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

Nikolina Ljepava, Aleksandar Jovanović and Aleksandar Aleksić ()
Additional contact information
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

Mathematics, 2023, vol. 11, issue 19, 1-22

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.

Keywords: customer satisfaction; fuzzy logic system; neural networks; multiple linear regression; ANFIS (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/19/4221/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/19/4221/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:19:p:4221-:d:1256332

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().