Intelligent Systems in Project Performance Measurement and Evaluation

Iranmanesh, Seyed Hossein; Hojati, Zahra Tavassoli

doi:10.1007/978-3-319-17906-3_23

Cited by 7 publications

(8 citation statements)

References 33 publications

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“…PMIS needs to handle that kind of situation. Establish well-defined automated processes between high management levels of the organization and the PMIS internal settings, promising new relationship features in the way on which these two entities communicate between them [138].…”

Section: Concerns On Recommend Practitioners Needmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

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Section: Concerns On Recommend Practitioners Needmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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“…As a result, R can be obtained from the above equation and placed in (27) during the update process. This is the way that risk analysis is performed in the proposed performance evaluation model.…”

Section: The Basic Notation Of the Kf Forecasting Modelmentioning

confidence: 99%

An Improved Performance Measurement Approach for Knowledge-Based Companies Using Kalman Filter Forecasting Method

Hasanzadeh

Shirani

Ardali

2016

Mathematical Problems in Engineering

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Performance measurement and forecasting are crucial for effective management of innovative projects in emerging knowledge-based companies. This study proposes an integrated performance assessment and forecasting model based on a combination of earned schedule methodology and the learning curve theory under risk condition. The operational performance is measured in terms of time and cost at completion indicators. As a novelty, the learning effects and Kalman filter forecasting method are employed to accurately estimate the future performance of the company. Furthermore, in order to predict the cost performance accurately, a logistic growth model is utilized. The validity of this integrated performance measurement model is demonstrated based on a case study. The computational results confirmed that the developed performance measurement framework provides, on average, more accurate forecast in terms of mean and standard deviation of the forecasting error for the future performance as against the traditional deterministic performance measurement methods.

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“…A new formula to calculate the (EAC) is presented [15], i.e., Estimate At Completion, of projects to hone EVMS. Their new method contains a formula which is composed of four variables: SPI, Scheduled Percent Complete by Duration (SPCD), Actual Percent Complete by Duration (APCD), and Sum of Durations to Due Time (SDDT).…”

Section: Evmmentioning

confidence: 99%

“…The other important aspect is that, unlike other methods, the SSA does not need any statistical assumptions, such as the stationarity of the series or the normality of the residuals. The basic concepts of the project progress generator have been shown by Iranmanesh and Tavassoli [15]. In this paper, the well-developed model with more details is presented.…”

Section: Project Progress Generator Frameworkmentioning

confidence: 99%

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Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis

2016

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During a construction project life cycle, project costs and time estimations contribute greatly to baseline scheduling. Besides, schedule risk analysis and project control are also influenced by the above factors. Although many papers have offered estimation techniques, little attempt has been made to generate project time series data as daily progressive estimations in different project environments that could help researchers in generating general and customized formulae in further studies. This paper, however, is an attempt to introduce a new simulation approach to reflect the data regarding time series progress of the project, considering the specifications and the complexity of the project and the environment where the project is performed. Moreover, this simulator can equip project managers with estimated information, which reassures them of the execution stages of the project although they lack historical data. A case study is presented to show the usefulness of the model and its applicability in practice. In this study, singular spectrum analysis has been employed to analyze the simulated outputs, and the results are separated based on their signal and noise trends. The signal trend is used as a point-of-reference to compare the outputs of a simulation employing S-curve technique results and the formulae corresponding to earned value management, as well as the life of a given project.

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Intelligent Systems in Project Performance Measurement and Evaluation

Cited by 7 publications

References 33 publications

Software Project Management Using Machine Learning Technique—A Review

Software Project Management Using Machine Learning Technique—A Review

An Improved Performance Measurement Approach for Knowledge-Based Companies Using Kalman Filter Forecasting Method

Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis

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