Identifying Industrial Productivity Factors with Artificial Neural Networks
Productivity is an important issue in recent literature because it encourages cost savings and efficiency in the use of industrial resources in all countries. However, the study of the factors that explain the productivity levels reached by the companies presents controversy, and the existing research demands new analysis models that can more accurately identify the causes of industrial productivity. The present study aims to develop a new model that allows determining with high accuracy the factors that explain productivity in the construction industry. For this, an important sample of industrial companies and techniques of artificial neural networks has been used. The results obtained provide levels of accuracy that exceed those obtained by the previous literature, and have allowed us to identify that the aspects related to turnover, liquidity, and growth of companies provide an excellent strategy for promoting industrial productivity.
Multi-Layer Perceptron; Construction Industry; Industrial Analysis
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