Architectural design of defective product inspection systems in the IoT-based automotive component industry

  • R Arif Firmansah Pradita University
  • Agus Anwar Pradita University
  • Haryono haryono Pradita University
Keywords: internet of things, system architecture, industry 4.0, archimate


Indonesia is one of the countries with the largest manufacturing industry in ASEAN, related to the Making Indonesia 4.0 program, which is a government program to prepare for the era of the digital sector and smart factory, therefore innovation and sustainable development are needed in terms of technology , in the manufacturing industry, one of which is the automotive industry. . Factors that determine the success of the manufacturing industry and its components include being able to reduce the number of reject products in the production process. The purpose of this research is to design an architectural design of a defective product inspection system based on Internet of thingss technology , which allows the quality control department to obtain more objective data in a fast time and make decisions based on analytical data, the research methodology is observation and literature study. , the result of the research is the blueprint architect of the defective product inspection system in the automotive component industry with an Archimate modeling  language, which shows that IoT technology  can be designed to prevent company losses caused by rejected products and improve the quality of domestic automotive industry products


[1] R. Poler et al., “An IoT-based reliable industrial data services for manufacturing quality control,” 2021. doi: 10.1109/ICE/ITMC52061.2021.9570203.
[2] J. Liu, F. Guo, H. Gao, M. Li, Y. Zhang, and H. Zhou, “Defect detection of injection molding products on small datasets using transfer learning,” J Manuf Process, vol. 70, no. August, pp. 400–413, 2021, doi: 10.1016/j.jmapro.2021.08.034.
[3] The Open Group, The Open Group Standard: ArchiMate 3.1 Specification. 2019.
[4] E. Manavalan and K. Jayakrishna, “A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements,” Comput Ind Eng, vol. 127, pp. 925–953, 2019, doi: 10.1016/j.cie.2018.11.030.
[5] M. Compare, P. Baraldi, and E. Zio, “Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0,” IEEE Internet Things J, vol. 7, no. 5, pp. 4585–4597, 2020, doi: 10.1109/JIOT.2019.2957029.
[6] C. Turner, O. Okorie, C. Emmanouilidis, and J. Oyekan, “Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review,” Comput Ind, vol. 136, p. 103593, 2022, doi: 10.1016/j.compind.2021.103593.
[7] D. R. Sjödin, V. Parida, M. Leksell, and A. Petrovic, “Smart Factory Implementation and Process Innovation: A Preliminary Maturity Model for Leveraging Digitalization in ManufacturingMoving to smart factories presents specific challenges that can be addressed through a structured approach focused on people, p,” Research Technology Management, vol. 61, no. 5, pp. 22–31, 2018, doi: 10.1080/08956308.2018.1471277.
[8] A. Mijuskovic, R. Bemthuis, A. Aldea, and P. Havinga, “An Enterprise Architecture based on Cloud, Fog and Edge Computing for an Airfield Lighting Management System,” Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOCW, vol. 2020-Octob, pp. 63–73, 2020, doi: 10.1109/EDOCW49879.2020.00021.
[9] A. Chehri, A. Zimmermann, R. Schmidt, and Y. Masuda, “Theory and Practice of Implementing a Successful Enterprise IoT Strategy in the Industry 4.0 Era,” Procedia Comput Sci, vol. 192, pp. 4609–4618, Jan. 2021, doi: 10.1016/J.PROCS.2021.09.239.
[10] S. Munirathinam, Industry 4.0: Industrial Internet of Things (IIOT), 1st ed., vol. 117, no. 1. Elsevier Inc., 2020. doi: 10.1016/bs.adcom.2019.10.010.
[11] D. Shah, J. Wang, and Q. P. He, “Feature engineering in big data analytics for IoT-enabled smart manufacturing – Comparison between deep learning and statistical learning,” Comput Chem Eng, vol. 141, p. 106970, Oct. 2020, doi: 10.1016/J.COMPCHEMENG.2020.106970.
[12] S. Kahveci, B. Alkan, M. H. Ahmad, B. Ahmad, and R. Harrison, “An end-to-end big data analytics platform for IoT-enabled smart factories: A case study of battery module assembly system for electric vehicles,” J Manuf Syst, vol. 63, pp. 214–223, Apr. 2022, doi: 10.1016/J.JMSY.2022.03.010.