Evaluating Organizational Effectiveness of Construction Industry Using Artificial Neural Networks

Abstract

In the construction industry we are struggling in selecting the best organization in the pretender stages. We select organization on the basis of lowest bid offered in a tender for particular contract having only the knowledge of reputation of work and the rate offered for the tender by the organization at the least we offer the contract to the organization. We do not pay much attention towards the methodologies adopted in solving the problems that arise the various sector in organization. So, there is an urge in finding out the functional efficiency of an organization. In the present scenario experts are trying out better solutions or enhancing organizational effectiveness. Concepts such as total quality management, reengineering, partnering, conformance to ISO standards, and other emerging management strategies are making headlines. However all of the techniques stress measurement and continuous assessments how the firm organized as important steps in improvement. Therefore it is clearly becoming essential for construction firms to develop valid methods of assessing and prediction their level of organizational effectiveness and hence achieve consistency in the projects performance. For that we need a novel approach for assessing the efficacy of the system used. In any organization the quality and the productivity depends upon the effectiveness of the systems implemented in the organization. So, it becomes necessary for evaluating the functional capability of the organization. We propose Artificial Neural Network (ANN) as decision aiding tool for evaluating the effectiveness and thereby reducing the flaws incorporated by the other techniques in use.