GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises
The adoption of models based on key performance indicators to diagnose and evaluate the competitiveness of companies has been presented as a trend in the operations’ management. These models are structured with different variables in complex interrelationships, making diagnosis and monitoring diffic...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Administrative Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3387/13/2/56 |
| _version_ | 1797623081980657664 |
|---|---|
| author | Jones Luís Schaefer Paulo Roberto Tardio Ismael Cristofer Baierle Elpidio Oscar Benitez Nara |
| author_facet | Jones Luís Schaefer Paulo Roberto Tardio Ismael Cristofer Baierle Elpidio Oscar Benitez Nara |
| author_sort | Jones Luís Schaefer |
| collection | DOAJ |
| description | The adoption of models based on key performance indicators to diagnose and evaluate the competitiveness of companies has been presented as a trend in the operations’ management. These models are structured with different variables in complex interrelationships, making diagnosis and monitoring difficult due to the number of variables involved, which is one of the main management challenges of Small and Medium-sized Enterprises. In this sense, this article proposes the Gain Information Artificial Neural Network (GIANN) method. GIANN is a method to optimize the number of variables of assessment models for the competitiveness and operational performance of Small and Medium-sized Enterprises. GIANN is a hybrid methodology combining Multi-attribute Utility Theory with Entropy and Information Gain concepts and computational modeling through Multilayer Perceptron Artificial Neural Network. The model used in this article integrates variables such as fundamental points of view, critical success factors, and key performance indicators. GIANN was validated through a survey of managers of Small and Medium-sized Enterprises in Southern Brazil. The initial model was adjusted, reducing the number of key performance indicators by 39% while maintaining the accuracy of the results of the competitiveness measurement. With GIANN, the number of variables to be monitored decreases considerably, facilitating the management of Small and Medium-sized Enterprises. |
| first_indexed | 2024-03-11T09:19:22Z |
| format | Article |
| id | doaj.art-e0f6e61d7f6042a2af679815162d6316 |
| institution | Directory Open Access Journal |
| issn | 2076-3387 |
| language | English |
| last_indexed | 2024-03-11T09:19:22Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Administrative Sciences |
| spelling | doaj.art-e0f6e61d7f6042a2af679815162d63162023-11-16T18:26:18ZengMDPI AGAdministrative Sciences2076-33872023-02-011325610.3390/admsci13020056GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized EnterprisesJones Luís Schaefer0Paulo Roberto Tardio1Ismael Cristofer Baierle2Elpidio Oscar Benitez Nara3Department of Production Engineering, Federal University of Santa Maria (UFSM), Santa Maria 97105-900, BrazilProduction and Systems Engineering Graduate Program, Pontifical Catholic University of Parana (PUCPR), Curitiba 80215-901, BrazilGraduate Program in Agro-Industrial Systems and Processes, Federal University of Rio Grande, Rio Grande 96203-900, BrazilProduction and Systems Engineering Graduate Program, Pontifical Catholic University of Parana (PUCPR), Curitiba 80215-901, BrazilThe adoption of models based on key performance indicators to diagnose and evaluate the competitiveness of companies has been presented as a trend in the operations’ management. These models are structured with different variables in complex interrelationships, making diagnosis and monitoring difficult due to the number of variables involved, which is one of the main management challenges of Small and Medium-sized Enterprises. In this sense, this article proposes the Gain Information Artificial Neural Network (GIANN) method. GIANN is a method to optimize the number of variables of assessment models for the competitiveness and operational performance of Small and Medium-sized Enterprises. GIANN is a hybrid methodology combining Multi-attribute Utility Theory with Entropy and Information Gain concepts and computational modeling through Multilayer Perceptron Artificial Neural Network. The model used in this article integrates variables such as fundamental points of view, critical success factors, and key performance indicators. GIANN was validated through a survey of managers of Small and Medium-sized Enterprises in Southern Brazil. The initial model was adjusted, reducing the number of key performance indicators by 39% while maintaining the accuracy of the results of the competitiveness measurement. With GIANN, the number of variables to be monitored decreases considerably, facilitating the management of Small and Medium-sized Enterprises.https://www.mdpi.com/2076-3387/13/2/56multi-attribute utility theoryartificial neural networkentropyinformation gainsmall and medium-sized enterprises |
| spellingShingle | Jones Luís Schaefer Paulo Roberto Tardio Ismael Cristofer Baierle Elpidio Oscar Benitez Nara GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises Administrative Sciences multi-attribute utility theory artificial neural network entropy information gain small and medium-sized enterprises |
| title | GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises |
| title_full | GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises |
| title_fullStr | GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises |
| title_full_unstemmed | GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises |
| title_short | GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises |
| title_sort | giann a methodology for optimizing competitiveness performance assessment models for small and medium sized enterprises |
| topic | multi-attribute utility theory artificial neural network entropy information gain small and medium-sized enterprises |
| url | https://www.mdpi.com/2076-3387/13/2/56 |
| work_keys_str_mv | AT jonesluisschaefer giannamethodologyforoptimizingcompetitivenessperformanceassessmentmodelsforsmallandmediumsizedenterprises AT paulorobertotardio giannamethodologyforoptimizingcompetitivenessperformanceassessmentmodelsforsmallandmediumsizedenterprises AT ismaelcristoferbaierle giannamethodologyforoptimizingcompetitivenessperformanceassessmentmodelsforsmallandmediumsizedenterprises AT elpidiooscarbeniteznara giannamethodologyforoptimizingcompetitivenessperformanceassessmentmodelsforsmallandmediumsizedenterprises |