GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises

Abstract

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.

Method of Analyzing Technological Data in Metric Space in the Context of Industry 4.0

“…Schaefer, J.L., Tardio, P.R., Barierle, I.C., Nara, E.O.B. [38] Linear models A linear model identifying the digital technologies used which have been shown to be sources of manufacturing competitiveness.…”

“…Proprietary methods relating to multifaceted competitive analysis are also being developed. An example of this is the hybrid GIANN method, which combines multi-attribute utility theory with the concepts of entropy and information extraction and computational modeling via a multi-layer perceptron artificial neural network [38]. Models directed at recognizing the correlation between the technological level and competitive success of companies are also being developed [39].…”

A Comprehensive Roadmap for Connecting Industry 4.0 Technologies to the Basic Model of Collaborative Planning, Forecasting, and Replenishment (CPFR)

“…As the third most mentioned technology, cloud computing can be a differentiator because it has unlimited storage capacity, facilitating decision-making, training, and collaboration among all supply chain stakeholders (Wang et al 2015). Its use in the supply chain creates competitive advantages for companies, allowing real-time visualization of all produced data, improving operations management, and ultimately, SCM (Schaefer et al 2023;Frank et al 2019). However, the most significant benefit is that it enhances collaboration and communication among SC stakeholders, facilitated through the use of applications that share information in real-time and improve decision-making, reducing costs and enhancing customer service (Wang et al 2015).…”

Promoting the International Competitiveness of Small and Medium-Sized Enterprises Through Cross-Border E-Commerce Development

“…The competitiveness of SMEs has always been an important issue in the field of management (Piperopoulos & Scase, 2009; Schaefer et al, 2023), and in the era of economic globalization, the competitiveness of SMEs is oriented toward the international market (Bilali, 2022). The international competitiveness of a company refers to its superior performance in market share, export trade, and financial performance compared to its competitors in international business operations (Moen & Servais, 2002; Peña-Vinces et al, 2017) or its ability to adapt to changes in the international market (Zheng et al, 2022).…”