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Quality Tools and Their Applications in Industry

Author

Listed:
  • Ana Maria Ifrim

    (University Politehnica of Bucharest, Romania)

  • Cătălin Ionuț Silvestru

    (University Politehnica of Bucharest, Romania)

  • Mihai-Alexandru Stoica

    (Bucharest University of Economic Studies, Romania)

  • Cristina Vasilica Icociu

    (University Politehnica of Bucharest, Romania)

  • Ionica Oncioiu

    (Titu Maiorescu University, Romania)

  • Marian Ernuț Lupescu

    (Bucharest University of Economic Studies, Romania)

Abstract

Almost all quality improvement methods require data collection and analysis to solve quality problems. The combination of six sigma and agile creates a six sigma agile methodology that aims to reach quality levels according to the Six Sigma requirements of 3.4 defects per million measurements. In order to achieve these objectives, it is necessary to know the industry well and implicitly the product or the analysed process. Thus, the correctness of these analyses depends on the collection of the data that will be analysed. The use of data analysis methods at each stage, especially in the measurement and analysis stages, is critically important for making strong decisions. The purpose of this article is to present the added value of the integration of six sigma and agile methodologies for IT projects. Thus, the integration of the two methodologies will lead to faster decision-making without the risk of an increase in the number of failures.

Suggested Citation

  • Ana Maria Ifrim & Cătălin Ionuț Silvestru & Mihai-Alexandru Stoica & Cristina Vasilica Icociu & Ionica Oncioiu & Marian Ernuț Lupescu, 2023. "Quality Tools and Their Applications in Industry," International Journal of Innovation in the Digital Economy (IJIDE), IGI Global, vol. 14(1), pages 1-11, January.
    • Handle: RePEc:igg:jide00:v:14:y:2023:i:1:p:1-11
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    References listed on IDEAS

    as
    1. Wang, Ching-Hsin & Chen, Kuen-Suan, 2020. "New process yield index of asymmetric tolerances for bootstrap method and six sigma approach," International Journal of Production Economics, Elsevier, vol. 219(C), pages 216-223.
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