Sustainable development (SD) has recently emerged as a critical axis in industrial strategic management and debate. Apart from the scarcity of resources, the influence of economic difficulties on human life and society has increased the relevance of SD. However, estimating and analyzing the SD of any business are still in its infancy. Thus, the SD of the sports goods industry is assessed using a multilayer artificial perceptron neural network (MLP) technique employing eight distinct characteristics connected to production efficiency, international and internal environment, export, and domestic development by using Statistica software. To discover an ideal model for SD in the sports goods business, stepwise models with varied input parameters were built. The identity activation function was used to create and train an error propagation algorithm-based network. The results of various eight network models revealed that, among other models, the accuracy of our fifth model, which incorporates production efficiency, international and internal environment, strategic management, and domestic distribution organization, gives the best answer. This model has the lowest root-mean-square error (RMSE) = 0.002 and the greatest R square = 0.922, demonstrating its efficacy and effectiveness in terms of SD. Based on these findings, it is possible to conclude that using artificial neural networks is beneficial in determining an optimal solution for SD and other similar problems since this tool is important and would be utilized for forecasting and decision-making in any business.
Keywords: Export development; Neural network approach; Production efficiency; Statistica; Strategic management.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.