scholarly journals INTERPRETIVE STRUCTURAL MODELING IN EARNED VALUE MANAGEMENT

2020 ◽  
Vol 26 (6) ◽  
pp. 524-533
Author(s):  
Morteza Bagherpour ◽  
Mohammad Khaje Zadeh ◽  
Amin Mahmoudi ◽  
Xiaopeng Deng
Keyword(s):  
Construction Projects ◽  
Total Duration ◽  
Value Management ◽  
Affecting Factors ◽  
Earned Value ◽  
Interpretive Structural Modeling ◽  
Earned Value Management ◽  
Factors Affecting ◽  
Engineering Managers ◽  
Practical Factors

The primary purpose of the current study is introducing a comprehensive approach to identify the relationship among different criteria in Earned Value Management (EVM). EVM is a well-known approach in project management context that can monitor schedule and cost performance indexes in projects simultaneously. The EVM detects current project performances and also predicts at completion costs of the project. In this study, employing Interpretive Structural Modelling, interactions which exist among affecting factors on EVM’s success are determined. First, all of the practical factors on EVM are determined and categorized into four main clusters; then the most effective ones are separated from the clusters; eventually, ISM is used based on eleven ultimate critical criteria. The results demonstrate that “Instability in the construction market” and “Macroeconomic indicators” are the most influencing factors affecting the EVM. Finally, a novel method for enhancing the performance of conventional EVM is presented. The proposed approach would be highly applicable for engineering managers who are willing to promote the current performance of the systems. Most studies have been previously carried out on the applications of the EVM in terms of improving final cost and total duration elapsed whereas there is not any particular study on the EVM issue which has stated the key factors that influence the EVM and lasting effect on the project performance. It should be noted that the proposed approach can be employed through the life cycle of any project particularly in construction projects.

scholarly journals Risk factors affecting the ability for earned value management to accurately assess the performance of infrastructure projects in Australia

2019 ◽  
Vol 19 (4) ◽  
pp. 550-569 ◽  
Author(s):  
Maan Nihad Ibrahim ◽  
David Thorpe ◽  
Muhammad Nateque Mahmood
Keyword(s):  
Risk Factors ◽  
Construction Projects ◽  
Project Performance ◽  
Value Management ◽  
Earned Value ◽  
Related Factors ◽  
Earned Value Management ◽  
Content Type ◽  
Factors Affecting ◽  
Factors Influencing

Purpose The purpose of this paper is to investigate a set of risk-related factors influencing the earned value management (EVM) concept as an assessment technique in evaluating the progress of modern sustainable infrastructure construction projects. Design/methodology/approach A qualitative research approach has been adopted for identifying risk-related factors influencing EVM concept from a literature review and through interviewing industry personnel, followed by an inductive process to form sets of key factors and their measuring items. Findings EVM is a common method for assessing project performance. A weakness of this approach is that EVM assessment in its current form does not measure the impact of a number of project performance factors that result from the complexity of modern infrastructure construction projects, and thus does not accurately assess their impact in this performance. This paper discusses and explains a range of potential risk factors to evaluating project performance such as sustainability, stakeholder requirements, communication, procurement strategy, weather, experience of staff, site condition, design issues, financial risk, subcontractor, government requirements and material. In addition, their measuring items were identified. Practical implications This research assists projects managers to improve the evaluation process of infrastructure construction performance by incorporating a range of factors likely to impact on that performance and which are not included in current EVM calculations. Originality/value This research addresses the need to include in the EVM calculation a range of risk factors affecting the performance of infrastructure projects in Australia and therefore makes this calculation a more reliable tool for assessing project performance.

scholarly journals Predicting Earned Value Indexes in Residential complexes’ Construction Projects Using Artificial Neural Network Model

2020 ◽  
Author(s):  
ibraheem Aidan ◽  
Firas Jaber ◽  
Duaa Al-Jeznawi ◽  
Faiq Al-Zwainy
Keyword(s):  
Performance Index ◽  
Construction Projects ◽  
Historical Data ◽  
Performance Indicator ◽  
Residential Buildings ◽  
Great Accuracy ◽  
Value Management ◽  
Earned Value ◽  
Earned Value Management ◽  
Cost Performance

The importance of this study may be defined by using the smart techniques to earned value indicators of residential buildings projects in Republic of Iraq, only one development intelligent forecasting model was presented to predict Schedule Performance Index (SPI), Cost Performance Index (CPI), and To Complete Cost Performance Indicator (TCPI) are defined as the dependent. The approach is principally influenced by the determining numerous factors which effect on the earned value management, that involves Iraqi historical data. In addition, six independent variables (F1: BAC, Budget at Completion., F2: AC, Actual Cost., F3, A%, Actual Percentage., F4: EV, Earned Value. F5: P%, Planning Percentage., and F6: PV, Planning Value) were arbitrarily designated and satisfactorily described for per construction project. It was found that ANN has the capability to envisage the dust storm with a great accuracy. The correlation coefficient (R) has been 90.00%, and typical accuracy percentage has been 89.00%.

Predicting the project time and costs using EVM based on gray numbers

2019 ◽  
Vol 26 (9) ◽  
pp. 2107-2119 ◽  
Author(s):  
Samira Nadafi ◽  
Seyed Hamed Moosavirad ◽  
Shahram Ariafar
Keyword(s):  
Completion Time ◽  
Construction Projects ◽  
Design Methodology ◽  
Value Management ◽  
Earned Value ◽  
Earned Value Management ◽  
Content Type ◽  
Project Completion ◽  
Practical Implications ◽  
Significant Point

PurposeThe purpose of this paper is to determine the project completion time and cost under non-deterministic conditions using interval gray numbers (IGNs).Design/methodology/approachThe earned value management (EVM) method based on the IGN has been developed.FindingsThe EVM method based on the IGN has been verified by a numerical example that can be applied to construction projects.Practical implicationsThe EVM method, based on the gray numbers, reduces the budget and time shortage risk. Also, using this method, the managers would not be restricted to provide very exact values in their progress reports in the non-deterministic conditions.Originality/valueOne notable and significant point in all projects during the execution process is to estimate the project completion time and cost. However, non-deterministic conditions for both planned and actual physical completion percentage of projects have not been considered for predicting the project completion time and cost in the literature. Therefore, the novelty of this paper is the prediction of project completion time and cost under non-deterministic conditions using IGN.

scholarly journals The Application and Challenges of Earned Value Management (EVM) As Cost Monitoring Tool in the Construction Industry

2018 ◽  
Vol 7 (3.36) ◽  
pp. 96
Author(s):  
Noorhidayah Sunarti ◽  
Zetty Pakir Mastan ◽  
Lum Seon Cin
Keyword(s):  
Project Management ◽  
Construction Industry ◽  
Time Management ◽  
Construction Projects ◽  
Value Management ◽  
Monitoring Tool ◽  
Earned Value ◽  
Management Tools ◽  
Earned Value Management ◽  
Implementation Level

Earned Value Management (EVM) is one of the fundamental approaches acting as a comprehensive project management and controlling technique for tracking the costs and examining project expenditures relative to the physical progress of work. Majority of the previous literature reviews and findings indicates the positive contributions of EVM in monitoring the project time-cost performance progressively and forecasting its future trends. However, EVM was not widely used as practically, the traditional cost and schedule monitoring tool is still very common in the construction industry. Thus, this research was conducted using quantitative method to the identified quantity surveying, project management and construction firms in Klang Valley area to achieve the objectives of; (1) to identify the implementation level of EVM in construction projects, (2) to recognize the EVM contribution as cost monitoring tool compared to the other mehods, and (3) to ascertain the challenges in using EVM. Based on the result, majority has reaffirmed that EVM is positively contribute to project cost monitoring and provide an overall effective cost management tools in their projects. Despite the major challenges identified in using EVM are due to the lack of EVM knowledge, expertise and experience by the user in the industry, the results also indicating that more construction players have come to realize that integration of cost and time management in EVM is beneficial to the construction industry.  

scholarly journals Predicting Performance Measurement of Residential Buildings Using an Artificial Neural Network

2021 ◽  
Vol 7 (3) ◽  
pp. 461-476
Author(s):  
Salah J. Mohammed ◽  
Hesham A. Abdel-khalek ◽  
Sherif M. Hafez
Keyword(s):  
Performance Index ◽  
Construction Projects ◽  
Residential Buildings ◽  
Machine Intelligence ◽  
Control Technique ◽  
Value Management ◽  
Earned Value ◽  
Earned Value Management ◽  
Cost Performance ◽  
Artificial Neural

Application Earned Value Management (EVM) as a construction project control technique is not very common in the Republic of Iraq, in spite of the benefit from EVA to the schedule control and cost control of construction projects. One of the goals of the present study is the employment machine intelligence techniques in the estimation of earned value; also this study contributes to extend the cognitive content of study fields associated with the earned value, and the results of this study are considered a robust incentive to try and do complementary studies, or to simulate a similar study in alternative new technologies. This paper is aiming at introducing a novel and alternative method of applying Artificial Intelligence Techniques (AIT) for earned value management of the construction projects through using Artificial Neural Networks (ANN) to build mathematical models to be used to estimate the Schedule Performance Index (SPI), Cost Performance Index (CPI) and to Complete Cost Performance Indicator (TCPI) in Iraqi residential buildings before and at execution stage through using web-based software to perform the calculations in the estimation quickly, accurately and without effort. ANN technique was utilized to produce new prediction models by applying the Backpropagation algorithm through Neuframe software. Finally, the results showed that the ANN technique shows excellent results of estimation when it is compared with MLR techniques. The results were interpreted in terms of Average Accuracy (AA%) equal to 83.09, 90.83, and 82.88%, also, correlation coefficient (R) equal to 90.95, 93.00, and 92.30% for SPI, CPI and TCPI respectively. Doi: 10.28991/cej-2021-03091666 Full Text: PDF

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