Data mining model for identifying project profitability variables

Chang, Andrew S.; Leu, Sou Sen

doi:10.1016/j.ijproman.2005.08.003

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…Construction organizations developed some tools and procedures to decrease the possible losses, and make their projects more profitable. These tools and strategies adopted by firms are based on the experience and knowledge of the firm's engineers [21]. These profitability-influencing variables are concerned with the initiation, bidding, contracting, execution, and closing stages of construction projects.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Profitability-Influencing Risk Factors for Construction Projects: A System Dynamics Approach

et al. 2022

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This study addressed the complexity involved in integrating the causative risk factors influencing construction profitability. Most of the existing studies cover the individual effects of profitability influencing factors. Very few focus on the systematic impact without incorporating the complexity and associated dynamics, presenting a gap targeted by the current study. The current study aimed to assess causative interrelations and interdependencies between profitability influencing risk factors (PIRF), through systems thinking (ST) and system dynamics (SD) modeling. The SD approach was used to evaluate the integrated impacts on profitability-influencing risk categories (PIRC) in construction projects. The causative influencing factors affecting construction profitability were identified through a comprehensive literature review. These were ranked using content analysis, and categorized into significant issues. Through 250 structured surveys and 15 expert opinion meetings, the path for quantitative and qualitative evaluations was prepared. Following these investigations, a causal loop diagram (CLD) was established using the ST technique, and the integrated effect was quantified using SD modeling. The study finds the rising cost of material, supply chain process, payment issues, planning and scheduling problems, financial difficulties, and effective control of manpower and equipment resources as the most critical PIRFs. The integrated effects of PIRFs on PIRC were quantified using SD modeling. This study helps field professionals with profitability-influencing factors, diagnosing issues, and integrating impacts regarding decision-making and policy formulation. For researchers, it presents a list of factors that can be investigated in detail, and the holistic interrelationships established.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling Profitability-Influencing Risk Factors for Construction Projects: A System Dynamics Approach

et al. 2022

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“…Authors of [5] made a comparative analysis of performance of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Neuro-Fuzzy System for prediction of level of severity of faults present in Java based object oriented software system. Data which comprises of project personnel data provides a rich resource for knowledge discovery and decision support [9]. Data mining results in decision through methods and not assumptions.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Prediction of Human Performance Capability during Software Development Using Classification

Gupta

Suma

2014

ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II

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The quality of human capital is crucial for software companies to maintain competitive advantages in knowledge economy era. Software companies recognize superior talent as a business advantage. They increasingly recognize the critical linkage between effective talent and business success. However, software companies suffering from high turnover rates often find it hard to recruit the right talents. There is an urgent need to develop a personnel selection mechanism to find the talents who are the most suitable for their software projects. Data mining techniques assures exploring the information from the historical projects depending on which the project manager can make decisions for producing high quality software. This study aims to fill the gap by developing a data mining framework based on decision tree and association rules to refocus on criteria for personnel selection. An empirical study was conducted in a software company to support their hiring decision for project members. The results demonstrated that there is a need to refocus on selection criteria for quality objectives. Better selection criteria was identified by patterns obtained from data mining models by integrating knowledge from software project database and authors research techniques.The paper is organized as follows: Section 2 provides the related work and background of data mining Algorithms. Section 3 presents the research methodology to derive at a conclusion while Section 4 depicts the obtained result. Section 5 discusses the summary of this paper and its future scope.

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“…Bineid and Fielding [15] used data mining techniques to explain the development of a dispatch reliability prediction method for passenger aircraft. Nazeri and Zhang [1] described the application of data mining to analyzing severe weather impacts on National Airspace System performance.…”

Section: Related Workmentioning

confidence: 99%

Data mining approaches for aircraft accidents prediction: An empirical study on Turkey airline

Christopher

Balamurugan²

2013

2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN)

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Data mining approaches have been successfully applied in different fields. Risk and safety have always been important considerations in aviation. There is a large amount of knowledge and data accumulation in aviation industry. These data can be store in the form of pilot reports, maintenance reports, accident reports or delay reports. This paper applied the decision tree model on accident reports of the Federal Aviation Administration (FAA) Accident / incident Data System database, contains 468 accident data records for all categories of aviation between the years of 1970 to 2011. The decision tree classifier is use to predict the warning level of the component as the class attribute. We have explored the use of the decision tree technique on aviation components data. Decision Tree induction algorithm is applied to generate the model and the generated model is used to predict the warning of accidents in the airline database. This work may be useful for Aviation Company to make better prediction.

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Data mining model for identifying project profitability variables

Cited by 14 publications

References 13 publications

Modeling Profitability-Influencing Risk Factors for Construction Projects: A System Dynamics Approach

Modeling Profitability-Influencing Risk Factors for Construction Projects: A System Dynamics Approach

Prediction of Human Performance Capability during Software Development Using Classification

Data mining approaches for aircraft accidents prediction: An empirical study on Turkey airline

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