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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
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    References listed on IDEAS

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    1. 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).
    2. 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.
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