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Inspection Plan Based on the Process Capability Index Using the Neutrosophic Statistical Method

Author

Listed:
  • Muhammad Aslam

    (Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia)

  • Mohammed Albassam

    (Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia)

Abstract

The Process Capability Index (PCI) has been widely used in industry to advance the quality of a product. Neutrosophic statistics is the more generalized form of classical statistics and is applied when the data from the production process or a product lot is incomplete, incredible, and indeterminate. In this paper, we will originally propose a variable sampling plan for the PCI using neutrosophic statistics. The neutrosophic operating function will be given. The neutrosophic plan parameters will be determined using the neutrosophic optimization solution. A comparison between plans based on neutrosophic statistics and classical statistics is given. The application of the proposed neutrosophic sampling plan will be given using company data.

Suggested Citation

  • Muhammad Aslam & Mohammed Albassam, 2019. "Inspection Plan Based on the Process Capability Index Using the Neutrosophic Statistical Method," Mathematics, MDPI, vol. 7(7), pages 1-10, July.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:7:p:631-:d:248895
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    References listed on IDEAS

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    1. Wu, Chien-Wei & Pearn, W.L. & Kotz, Samuel, 2009. "An overview of theory and practice on process capability indices for quality assurance," International Journal of Production Economics, Elsevier, vol. 117(2), pages 338-359, February.
    2. Pearn, W.L. & Wu, Chien-Wei, 2007. "An effective decision making method for product acceptance," Omega, Elsevier, vol. 35(1), pages 12-21, February.
    3. Muhammad Aslam & Muhammad Azam & Chi‐Hyuck Jun, 2015. "Various repetitive sampling plans using process capability index of multiple quality characteristics," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(6), pages 823-835, November.
    4. S Balamurali & Muhammad Aslam & Ahmad Liaquat, 2018. "Determination of a new mixed variable lot-size multiple dependent state sampling plan based on the process capability index," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(3), pages 615-627, February.
    5. S. Balamurali & Chi‐Hyuck Jun, 2011. "A new system of skip‐lot sampling plans having a provision for reducing normal inspection," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(3), pages 348-363, May.
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    Cited by:

    1. Iván E. Villalón-Turrubiates & Rogelio López-Herrera & Jorge L. García-Alcaraz & José R. Díaz-Reza & Arturo Soto-Cabral & Iván González-Lazalde & Gerardo Grijalva-Avila & José L. Rodríguez-Álvarez, 2022. "A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE," Mathematics, MDPI, vol. 10(16), pages 1-27, August.

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