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MCDM-Based R&D Project Selection: A Systematic Literature Review

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  • Dalton Garcia Borges de Souza

    (Division of Computer Science, Aeronautics Institute of Technology, São José dos Campos 12.228-900, Brazil
    Institute of Science and Technology, Federal University of Sao Paulo, São José dos Campos 12.247-014, Brazil
    Lorena School of Engineering, University of São Paulo, Lorena 12.602-810, Brazil)

  • Erivelton Antonio dos Santos

    (Institute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá 37.500-903, Brazil
    Department of Administration Course, José do Rosário Vellano University, Alfenas 37.132-440, Brazil)

  • Nei Yoshihiro Soma

    (Division of Computer Science, Aeronautics Institute of Technology, São José dos Campos 12.228-900, Brazil
    Institute of Science and Technology, Federal University of Sao Paulo, São José dos Campos 12.247-014, Brazil
    Department of Mechanical Engineering, University of Taubate, Taubaté 12.020-270, Brazil)

  • Carlos Eduardo Sanches da Silva

    (Institute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá 37.500-903, Brazil
    Secretariat of Higher Education, Ministry of Education, Brasília 70.047-900, Brazil)

Abstract

From small spin-offs deploying innovative software to big pharmaceutical complexes making vaccines, Research and Development (R&D) Project Portfolio Selection (PPS) is an essential strategic process for various companies. It was never easy to select a set of projects among many feasible possibilities, even for yesterday’s paces. However, the world is rapidly changing, and so is R&D PPS. The portfolio objectives excel profit in the same manner that model constraints go beyond budget limitations. In parallel, project selection approaches and solving algorithms followed the increase of computational power. Despite all those changes, the importance of Multi-Criteria Decision Making (MCDM) methods and the decision criteria used for R&D PPS, there is still room for a systematic literature review (SLR) for the topic. Thus, this paper offers an SLR of the existing literature from the half-century, 1970, and onward MCDM-based R&D PPS performed in Scopus and Web of Science Core Collection. We provide a comprehensive picture of this field, show how it is changing, and highlight standard practices and research opportunities in the area. We perform a broad classification of the MCDM methods, categorized by the nature of alternatives, types of integration approach, the MCDM method itself, and types of uncertainty, by the 66 studies in the SLR database. The portfolios’ classification obeys the application domain and the number of projects. We have also explored all the 263 criteria found in the literature by grouping them according to experts from five Brazilian R&D organizations that together manage portfolios valued around US$ 5 billion a year, accounting for 38% of all Brazilian annual expenditure in R&D projects. We also include a bibliometric analysis of the considered papers and research opportunities highlighted or not explored by researchers. Given the increasing number of decision-making approaches and new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the usage of MCDM methods and decision criteria in R&D PPS.

Suggested Citation

  • Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Nei Yoshihiro Soma & Carlos Eduardo Sanches da Silva, 2021. "MCDM-Based R&D Project Selection: A Systematic Literature Review," Sustainability, MDPI, vol. 13(21), pages 1-34, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11626-:d:661413
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    References listed on IDEAS

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    1. Fatima-Zahra Eddoug & Rajaa Benabbou & Jamal Benhra, 2023. "Adapting P2M Framework for Innovation Program Management Through a Lean-Agile Approach," International Journal of Information Technology Project Management (IJITPM), IGI Global, vol. 14(1), pages 1-18, January.
    2. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.

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