Project selection in project portfolio management: An artificial neural network model based on critical success factors

Costantino, Francesco; Gravio, Giulio Di; Nonino, Fabio

doi:10.1016/j.ijproman.2015.07.003

Cited by 149 publications

(90 citation statements)

References 69 publications

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“…In the soft computing field, it is suggested that AI would yield better predictions than traditional methods. Costantino and Nonino [23] developed an artificial neural network (ANN) for extracting the experience of project managers from a set of past successes and failures to classify the grade of project risk. Mousavi et al [24] proposed an effective AI model applying fuzzy logic and developing new neural networks to improve the decisions for construction project selection.…”

Section: Artificial Intelligence Optimization Methodsmentioning

confidence: 99%

A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

2017

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In today's rapidly changing economy, technology companies have to make decisions on research and development (R&D) projects investment on a routine bases with such decisions having a direct impact on that company's profitability, sustainability and future growth. Companies seeking profitable opportunities for investment and project selection must consider many factors such as resource limitations and differences in assessment, with consideration of both qualitative and quantitative criteria. Often, differences in perception by the various stakeholders hinder the attainment of a consensus of opinion and coordination efforts. Thus, in this study, a hybrid model is developed for the consideration of the complex criteria taking into account the different opinions of the various stakeholders who often come from different departments within the company and have different opinions about which direction to take. The decision-making trial and evaluation laboratory (DEMATEL) approach is used to convert the cause and effect relations representing the criteria into a visual network structure. A consistent fuzzy preference relations based analytic network process (CFPR-ANP) method is developed to calculate the preference-weights of the criteria based on the derived network structure. The CFPR-ANP is an improvement over the original analytic network process (ANP) method in that it reduces the problem of inconsistency as well as the number of pairwise comparisons. The combined complex proportional assessment (COPRAS-G) method is applied with fuzzy grey relations to resolve conflicts arising from differences in information and opinions provided by the different stakeholders about the selection of the most suitable R&D projects. This novel combination approach is then used to assist an international brand-name company to prioritize projects and make project decisions that will maximize returns and ensure sustainability for the company.

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Section: Artificial Intelligence Optimization Methodsmentioning

confidence: 99%

A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

2017

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“…Financial and non-financial indicators at the project level could assist the decision-making process. According to Costantino, Di Gravio and Nonino (2015), deciding on project critical success factors is an important criterion for PPM, as decision makers deal with probable causes of failures during project selection processes. These authors argued that using artificial neutral networks provides a simpler approach for top manager engagement in the decision-making, facilitating communication loops between project managers and project portfolio managers to assess the riskiness of project success based on the project managers' past experience (Costantino et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to Costantino, Di Gravio and Nonino (2015), deciding on project critical success factors is an important criterion for PPM, as decision makers deal with probable causes of failures during project selection processes. These authors argued that using artificial neutral networks provides a simpler approach for top manager engagement in the decision-making, facilitating communication loops between project managers and project portfolio managers to assess the riskiness of project success based on the project managers' past experience (Costantino et al, 2015). Maged (2008) describes a multiple criteria decision-making (MCDM) model to find the optimized solutions for R&D projects where resources dependencies pose constraints on the decisionmaking process for project selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision-making in project portfolio management: using the Cynefin framework to understand the impact of complexity

Shalbafan¹,

Leigh²,

Pollack³

et al. 2017

International Research Network on Organizing by Projects (IRNOP) 2017

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“…Según el marco referencial consultado para este trabajo, se han encontrado tres líneas de estudio en los temas de análisis y gestión de proyectos a gran escala desde la inteligencia computacional: (a) técnicas de inteligencia computacional para la selección de proyectos según estructura y escala tecnológica [6]- [8]; (b) algoritmos computacionales para problemas de agendamiento y costo en proyectos específicos [9]- [11], y (c) sistemas de predicción deléxito de proyectos basados en redes neuronales [16]. Sin embargo, no se encontraron referencias que aborden la estimación del avance de megaproyectos de inversión pública desde estas técnicas.…”

Section: Inteligencia Computacional En La Gestión De Proyectosunclassified

“…Se presentará una breve introducción a las mismas invitando al lector a que profundice a través de la consulta de [19] y [20]. En cuanto al marco de interés de este trabajo, se han reportado en la literatura modelos de gestión de proyectos que incluyen redes neuronales y SVM (máquina de soporte vectorial), a manera de ejemplo se mencionan algunos: selecciónóptima de proyectos de innovación tecnológica aplicandoárboles de decisión neurodifusos [6], agendamiento de proyectos según teoría de restricción de recursos con técnica de red neuronal basado en modelo de colonias de hormigas [10], sistemas de selección en portafolio de proyectos con redes neuronales y sistemas difusos [8], [16], predicción de la duración de proyectos con SVM [21], [22] e identificación del riesgo en proyectos por medio de una combinación entre SVM y algoritmos de colonias de hormigas [23].…”

Section: Inteligencia Computacional En La Gestión De Proyectosunclassified

Clasificación Automática del Avance de Megaproyectos de Inversión Pública en Colombia, desde un Enfoque Técnico, Organizacional y Ambiental

Gutiérrez¹,

Melgarejo

2017

ing.

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Context: The TOE (Technical, Organizational, and Environmental) framework for the analysis of large scale projects is considered as the basis for the development of megaproject progress classification in accordance with the needs of the national planning agency in Colombia.Method: Classification of a megaproject progress is supported in the selection of several features taken from the TOE. These feature set is used to configure a database from the projects registered in the project-surveillance platform of the national planning agency in Colombia. The database is used to train two classification models. Information about 3200 projects from 2008 to 2012 was used, covering four economic sectors (Environment and sustainable development, Energy and mining, Health and social care and transportation). Debugging of the database was carried out by an analytic and quantitative approach. Model training and validation were computed with 70 % and 30 % of data respectively.Results: Obtained models have similar performances beyond 70 % in precision and agree in relevant input features.Conclusions: This work is a starting point to develop an automatic tool that can be used by the national planning agency of Colombia in the a-priori evaluation of delays in public investment Megaprojects.

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Project selection in project portfolio management: An artificial neural network model based on critical success factors

Cited by 149 publications

References 69 publications

A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

Decision-making in project portfolio management: using the Cynefin framework to understand the impact of complexity

Clasificación Automática del Avance de Megaproyectos de Inversión Pública en Colombia, desde un Enfoque Técnico, Organizacional y Ambiental

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