A mathematical model to identify companies' adaptation variables to cyber-physical production systems
DOI:
https://doi.org/10.5902/1983465988466Keywords:
Production planning and control, Industry 4.0, Benchmarking, Logistic regression, SurveyAbstract
Purpose: With the fourth industrial revolution, it becomes necessary to identify how companies are adapting to the use of technologies aimed at cyber-physical production systems. Therefore, this article aimed to develop a mathematical model to identify the degree of maturity of cyber-physical production systems.
Design/methodology/approach: The methodology draws from theoretical definitions, followed by a survey, in which 61 medium and large companies from the South of Brazil in the textile and clothing sector participated. Afterwards, a mathematical model was developed with logistic regression.
Findings: The results showed that the maturity level was classified as intermediate in the validated textile companies, with opportunities for advances in the implementation of real-time monitoring and other technologies of industry 4.0. This result was expected, given the field studies and observations with companies, which reveal that the mathematical model is valid and can be replicated in other organizations.
Research limitations/implications: The restriction is that the model was tested in the reality of the Brazilian textile industries.
Originality/value: The originality is to propose a model for using cyber-physical systems as a complement to decision-making in production scheduling with ERP that have finite capacity and thus adding an APS algorithm to assist in monitoring production.
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Addo-Tenkorang, R., & Helo, P. (2011, October 19). Enterprise Resource Planning (ERP): A Review Literature Report. Proceedings of the World Congress on Engineering and Computer Science.
Alff, L. A., Kipper, L. M., Helfer, D. G., Tedesco, L. P., Furtado, J. C., & Goecks, L. S. (2022). Framework to Support the Implementation of an Intelligent Factory. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4308226 DOI: https://doi.org/10.2139/ssrn.4308226
Allaoui, H., Guo, Y., & Sarkis, J. (2019). Decision support for collaboration planning in sustainable supply chains. Journal of Cleaner Production, 229, 761–774. https://doi.org/10.1016/j.jclepro.2019.04.367 DOI: https://doi.org/10.1016/j.jclepro.2019.04.367
Alquthami, T., Zulfiqar, M., Kamran, M., Milyani, A. H., & Rasheed, M. B. (2022). A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid. IEEE Access, 10, 48419–48433. https://doi.org/10.1109/ACCESS.2022.3171270 DOI: https://doi.org/10.1109/ACCESS.2022.3171270
Andronie, M., Lăzăroiu, G., Ștefănescu, R., Uță, C., & Dijmărescu, I. (2021). Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review. Sustainability, 13(10), 5495. https://doi.org/10.3390/su13105495 DOI: https://doi.org/10.3390/su13105495
Babaei, M., Gholami, Z., & Altafi, S. (2015). Challenges of Enterprise Resource Planning implementation in Iran large organizations. Information Systems, 54, 15–27. https://doi.org/10.1016/j.is.2015.05.003 DOI: https://doi.org/10.1016/j.is.2015.05.003
Bueno, A., Godinho Filho, M., & Frank, A. G. (2020). Smart production planning and control in the Industry 4.0 context: A systematic literature review. Computers & Industrial Engineering, 149, 106774. https://doi.org/10.1016/j.cie.2020.106774 DOI: https://doi.org/10.1016/j.cie.2020.106774
Cardoso Ermel, A. P., Lacerda, D. P., Morandi, M. I. W. M., & Gauss, L. (2021). Literature Reviews. Springer International Publishing. https://doi.org/10.1007/978-3-030-75722-9 DOI: https://doi.org/10.1007/978-3-030-75722-9
Cloppenburg, F., Münkel, A., Gloy, Y., & Gries, T. (2017). Industry 4.0 – How will the nonwoven production of tomorrow look like? IOP Conference Series: Materials Science and Engineering, 254(13), 132001. https://doi.org/10.1088/1757-899X/254/13/132001 DOI: https://doi.org/10.1088/1757-899X/254/13/132001
Corrar, L. J., Dias, J. M. F., & Paulo, E. (2007). Análise multivariada para os cursos de administração, ciências contábeis e economia (1st ed.). Atlas.
Dal Forno, A. J., Bataglini, W. V., Steffens, F., & Ulson de Souza, A. A. (2023). Industry 4.0 in textile and apparel sector: a systematic literature review. Research Journal of Textile and Apparel, 27(1), 95–117. https://doi.org/10.1108/RJTA-08-2021-0106 DOI: https://doi.org/10.1108/RJTA-08-2021-0106
Elliott, R. (2013). Manufacturing Execution System (MES) An Examination of Implementation Strategy [Thesis]. Faculty of California Polytechnic State University.
Enrique, D. V., Marcon, É., Charrua-Santos, F., & Frank, A. G. (2022). Industry 4.0 enabling manufacturing flexibility: technology contributions to individual resource and shop floor flexibility. Journal of Manufacturing Technology Management, 33(5), 853–875. https://doi.org/10.1108/JMTM-08-2021-0312 DOI: https://doi.org/10.1108/JMTM-08-2021-0312
Farooq, B., Bao, J., Li, J., Liu, T., & Yin, S. (2020). Data-Driven Predictive Maintenance Approach for Spinning Cyber-Physical Production System. Journal of Shanghai Jiaotong University (Science), 25(4), 453–462. https://doi.org/10.1007/s12204-020-2178-z DOI: https://doi.org/10.1007/s12204-020-2178-z
Goldenberg, M. (1997). A arte de pesquisar: como fazer pesquisa qualitativa em ciências sociais (17th ed.). Record.
Guise, A., Oliveira, J., Teixeira, S., & Silva, Â. (2023). Development of tools to support the production planning in a textile company. Procedia Computer Science, 219, 889–896. https://doi.org/10.1016/j.procs.2023.01.364 DOI: https://doi.org/10.1016/j.procs.2023.01.364
Helfer, D. G., Alff, L. A., Kipper, L. M., & Tedesco, L. P. (2021). Nível de maturidade para indústria 4.0: Um estudo de caso em empresa de parafusos / Maturity level for industry 4.0: A case study in a screw company. Brazilian Journal of Development, 7(11), 102801–102818. https://doi.org/10.34117/bjdv7n11-077 DOI: https://doi.org/10.34117/bjdv7n11-077
Hvolby, H.-H., & Steger-Jensen, K. (2010a). Technical and industrial issues of Advanced Planning and Scheduling (APS) systems. Computers in Industry, 61(9), 845–851. https://doi.org/10.1016/j.compind.2010.07.009
Hvolby, H.-H., & Steger-Jensen, K. (2010b). Technical and industrial issues of Advanced Planning and Scheduling (APS) systems. Computers in Industry, 61(9), 845–851. https://doi.org/10.1016/j.compind.2010.07.009 DOI: https://doi.org/10.1016/j.compind.2010.07.009
Jang, S., Choi, J. Y., Yoo, E. S., Lim, D. Y., Lee, J. Y., Kim, J. K., & Pang, C. (2021). Printable wet-resistive textile strain sensors using bead-blended composite ink for robustly integrative wearable electronics. Composites Part B: Engineering, 210, 108674. https://doi.org/https://doi.org/10.1016/j.compositesb.2021.108674 DOI: https://doi.org/10.1016/j.compositesb.2021.108674
Jeon, B. W., Um, J., Yoon, S. C., & Suk-Hwan, S. (2017). An architecture design for smart manufacturing execution system. Computer-Aided Design and Applications, 14(4), 472–485. https://doi.org/10.1080/16864360.2016.1257189 DOI: https://doi.org/10.1080/16864360.2016.1257189
Jeong, Y.-M., Son, I., & Baek, S.-H. (2019). Binder–free of NiCo–layered double hydroxides on Ni–coated textile for wearable and flexible supercapacitors. Applied Surface Science, 467–468, 963–967. https://doi.org/https://doi.org/10.1016/j.apsusc.2018.10.252 DOI: https://doi.org/10.1016/j.apsusc.2018.10.252
Jiang, Y., Yin, S., & Kaynak, O. (2018). Data-Driven Monitoring and Safety Control of Industrial Cyber-Physical Systems: Basics and Beyond. IEEE Access, 6, 47374–47384. https://doi.org/10.1109/ACCESS.2018.2866403 DOI: https://doi.org/10.1109/ACCESS.2018.2866403
Jung, W.-K., Kim, D.-R., Lee, H., Lee, T.-H., Yang, I., Youn, B. D., Zontar, D., Brockmann, M., Brecher, C., & Ahn, S.-H. (2021). Appropriate Smart Factory for SMEs: Concept, Application and Perspective. International Journal of Precision Engineering and Manufacturing, 22(1), 201–215. https://doi.org/10.1007/s12541-020-00445-2 DOI: https://doi.org/10.1007/s12541-020-00445-2
Khedher, A. Ben, Henry, S., & Bouras, A. (2011). Integration between MES and Product Lifecycle Management. ETFA2011, 1–8. https://doi.org/10.1109/ETFA.2011.6058993 DOI: https://doi.org/10.1109/ETFA.2011.6058993
Kim, J. H. (2017). A Review of Cyber-Physical System Research Relevant to the Emerging IT Trends: Industry 4.0, IoT, Big Data, and Cloud Computing. Journal of Industrial Integration and Management, 02(03), 1750011. https://doi.org/10.1142/S2424862217500117 DOI: https://doi.org/10.1142/S2424862217500117
Kipper, L. M., Furstenau, L. B., Hoppe, D., Frozza, R., & Iepsen, S. (2020). Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysis. International Journal of Production Research, 58(6), 1605–1627. https://doi.org/10.1080/00207543.2019.1671625 DOI: https://doi.org/10.1080/00207543.2019.1671625
Kjellsdotter Ivert, L., & Jonsson, P. (2014). When should advanced planning and scheduling systems be used in sales and operations planning? International Journal of Operations & Production Management, 34(10), 1338–1362. https://doi.org/10.1108/IJOPM-03-2011-0088 DOI: https://doi.org/10.1108/IJOPM-03-2011-0088
Ku, C.-C., Chien, C.-F., & Ma, K.-T. (2020). Digital transformation to empower smart production for Industry 3.5 and an empirical study for textile dyeing. Computers & Industrial Engineering, 142, 106297. https://doi.org/10.1016/j.cie.2020.106297 DOI: https://doi.org/10.1016/j.cie.2020.106297
Kumar, V., Agrawal, T. K., Wang, L., & Chen, Y. (2017). Contribution of traceability towards attaining sustainability in the textile sector. Textiles and Clothing Sustainability, 3(1), 5. https://doi.org/10.1186/s40689-017-0027-8 DOI: https://doi.org/10.1186/s40689-017-0027-8
Kusi-Sarpong, S., Gupta, H., Khan, S. A., Chiappetta Jabbour, C. J., Rehman, S. T., & Kusi-Sarpong, H. (2023). Sustainable supplier selection based on industry 4.0 initiatives within the context of circular economy implementation in supply chain operations. Production Planning & Control, 34(10), 999–1019. https://doi.org/10.1080/09537287.2021.1980906 DOI: https://doi.org/10.1080/09537287.2021.1980906
Levin, J. (1987). Estatística aplicada à ciências humanas (2nd ed.). Harbra.
Li, H., Wang, Y., Zhao, P., Zhang, X., & Zhou, P. (2015). Cutting tool operational reliability prediction based on acoustic emission and logistic regression model. Journal of Intelligent Manufacturing, 26(5), 923–931. https://doi.org/10.1007/s10845-014-0941-4 DOI: https://doi.org/10.1007/s10845-014-0941-4
Liu, Y., Peng, Y., Wang, B., Yao, S., & Liu, Z. (2017). Review on cyber-physical systems. IEEE/CAA Journal of Automatica Sinica, 4(1), 27–40. https://doi.org/10.1109/JAS.2017.7510349 DOI: https://doi.org/10.1109/JAS.2017.7510349
Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10. https://doi.org/https://doi.org/10.1016/j.jii.2017.04.005 DOI: https://doi.org/10.1016/j.jii.2017.04.005
Lugaresi, G., Alba, V. V., & Matta, A. (2021). Lab-scale Models of Manufacturing Systems for Testing Real-time Simulation and Production Control Technologies. Journal of Manufacturing Systems, 58, 93–108. https://doi.org/https://doi.org/10.1016/j.jmsy.2020.09.003 DOI: https://doi.org/10.1016/j.jmsy.2020.09.003
Luu, H., Ferreira, F., & Marques, A. D. (2019). Digitisation and Industry 4.0 in the Portuguese T&C sector. Industria Textila, 70(4), 342–345. https://doi.org/10.35530/IT.070.04.1612 DOI: https://doi.org/10.35530/IT.070.04.1612
Magalhães, L. C., Magalhães, L. C., Ramos, J. B., Moura, L. R., de Moraes, R. E. N., Gonçalves, J. B., Hisatugu, W. H., Souza, M. T., de Lacalle, L. N. L., & Ferreira, J. C. E. (2022). Conceiving a Digital Twin for a Flexible Manufacturing System. Applied Sciences, 12(19), 9864. https://doi.org/10.3390/app12199864 DOI: https://doi.org/10.3390/app12199864
Manglani, H., Hodge, G. L., & Oxenham, W. (2019). Application of the Internet of Things in the textile industry. Textile Progress, 51(3), 225–297. https://doi.org/10.1080/00405167.2020.1763701 DOI: https://doi.org/10.1080/00405167.2020.1763701
Mantravadi, S., & Møller, C. (2019). An Overview of Next-generation Manufacturing Execution Systems: How important is MES for Industry 4.0? Procedia Manufacturing, 30, 588–595. https://doi.org/10.1016/j.promfg.2019.02.083 DOI: https://doi.org/10.1016/j.promfg.2019.02.083
Marconi, M. de A., & Lakatos, E. M. (2010). Fundamentos de metodologia científica (7th ed.). Atlas.
Masood, T., & Sonntag, P. (2020). Industry 4.0: Adoption challenges and benefits for SMEs. Computers in Industry, 121, 103261. https://doi.org/10.1016/j.compind.2020.103261 DOI: https://doi.org/10.1016/j.compind.2020.103261
Mathew, D., & Brintha, N. C. (2023). Artificial Intelligence in the Field of Textile Industry: A Systematic Study on Machine Learning and Neural Network Approaches. In Recent Trends in Computational Intelligence and Its Application (pp. 222–228). CRC Press. https://doi.org/10.1201/9781003388913-30 DOI: https://doi.org/10.1201/9781003388913-30
Mattar, F. N. (2012). Pesquisa de marketing: edição compacta (2nd ed.). GEN Atlas.
Mellado, J., & Núñez, F. (2022). Design of an IoT-PLC: A containerized programmable logical controller for the industry 4.0. Journal of Industrial Information Integration, 25, 100250. https://doi.org/10.1016/j.jii.2021.100250 DOI: https://doi.org/10.1016/j.jii.2021.100250
Merhi, M. I., & Harfouche, A. (2023). Enablers of artificial intelligence adoption and implementation in production systems. International Journal of Production Research, 1–15. https://doi.org/10.1080/00207543.2023.2167014 DOI: https://doi.org/10.1080/00207543.2023.2167014
Minussi, J. A., Damacena, C., & Ness Jr, W. L. (2002). Um modelo de previsão de solvência utilizando regressão logística. Revista de Administração Contemporânea, 6(3), 109–128. https://doi.org/10.1590/S1415-65552002000300007 DOI: https://doi.org/10.1590/S1415-65552002000300007
Naedele, M., Chen, H.-M., Kazman, R., Cai, Y., Xiao, L., & Silva, C. V. A. (2015). Manufacturing execution systems: A vision for managing software development. Journal of Systems and Software, 101, 59–68. https://doi.org/10.1016/j.jss.2014.11.015 DOI: https://doi.org/10.1016/j.jss.2014.11.015
Öztürk, C., & Ornek, A. M. (2014). Operational extended model formulations for Advanced Planning and Scheduling systems. Applied Mathematical Modelling, 38(1), 181–195. https://doi.org/10.1016/j.apm.2013.05.049 DOI: https://doi.org/10.1016/j.apm.2013.05.049
Park, K. T., Kang, Y. T., Yang, S. G., Zhao, W. Bin, Kang, Y.-S., Im, S. J., Kim, D. H., Choi, S. Y., & Do Noh, S. (2020). Cyber Physical Energy System for Saving Energy of the Dyeing Process with Industrial Internet of Things and Manufacturing Big Data. International Journal of Precision Engineering and Manufacturing-Green Technology, 7(1), 219–238. https://doi.org/10.1007/s40684-019-00084-7 DOI: https://doi.org/10.1007/s40684-019-00084-7
Peng, C.-Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. Journal of Educational Research. The Journal of Educational Research, 96(1), 3–14. DOI: https://doi.org/10.1080/00220670209598786
Pereira, F., Carvalho, V., Vasconcelos, R., & Soares, F. (2022). A Review in the Use of Artificial Intelligence in Textile Industry (pp. 377–392). https://doi.org/10.1007/978-3-030-79168-1_34 DOI: https://doi.org/10.1007/978-3-030-79168-1_34
Petri, R. F., Forno, A. J. D., De Souza, A. A. U., & Kipper, L. M. (2021). Uma análise das publicações no SIMPEP sobre indicadores da manufatura enxuta e a indústria 4.0 no segmento têxtil. In E. M. Pinheiro, P. M. de A. Filho, & G. T. F. Coelho (Eds.), ENGENHARIA 4.0: A era da produção inteligente (1st ed., Vol. 5, pp. 385–399). Editora Pascal.
Petri, R. F., Maestri, G., Bessa, G. C., Steffens, F., Oliveira, F. R., Dal Forno, A. J., Rincon, L. M., Merlini, C., & de Souza, A. A. U. (2021). Estimativa de benefícios na implementação de projeto de automação da etiquetagem de embalagens na indústria têxtil. The Academic Society Journal, 29–44. https://doi.org/10.32640/tasj.2021.5.29 DOI: https://doi.org/10.32640/tasj.2021.5.29
Rathore, Dr. B. (2023). Future of Textile: Sustainable Manufacturing & Prediction via ChatGPT. Eduzone : International Peer Reviewed/Refereed Academic Multidisciplinary Journal, 12(01), 52–62. https://doi.org/10.56614/eiprmj.v12i1y23.253 DOI: https://doi.org/10.56614/eiprmj.v12i1y23.253
Sacomano, J. B., Gonçalves, R. F., Bonilla, S. H., Silva, M. T. da, & Sátyro, W. C. (2020). Indústria 4.0: Conceitos e Fundamentos (Vol. 1). Editora Blucher.
Sadiku, M. N. O., Wang, Y., Cui, S., & Musa, S. M. (2017). Cyber-Physical Systems: A Literature Review. European Scientific Journal, ESJ, 13(36), 52. https://doi.org/10.19044/esj.2017.v13n36p52 DOI: https://doi.org/10.19044/esj.2017.v13n36p52
Saenz de Ugarte, B., Artiba, A., & Pellerin, R. (2009). Manufacturing execution system – a literature review. Production Planning & Control, 20(6), 525–539. https://doi.org/10.1080/09537280902938613 DOI: https://doi.org/10.1080/09537280902938613
Schwarz, I., & Kovačević, S. (2017). Textile Application: From Need to Imagination. In Textiles for Advanced Applications. InTech. https://doi.org/10.5772/intechopen.68376 DOI: https://doi.org/10.5772/intechopen.68376
Seraj, E., Wang, Z., Paleja, R. R., Martin, D., Sklar, M., Patel, A., & Gombolay, M. C. (2022). Learning Efficient Diverse Communication for Cooperative Heterogeneous Teaming. 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022, 1173–1182.
Shahriar, M. M., Parvez, M. S., Islam, M. A., & Talapatra, S. (2022). Implementation of 5S in a plastic bag manufacturing industry: A case study. Cleaner Engineering and Technology, 8, 100488. https://doi.org/10.1016/j.clet.2022.100488 DOI: https://doi.org/10.1016/j.clet.2022.100488
Shao, J., He, X., Wang, J., Bai, X., Lei, X., & Liu, C. (2015). Design of Textile Manufacturing Execution System Based on Big Data. Journal of Mechanical Engineering, 51(5), 160–170. https://doi.org/10.3901/JME.2015.05.160 DOI: https://doi.org/10.3901/JME.2015.05.160
Shen, Y.-C., Chen, P.-S., & Wang, C.-H. (2016). A study of enterprise resource planning (ERP) system performance measurement using the quantitative balanced scorecard approach. Computers in Industry, 75, 127–139. https://doi.org/10.1016/j.compind.2015.05.006 DOI: https://doi.org/10.1016/j.compind.2015.05.006
Sikka, M. P., Sarkar, A., & Garg, S. (2022). Artificial intelligence (AI) in textile industry operational modernization. Research Journal of Textile and Apparel. https://doi.org/10.1108/RJTA-04-2021-0046 DOI: https://doi.org/10.1108/RJTA-04-2021-0046
Song, C. H., Elvers, D., & Leker, J. (2017). Anticipation of converging technology areas — A refined approach for the identification of attractive fields of innovation. Technological Forecasting and Social Change, 116, 98–115. https://doi.org/10.1016/j.techfore.2016.11.001 DOI: https://doi.org/10.1016/j.techfore.2016.11.001
Sousa, T. B. de, Camparotti, C. E. S., Guerrini, F. M., Silva, A. L. da, & Azzolini Júnior, W. (2014). AN OVERVIEW OF THE ADVANCED PLANNING AND SCHEDULING SYSTEMS. Independent Journal of Management & Production, 5(4). https://doi.org/10.14807/ijmp.v5i4.239 DOI: https://doi.org/10.14807/ijmp.v5i4.239
Souza, K. D. L., Nascimento, I. B. do, Keine, S., & Fleig, R. (2021). Desenvolvimento de um Sistema de Execução de Manufatura (MES) no Planejamento e Controle de Produção: uma Aplicação da Indústria 4.0 no Processo de Fabricação de Tubos de Aço. Produto & Produção, 22(1). https://doi.org/10.22456/1983-8026.102371 DOI: https://doi.org/10.22456/1983-8026.102371
Storch, L. A., Nara, E. O. B., & Kipper, L. M. (2013). The use of process management based on a systemic approach. International Journal of Productivity and Performance Management, 62(7), 758–773. https://doi.org/10.1108/IJPPM-12-2012-0134 DOI: https://doi.org/10.1108/IJPPM-12-2012-0134
Taghaboni‐Dutta, F., Trappey, A. J. C., Trappey, C. V., & Wu, H. (2009). An exploratory RFID patent analysis. Management Research News, 32(12), 1163–1176. https://doi.org/10.1108/01409170911006911 DOI: https://doi.org/10.1108/01409170911006911
Taha, M. F., ElMasry, G., Gouda, M., Zhou, L., Liang, N., Abdalla, A., Rousseau, D., & Qiu, Z. (2022). Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview. Chemosensors, 10(8), 303. https://doi.org/10.3390/chemosensors10080303 DOI: https://doi.org/10.3390/chemosensors10080303
Urbina Coronado, P. D., Lynn, R., Louhichi, W., Parto, M., Wescoat, E., & Kurfess, T. (2018). Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system. Journal of Manufacturing Systems, 48, 25–33. https://doi.org/10.1016/j.jmsy.2018.02.002 DOI: https://doi.org/10.1016/j.jmsy.2018.02.002
Wang, L.-C., Chen, C.-C., Liu, J.-L., & Chu, P.-C. (2021). Framework and deployment of a cloud-based advanced planning and scheduling system. Robotics and Computer-Integrated Manufacturing, 70, 102088. https://doi.org/10.1016/j.rcim.2020.102088 DOI: https://doi.org/10.1016/j.rcim.2020.102088
Witsch, M., & Vogel-Heuser, B. (2012). Towards a Formal Specification Framework for Manufacturing Execution Systems. IEEE Transactions on Industrial Informatics, 8(2), 311–320. https://doi.org/10.1109/TII.2012.2186585 DOI: https://doi.org/10.1109/TII.2012.2186585
Yin, S., Bao, J., Zhang, Y., & Huang, X. (2017). M2M Security Technology of CPS Based on Blockchains. Symmetry, 9(9), 193. https://doi.org/10.3390/sym9090193 DOI: https://doi.org/10.3390/sym9090193
Younus, M., Peiyong, C., Hu, L., & Yuqing, F. (2010). MES development and significant applications in manufacturing -A review. 2010 2nd International Conference on Education Technology and Computer, V5-97-V5-101. https://doi.org/10.1109/ICETC.2010.5530040 DOI: https://doi.org/10.1109/ICETC.2010.5530040
Yue, L., Wang, L., Niu, P., & Zheng, N. (2019). Building a reference model for a Manufacturing Execution System (MES) platform in an Industry 4.0 context. Journal of Physics: Conference Series, 1345(6), 062002. https://doi.org/10.1088/1742-6596/1345/6/062002 DOI: https://doi.org/10.1088/1742-6596/1345/6/062002
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