Somayeh Maleki; Farzad Movahedi Sobhani
Volume 2, Issue 2 , June 2015, , Pages 86-96
Abstract
The objective of this study is to investigate the relationship between the ability to apply quality cost information and planning and implementing quality improvement activities. The factors leading to increase in application capacity of quality cost information in organizations are also explored in ...
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The objective of this study is to investigate the relationship between the ability to apply quality cost information and planning and implementing quality improvement activities. The factors leading to increase in application capacity of quality cost information in organizations are also explored in this study. The research model explains how the application capacity of quality cost information affects the planning and implementation of quality improvement activities. The research model and assumptions are examined through “structural equation model” testing approach. The findings of this research show that there is a positive and direct relationship between the application capacity of quality cost information and planning and implementation of quality improvements activities. The data used in the present study were gathered from 102 Iranian companies manufacturing automotive spare parts. Due to the volume of samples, there are limitations in generalizing the study results. This study, through identifying the factors influencing the increase in ability of an organization to apply quality cost information, offers a framework for investment and improvement of these factors with the aim of using quality cost information.
Farzad Movahedi Sobhani; Tahereh Madadi
Volume 2, Issue 1 , March 2015, , Pages 15-33
Abstract
The main purpose of this paper is to investigate the suitability of diverse data mining techniques for construction delay analysis. Data of this research obtained from 120 Iranian construction projects. The analysis consists of developing and evaluating various data mining models for factor selection, ...
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The main purpose of this paper is to investigate the suitability of diverse data mining techniques for construction delay analysis. Data of this research obtained from 120 Iranian construction projects. The analysis consists of developing and evaluating various data mining models for factor selection, delay classification, and delay prediction. The results of this research indicate that with respect to accuracy and correlation indexes, genetic algorithm with K-NN learning model is the most suitable model for factor selection. By conducting the genetic algorithm, eight significant variables causing construction delay are identified as: Changes in project manager, Difficulties in financing project by owner, Number of employees, Project duration, Unforeseen events, Project Location, Number of equipment, How to get the project. This research also revealed that in the case of delay classification and prediction, respectively, bagging decision tree and bagging neural network has the least amount of error in comparison with other techniques. In addition, to compare the diversity of data mining methods, the optimized parameter vectors of the selected models were also identified.
