Please use this identifier to cite or link to this item:
Title: Identifying Industrial Productivity Factors with Artificial Neural Networks
Authors: Gutiérrez-Ruiz, A Manuel
Valcarce-Ruiz, Lucía
Becerra-Vicario, Rafael
Ruíz-Palomo, Daniel
Keywords: Multi-Layer Perceptron;Construction Industry;Industrial Analysis
Issue Date: Jun-2020
Publisher: NISCAIR-CSIR, India
Abstract: 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.
Page(s): 534-536
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(06) [June 2020]

Files in This Item:
File Description SizeFormat 
JSIR 79(6) 534-536.pdf513.59 kBAdobe PDFView/Open

Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.