Search results

This study aims to review earned value management (EVM)-relative methods, including the original EVM, earned schedule method (ESM) and earned duration management (EDM(t)). This study then proposes a general implementation procedure and some basic principles for the selection of EVM-relative methods.

After completing an intensive literature review, this study conducts a case study to examine the forecasting performance of project duration using the EVM, ESM and EDM(t) methods.

When the project is expected to finish on time, ESM with a performance factor equal to 1 is the recommended method. EDM(t) would be the most reliable method during a project's entire lifetime if EDM(t) is expected to be delayed based on past experience.

As this research conducts a case study with only one building construction project, the results might not hold true for all types of construction projects.

EVM, ESM and EDM(t) are simple and data-accessible methods. With the help of a general implementation procedure, applying all three methods would be better. The power of the three methods is definitely larger than that of choosing only one for complex construction projects.

Previous studies have discussed the advantages and disadvantages of EVM, ESM and EDM(t). This study amends the available outcomes. Thus, for schedulers or researchers interested in implementing EVM, ESM and EDM(t), this study can provide more constructive instructions.

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.

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 an integrated earned value management (EVM) approach to control quality, cost, schedule and risk of projects.

This study represents a new EVM framework by considering a quality control index. Particularly, some control indices and cumulative buffers are defined by two proposed, methods, namely the linear- and Taguchi-based methods. These methods are implemented in three different projects in different industries.

According to the results, integration of the quality index creates a better control situation by providing more accurate information. Hence, project managers could comprehensively monitor the status of important factors to make more precise decisions while maintaining the simplicity of their analysis.

From the methodological and theoretical features, this paper offers new visions because, to the best of authors’ knowledge, no comparable study has been conducted before.

The purpose of this paper is to determine the project completion time and cost under non-deterministic conditions using interval gray numbers (IGNs).

The earned value management (EVM) method based on the IGN has been developed.

The EVM method based on the IGN has been verified by a numerical example that can be applied to construction projects.

The 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.

One 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.

Prefabricated construction is often hindered by scheduling delays. This paper aims to propose a schedule delay prediction model system, which can provide the key information for controlling the delay effects of risk-related factors on scheduling in prefabricated construction.

This paper combines SD (System Dynamics) and BP (Back Propagation) neural network to predict risk related delays. The SD-based prediction model focuses on dynamically presenting the interrelated impacts of risk events and activities along with workflow. While BP neural network model is proposed to evaluate the delay effect for a single risk event disrupting a single job, which is the necessary input parameter of SD-based model.

The established model system is validated through a structural test, an extreme condition test, a sensitivity test, and an error test, and shows an excellent performance on aspect of reliability and accuracy. Furthermore, 5 scenarios of case application during 3 different projects located in separate cities prove the prediction model system can be applied in a wide range.

This paper contributes to academic research on combination of SD and BP neural network at the operational level prediction, and a practical prediction tool supporting managers to take decision-making in a timely manner against delays.

Integrated project delivery (IPD) is highly recommended to be utilised with building information management (BIM), specifically with BIM level-3 implementation process. Extant literature highlights the financial management challenges facing the proposed integration. These challenges are mainly related to the IPD compensation and the conventional cost control approaches that are not consistent with IPD principles. As such, this paper presents an integration of several methods to support automating risk/reward sharing amongst project parties thus enhancing IPD core team members’ relationship.

The literature review was used to highlight the challenges that face the IPD-based cost management practices such as the risk sharing/reward sharing amongst IPD core team members and potential methods to bridge the revealed IPD gap. A framework was developed by integrating the activity-based costing (ABC) – as a method to analyse the cost structure – and earned value management (EVM) to develop mathematical models that can determine the three main IPD financial transactions (i.e. …) fairly. To demonstrate the applicability of the developed system, a real-life case study was used, in which, promising results were collected in regard to visualising the cost control data and understanding of the accumulative status of the project cost and schedule for team members.

A centralised cost management system (CCMS) for IPD is developed to enable the IPD cost structure as well as automating the risk-sharing/reward-sharing calculations. This system is linked with a web-based management system to display the output of proposed risk-sharing/reward-sharing models. Moreover, a novel grid is developed to show the project status graphically and to respect the diversity in core team members backgrounds. In addition, the case study showed that the proposed integration of different methods (ABC, EVM, BIM and web-based management system) is interoperable and applicable.

This research presents a comprehensive solution to the most revealed challenges in cost management practices in IPD implementation. The outcome of this research contributes to the body of knowledge through presenting new extensions of the EVM to be used with the IPD approach to calculate risk/reward. Moreover, the implementation of the proposed tools such as centralised cost management system (CCMS) and CCMS for IPD web system will enhance/foster the implementation of the IPD in conjunction with BIM process.

This study aims to analyze the difficulties observed in the management of three hydroelectric turbine projects developed by a large multinational company in the metal-mechanic segment, identifying those most critical.

Through a bibliographic search, difficulties throughout project management were identified. These difficulties were used as a research protocol to carry out three case studies in a hydroelectric turbine manufacturer. The projects selection aimed to contemplate different characteristics to encompass the diversity of their typologies. Data were analyzed through content analysis technique and the difficulties were ordered via Grey Relational Analysis (GRA).

Qualitative data analysis provided specific insights regarding the characteristics of each project. When analyzing the difficulties of hydroelectric turbine projects management via GRA, it is observed that those difficulties most evidenced were related to integration and communication, suppliers management and objectives definition.

The main contribution of this research lies in the sector under analysis. The literature about hydroelectric turbine projects is scarce. In addition, no similar studies were found in the literature.

The purpose of this paper is to propose models of duration for maturity gap risk management in Islamic banks.

A thorough review of literature on duration modeling, duration measurement in Islamic banks and Shariah compliance has been conducted to set parameters to develop Shariah-compliant maturity gap risk management mechanism.

Models based on durations of earning assets and return bearing liabilities using various rates of return earned and paid, benchmark rates and industry standards commonly used by Islamic and conventional banks.

Increased Shariah compliance has threefold impact. Firstly, it will increase trust of customers. Secondly, it will help improve profitability by reducing non-Shariah compliance penalties from the regulators. And finally, it will enhance market capitalization and returns stability to investors because of enhanced customer base, increased level of trust and increased profitability.

This research proposes Shariah-compliant maturity gap risk management models based on the concept of duration according to recommendations of Bank for International Settlements. As there is no such maturity gap risk management mechanism that meets the requirements of Shariah using benchmarks that are common between Islamic and conventional banks; therefore, this research presents risk management solutions that can be applied simultaneously in the entire banking sector.

The purpose of this paper is to develop models to forecast final budget and duration of a highway construction project during construction stage.

Highway construction project data are collected and analyzed to find out factors affecting project final budget and duration before developing the forecasting models, research for which is based on the principle of Artificial Neural Network (ANN). The forecasting results obtained from the proposed method are compared with those obtained from the current method based on earned value.

Factors affecting final budget and duration are presented. The forecasting results obtained from the proposed method based on ANN application are more accurate and stable than those obtained from the current method based on earned value.

Factors affecting final budget and duration may differ if applied in other countries, since the project data were collected in the Kingdom of Thailand. The forecasting models, therefore, must be reconsidered for better outcomes.

The study presents a useful tool for the highway construction project manager to predict project final budget and duration. The results can potentially provide early warning of over‐budget and schedule delay.

The ANN models to forecast final budget and duration of highway construction projects during the construction stage, developed by using project data reflecting continual and seasonal cycle data, can provide better predicting results.