Digital twin supporting environmental performance evaluation according to ISO certification: an application case in the tire industry

References

• Abdullah, T., & Dr Murat, A. (2019). Digital transformation design of Banbury mixing unit in tire manufacturing. In 2019 International Conference on Applied Automation and Industrial Diagnostics (ICAAID), IEEE, Elazig, Turkey (pp. 1–32). https://doi.org/10.1109/ICAAID.2019.8934975
   Google Scholar
• Abou-Ali, M. G., & Khamis, M. (2003). TIREDDX: An integrated intelligent defects diagnostic system for tire production and service. Expert Systems with Applications, 24(3), 247–259. https://doi.org/10.1016/S0957-4174(02)00153-7
   Web of Science ®Google Scholar
• Acerbi, F., Rocca, R., Fumagalli, L., Taisch, M., Acerbi, F., Rocca, R., Fumagalli, L., & Taisch, M. (2023). Enhancing the cosmetics industry sustainability through a renewed sustainable supplier selection model. Production & Manufacturing Research, 11(1). https://doi.org/10.1080/21693277.2022.2161021
   Google Scholar
• Acerbi, F., Sassanelli, C., & Taisch, M. (2022). A conceptual data model promoting data-driven circular manufacturing. Operations Management Research, 1–20. https://doi.org/10.1007/s12063-022-00271-x
   Web of Science ®Google Scholar
• Acerbi, F., Sassanelli, C., & Taisch, M. (2024). A maturity model enhancing data-driven circular manufacturing. Production Planning & Control, 2322608, 1–19. https://doi.org/10.1080/09537287.2024.2322608
   Google Scholar
• Ali, S. M., Appolloni, A., Cavallaro, F., D’Adamo, I., DiVaio, A., Ferella, F., Gastaldi, M., Ikram, M., Kumar, N. M., Martin, M. A., Nizami, A.-S., Ozturk, I., Riccardi, M. P., Rosa, P., Santibañez Gonzalez, E. D. R., Sassanelli, C., Settembre-Blundo, D., Singh, R. K., Smol, M., & Tsalidis G.A. (2023). Development goals towards sustainability. Sustainability (Switzerland), 15(9443), 1–11. https://doi.org/10.3390/su15129443
   Google Scholar
• Alptekin, B., Tunaboylu, B., Selim, Z., & Perlo, P. (2021). Smart manufacturing of electric vehicles. Digital Maturity Assessment Model for Smart Agriculture, 289–301. https://doi.org/10.1007/978-3-030-62784-3_24
   Google Scholar
• Anderl, R., Haag, S., Schützer, K., & Zancul, E. (2021). Digital twin technology-an approach for Industrie 4.0 vertical and horizontal lifecycle integration. IT - Information Technology, 60(3), 125–132. https://doi.org/10.1515/itit-2017-0038
   Google Scholar
• Anthony Howard, D., Ma, Z., Mazanti Aaslyng, J., & Norregaard Jorgensen, B. (2020). Data architecture for digital twin of commercial greenhouse production. Proceedings - 2020 RIVF International Conference on Computing and Communication Technologies, RIVF 2020. https://doi.org/10.1109/RIVF48685.2020.9140726
   Google Scholar
• Araujo-Morera, J., Verdejo, R., López-Manchado, M. A., & Hernández Santana, M. (2021). Sustainable mobility: The route of tires through the circular economy model. Waste Management, 126, 309–322. https://doi.org/10.1016/j.wasman.2021.03.025
   PubMed Web of Science ®Google Scholar
• Bhatti, G., Mohan, H., & Raja Singh, R. (2021). Towards the future of smart electric vehicles: Digital twin technology. Renewable and Sustainable Energy Reviews, 141(January), 110801. https://doi.org/10.1016/j.rser.2021.110801
   Google Scholar
• Bocken, N. M. P., de Pauw, I., Bakker, C. A., & van der Grinten, B. (2016). Product design and business model strategies for a circular economy. Journal of Industrial and Production Engineering, 33(5), 308–320. https://doi.org/10.1080/21681015.2016.1172124
   Web of Science ®Google Scholar
• Branca, T. A., Fornai, B., Colla, V., Murri, M. M., Streppa, E., & Schröder, A. J. (2020). The challenge of digitalization in the steel sector. Metals, 10(2), 1–23. https://doi.org/10.3390/met10020288
   Web of Science ®Google Scholar
• Caccami, M. C., Amendola, S., & Occhiuzzi, C. (2019). Method and system for reading RFID tags embedded into tires on conveyors. In 2019 IEEE International Conference on RFID Technology and Applications (RFID-TA) (pp. 141–144). https://doi.org/10.1109/RFID-TA.2019.8892245
   Google Scholar
• Calış Duman, M., & Akdemir, B. (2021). A study to determine the effects of industry 4.0 technology components on organizational performance. Technological Forecasting and Social Change, 167(February), 0–3. https://doi.org/10.1016/j.techfore.2021.120615
   Google Scholar
• Chen, W., Zhang, J., Chi, C., Luo, Y., Ding, X., & Ma, B. (2022). Innovative Blockchain-Based application of carbon footprint of products: A case study in textile and apparel industry. 2022 IEEE 8th International Conference on Computer and Communications, ICCC 2022 (pp. 1350–1355). https://doi.org/10.1109/ICCC56324.2022.10065849
   Google Scholar
• Chernoded, A. I., Vasiliev, V. A., Tsyrkov, A. V., Aleksandrova, S. V., & Edel, M. F. (2022). Artificial intelligence techniques within a technological process of manufacturing automated control systems. Proceedings of the 2022 International Conference “Quality Management, Transport and Information Security, Information Technologies”, IT and QM and IS 2022 (pp. 77–82). https://doi.org/10.1109/ITQMIS56172.2022.9976502
   Google Scholar
• Chiappetta Jabbour, C. J., Fiorini, P. D. C., Ndubisi, N. O., Queiroz, M. M., & Piato, É. L. (2020). Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda. Science of the Total Environment, 725, 138177. https://doi.org/10.1016/j.scitotenv.2020.138177
   PubMed Web of Science ®Google Scholar
• Colangelo, G., Facchini, F., Ranieri, L., Starace, G., & Vitti, M. (2023). Assessment of carbon emissions’ effects on the investments in conventional and innovative waste-to-energy treatments. Journal of Cleaner Production, 388(135849), 1–15. https://doi.org/10.1016/j.jclepro.2023.135849
   Google Scholar
• Cozza, G., D’Adamo, I., & Rosa, P. (2023). Circular manufacturing ecosystems: Automotive printed circuit boards recycling as an enabler of the economic development. Production & Manufacturing Research, 11(2182837), 1–23. https://doi.org/10.1080/21693277.2023.2182837
   Google Scholar
• Cuartas, M., Ruiz, E., Ferreño, D., Setién, J., Arroyo, V., & Gutiérrez-Solana, F. (2021). Machine learning algorithms for the prediction of non-metallic inclusions in steel wires for tire reinforcement. Journal of Intelligent Manufacturing, 32(6), 1739–1751. https://doi.org/10.1007/s10845-020-01623-9
   Web of Science ®Google Scholar
• Cugno, M., Castagnoli, R., & Büchi, G. (2021). Openness to industry 4.0 and performance: The impact of barriers and incentives. Technological Forecasting and Social Change, 168, 120756. https://doi.org/10.1016/j.techfore.2021.120756
   Web of Science ®Google Scholar
• Dai, Y., & Zhang, Y. (2022). Adaptive digital twin for vehicular edge computing and networks. Journal of Communications and Information Networks, 7(1), 48–59. https://doi.org/10.23919/JCIN.2022.9745481
   Google Scholar
• Dallasega, P., Rauch, E., & Linder, C. (2018). Industry 4.0 as an enabler of proximity for construction supply chains: A systematic literature review. Computers in Industry, 99(April), 205–225. https://doi.org/10.1016/j.compind.2018.03.039
   Google Scholar
• De Carolis, A., Macchi, M., Boonserm, K. M. P. B., & Terzi, S. (2017). Maturity models and tools for enabling smart manufacturing systems characterization. In J. R. P. Chiabert, A. Bouras, & F. Noël (Eds.), 14th IFIP international conference on product lifecycle management (pp. 23–35). Springer.
   Google Scholar
• de Freitas, B. S. B., Munhoz, I. P., & Mendonça, F. C. (2021). Indicators for assessing sustainability and productivity in companies with implementation of industry 4.0 in Mercosur. Smart Innovation, Systems and Technologies, 233, 33–40. https://doi.org/10.1007/978-3-030-75680-2_5
   Google Scholar
• Delanoë, P., Tchuente, D., & Colin, G. (2023). Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions. Journal of Environmental Management, 331(January), 117261. https://doi.org/10.1016/j.jenvman.2023.117261
   PubMedGoogle Scholar
• Eirinakis, P., Lounis, S., Plitsos, S., Arampatzis, G., Kalaboukas, K., Kenda, K., Lu, J., Rožanec, J. M., & Stojanovic, N. (2022). Cognitive digital twins for resilience in production: A conceptual framework. Information, 13(1), 33. https://doi.org/10.3390/info13010033
   Google Scholar
• Elbadawi, M., Basit, A., & Gaisford, S. (2023). Energy consumption and carbon footprint of 3D printing in pharmaceutical manufacture. International Journal of Pharmaceutics, 639(March), 122926. https://doi.org/10.1016/j.ijpharm.2023.122926
   PubMedGoogle Scholar
• The Ellen MacArthur Foundation. (2013). Towards the circular economy economic and business rationale for an accelerated transition. https://www.ellenmacarthurfoundation.org/assets/downloads/publications/Ellen-MacArthur-Foundation-Towards-the-Circular-Economy-vol.1.pdf
   Google Scholar
• Erauskin-Tolosa, A., Zubeltzu-Jaka, E., Heras-Saizarbitoria, I., & Boiral, O. (2020). ISO 14001, EMAS and environmental performance: A meta-analysis. Business Strategy and the Environment, 29(3), 1145–1159. https://doi.org/10.1002/bse.2422
   Web of Science ®Google Scholar
• Facchini, F., Ranieri, L., & Vitti, M. (2021). A neural network model for decision-making with application in sewage sludge management. Applied Sciences (Switzerland), 11(5434), 1–17. https://doi.org/10.3390/app11125434
   Google Scholar
• Finke, C., Dewitz, P., Groth, M., Gehrke, J., Schumann, M., & Marahrens, T. (2023). Design and Implementation of Hierarchical Digital Twins in Industrial Production Environments. In Proceedings of the 56th Hawaii International Conference on System Sciences | 2023 Design (pp. 1448–1457).
   Google Scholar
• Golovina, T., Polyanin, A., Adamenko, A., Khegay, E., & Schepinin, V. (2020). Digital twins as a new paradigm of an industrial enterprise. International Journal of Technology, 11(6), 1115–1124. https://doi.org/10.14716/ijtech.v11i6.4427
   Web of Science ®Google Scholar
• Howard, D. A., Zheng, M., & Norregaard, B. (2020, September). Digital twin framework for energy efficient greenhouse industry 4. 0 digital twin framework for energy efficient the greenhouse sector has been identified as a sector with significant unexploited. 0–4. https://doi.org/10.1007/978-3-030-58356-9
   Google Scholar
• Hübner, D., Moghayedi, A., & Michell, K. (2022). The impact of industry 4.0 technologies on the environmental sustainability of commercial property by reducing the energy consumption. IOP Conference Series: Earth and Environmental Science, 1101(6), 0–8. https://doi.org/10.1088/1755-1315/1101/6/062018
   Google Scholar
• Imarc Group, T. I. M. A. R. and C. G.-I. (2020). Global tire market to grow at 2.88% during 2023–2028, impelled by advent of airless or Non-Pneumatic tires. The International Market Analysis Research and Consulting Group - IMARC Group. https://www.imarcgroup.com/global-tire-market
   Google Scholar
• Islam, M. T., Iyer-Raniga, U., & Trewick, S. (2022). Recycling perspectives of circular business models: A review. Recycling, 7(5), 79. https://doi.org/10.3390/recycling7050079
   Google Scholar
• ISO - International Organization for Standardization. (2023). https://www.iso.org/home.html
   Google Scholar
• ISO survey. (2022). ISO survey of certifications to management system standards – ISO survey 2022 results - number of certificates and sites per country and the number of sector overall. https://www.iso.org/committee/54998.html?t=KomURwikWDLiuB1P1c7SjLMLEAgXOA7emZHKGWyn8f3KQUTU3m287NxnpA3DIuxm&view=documents#section-isodocuments-top
   Google Scholar
• ISO/TC. (2019, September). The ISO survey of management system certifications – 2018 – explanatory note background the ISO survey of certifications is an annual survey of the number of valid certificates to. ISO Survey, 2017–2018.
   Google Scholar
• Jankovic-Zugic, A., Medic, N., Pavlovic, M., Todorovic, T., & Rakic, S. (2023). Servitization 4. 0 as a trigger for sustainable business: Evidence from automotive digital supply chain. Sustainability (Switzerland), 15(2217), 1–13. https://doi.org/10.3390/su15032217
   Google Scholar
• Javaid, M., Haleem, A., Pratap Singh, R., Khan, S., & Suman, R. (2022). Sustainability 4.0 and its applications in the field of manufacturing. Internet of Things and Cyber-Physical Systems, 2(June), 82–90. https://doi.org/10.1016/j.iotcps.2022.06.001
   Google Scholar
• Javied, T., Bakakeu, J., Gessinger, D., & Franke, J. (2018). Strategic energy management in industry 4.0 environment. 12th Annual IEEE International Systems Conference, SysCon 2018 - Proceedings (pp. 1–4). https://doi.org/10.1109/SYSCON.2018.8369610
   Google Scholar
• Jena, M. C., Mishra, S. K., & Moharana, H. S. (2020). Application of industry 4.0 to enhance sustainable manufacturing. Environmental Progress and Sustainable Energy, 39(1), 0–11. https://doi.org/10.1002/ep.13360
   Web of Science ®Google Scholar
• Jiménez, M., Espinosa, M. D. M., Domínguez, M., Romero, M., & Awad, T. (2021). Adaptation of the lean 6S methodology in an industrial environment under sustainability and industry 4.0 criteria. Sustainability (Switzerland), 13(22), 1–13. https://doi.org/10.3390/su132212449
   Web of Science ®Google Scholar
• Kagermann, H., Lukas, W.-D., & Wahlster, W. (2011). Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. VDI Nachrichten, 13(11). https://www.dfki.de/fileadmin/user_upload/DFKI/Medien/News_Media/Presse/Presse-Highlights/vdinach2011a13-ind4.0-Internet-Dinge.pdf
   Google Scholar
• Kampker, A., Heimes, H. H., Dorn, B., Clever, H., Ludwigs, R., Li, R., & Drescher, M. (2023). Concept for digital product twins in battery cell production. World Electric Vehicle Journal, 14(4), 108. https://doi.org/10.3390/wevj14040108
   Google Scholar
• Katarzyna, P., Izabela, P., Patrycja, B. W., Weronika, K., & Andrzej, T. (2020). LCA as a tool for the environmental management of car tire manufacturing. Applied Sciences (Switzerland), 10(20), 1–25. https://doi.org/10.3390/app10207015
   Web of Science ®Google Scholar
• Kirchherr, J., Reike, D., & Hekkert, M. (2017). Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling, 127, 221–232. https://doi.org/10.1016/J.RESCONREC.2017.09.005
   Web of Science ®Google Scholar
• Kovalyov, S. P. (2022). Distributed energy resources management: From digital twin to digital platform. IFAC-Papersonline, 55(9), 460–465. https://doi.org/10.1016/j.ifacol.2022.07.080
   Google Scholar
• Laayati, O., Bouzi, M., & Chebak, A. (2022). Smart energy management system: Design of a monitoring and peak load forecasting system for an experimental open-pit mine. Applied System Innovation, 5(1), 1–18. https://doi.org/10.3390/asi5010018
   Google Scholar
• Lacy, P., Rutqvist, J., & Lamonica, B. (2016). Circular economy (EGEA (Ed.)).
   Google Scholar
• Lattanzi, L., Raffaeli, R., Peruzzini, M., & Pellicciari, M. (2021). Digital twin for smart manufacturing: A review of concepts towards a practical industrial implementation. International Journal of Computer Integrated Manufacturing, 34(6), 567–597. https://doi.org/10.1080/0951192X.2021.1911003
   Web of Science ®Google Scholar
• Maeda, J. (2006). The laws of simplicity. Design, technology, business, life. MIT Press.
   Google Scholar
• Magargle, R., Johnson, L., Mandloi, P., Davoudabadi, P., Kesarkar, O., Krishnaswamy, S., Batteh, J., & Pitchaikani, A. (2017). A Simulation-Based digital twin for Model-Driven health monitoring and predictive maintenance of an automotive braking system. Proceedings of the 12th International Modelica Conference (Vol. 132. pp. 35–46, Prague, Czech Republic. May 15–17, 2017. https://doi.org/10.3384/ecp1713235
   Google Scholar
• Margherita, E. G., & Braccini, A. M. (2020, August). Industry 4.0 technologies in flexible manufacturing for sustainable organizational value: Reflections from a multiple case study of Italian manufacturers. Information Systems Frontiers, 25(3), 995–1016. https://doi.org/10.1007/s10796-020-10047-y
   Web of Science ®Google Scholar
• Massaro, A., Dipierro, G., Cannella, E., & Galiano, A. M. (2020). Comparative analysis among discrete Fourier transform, K-means and artificial neural networks image processing techniques oriented on quality control of assembled tires. Information (Switzerland), 11(5), 257. https://doi.org/10.3390/INFO11050257
   Google Scholar
• Massaro, A., Manfredonia, I., Galiano, A., & Contuzzi, N. (2019). Inline image vision technique for tires industry 4.0: Quality and defect monitoring in tires assembly. 54–57.
   Google Scholar
• Medojevic, M., Marjanovic, U., & Lalic, B. (1983). Exploring the impact of industry 4.0 concepts on energy and environmental management systems: Evidence from Serbian manufacturing companies. Computers in Industry, 4(4), 411–416. https://doi.org/10.1016/0166-3615(83)90009-x
   Google Scholar
• Morrow, M. J., & Zarrebini, M. (2019). Blockchain and the tokenization of the individual: Societal implications. Future Internet, 11(10), 1–12. https://doi.org/10.3390/FI11100220
   Web of Science ®Google Scholar
• Orellana, F., & Torres, R. (2019). From legacy-based factories to smart factories level 2 according to the industry 4.0. International Journal of Computer Integrated Manufacturing, 32(4–5), 441–451. https://doi.org/10.1080/0951192X.2019.1609702
   Web of Science ®Google Scholar
• Patsavellas, J., & Salonitis, K. (2019). The carbon footprint of manufacturing digitalization: Critical literature review and future research agenda. Procedia CIRP, 81(March), 1354–1359. https://doi.org/10.1016/j.procir.2019.04.026
   Google Scholar
• Perez-Castillo, R., Carretero, A. G., Rodriguez, M., Caballero, I., Piattini, M., Mate, A., Kim, S., & Lee, D. (2018). Data quality best practices in IoT environments. Proceedings - 2018 International Conference on the Quality of Information and Communications Technology, QUATIC 2018 (pp. 272–275). https://doi.org/10.1109/QUATIC.2018.00048
   Google Scholar
• Phuong, N. A., & Guidat, T. (2018). Sustainable value stream mapping and technologies of industry 4.0 in manufacturing process re-configuration: A case study in an apparel company. 85–90.
   Google Scholar
• Piotrowska, K., Kruszelnicka, W., Bałdowska-Witos, P., Kasner, R., Rudnicki, J., Tomporowski, A., Flizikowski, J., & Opielak, M. (2019). Assessment of the environmental impact of a car tire throughout its lifecycle using the LCA method. Materials, 12(24), 1–25. https://doi.org/10.3390/MA12244177
   Web of Science ®Google Scholar
• Porter, M. E., & Heppelmann, J. E. (2014, November). How smart, connected products are transforming competition. Harvard Business Review, 1–30. https://doi.org/10.1017/CBO9781107415324.004
   Web of Science ®Google Scholar
• Richardson, K., Al, E., Lucht, W., Bendtsen, J., Cornell, S. E., Donges, J. F., Drüke, M., Fetzer, I., Bala, G., von Bloh, W., Feulner, G., Fiedler, S., Gerten, D., Gleeson, T., Hofmann, M., Huiskamp, W., Kummu, M., Mohan, C. … Weber, L. (2023). Supplementary materials for earth beyond six of nine planetary boundaries. Science Advances, 9(37), 2458. https://doi.org/10.1126/sciadv.adh2458
   Google Scholar
• Roblek, V., Meško, M., & Krapež, A. (2016). A complex view of industry 4.0. SAGE Open, 6(2), 1–11. https://doi.org/10.1177/2158244016653987
   Web of Science ®Google Scholar
• Rosa, P., Sassanelli, C., Urbinati, A., Chiaroni, D., & Terzi, S. (2020). Assessing relations between circular economy and industry 4.0: A systematic literature review. International Journal of Production Research, 58(6), 1662–1687. https://doi.org/10.1080/00207543.2019.1680896
   Web of Science ®Google Scholar
• Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. https://doi.org/10.1007/s12599-014-0334-4
   Google Scholar
• Santos, N. A. D., Ruggero, S. M., Sacomano, J. B., Estender, A. C., & Da Silva, M. T. (2020). The impact of industry 4.0 connectivity on the collaboration along Brazilian automotive supply chain. IFIP Advances in Information and Communication Technology, 592 IFIP (pp. 381–388. https://doi.org/10.1007/978-3-030-57997-5_85
   Google Scholar
• Sassanelli, C., Garza-Reyes, J. A., Liu, Y., Pacheco, D. A. D. J., & Luthra, S. (2023). The disruptive action of industry 4.0 technologies cross-fertilizing circular economy throughout society. Computers & Industrial Engineering, 183(109548), 1–5. https://doi.org/10.1016/j.cie.2023.109548
   Google Scholar
• Sassanelli, C., Pezzotta, G., Pirola, F., Rossi, M., & Terzi, S. (2019). The PSS design GuRu methodology: Guidelines and rules generation to enhance Product Service Systems (PSS) detailed design. Journal of Design Research, 17(2/3/4), 125–162. https://doi.org/10.1504/JDR.2019.105756
   Google Scholar
• Sassanelli, C., Rossi, M., & Terzi, S. (2020). Evaluating the smart maturity of manufacturing companies along the product development process to set a PLM project roadmap. International Journal of Product Lifecycle Management, 12(3), 185–209. https://doi.org/10.1504/IJPLM.2020.109789
   Google Scholar
• Sassanelli, C., Urbinati, A., Rosa, P., Chiaroni, D., & Terzi, S. (2020). Addressing circular economy through design for X approaches: A systematic literature review. Computers in Industry, 120(103245), 1–23. https://doi.org/10.1016/j.compind.2020.103245
   Google Scholar
• Schöggl, J. P., Rusch, M., Stumpf, L., & Baumgartner, R. J. (2023). Implementation of digital technologies for a circular economy and sustainability management in the manufacturing sector. Sustainable Production and Consumption, 35, 401–420. https://doi.org/10.1016/j.spc.2022.11.012
   Web of Science ®Google Scholar
• Shakenov, A., Sładkowski, A., & Stolpovskikh, I. (2022). Haul road condition impact on tire life of mining dump truck. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 6(6), 25–29. https://doi.org/10.33271/nvngu/2022-6/025
   Google Scholar
• Shinohara, A. C., Da Silva, E. H. D. R., De Lima, E. P., Deschamps, F., & Da Costa, S. E. G.(2017). Critical success factors for digital manufacturing implementation in the context of industry 4.0. In In Coperich, K. (Ed.), Proceedings of the 2017 Industrial and Systems Engineering Conference, Institute of Industrial and Systems Engineers (IISE), Pittsburgh, Pennsylvania, USA (pp. 199–204).
   Google Scholar
• Sousa, J., Mendonça, J. P., & Machado, J. (2022). A generic interface and a framework designed for industrial metrology integration for the internet of things. Computers in Industry, 138, 103632. https://doi.org/10.1016/j.compind.2022.103632
   Web of Science ®Google Scholar
• Stevanov, B., Sremcev, N., Lazarevic, M., Anderla, A., Sladojevic, S., & Vidicki, P. (2022). Optimization of the subassembly production process using simulation. International Journal of Simulation Modelling, 21(4), 663–674. https://doi.org/10.2507/IJSIMM21-4-633
   Web of Science ®Google Scholar
• Sujová, E., Bambura, R., Vysloužilová, D., & Koleda, P. (2023). Use of the digital twin concept to optimize the production process of engine blocks manufacturing. Production Engineering Archives, 29(2), 168–174. https://doi.org/10.30657/pea.2023.29.20
   Google Scholar
• Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C. Y., & Nee, A. Y. C. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935–3953. https://doi.org/10.1080/00207543.2018.1443229
   Web of Science ®Google Scholar
• Torres, L. C. C. (2022). Case study: Simulation and artificial intelligence application for the optimization of the hauling and loading process in an open-pit mining system. IFAC-Papersonline, 55(39), 265–269. https://doi.org/10.1016/j.ifacol.2022.12.032
   Google Scholar
• Tsai, W. H., Lu, Y. H., & Hsieh, C. L. (2022). Comparison of production decision-making models under carbon tax and carbon rights trading. Journal of Cleaner Production, 379, 134462. https://doi.org/10.1016/j.jclepro.2022.134462
   Web of Science ®Google Scholar
• Ulrich, K. T., & Eppinger, S. D. (2012). Product design and development. Product Design and Development, 384. https://doi.org/10.1016/B978-0-7506-8985-4.00002-4
   Google Scholar
• United Nations. (2015). Transforming our world: The 2030 agenda for Sustainable Development | Department of Economic and Social Affairs. https://sdgs.un.org/2030agenda
   Google Scholar
• Upadhyay, A., Mukhuty, S., Kumar, V., & Kazancoglu, Y. (2021). Blockchain technology and the circular economy: Implications for sustainability and social responsibility. Journal of Cleaner Production, 293, 126130. https://doi.org/10.1016/j.jclepro.2021.126130
   Web of Science ®Google Scholar
• Yin, P. Y., Chen, H. M., Cheng, Y. L., Wei, Y. C., Huang, Y. L., & Day, R. F. (2021). Minimizing the makespan in flowshop scheduling for sustainable rubber circular manufacturing. Sustainability (Switzerland), 13(5), 1–18. https://doi.org/10.3390/su13052576
   Web of Science ®Google Scholar
• Yu, W., Patros, P., Young, B., Klinac, E., & Walmsley, T. G. (2022). Energy digital twin technology for industrial energy management: Classification, challenges and future. Renewable and Sustainable Energy Reviews, 161(March), 112407. https://doi.org/10.1016/j.rser.2022.112407
   Google Scholar
• Zakrajsek, A. J., & Mall, S. (2017). The development and use of a digital twin Model for tire touchdown health monitoring. In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Grapevine, Texas. https://doi.org/10.2514/6.2017-0863
   Google Scholar
• Zhou, J., Ong, T. S., Mastellone, L., & DiVaio, A. (2023). The effect of disciplinary inspection commission participation on the financing constraints of Chinese state-owned enterprises: A circular economy perspective. Production and Manufacturing Research, 11(1). https://doi.org/10.1080/21693277.2023.2214811
   Google Scholar
• Zimmermann, V., Heimicke, J., Albers, A., & Reiß, N. (2019). Acceptance modelling in product development - case study: Connected systems for industry 4.0 solutions. IOP Conference Series: Materials Science and Engineering, 520(1), 0–10. https://doi.org/10.1088/1757-899X/520/1/012011
   Google Scholar
• Zolkin, A. L., Matvienko, E. V., & Shavanov, M. V. (2021). Perspectives for the use of digital technologies in the agricultural sector in order to optimize agricultural operational processes. IOP Conference Series: Earth and Environmental Science, 666(3), 032081. https://doi.org/10.1088/1755-1315/666/3/032081
   Google Scholar
• Zoubek, M., Poor, P., Broum, T., Basl, J., & Simon, M. (2021). Industry 4.0 maturity model assessing environmental attributes of manufacturing company. Applied Sciences (Switzerland), 11(11), 5151. https://doi.org/10.3390/app11115151
   Web of Science ®Google Scholar