IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i23p16206-d1285589.html
   My bibliography  Save this article

Enhancing Aerospace Industry Efficiency and Sustainability: Process Integration and Quality Management in the Context of Industry 4.0

Author

Listed:
  • Gheorghe Ioan Pop

    (S.C. Universal Alloy Corporation Europe S.R.L. Dumbravița 244A, 437145 Maramures, Romania)

  • Aurel Mihail Titu

    (Industrial Engineering and Management Department, Faculty of Engineering, “Lucian Blaga” University of Sibiu, 10 Victoriei Street, 550024 Sibiu, Romania
    The Academy of Romanian Scientists, 3 Ilfov Street, 030167 Bucharest, Romania)

  • Alina Bianca Pop

    (Faculty of Engineering, Department of Engineering and Technology Management, Northern University Centre of Baia Mare, Technical University of Cluj-Napoca, 62A Victor Babes Street, 430083 Baia Mare, Romania)

Abstract

This paper delves into the multifaceted domain of the aerospace industry, examining its evolution, current challenges, and imperative focus on quality management and process integration. The aerospace sector, driven by technological advancements and a burgeoning global demand for air travel and freight transport, necessitates a thorough analysis of its industrial fabric and operational intricacies. This research endeavors to analyze the dynamics of the aerospace industry, pinpoint its challenges, and propose an integrated approach to enhance efficiency, quality, and sustainability. The primary goals encompass understanding the evolving industry landscape, identifying critical challenges, and offering innovative solutions by amalgamating the principles of Industry 4.0 into quality management and processes within the aerospace sector. Through an in-depth exploration of various facets, this research underscores the pivotal role of efficient processes and integrated quality management in achieving sustainable growth and competitiveness in the aerospace industry. By aligning with the paradigm of Industry 4.0, organizations can optimize their operations and contribute to the industry’s advancement, delivering safer and more cost-effective aerospace products. The study adopts a multifaceted approach, incorporating an extensive literature review, a critical analysis of industry trends, the examination of quality management frameworks, and a thorough evaluation of the integration potential of Industry 4.0 technologies. The research also involves case studies and expert insights to validate the proposed approach. The investigation reveals that by leveraging Industry 4.0 technologies and embracing an integrated approach to quality management, the aerospace industry can significantly enhance operational efficiency, product quality, and overall sustainability. The seamless integration of processes and the implementation of advanced quality frameworks pave the way for a more competitive and future-ready aerospace industry, meeting the evolving demands of a globalized world.

Suggested Citation

  • Gheorghe Ioan Pop & Aurel Mihail Titu & Alina Bianca Pop, 2023. "Enhancing Aerospace Industry Efficiency and Sustainability: Process Integration and Quality Management in the Context of Industry 4.0," Sustainability, MDPI, vol. 15(23), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16206-:d:1285589
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/23/16206/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/23/16206/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agnieszka Bitkowska, 2020. "The relationship between Business Process Management and Knowledge Management - selected aspects from a study of companies in Poland," Journal of Entrepreneurship, Management and Innovation, Fundacja Upowszechniająca Wiedzę i Naukę "Cognitione", vol. 16(1), pages 169-193.
    2. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pan, Yongjun & Sun, Yu & Li, Zhixiong & Gardoni, Paolo, 2023. "Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Phan, Hieu Chi & Dhar, Ashutosh Sutra & Bui, Nang Duc, 2023. "Reliability assessment of pipelines crossing strike-slip faults considering modeling uncertainties using ANN models," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    5. Liu, Jiale & Wang, Huan, 2024. "A brain-inspired energy-efficient Wide Spiking Residual Attention Framework for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Yuan, Zixia & Xiong, Guojiang & Fu, Xiaofan & Mohamed, Ali Wagdy, 2023. "Improving fault tolerance in diagnosing power system failures with optimal hierarchical extreme learning machine," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    7. Chen, Edward & Bao, Han & Dinh, Nam, 2024. "Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    8. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Wang, Weicheng & Chen, Jinglong & Zhang, Tianci & Liu, Zijun & Wang, Jun & Zhang, Xinwei & He, Shuilong, 2023. "An asymmetrical graph Siamese network for one-classanomaly detection of engine equipment with multi-source fusion," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    10. Hieu T. T. L. Pham & Mahdi Rafieizonooz & SangUk Han & Dong-Eun Lee, 2021. "Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction," Sustainability, MDPI, vol. 13(24), pages 1-37, December.
    11. Bakeer, Tammam, 2023. "General partial safety factor theory for the assessment of the reliability of nonlinear structural systems," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    12. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    13. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    14. Bo, Yimin & Bao, Minglei & Ding, Yi & Hu, Yishuang, 2024. "A DNN-based reliability evaluation method for multi-state series-parallel systems considering semi-Markov process," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    15. Zhang, Qing & Tang, Lv & Xuan, Jianping & Shi, Tielin & Li, Rui, 2023. "An uncertainty relevance metric-based domain adaptation fault diagnosis method to overcome class relevance caused confusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    16. Alkhaleel, Basem A., 2024. "Machine learning applications in the resilience of interdependent critical infrastructure systems—A systematic literature review," International Journal of Critical Infrastructure Protection, Elsevier, vol. 44(C).
    17. Ma, Yulin & Li, Lei & Yang, Jun, 2022. "Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    18. Chehade, Abdallah & Savargaonkar, Mayuresh & Krivtsov, Vasiliy, 2022. "Conditional Gaussian mixture model for warranty claims forecasting," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    19. Zheng, Shuwen & Wang, Chong & Zio, Enrico & Liu, Jie, 2024. "Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    20. Zaitseva, Elena & Levashenko, Vitaly & Rabcan, Jan, 2023. "A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 229(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16206-:d:1285589. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.