Artificial Intelligence Applied to Project Success: A Literature Review

Martínez, Daniel Magaña; Rodríguez, Juan Carlos Fernandez

doi:10.9781/ijimai.2015.3510

Cited by 36 publications

(13 citation statements)

References 44 publications

(44 reference statements)

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“…These models, owing to their robust nature, are more accurate than linear models are (e.g. [61], [65]). Furthermore, where there are nonlinear relationships between input and output variables, as well as where there is less information available on the relationships between variables, parametric methods fail to provide an accurate prediction.…”

Section: B Comparison With Previous Methodsmentioning

confidence: 99%

“…In this paper it is found that expert judgment could be used for variable evaluation, adding value to the quality of data collected in key project management areas. Although the complexity of software projects makes using expert judgment difficult to accurately predict the future, the expert judgment itself may be used in conjunction with other methods, such as has been used as an adjustment factor in parametric models [65] or to create Bayesian models [92]. Further, metrics selection based on the expert judgment was a method used in one of the first applications of artificial intelligence to project success, the model of which was created by applying a Bayesian classifier to estimate the project outcome (e.g.…”

Section: B Comparison With Previous Methodsmentioning

confidence: 99%

“…Hitherto research reported in the literature involving the application of ANNs to project success concerns the objectives of project outcome prediction and CSF identification, as found in [57] - [65]. The main challenge is that an accurate prediction of project success has proven difficult due to the complex relationship between project success and CSFs, resulting in the application of multipleinput single-output models, where output variables are simple success measures.…”

Section: Literature Reviewmentioning

confidence: 99%

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Systems Thinking in Software Projects-an Artificial Neural Network Approach

et al. 2020

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The authors discuss why the current conceptual base of project management research and practice continues to attract criticism since it does not adequately address the complexity that leads to software-project failure. To do so, the study explores systems thinking and artificial neural networks to shed light on complexity in software-project behavior using nonlinear functional relationships between critical success factors and project success to utilize their connectedness as an approach in order to create projectoutcome prediction models. The artificial neural networks were used to create two project-outcome prediction models: one for a binary classification task to discriminate failed from successful projects using a multi-input-single-output configuration and one for a multi-task binary classification to discriminate success from failure in multiple project-success dimensions using a multi-input multi-output configuration. The results yielded high-performance values for a binary classification task, performed to predict overall project success, and slightly lower performance values for the multi-task binary classification, which was also performed to predict success in project-success dimensions. It was found that the nonlinear behavior of critical success factors may be used to create prediction models, by embedding equifinality and connectedness constructs that prove to be useful to understand projects as complex, multi-loop, and nonlinear systems. Further research is needed to investigate the causality between critical success factors in order to explore the possible propagation of critical success factors within a project system network and its implications on project success. INDEX TERMS Artificial neural networks, critical success factors, project success, prediction models, systems thinking.

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Section: B Comparison With Previous Methodsmentioning

confidence: 99%

Section: B Comparison With Previous Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Systems Thinking in Software Projects-an Artificial Neural Network Approach

et al. 2020

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“…Algorithms such as naïve Bayes, rule induction, decision tree, boosted decision tree, random forest and stochastic gradient tree boosting have all been used to generate predictions (Mahfouz and Kandil, 2012). Artificial-intelligence algorithms, such as the CI 23,1 Bayesian model, evolutionary fuzzy neural inference model, neural networks, fast messy genetic algorithm, k-means clustering, bootstrap aggregating neural networks and adaptive boosting neural networks, have all been applied to predict project success and to find the critical success factors (Magaña Martínez and Fernandez-Rodriguez, 2015). A model to predict delays in construction logistics was developed using J48 decision tree and naïve Bayes, and the accuracy obtained by both classifiers was compared (Asadi et al, 2015).…”

Section: Machine Learning In Classificationmentioning

confidence: 99%

Machine learning for the identification of competent project managers for construction projects in Nepal

Karki

Hadikusumo

2021

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Purpose Project manager’s competency is crucial in the construction sector for the successful completion of projects, particularly in the case of developing countries like Nepal. Therefore, it is very essential to select competent project managers by finding the competency factors required by them. Hence, this study aims to identify the characteristics of competent project managers by expert opinion method and to evaluate their competency level by a questionnaire survey to develop a prediction model using a supervised machine learning approach via Waikato Environment for Knowledge Analysis (WEKA), a machine learning tool which predicts Project manager’s performance as “Higher than expected,” “Expected” or “Lower than expected” for the medium complexity construction projects of Nepal (from US$200,000 up to US$10M). Design/methodology/approach The data collection procedure for this research is based on an expert opinion method and survey. Expert opinion method is conducted to find the characteristics of a competent project manager by validating the top 15 competency factors based on literature review. The survey is conducted with the top management to assess their project manager’s competency level. Both qualitative and quantitative approaches are used to collect data for classification and prediction in WEKA, a machine learning tool. Findings The results illustrate that the project managers in Nepal have a high score in leadership skills, personal characteristics, team development and delegation, communication skills, technical skills, problem-solving/coping with situation skills and stakeholder/relationship management skills. Furthermore, among the seven classifiers (naïve Bayes, sequential minimal optimization [SMO], multilayer perceptron, logistic, KStar, J48 and random forest), the accuracy given by the SMO algorithm is highest of all in both the percentage split and k-folds cross validation method. The model developed using SMO classifier by k-folds cross-validation (k = 10) is acknowledged as a final model. Research limitations/implications This research focuses to develop a prediction model to predict and analyze the competency of project managers by applying a supervised machine learning approach. Seven extensively used algorithms (Naïve Bayes, SMO, multilayer perceptron, logistic, KStar, J48, random forest) are used to check the accuracy of models and an algorithm that gives the highest accuracy is adopted. Data collection for this research is carried out by expert opinion method to validate the characteristics (factors) essential for competent project managers in the first round and the description of each factor as high, medium and low is inquired with the same experts in the second round. After an expert opinion, a structured questionnaire is prepared for the survey to assess the competency level of project managers (PMs). The competency level of PMs working under government funded, foreign aided or private projects from the contractor’s side is measured. This research is limited to the medium scale construction projects of Nepal. Practical implications This model can be a huge asset in the human resource department of construction companies as it helps to know the performance level of project managers in terms of “Higher than expected,” “Expected” or “Lower than expected” for the medium complexity construction projects of Nepal. Also, the model will assist human intelligence to make the decision while recruiting a new project manager/s for different types of projects at a time. Moreover, the model can be used for self-assessment of project manager/s to know their performance level. The model can be used to develop a user friendly interface system or an application such that it can be conveniently used anywhere any time. Social implications This research shows that most of the project managers working in a medium complexity construction project of Nepal are male, maximum of them hold bachelor’s degree and study for road projects. Furthermore, most of the project managers scored high in leadership skills, personal characteristics, communication skills, technical skills, problem-solving/coping with situation skills, team development and delegation and stakeholder/relationship management skills. The model has given the “Personal characteristics” attribute the highest weightage. Likewise, other attributes having high weightage are communication skills, analytical abilities, project budget, stakeholder/relationship management, team development and delegation and time management skills. Originality/value This research was conducted to find the competency factors and to study the competency level of project managers in Nepal to develop a prediction model to predict the PM’s performance using a machine learning approach in medium scale construction projects. There is a lack of research to develop a model that predicts project manager’s competency using the machine learning approach. Therefore, the predictive model developed here helps in the identification of a competent project manager as it will be advantageous for project completion with a high success rate.

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“…Artificial intelligence (AI) can improve decision-making in complex environments with clear objectives. A study concluded that, in terms of accuracy, artificial intelligence tools outperform traditional tools [5]. The value of AI can only be activated as humans and machines function complementarily integrated.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Model Performance in IT Project Cost and Duration Prediction

Pang¹

2023

Adv. sci. technol. eng. syst. j.

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Traditional project planning in effort and duration estimation techniques remain low to medium accurate. This study seeks to develop a highly reliable and efficient hybrid Machine Learning model that can improve cost and duration prediction accuracy. This experiment compared the performance of five machine learning models across three different datasets and six performance indicators. Then the best model was verified with three other types of live project data. The results indicated that the MLR-DNN is a highly reliable, effective, consistent, and accurate machine learning model with a significant increase in accuracy over conventional predictive project management tools. The finding pointed out a potential gap in the relationship between dataset quality and the Machine Learning model's performance.

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Artificial Intelligence Applied to Project Success: A Literature Review

Cited by 36 publications

References 44 publications

Systems Thinking in Software Projects-an Artificial Neural Network Approach

Systems Thinking in Software Projects-an Artificial Neural Network Approach

Machine learning for the identification of competent project managers for construction projects in Nepal

Hybrid Machine Learning Model Performance in IT Project Cost and Duration Prediction

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