IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i16p3000-d892689.html
   My bibliography  Save this article

A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

Author

Listed:
  • Iván E. Villalón-Turrubiates

    (Department of Doctoral Program in Engineering Sciences, Western Institute of Technology and Higher Education, Tlaquepaque 45604, Mexico)

  • Rogelio López-Herrera

    (Department of Doctoral Program in Engineering Sciences, Western Institute of Technology and Higher Education, Tlaquepaque 45604, Mexico)

  • Jorge L. García-Alcaraz

    (Department of Industrial Engineering, Autonomous University of Ciudad Juárez, Ciudad Juárez 32310, Mexico)

  • José R. Díaz-Reza

    (Division of Research and Postgraduate Studies, National Technology of Mexico, Technological Institute of Ciudad Juárez, Ciudad Juárez 32310, Mexico)

  • Arturo Soto-Cabral

    (Department of Industrial Engineering, National Technology of México, Technological Institute of Durango, Durango 34080, Mexico)

  • Iván González-Lazalde

    (Department of Industrial Engineering, National Technology of México, Technological Institute of Durango, Durango 34080, Mexico)

  • Gerardo Grijalva-Avila

    (Department of Manufacturing Engineering, Polytechnic University of Durango, Durango 34306, Mexico)

  • José L. Rodríguez-Álvarez

    (Department of Doctoral Program in Engineering Sciences, Western Institute of Technology and Higher Education, Tlaquepaque 45604, Mexico
    Department of Management Engineering, National Technology of México, Higher Technological Institute of the Los Llanos Region, Guadalupe Victoria 34700, Mexico)

Abstract

The capability analysis of a process against requirements is often an instrument of change. The traditional and fuzzy process capability approaches are the most useful statistical techniques for determining the intrinsic spread of a controlled process for establishing realistic specifications and use for comparative processes. In the industry, the traditional approach is the most commonly used instrument to assess the impact of continuous improvement projects. However, these methods used to evaluate process capability indices could give misleading results because the dataset employed corresponds to the final product/service measures. This paper reviews an alternative procedure to assess the fuzzy process capability indices based on the statistical methodology involved in the modeling and design of experiments. Firstly, a model with reasonable accuracy is developed using a neural network approach. This model is embedded in a graphic user interface (GUI). Using the GUI, an experimental design is carried out, first to know the membership function of the process variability and then include this variability in the model. Again, an experimental design identifies the improved operating conditions for the significative independent variables. A new dataset is generated with these operating conditions, including the minimum error reached for each independent variable. Finally, the GUI is used to get a new prediction for the response variable. The fuzzy process capability indices are determined using the triangular membership function and the predicted response values. The feasibility of the proposed method was validated using a random data set corresponding to the basis weight of a papermaking process. The results indicate that the proposed method provides a better overview of the process performance, showing its true potential. The proposed method can be considered non-invasive.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:3000-:d:892689
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/16/3000/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/16/3000/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Armin Falk & James J. Heckman, 2009. "Lab Experiments are a Major Source of Knowledge in the Social Sciences," Working Papers 200935, Geary Institute, University College Dublin.
    2. Lee, Hong Tau, 2001. "Cpk index estimation using fuzzy numbers," European Journal of Operational Research, Elsevier, vol. 129(3), pages 683-688, March.
    3. John Antonakis & Samuel Bendahan & Philippe Jacquart & Rafael Lalive, 2010. "On making causal claims : A review and recommendations," Post-Print hal-02313119, HAL.
    4. Editors The, 2008. "From the Editors," Basic Income Studies, De Gruyter, vol. 2(2), pages 1-3, January.
    5. Moreira, M.O. & Balestrassi, P.P. & Paiva, A.P. & Ribeiro, P.F. & Bonatto, B.D., 2021. "Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Chen, K.S. & Chen, T.W., 2008. "Multi-process capability plot and fuzzy inference evaluation," International Journal of Production Economics, Elsevier, vol. 111(1), pages 70-79, January.
    7. Hsu, Bi-Min & Shu, Ming-Hung, 2008. "Fuzzy inference to assess manufacturing process capability with imprecise data," European Journal of Operational Research, Elsevier, vol. 186(2), pages 652-670, April.
    8. Ali Shabani & Saralees Nadarajah & Mojtaba Alizadeh, 2018. "The (α, β)-cut control charts for process average based on the generalised intuitionistic fuzzy number," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(2), pages 392-406, January.
    9. Gulbay, Murat & Kahraman, Cengiz, 2006. "Development of fuzzy process control charts and fuzzy unnatural pattern analyses," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 434-451, November.
    10. Editors The, 2008. "From the Editors," Basic Income Studies, De Gruyter, vol. 3(1), pages 1-1, July.
    11. A. Parchami & M. Mashinchi, 2010. "A new generation of process capability indices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(1), pages 77-89.
    12. 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.
    13. Laoun, Brahim & Kasat, Harshal A. & Ahmad, Riaz & Kannan, Arunachala M., 2018. "Gas diffusion layer development using design of experiments for the optimization of a proton exchange membrane fuel cell performance," Energy, Elsevier, vol. 151(C), pages 689-695.
    14. Wu, Chien-Wei, 2009. "Decision-making in testing process performance with fuzzy data," European Journal of Operational Research, Elsevier, vol. 193(2), pages 499-509, March.
    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. Lude, Maximilian & Prügl, Reinhard, 2021. "Experimental studies in family business research," Journal of Family Business Strategy, Elsevier, vol. 12(1).
    2. Gholamreza Hesamian & Mohamad Ghasem Akbari, 2021. "A process capability index for normal random variable with intuitionistic fuzzy information," Operational Research, Springer, vol. 21(2), pages 951-964, June.
    3. Yang, Miles M. & Li, Tianchen & Wang, Yue, 2020. "What explains the degree of internationalization of early-stage entrepreneurial firms? A multilevel study on the joint effects of entrepreneurial self-efficacy, opportunity-motivated entrepreneurship,," Journal of World Business, Elsevier, vol. 55(6).
    4. Zapata, Cindy P. & Hayes-Jones, Laura C., 2019. "The consequences of humility for leaders: A double-edged sword," Organizational Behavior and Human Decision Processes, Elsevier, vol. 152(C), pages 47-63.
    5. Rosalie L Tung & Günter K Stahl, 2018. "The tortuous evolution of the role of culture in IB research: What we know, what we don’t know, and where we are headed," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 49(9), pages 1167-1189, December.
    6. Laurent, Catherine E. & Berriet-Solliec, Marielle & Kirsch, Marc & Labarthe, Pierre & Trouve, Aurelie, 2010. "Multifunctionality Of Agriculture, Public Policies And Scientific Evidences: Some Critical Issues Of Contemporary Controversies," APSTRACT: Applied Studies in Agribusiness and Commerce, AGRIMBA, vol. 4(1-2), pages 1-6.
    7. Hsu, Dan K. & Burmeister-Lamp, Katrin & Simmons, Sharon A. & Foo, Maw-Der & Hong, Michelle C. & Pipes, Jesse D., 2019. "“I know I can, but I don't fit”: Perceived fit, self-efficacy, and entrepreneurial intention," Journal of Business Venturing, Elsevier, vol. 34(2), pages 311-326.
    8. Batistič, Saša & Černe, Matej & Kaše, Robert & Zupic, Ivan, 2016. "The role of organizational context in fostering employee proactive behavior: The interplay between HR system configurations and relational climates," European Management Journal, Elsevier, vol. 34(5), pages 579-588.
    9. Dan K. Hsu & Johan Wiklund & Richard D. Cotton, 2017. "Success, Failure, and Entrepreneurial Reentry: An Experimental Assessment of the Veracity of Self–Efficacy and Prospect Theory," Entrepreneurship Theory and Practice, , vol. 41(1), pages 19-47, January.
    10. Krueger, Norris & Bogers, Marcel L.A.M. & Labaki, Rania & Basco, Rodrigo, 2021. "Advancing family business science through context theorizing: The case of the Arab world," Journal of Family Business Strategy, Elsevier, vol. 12(1).
    11. Jana Schmutzler & Edward Lorenz, 2018. "Tolerance, agglomeration, and enterprise innovation performance: a multilevel analysis of Latin American regions," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 27(2), pages 243-268.
    12. Choi, James J. & Haisley, Emily & Kurkoski, Jennifer & Massey, Cade, 2017. "Small cues change savings choices," Journal of Economic Behavior & Organization, Elsevier, vol. 142(C), pages 378-395.
    13. Catherine Welch & Eriikka Paavilainen-Mäntymäki & Rebecca Piekkari & Emmanuella Plakoyiannaki, 2022. "Reconciling theory and context: How the case study can set a new agenda for international business research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(1), pages 4-26, February.
    14. Sirola, Nina & Pitesa, Marko, 2018. "The macroeconomic environment and the psychology of work evaluation," Organizational Behavior and Human Decision Processes, Elsevier, vol. 144(C), pages 11-24.
    15. Weber, Ellen & Büttgen, Marion & Bartsch, Silke, 2022. "How to take employees on the digital transformation journey: An experimental study on complementary leadership behaviors in managing organizational change," Journal of Business Research, Elsevier, vol. 143(C), pages 225-238.
    16. Sturt W Manning & Brita Lorentzen & Lynn Welton & Stephen Batiuk & Timothy P Harrison, 2020. "Beyond megadrought and collapse in the Northern Levant: The chronology of Tell Tayinat and two historical inflection episodes, around 4.2ka BP, and following 3.2ka BP," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-38, October.
    17. Ramli, Azizul Azhar & Watada, Junzo & Pedrycz, Witold, 2011. "Real-time fuzzy regression analysis: A convex hull approach," European Journal of Operational Research, Elsevier, vol. 210(3), pages 606-617, May.
    18. Milazzo, M.F. & Spina, F. & Primerano, P. & Bart, J.C.J., 2013. "Soy biodiesel pathways: Global prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 579-624.
    19. Ravi KANBUR & Lucas RONCONI, 2018. "Enforcement matters: The effective regulation of labour," International Labour Review, International Labour Organization, vol. 157(3), pages 331-356, September.
    20. Meuleman, Miguel & Wright, Mike, 2011. "Cross-border private equity syndication: Institutional context and learning," Journal of Business Venturing, Elsevier, vol. 26(1), pages 35-48, January.

    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:jmathe:v:10:y:2022:i:16:p:3000-:d:892689. 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.