Earned Value Management Research Articles

Project Controls Model for Sustainable Construction Projects

As sustainability rises in significance in the construction industry, there is a need to adopt new project management techniques to help achieve increases in sustainability objectives. There have been several research works that have investigated the integration of sustainability in construction. However, there is a gap in addressing performance measurement and evaluation during the construction phase. Traditional project performance evaluation methods use the Earned Value Management (EVM) technique to assess time, cost, and scope performance. However, EVM does not address the performance of sustainability goals. The main objective of this paper is to develop a new project controls method for sustainable construction projects. Sustainability performance indicators can play an essential role in advancing the practice of sustainable construction. Accordingly, a Sustainable Earned Value Management (SEVM) model incorporating sustainability indicators was developed to monitor the attainment of sustainability objectives in construction projects in addition to the traditional objectives. The proposed SEVM model uses the construction planning optimization model to prepare the baseline plan to monitor the project’s time, cost, and sustainability performance. The proposed model enhances the monitoring and control of sustainable construction projects in terms of time, cost, and sustainability using sustainability indicators.

The Project Performance Evaluation of PLBN Project Using Earned Value Management Method

Process of monitoring and controlling projects requires data evaluation activities in the order to findout whether the project is in accordance with the plan to avoid an incompatibility between theplanning and the actual time. The failure of construction project almost reached the figure of 20% -40% which occurred in the project monitoring and controlling process. The purpose of this researchis analysis project performance with earned value management method at PLBN Project. Thisresearch was carried out by several stages: data collection, data processing and analysis. From theresults of the performance index analysis show that the project’s performance on the constructionproject is still not good. At week 16 the CPI and SPI values are 0.94 and 0.85. Both values can beinterpreted that the costs incurred are greater than planning and the implementation time is longerthan planning. The result of EAC is Rp. 2.423.695.294 with an estimated completion time is 21weeks which indicates that there is an additional 3 weeks of completion.

Proposal and appraisal of novel indicators and performance metrics for project monitoring

Project monitoring is a critical process for guaranteeing its excellence, ensuring the delivery of activities both on planned time and budget. Despite its importance, numerous discussions have arisen regarding which indicator is the most appropriate, and how and when to measure it. The primary objective of this article is to evaluate novel duration indicators based on the Earned Value Management (EVM), Earned Schedule Management (ESM) and Earned Duration Management (EDM) methodologies, and to assess new performance metrics. This involves the determination of the limits of the control charts through extensive Monte Carlo Simulations and their performance under several out-of-control scenarios. The robustness of the control charts is tested using performance metrics within a statistical control approach. The secondary objective consists of evaluating the impact of new probability distributions that describe the project activities, such as the Gamma and Conway–Maxwell–Poisson (CMP) distributions. The contributions of this study are threefold: firstly, the Duration Performance Index (DPI) from the EDM is a reliable indicator for monitoring the overall project duration. Secondly, the probability of overreaction (PO) and the accuracy (Ac) are the performance metrics that most produced robust indicators. Lastly, it is observed that different probability distributions, specifically Gamma and Conway–Maxwell–Poison (CMP) distributions, seem to interfere with the correct generation of control charts signals, although they do not significantly affect the overall duration of the entire project.

Predicting Students' Academic Performances Using Machine Learning Algorithms in Educational Data Mining

In every culture and era, education is considered the most fundamental reality and rule that societies prioritize and deem essential. Throughout the process spanning thousands of years, from the emergence of writing to the present day, education has undergone various forms and formats of change. Education has been a continuous guide for shaping, influencing, sustaining societies, and maintaining its dynamics throughout these historical processes. The continuous evolution and growth of education systems and formats worldwide, with changes affecting the quality of education, have the potential to influence nations and societies in every field, ultimately leading to the emergence of an informed society, achievable only through quality education. In this study, the aim is to determine the factors affecting students' academic performance and predict students' end-of-term academic grades using machine learning algorithms within the scope of Earned Value Management (EVM). Such studies have great potential to increase efficiency in education, improve student achievement and improve education policies. With the use of machine learning algorithms, these goals can be achieved more quickly and efficiently. Five different machine learning algorithms, namely RF, KA, KNN, SVM, and NB, have been employed in the study. Binary and multiclass classification methods were used in prediction processes, and among these methods, the Random Forest (RF) algorithm achieved the highest success prediction rates of 0.97 and 0.93, respectively, in both classification methods.

APPLICATIONS OF NON-TRADITIONAL EARNED VALUE MANAGEMENT MODELS IN PROJECT ANALYTICS

Effective management of financial resources in projects is crucial for project success. Often, difficulties with financial resources, such as budget overruns, lead to unfavorable consequences that directly impact the successful completion of the project, the quality of the outcome, and stakeholder satisfaction. Therefore, identifying and developing tools for the effective management of financial resources, is of paramount importance. The purpose of this study is to apply non-traditional earned value management (EVM) models based on machine learning to predict project costs. To achieve the research objectives, previous literature on the topic was analyzed, a dataset of past projects was prepared, and a machine learning model was applied. The study found that non-traditional models, such as the regression algorithm AdaBoost, produced results close to the actual costs. The research indicates that the developed model could become an indispensable tool for project management and business decision-making, as it demonstrates the ability to adapt to various conditions and make accurate forecasts.

Improving Project Forecasting Accuracy by Developing the Normalised Project Management Baseline

Periodic project reporting using Earned Value Management (EVM) and Earned Duration Management (EDM) uses performance metrics to communicate current and forecast future project performance. The likely accuracy ranges of schedule and cost forecasts are commonly expressed as a simple plus or minus percentage or value. This paper introduces the Normalised Project Management Baseline (NPMB), a single (x, y) plot of the normalised EDM and EVM Project Management Baselines to identify whether periodic project forecasts are optimistic (low) or pessimistic (high), as well as informing the magnitude of variation in the forecast. The NPMB is tested using six real-life completed projects and it is shown to be highly suited to identifying schedule forecast optimism, pessimism, and variations, whilst results for cost are highly promising and warrant further investigation. For project practice the NPMB increases awareness of performance variations, improving forecasting accuracy and confidence in final forecast ranges. For academia, the NPMB introduces new research directions by improving forecasting accuracy and early warning processes by improving the accuracy of periodic project performance observations. This paper contributes to project management literature as the NPMB is the first attempt to improve forecasting accuracy by identifying variations in the periodic project performance observations themselves.

Earned Value Management of Commercial Project: A Case Study Of An LPG Bottling Plant

Earned Value Management of Commercial Project: A Case Study Of An LPG Bottling Plant

Lean project planning – Bridging last planner system and earned value management

Earned Value Management (EVM) has become a de facto standard for project planning after decades of applications worldwide, and most industry contracts that require some of the reporting features of EVM. However, EVM has shortcomings such as too early release of work and no process perspective to facilitate improvements. The Last Planner System (LPS) was developed to overcome the shortcomings of EVM utilizing insights from lean manufacturing. Unfortunately, LPS has not reached the same level of acceptance as EVM. A new approach, Lean Project Planning (LPP), is developed to take advantage of the strengths of both approaches. In this paper, LPS and EVM are compared to each other, and the resulting LPP is presented. The paper discusses a LPP implementation across the project portfolio at a Norwegian pressure vessel fabrication company. The results show that LPP provided accurate Estimate At Completion (EAC) for both costs and delivery times despite taking on many major Variation Orders. The approach is arguably a step forward for turning plans into planning in project-based industries such as shipbuilding and fabrication of pressure vessels. The complexity of pressure vessel fabrication stress-tested the LPP, and some improvements were identified for future work.

Analysis of project work using earned value management and precedence diagram method in manufacturing system projects

PT. Weiss Tech has received a project order for the fabrication and installation of coffee packaging production machines from PT.XYZ, scheduled to start on June 19, 2023, with a deadline of June 24, 2024, spanning 54 weeks. The project has been ongoing for 16 weeks with an update indicating that only 20% of the planned progress has been achieved, which suggests that it may not be completed on time. To evaluate the project's performance, the EVM method will be used to identify critical tasks and reduce the project duration using the Precedence Diagram Method with the help of MS Project software. The evaluation shows negative values for CV and SV variances, as well as performance index SPI<1 and CPI<1, indicating poor project performance. The critical tasks identified include electrical & instrument installation, machine & utility installation, trial work, assembly, quality control, polishing, milling, turning, cutting, and design. Design work has been identified as the most influential on the critical path. By rescheduling the project plan, positive values for CV and SV have been achieved. With the SPI value of 1.13 and CPI value of 1, it can be concluded that future project performance will be completed faster and within budget.

Improving the effectiveness of project scheduling by using Earned Value Management and Artificial Neural Network

Improving the effectiveness of project scheduling by using Earned Value Management and Artificial Neural Network

Earned Value Management in Intralogistics: A Case Study in Mexican Manufacturing

Earned Value Management (EVM) is a project management tool primarily used in engineering and project management to assess performance in terms of cost and schedule. Earned Value Management/Scheduling systems (EVM/ES) have been fundamental in project control, providing key metrics that measure deviations between planned and actual performance in terms of time and cost. However, its application as a project control technique is not very common in Mexico. In this article, EVM was applied to the intralogistics improvement of a manufacturing process in the automotive sector located in Aguascalientes, Mexico. The case study concluded in February 2024 and includes the project scope, scheduling charts, physical progress reports, and budgeted versus actual cost reports. The aim of this article is to provide practical evidence on how to apply EVM in manufacturing projects in Mexico. This will enable project professionals to more effectively utilize EVM for schedule and cost control in their manufacturing projects, with a specific emphasis on intralogistics.

Time and Cost Management in Water Resources Projects Utilizing the Earned Value Method

Today, the development of infrastructure and construction projects in developing countries is of paramount importance. Water projects, in particular, play a crucial role in advancing agriculture, industry, and the economy. The success of these projects hinges on the effective management of time, cost, and quality. The most critical indicator of a project's success is its completion within the forecasted time and budget leading up to its operation. This study aims to assess the impact of Earned Value Management (EVM) 's impact on managing project schedules and budgets in water resource projects. The earned value method was employed to ascertain if the project was ahead or behind schedule and whether the costs were within the budget or had overrun. The values of the Earned Value Index were analyzed at three stages of project implementation. Examining the Schedule Variance (SV) at various phases indicates that a lower percentage is more advantageous for the project. Initially, the SV was 40%, signaling a delay mainly due to the delayed start of operations in the injection gallery and access points. This improved to 25% in the second stage, with the commencement of previously lagging activities. The schedule variance decreased to 11.6% in the final stage, aligning closer to the project schedule's end. Additionally, the Schedule Performance Index (SPI) value, which reflects the efficiency of project management in terms of timeliness, improved from a weak start of 0.6 to 0.88 (with 1.0 indicating that project performance is on target).

Performance Control of Road and Bridge Preservation Projects Using Earned Value Management (EVM) Method

Performance Control of Road and Bridge Preservation Projects Using Earned Value Management (EVM) Method

Artificial Intelligence Models for Predicting Budget Expenditures

This study develops and tests a machine learning (ML)-based cost forecasting model against traditional earned value management (EVM) techniques. Utilizing Python for ML implementation, the research applies algorithms to a dataset of completed projects globally, evaluating their performance with metrics like mean absolute percentage error (MAPE) and percentage error (PE). The results confirmed that ML give more accurate results than the traditional methods. Thus, the initial rate showing that XGBoost is more accurate than the traditional method using Index-2 is 88%. In 23 of 25 randomly selected projects, this algorithm was more accurate. At the middle stage, the same frequency is 92.6%; later stage, the selected criterion further confirms that the ML algorithm is more accurate than the traditional method, accounting for 75% of 21 projects out of 28. By introducing ML into project management forecasting, managers could spend less time on the technical tasks in their projects. Despite its effectiveness, the study's scope is limited by a small sample size of 110 projects and the testing of only three algorithms. Future research is suggested to expand the dataset and explore additional algorithms, including neural networks and tree-based methods, to enhance forecasting precision.

Evaluasi Keterlambatan Proyek Pembangunan Jembatan Gantung Bangun Jawa I Kabupaten Serdang Bedagai

Delays in construction projects often result in losses for construction actors, and can be caused by internal, external, and other factors such as natural, management, material, and environmental disasters. Research was conducted on the Java I Construction Suspension Bridge project, Serdang Bedagai Regency to evaluate delays in terms of cost and time. The goal is to evaluate the schedule of the project plan and implementation and find out the cause. Evaluation using the Earned Value Management method shows that project implementation tends to be slow, but costs are still below budget with an estimated Rp. 2,454,274,500,- and 36 weeks. Delays are caused by the engineering justification process, late delivery of bridge trusses, and instability of service provider cash flow. This research is expected to provide a reference for construction actors to anticipate delays in other projects.

Project cost forecasting based on earned value management and Markov chain

Project cost forecasting based on earned value management and Markov chain

Improving Project Estimates at Completion through Progress-Based Performance Factors

From a managerial perspective, project success hinges on estimates at completion as they allow tailoring response actions to cost and schedule overruns. While the literature is moving towards sophisticated approaches, standard methodologies, such as Earned-Value Management (EVM) and Earned Schedule (ES), are barely implemented in certain contexts. Therefore, it is necessary to improve performance forecasting without increasing its difficulty. The objective of this study was twofold. First, to guide modeling and implementing project progress within cost and to schedule Performance Factors (PFs). Second, to test several PFs utilized within EVM and ES formulae to forecast project cost and duration at completion. Progress indicators dynamically adjust the evaluation approach, shifting from neutral to conservative as the project progresses, either physically or temporally. This study compared the performance of the progress-based PFs against EVM and ES standard, combined, and average-based PFs on a dataset of 65 real construction projects, in both cost and duration forecasting. The results show that progress-based PFs provide more accurate, precise, and timely forecasts than other PFs. This study allows practitioners to select one or more of the proposed PFs, or even to develop one, following the guidelines provided, to reflect best their assumptions about the future course of project performance.

Enhancing project success: the impact of sociotechnical integration on project and program management using earned value management systems

PurposeEarned value management systems (EVMS), also called integrated project and program management systems, have been greatly examined in the literature, which has typically focused on their technical aspects rather than social. This study aims to hypothesize that improving both the technical maturity of EVMS and the social environment elements of EVMS applications together will significantly impact project performance outcomes. For the first time, empirical evidence supports a strong relationship between EVMS maturity and environment.Design/methodology/approachData was collected from 35 projects through four workshops, attended by 31 industry practitioners with an average of 19 years of EVMS experience. These experts, representing 23 organizations, provided over 2,800 data points on sociotechnical integration and performance outcomes, covering projects totaling $21.8 billion. Statistical analyses were performed to derive findings on the impact of technical maturity and social environment on project success.FindingsThe results show statistically significant differences in cost growth, compliance, meeting project objectives and business drivers and customer satisfaction, between projects with high EVMS maturity and environment and projects with poor EVMS maturity and environment. Moreover, the technical and social dimensions were found to be significantly correlated.Originality/valueKey contributions include a novel and tested performance-driven framework to support integrated project management using EVMS. The adoption of this detailed assessment framework by government and industry is driving a paradigm shift in project management of some of the largest and most complex projects in the U.S.; specifically transitioning from a project assessment based upon a binary approach for EVMS technical maturity (i.e. compliant/noncompliant to standards) to a wide-ranging scale (i.e. 0–1,000) across two dimensions.

Integration of Java Programming-Based Pert and Earned Value Management Methods Based on Residential Precast Case Study to Improve Project Cost Efficiency

The need for residential houses is still high for property and construction commodities. The availability of land and market prices for residential houses continue to increase yearly. Only 45.04% of Jakarta residents had a home ownership status in 2020. 2021 is the precast building mode. It is justified that precast construction structures can save 35% on construction costs and are 27.33% more reliable than ordinary concrete structures. The research examines the integration of the PERT method and earned value management (EVM) based on Java programming in residential precast case studies. The results of this study are that the implementation of the PERT method can increase the efficiency of project duration by 6%, and the implementation of the EVM method can increase project cost efficiency by 7% with the indicators Schedule Performance Index (SPI) = 1.01 (under schedule) and Cost Performance Index = 1, 07 (Under Budget)

An Effective Earned Value Management System (EVMS) is a Team Sport

We present a novel framework to assess 27 earned value management system (EVMS) environment factors and investigate their impact on project performance. A study of the EVMS literature, a survey of practitioners, and focus group meetings with 36 EVMS industry and government experts, supported the development of the framework. Focus groups involving 80 practitioners refined and tested it. We analyzed performance data from 35 completed projects/programs representing over US$21.8 billion in total cost; a project demonstrating a positive environment could save up to 25% in cost versus baseline. Practitioners can develop an effective EVMS environment by following the provided guidance.