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Cited by 1Digital Transformation in IndustryManagement Systems and Quality ImprovementManagement and Optimization Techniques

Integrating Industry 4.0 Technologies into 8D Methodologies: A Case Study in the Automotive Industry

Open access
Oancea, Alexandru-Vasile, Ionescu, Nadia, Rontescu, Corneliu, Ionescu, Laurentiu-Mihai, Misztal, Agnieszka, Bogatu, Ana-Maria, Cicic, Dumitru-Titi, Pirvu, Valentin·2025·Applied Sciences
DOI 10.3390/app152011262
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Abstract

This paper presents the application of the 8D method to a case study in the automotive industry, combined with the development of a pilot framework that integrates Industry 4.0 technologies—Artificial Intelligence (AI), Internet of Things (IoT), and advanced IT tools. Unlike traditional 8D implementations, the proposed solution introduces several innovations: (i) AI-based complaint analysis with automated keyword detection and classification and localization of batches in the warehouses achieving a 100% accuracy rate in categorizing root causes; (ii) IoT-enabled non-invasive monitoring of production parameters for tests and logs, reducing fault rate to 0 for current case study; (iii) digital cataloging and storage of corrective and preventive actions, improving retrieval speed of past solutions by 300 times (from 300 min to less than 1 min); and (iv) automated reporting tools, cutting the report preparation time from hours to minutes. Through these contributions, the system enhances both responsiveness and prevention, leading to an observed 100% reduction in fault recurrence associated with current case study and an estimated 75% decrease in customer complaints regarding defects occurring throughout the production sector where the pilot was implemented. In addition to the practical improvements, this work extends the literature on digitalized quality management by demonstrating how the 8D methodology can be effectively re-engineered with Industry 4.0 components. Comparative analysis against the enterprise’s conventional 8D system and against recent alternative approaches confirms that our framework provides measurable benefits in the speed, accuracy, and sustainability of problem-solving processes.

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  • The risk management system is fully aligned with the VDA 2019 requirements, demonstrating its effectiveness.

    “Consequently, the risk management system is fully aligned with the VDA 2019 requirements, demonstrating its effectiveness [33].”

    From An Intelligent Framework for Implementing AIAG–VDA FMEA and Action Priority (AP) Assessment (2026) · cites this paper as [33]

    The Conclusions (page unavailable) say that FMEA was applied according to VDA-AIAG and enabled proactive risk assessment. The Additional information section (page unavailable) reports low Action Priority for all failure effects after corrective actions. These passages support the risk-control approach and its reported results, but do not establish that the entire risk management system is fully aligned with VDA 2019 requirements. (LLM, conf=0.85)

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Research works printed but not linked: 6

  1. Mahmood, K. Solving Manufacturing Problems with 8D Methodology: A Case Study of Leakage Current in a Production. Company. J. Electr. Electron. Eng. 2023, 2, 1-18.
  2. Barosani, S.; Bhalwankar, N.; Deshmukh, V.; Kokane, S.; Kulkarni, P.R. A review on 8D problem solving process. Int. Res. J. Eng. Technol. 2017, 4, 529-535.
  3. Kumar, S.; Verma, M.; Dubey, D. Reducing the defects and improving the quality of manufacturing product (CT wheel/crain part) using 8D problem solving tool. Indian. J. Sci. Res. 2023, 3, 47-55.
  4. Kovács, T.A., Stadler, R.G., Daruka, N., Eds.; Advanced Sciences and Technologies for Security Applications; Springer: Cham, Switzerland, 2025.
  5. Ichimov, M.A.M.; Popescu, M.V.; Negoita, O.D.; Costea-Marcu, I.C.; Moiceanu, G. Proposal for software management solution to prevent potential issues. U.P.B. Sci. Bull. Ser. C 2025, 87, 107-120.
  6. Bozyi git, F.; Do gan, O.; Kılınç, D. Categorization of customer complaints in food industry using machine learning approaches. J. Intell. Syst. Theory Appl. 2022, 5, 85-91. [CrossRef] · DOI

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Authors
Oancea, A.-V., Ionescu, N., Rontescu, C., Ionescu, L.-M., Misztal, A., Bogatu, A.-M., Cicic, D.-T., & Pirvu, V.
Affiliations
  • Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
  • Doctoral School, Faculty of Industrial Engineering and Robotic, National University of Science and Technology Politehnica Bucharest, 313 Splaiul Independenței, 060042 Bucharest, Romania
  • Faculty of Electronics, Communications and Computers, Pitești University Centre, National University of Science and Technology Politehnica Bucharest, 313 Splaiul Independenței, 060042 Bucharest, Romania
  • Poznań University of Technology (PL)
Corresponding author
  • Ionescu, Nadia
  • Bogatu, Ana-Maria
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2025-10-21
Volume
15
Issue
20
Pages
11262
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app152011262
OpenAlex
W4415403313
Cited by
1
References
18
Citations by year
2026: 1
FWCI
0.49
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Management Systems and Quality Improvement
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Management and Optimization Techniques
Keywords
Industry 4.0, automotive quality management, root cause analysis, complaint analysis, artificial intelligence, Internet of Things, corrective and preventive actions, automated reporting

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