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Industrial Application of the ANFIS Algorithm—Customer Satisfaction Assessment in the Dairy Industry

Author

Listed:
  • Nikolina Ljepava

    (College of Business Administration, American University in the Emirates, Dubai P.O. Box 503000, United Arab Emirates)

  • Aleksandar Jovanović

    (Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia)

  • Aleksandar Aleksić

    (Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia)

Abstract

As a part of the food industry, the dairy industry is one of the most important sectors of the process industry, keeping in mind the number of employees in that sector, the share in the total industrial production, and the overall value added. Many strategies have been developed over time to satisfy customer needs and assess customer satisfaction. This paper proposes an innovative model based on adaptive neuro-fuzzy inference system (ANFIS) and elements of the ACSI (American customer satisfaction index) for assessing and monitoring the level of customer satisfaction in a dairy manufacturing company where there are no large seasonal variations. In terms of an innovative approach, the base of fuzzy logic rules is determined by applying the fuzzy Delphi technique for the application of the ANFIS algorithm and assessment of customer satisfaction. The verification of the model is delivered by testing a real sample from a company of the dairy industry. As decisions on the strategic company level may be impacted by customer satisfaction, the company management should choose the most precise methodology for customer satisfaction assessment. The results are compared with other methods in terms of mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percentage error (MAPE). Results show that ANFIS outperformed other methods used for assessing the level of customer satisfaction, such as case-based reasoning and multiple linear regression.

Suggested Citation

  • Nikolina Ljepava & Aleksandar Jovanović & Aleksandar Aleksić, 2023. "Industrial Application of the ANFIS Algorithm—Customer Satisfaction Assessment in the Dairy Industry," Mathematics, MDPI, vol. 11(19), pages 1-22, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4221-:d:1256332
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    References listed on IDEAS

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    1. Miklós Pakurár & Hossam Haddad & János Nagy & József Popp & Judit Oláh, 2019. "The Service Quality Dimensions that Affect Customer Satisfaction in the Jordanian Banking Sector," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    2. Snežana Nestić & Ranka Gojković & Tijana Petrović & Danijela Tadić & Predrag Mimović, 2022. "Quality Performance Indicators Evaluation and Ranking by Using TOPSIS with the Interval-Intuitionistic Fuzzy Sets in Project-Oriented Manufacturing Companies," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    3. Ashley S. Otto & David M. Szymanski & Rajan Varadarajan, 2020. "Customer satisfaction and firm performance: insights from over a quarter century of empirical research," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 543-564, May.
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    1. Bingjie Zhang & Jian Wang & Xiaoling Gong & Zhanglei Shi & Chao Zhang & Kai Zhang & El-Sayed M. El-Alfy & Sergey V. Ablameyko, 2023. "First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization," Mathematics, MDPI, vol. 12(1), pages 1-14, December.

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