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There are many potential measures of performance for evaluating the success of a construction project. All address performance in three key areas: scope, schedule and budget. In a performance measurement framework where senior management wishes to minimise the numberof performance measures it employs, while ensuring maximal coverage or visibility into the programme, having a tool that captures each of the three areas would be ideal. Project managers have used Earned Value Management (EVM) for over 40 years to track actual schedule progress and actual costs against project plans. Earned Value Management has traditionally been applied to individual projects on which the manager is accountable for both schedule and cost variances. This paper proposes methods to apply EVM principles to allow: (1) analysis of portfolios of construction projects; (2) incorporation of the analysis into an innovative pay‐for‐performance human resources practice; and (3) use of regression analysis to develop baseline earned value curves. These extensions fit the needs of many government managers, who oversee a range of projects by multiple contractors, and whose cost risk is primarily due to schedule slips and change orders. Earned Value Management is described in the context of project oversight, and a dashboard system of performance measures is proposed for quickly assessing individual projects and portfolios. A method of generating standardised planned value curves is then specified, based on data from previous and ongoing projects. The paper concludes by showing how the US General Services Administration’s Public Buildings Service is using these methods to analyse and oversee its portfolio of new construction and major repair and alteration projects.

Project monitoring is one of the important activities in the management of a project and its main objective is to supply the necessary information for proper control of the project based on the project plan. Monitoring of a project covers different issues related to costs, resources, problems, progress etc. All these issues are dependent on many other sub‐issues and any changes in any of them influences the whole project monitoring system. EArned value analysis is one of the tools used for monitoring the progress and costs of the project is composed of several factors. In the present work, the activity of project monitoring has been treated as a general system and earned value analysis as a specific system for modeling and simulating the dynamics involved therein. System dynamics methodology has been applied for this purpose. A case study has been conducted on a real‐world software project in an IT organization. The results show that the system of earned value analysis is highly influenced by the variations in the estimated cost of unit effort, delay in an activity and estimated effort of work package.

The Ariba Implementation at MED-X case is designed to teach students how to analyze a program that is experiencing problems and recommend solutions. Specifically, the case introduces students to earned value analysis and program oversight for an e-procurement technology program. The case centers on MED-X's need to quickly discover why the company's e-procurement implementation project was not going according to plan. Once a cause has been discovered, students will need to make a recommendation to fix the problem. Data for the simplified program, consisting of two concurrent projects, is given to students, who should in turn analyze the project using earned value analysis. The case is an easy introduction to program management and oversight for executives and MBA students, and teaches the essentials of earned value project management.

Students will learn how to control and act in oversight of large complex programs, as well as how to apply earned value metrics to analyze a simplified program consisting of two projects. Analyzing the project enables students to learn the strengths and pitfalls of the earned value approach. From a management decision perspective, the case gives students the tools to succinctly answer the questions: How much will the project cost? How long will it take? What is wrong with the project?

Delineating where stability occurs in a contract provides the window of opportunity for procurement officials to positively affect cost and schedule outcomes. While the concept of a Cost Performance Index (CPI) "stability rule" has been routinely cited by Earned Value Management (EVM) authors since the early 1990's, more recent research questions the veracity of this stability rule. This paper resolves the controversy by demonstrating that the definition of stability matters. We find a morphing of the stability definition over time, with three separate definitions permeating the literature. Next, an analysis of Department of Defense contracts for both cost and schedule stability properties finds that the veracity of the stability rule is intricately tied to the definition used.

Earned Value Analysis (EVA) is an accepted theoretical technique advocated for the control of projects. This paper attempts to refine and improve the performance of traditional EVA by the introduction of a hybrid methodology based on work packages and logical time analysis entitled Work Package Methodology (WPM). The proposed WPM provides the means to periodically update project cost and time performance by restricting EVA calculations to individual work packages. These are then subjected to a logical time analysis to determine the predicted project cost and time to completion. A comparative analysis between WPM and EVA is then undertaken using adapted test data derived from knowledge of previous projects to identify the reasons for variation in the results obtained from both methods. The evaluation of the test results indicates that when the Cost Performance Index (CPI) and the Schedule Performance Index (SPI) are well above or below unity then, especially in the early stages of the project, traditionally applied EVA predictions can be un‐realiable and require further investigation and evaluation. WPM provides a vehicle for judging the performance of EVA by applying an alternative logical time and cost utilizing work sequence and construction methods. The predictive performance of EVA is refined by these means.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

The purpose of this paper is to propose a decision support framework that can be used by decision-makers to identify the most convenient disruption analysis (DA) methods for megaprojects and their stakeholders.

The framework was initially developed by conducting a comprehensive literature review to obtain extensive knowledge about disruption management and megaprojects. Focus group discussion (FGD) sessions with the participation of the construction practitioners were then organized to validate and strengthen the findings of the literature review. Consequently, 17 selection factors were identified and categorized as requirement, ability and outcome. Lastly, the most convenient DA methods for megaprojects were identified by performing integrated fuzzy analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) analysis. Additionally, consistency analysis was also conducted to verify the reliability of the results.

The results revealed that the measured mile method is the most appropriate DA method for megaprojects. In case the measured mile method cannot be adopted due to various technical and contractual reasons, the decision-makers are proposed to consider program analysis, work or trade sampling, earned value analysis and control chart method, respectively. Second, the selection factors such as “Comprehensible analysis procedure,” “Existing knowledge and experience about a particular DA method,” “Ability to resolve greater number of disruption events,” “Ability to resolve complex disruption events,” “Ability to exclude factors that are not under the owner's responsibility” and “General acceptance by practitioners, courts, and arbitration, etc.” were given the top priority by the experts, highlighting the critical aspects of the DA methods.

Disruption claims in megaprojects are very critical for the contractors to compensate for the losses stemming from disruption events. Although the effective use of DA methods maximizes the accuracy and reliability of disruption claims, decision-makers can barely implement these methods adequately since past studies neglect to present extensive knowledge about the most convenient DA methods for megaprojects. Thus, developing a decision support framework for the selection of DA methods, this study is the earliest attempt that examines the mechanisms and inherent differences of DA methods. Additionally, owing to the robustness and versatility of this research approach, the research approach could be replicated also for future studies focusing on other project-based industries since disruption is also a challenging issue for many other industries.

Earned Value Management (EVM) is a project monitoring and control technique that enables the forecasting of a project's duration. Many EVM metrics and project duration forecasting methods have been proposed. However, very few studies have compared their accuracy and stability.

This paper presents an exhaustive stability and accuracy analysis of 27 deterministic EVM project duration forecasting methods. Stability is measured via Pearson's, Spearman's and Kendall's correlation coefficients while accuracy is measured by Mean Squared and Mean Absolute Percentage Errors. These parameters are determined at ten percentile intervals to track a given project's progress across 4,100 artificial project networks with varied topologies.

Findings support that stability and accuracy are inversely correlated for most forecasting methods, and also suggest that both significantly worsen as project networks become increasingly parallel. However, the AT + PD-ESmin forecasting method stands out as being the most accurate and reliable.

Implications of this study will allow construction project managers to resort to the simplest, most accurate and most stable EVM metrics when forecasting project duration. They will also be able to anticipate how the project topology (i.e., the network of activity predecessors) and the stage of project progress can condition their accuracy and stability.

Unlike previous research comparing EVM forecasting methods, this one includes all deterministic methods (classical and recent alike) and measures their performance in accordance with several parameters. Activity durations and costs are also modelled akin to those of construction projects.

The purpose of this paper is to contribute to the diffusion of earned value management (EVM) as a practicable methodology to monitor facility construction and renovation projects in the context of the European industry.

First, a review of the literature reveals how EVM evolved as a tool for facility construction project monitoring together with specific concerns for its application. Then, a review of EVM practice and trends in Europe is provided and finally, applicability and viability of the method is proved through a case demonstration.

EVM practice in the European construction industry is found to be lagging behind other experienced countries and industries, despite EVM having been found to be applicable, adaptable, and predictive of integrated final cost and schedule of facility construction projects. In particular, cost estimate at completion is forecasted by a simple cost performance index (CPI), while for the time estimate at completion, the earned schedule concept is revealed as an accurate predictor.

The paper urges the need for research of a European standard as a primary factor for successful diffusion of EVM usage in architecture, engineering and construction projects.

This paper helps practitioners to understand the adaptability of EVM practice in the European construction industry and to apply EV tools for effective monitoring of the performance of their projects.

Current trends of EVM practice in the European construction context are presented and suggestions for sustaining the diffusion of EVM are given.

The purpose of the paper is to develop a method to integrate the schedule-based analysis with a productivity-based analysis to prove and support the result of the damages calculation.

In this paper, a “cost and schedule impact integration” (CSI₂) model is proposed to objectively show and estimate lost productivity due to changes in construction projects.

A schedule-based analysis to include separate tracking of change order costs can be used to predict productivity due to the delay and disruption; changes in construction projects almost always result in delay and disruption. However, the schedule-based analysis needs to be integrated with a productivity-based analysis to prove and support the result of the damages calculation.

The results of this study expand upon construction practices for proving and quantifying lost productivity due to changes in construction projects.

The contribution of the paper is summarized as the introduction of a “schedule impact analysis” into a “cost impact analysis” technique to assess the damages, as well as to demonstrate the labor productivity impact due to delay and disruption in construction projects.