.org/10.54050/prj2222764.

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Revolutionising Industry 5.0: unlocking AI-at-the-Edge for sustainable manufacturing transformation AI REDGIO 5.0 aims to accelerate the digital transformation of manufacturing SMEs by integrating AI-at-the-Edge and Industry 5.0 systems. The project focuses on developing AI-driven solutions that enhance operational efficiency, sustainability and human-centred practices. Through SME-driven experiments and collaboration across regions, it seeks to foster innovation, improve manufacturing processes and promote a sustainable, resilient industry.

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26

Rajawat, Anand Singh, S. B. Goyal, Chetan Chauhan, Pradeep Bedi, Mukesh Prasad, and Tony Jan. "Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm." Electronics 12, no. 1 (2023): 217. http://dx.doi.org/10.3390/electronics12010217.

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Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient to respond to changes in real time. This study examines the use of RL in a wide range of adaptive cognitive systems with IIoT-edges in manufacturing processes. We propose a cognitive adaptive system using IIoT with RL (CAS-IIoT-RL) and our experimen

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27

Agote-Garrido, Alejandro, Alejandro M. Martín-Gómez, and Juan Ramón Lama-Ruiz. "Manufacturing System Design in Industry 5.0: Incorporating Sociotechnical Systems and Social Metabolism for Human-Centered, Sustainable, and Resilient Production." Systems 11, no. 11 (2023): 537. http://dx.doi.org/10.3390/systems11110537.

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This paper delves into the concept of social metabolism as a foundation for the development of sociotechnical systems in Industry 5.0. The study conducts an analysis of the existing methods and approaches for designing sociotechnical systems, and reviews publications that utilize such systems to incorporate Industry 4.0 technologies into manufacturing processes. Additionally, it examines the three key factors of Industry 5.0 and the enabling framework of Industry 4.0 technologies. Based on these investigations, a theoretical model is proposed for manufacturing system design, employing sociotec

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28

Bauer, Dennis, Thomas Bauernhansl, and Alexander Sauer. "Improvement of Delivery Reliability by an Intelligent Control Loop between Supply Network and Manufacturing." Applied Sciences 11, no. 5 (2021): 2205. http://dx.doi.org/10.3390/app11052205.

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Manufacturing companies operate in an environment characterized as increasingly volatile, uncertain, complex and ambiguous. At the same time, their customer orientation makes it increasingly important to ensure high delivery reliability. Manufacturing sites within a supply network must therefore be resilient against events from the supply network. This requires deeper integration between the supply network and manufacturing control. Therefore, this article presents a concept to connect supply network and manufacturing more closely by integrating events from the supply network into manufacturin

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Bauer, Dennis, Thomas Bauernhansl, and Alexander Sauer. "Improvement of Delivery Reliability by an Intelligent Control Loop between Supply Network and Manufacturing." Applied Sciences 11, no. 5 (2021): 2205. https://doi.org/10.3390/app11052205.

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Manufacturing companies operate in an environment characterized as increasingly volatile, uncertain, complex and ambiguous. At the same time, their customer orientation makes it increasingly important to ensure high delivery reliability. Manufacturing sites within a supply network must therefore be resilient against events from the supply network. This requires deeper integration between the supply network and manufacturing control. Therefore, this article presents a concept to connect supply network and manufacturing more closely by integrating events from the supply network into manufacturin

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30

Afzal, Muhammad Jahanzaib, Ar Ahsan Khalil, Muhammad Islam, et al. "Strategies for Smart Manufacturing Industry 5.0: High Quality Development for the Future." European Journal of Theoretical and Applied Sciences 2, no. 3 (2024): 913–25. http://dx.doi.org/10.59324/ejtas.2024.2(3).71.

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The proposal of Industry 5.0 is an effort and attempt to shape a new type of industrialization using human defined values. The core elements of Industry 5.0, including human centeredness, sustainable development, and resilience, are all different forms of high-quality development. This article proposes four corporate value innovation strategies - rainforest strategy, wetland strategy, grassland strategy, and oasis strategy - to provide management suggestions for traditional manufacturing enterprises to shift to smart manufacturing Industry 5.0. Human-robot collaboration, AI and machine learnin

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Muhammad, Jahanzaib Afzal, Ahsan Khalil Ar., Islam Muhammad, et al. "Strategies for Smart Manufacturing Industry 5.0: High Quality Development for the Future." European Journal of Theoretical and Applied Sciences 2, no. 3 (2024): 913–25. https://doi.org/10.59324/ejtas.2024.2(3).71.

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The proposal of Industry 5.0 is an effort and attempt to shape a new type of industrialization using human defined values. The core elements of Industry 5.0, including human centeredness, sustainable development, and resilience, are all different forms of high-quality development. This article proposes four corporate value innovation strategies - rainforest strategy, wetland strategy, grassland strategy, and oasis strategy - to provide management suggestions for traditional manufacturing enterprises to shift to smart manufacturing Industry 5.0. Human-robot collaboration, AI and machine learnin

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32

Lee, Hyuk. "Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers." Applied Sciences 15, no. 4 (2025): 1771. https://doi.org/10.3390/app15041771.

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As edge computing environments become increasingly dynamic, the need for efficient job scheduling and proactive fault prevention is becoming paramount. In such environments, minimizing machine downtime and maintaining productivity are critical challenges. In this paper, we propose an integrated approach to scheduling optimization that combines deep learning-based fault prediction with Satisfiability Modulo Theories (SMT)-based scheduling techniques. The proposed system predicts fault probabilities for machines in real time by leveraging operational state features such as temperature, vibration

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33

Marinagi, Catherine, Panagiotis Reklitis, Panagiotis Trivellas, and Damianos Sakas. "The Impact of Industry 4.0 Technologies on Key Performance Indicators for a Resilient Supply Chain 4.0." Sustainability 15, no. 6 (2023): 5185. http://dx.doi.org/10.3390/su15065185.

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The term “Resilient Supply Chain 4.0” incorporates two research areas: Industry 4.0 and Supply Chain Resilience (SCRes). Industry 4.0 technologies include innovations such as the Internet of Things (IoT), Cyber-Physical Systems (CPS), Augmented Reality (AR), Cloud Computing (CC), the Internet of Services (IoS), Big Data Analytics (BDA), Artificial Intelligence (AI), Digital Twins (DT), Blockchain (BC), Industrial Robotics (IR), and Additive Manufacturing (AM). Industry 4.0 technologies do not have a direct impact on SCRes, but on resilience elements such as flexibility, redundancy, visibility,

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34

Kaeo-Tad, Najavadh, Chawalit Jeenanunta, Kwanchanok Chumnumporn, Thanapatra Nitisahakul, and Vararat Sanprasert. "Resilient manufacturing: case studies in Thai automotive industries during the COVID-19 pandemic." Engineering Management in Production and Services 13, no. 3 (2021): 99–113. http://dx.doi.org/10.2478/emj-2021-0024.

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Abstract The coronavirus pandemic is a crisis that disrupts the global supply chain and slows down the world economy. A significant challenge for the companies in the manufacturing sector is to mitigate risks related to the COVID-19 outbreak. Grounded in the resilience theory, resilience (i.e., prepare, prevent, protect, respond, and recover processes) aims to manage risks and ensure business continuity. This research aims to identify the key factors for the resilient manufacturing management of Thai automotive industries during the COVID-19 outbreak. Three outstanding companies of tier 1 auto

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Chenaru, Oana, Stefan Mocanu, Radu Dobrescu, and Maximilian Nicolae. "Enhancing Antifragile Performance of Manufacturing Systems through Predictive Maintenance." Applied Sciences 12, no. 23 (2022): 11958. http://dx.doi.org/10.3390/app122311958.

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Antifragility was introduced as a term no later than 10 years ago. As presented by Taleb, antifragility means that a system becomes more resilient and more robust with every harmful and/or stressful action it is confronted with. This paper is based on a study which aimed to use the concept of antifragility during the design stage of a self-improving system. This way, it is expected to obtain a fast adaptive system capable of functioning at optimal parameters even when it works under adverse conditions or faces unforeseen changes in the environment. Assuming that an antifragile system not only

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36

Wang, Tao-Hua, Hao-Chiang Koong Lin, and Cheng-Tsung Li. "Emerging trends in sustainable design: Integrating museums for a resilient future." Building Engineering 1, no. 1 (2023): 268. http://dx.doi.org/10.59400/be.v1i1.268.

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The paper unfolds the intricate tapestry of sustainable design, weaving in the pivotal role of renewable energy systems and astutely tying it to the eco-conscious blueprint of modern museums. The introduction sheds light on the potent impact of sustainable design in mitigating environmental hurdles and fostering a greener, more sustainable future, with a nuanced nod towards sustainability in museum management and design. Motivated by an aspiration to traverse diverse viewpoints and methodologies in sustainable design, the research stretches across numerous sectors—encompassing construction, ar

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Wang, Junliang, Peng Zheng, Wei Qin, Tengda Li, and Jie Zhang. "A novel resilient scheduling paradigm integrating operation and design for manufacturing systems with uncertainties." Enterprise Information Systems 13, no. 4 (2018): 430–47. http://dx.doi.org/10.1080/17517575.2018.1526322.

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Purba, Humiras Hardi, Choesnul Jaqin, Siti Aisyah, and Mutiara Nabilla. "Analysis of lean-agile-resilient-green (LARG) implementation in the electric car industry in Indonesia." Jurnal Sistem dan Manajemen Industri 8, no. 1 (2024): 61–72. http://dx.doi.org/10.30656/jsmi.v8i1.7674.

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Vehicle type approval (VTA) total registration of electronic vehicles in Indonesia for the accumulation period until August 2023 is 81,525 units with a composition of 4-wheeled vehicles 18,300 units. The use of electric vehicles is still a tiny portion compared to the motorized vehicle population in Indonesia, which will reach more than 146 million units in 2022. It is different from developments in Europe, the United States, and China, where more research into the use of electric vehicles is being carried out. The readiness of the automotive industry system to produce electric vehicles is abs

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Kadam, Akash Abaji, Srinivas Reddy Kosna, and Supriya Akash Kadam. "A theoretical framework for human-centric cyber-physical production systems in industry 5.0: Enabling resilient, autonomous, and adaptive manufacturing." Review of Computer Engineering Research 12, no. 1 (2025): 1–10. https://doi.org/10.18488/76.v12i1.4157.

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This paper intends to put forward a conceptual framework for the development of Cyber-Physical Production Systems (CPPS), which integrates AI, ML, and IoT technologies with human-centric design principles. Industry 5.0 is focused on a human-centric, resilient, and adaptive manufacturing system where harmony in human-machine collaboration exists. The proposed framework addresses some of the challenges facing currently trending ecologies of manufacturing. In other words, it lets CPPS automatically adjust to changes in the environment and keep learning from real-time data and interactions between

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40

Xu, Xun, Lihui Wang, Livan Fratini, Ihab Ragai, and Andrew Yeh Ching Nee. "Smart and resilient manufacturing in the wake of COVID-19." Journal of Manufacturing Systems 60 (July 2021): 707–8. http://dx.doi.org/10.1016/j.jmsy.2021.07.025.

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Ito, Adriana, Malin Hagström, Jon Bokrantz, et al. "Improved root cause analysis supporting resilient production systems." Journal of Manufacturing Systems 64 (July 2022): 468–78. http://dx.doi.org/10.1016/j.jmsy.2022.07.015.

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42

Tanaya, Prianggada Indra. "The State of The Knowledge of Cloud Computing Technology for Manufacturing Automation Systems." Jurnal Rekayasa Sistem Industri 13, no. 1 (2024): 79–90. http://dx.doi.org/10.26593/jrsi.v13i1.6908.79-90.

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The manufacturing sector is grappling with the need to adapt to rapid technological changes, and leveraging cloud computing is becoming crucial for staying competitive and resilient. The presentation of knowledge in this work focuses on highlighting the specific challenges and opportunities in integrating cloud technologies into manufacturing systems. It aims to answer critical questions such as how cloud computing can enhance manufacturing automation, solve problems, and benefit to the industry. The methodology employed in this research takes a comprehensive and top-down approach, aligning an

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43

Yewande Mariam Ogunsuji, Olamide Raimat Amosu, Divya Choubey, Bibitayo Ebunlomo Abikoye, Praveen Kumar, and Stanley Chidozie Umeorah. "Sourcing renewable energy components: building resilient supply chains, reducing dependence on foreign suppliers, and enhancing energy security." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 251–62. http://dx.doi.org/10.30574/wjarr.2024.23.2.2297.

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The transition to renewable energy is essential for mitigating climate change and ensuring long-term energy security. As global demand for renewable energy technologies grows, establishing resilient supply chains becomes crucial. This paper examines strategies to build robust supply chains, reduce reliance on foreign suppliers, and enhance energy security. A major challenge in renewable energy is the dependence on a limited number of international suppliers, which risks supply disruptions due to geopolitical tensions, trade restrictions, or natural disasters. Diversifying suppliers can mitigat

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Yewande, Mariam Ogunsuji, Raimat Amosu Olamide, Choubey Divya, Ebunlomo Abikoye Bibitayo, Kumar Praveen, and Chidozie Umeorah Stanley. "Sourcing renewable energy components: building resilient supply chains, reducing dependence on foreign suppliers, and enhancing energy security." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 251–62. https://doi.org/10.5281/zenodo.14835945.

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The transition to renewable energy is essential for mitigating climate change and ensuring long-term energy security. As global demand for renewable energy technologies grows, establishing resilient supply chains becomes crucial. This paper examines strategies to build robust supply chains, reduce reliance on foreign suppliers, and enhance energy security. A major challenge in renewable energy is the dependence on a limited number of international suppliers, which risks supply disruptions due to geopolitical tensions, trade restrictions, or natural disasters. Diversifying suppliers can mitigat

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Rakic, Slavko, Nenad Medic, Janika Leoste, Teodora Vuckovic, and Ugljesa Marjanovic. "Development and Future Trends of Digital Product-Service Systems: A Bibliometric Analysis Approach." Applied System Innovation 6, no. 5 (2023): 89. http://dx.doi.org/10.3390/asi6050089.

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As a plan, Industry 4.0 encourages manufacturing companies to switch from conventional Product-Service Systems to Digital Product-Service Systems. Systems of goods, services, and digital technologies known as “Digital Product-Service Systems” are provided to improve consumer satisfaction and business success in the marketplace. Previous studies have looked into various elements of this area for industrial companies and academic institutions. Digital Product-Service Systems’ overall worth and expected course of growth are still ignored. The authors use bibliometric analysis to organize the body

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Keshavarzi, Elham, Matthew McIntire, and Christopher Hoyle. "A dynamic design approach using the Kalman filter for uncertainty management." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, no. 2 (2017): 161–72. http://dx.doi.org/10.1017/s0890060417000051.

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AbstractIt is desirable for complex engineered systems to be resilient to various sources of uncertainty throughout their life cycle. Such systems are high in cost and complexity, and often incorporate highly sophisticated materials, components, design, and other technologies. There are many uncertainties such systems will face throughout their life cycles due to changes in internal and external conditions, or states of interest, to the designer, such as technology readiness, market conditions, or system health. These states of interest affect the success of the system design with respect to t

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Bajestani, Mahdi Sadeqi, Mohammad Mahruf Mahdi, Duhwan Mun, and Duck Bong Kim. "Human and Humanoid-in-the-Loop (HHitL) Ecosystem: An Industry 5.0 Perspective." Machines 13, no. 6 (2025): 510. https://doi.org/10.3390/machines13060510.

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As manufacturing transitions into the era of Industry 5.0, the demand for systems that are not only intelligent but also human-centric, resilient, and sustainable is becoming increasingly critical. This paper introduces the Human and Humanoid-in-the-Loop (HHitL) ecosystem, a novel framework that integrates both humans and humanoid robots as collaborative agents within cyber–physical manufacturing environments. Building on the foundational principles of Industry 5.0, the paper presents a 6P architecture that includes participation, purpose, preservation, physical assets, persistence, and projec

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Antompietri, German Marino Rojas, Ravindra Thamma, and Erin-Leigh Beecher. "Industry 4.0 Innovations: The Synergy of Autonomous Systems, IoT, and Systems Integration in Industry 4.0." International Journal of Emerging Technology and Advanced Engineering 15, no. 4 (2025): 1–14. https://doi.org/10.46338/ijetae0425_01.

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The transformative impact of Industry 4.0 on contemporary manufacturing and industrial operations is examined through advancements in autonomous systems, systems integration, and the Internet of Things (IoT). This study thoroughly explores the synergistic effects of these technologies in developing smart and efficient production ecosystems. Autonomous systems, such as robotics and intelligent machines, are crucial in executing complex tasks with minimal human intervention, resulting in increased productivity, safety, and operational precision. Systems integration establishes a vital framework

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Ilkevich, Sergey. "Sustainable and resilient strategies for entering international markets for Russian manufacturing companies based on digital platform interactions." E3S Web of Conferences 531 (2024): 05007. http://dx.doi.org/10.1051/e3sconf/202453105007.

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The focus of the article is the problem of forming sustainable and resilient (including in the face of external restrictions) strategies for entering international markets for Russian manufacturing companies based on mechanisms of platform interactions, taking into account the fact that in the current conditions of the platform economy, value creation is the result of a new combination of information, physical products and real services provided by various types of complementors. Based on a theoretical review and interviews with a focus group of 23 representatives of Russian manufacturing comp

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Sreeperambuduru, Rukmini Kumar. "The Intelligent PLM Ecosystem: How AI is Transforming Core Tools." European Journal of Computer Science and Information Technology 13, no. 20 (2025): 16–29. https://doi.org/10.37745/ejcsit.2013/vol13n201629.

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Artificial Intelligence is transforming Product Lifecycle Management (PLM) systems across industries, revolutionizing how organizations design, develop, and maintain products throughout their lifecycle. The integration of AI technologies has enhanced core PLM tools, from requirements management to manufacturing integration, enabling more intelligent decision-making and automated processes. Through advanced capabilities such as digital twins, predictive analytics, and machine learning algorithms, organizations are achieving significant improvements in operational efficiency, quality control, an

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