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.

The digital twin (DT), which involves creating a virtual replica of a physical asset or system, has emerged as a transformative set of tools across various industries. In the oil and gas (O&G) industry, the development of DTs represents a significant evolution in how companies manage complex operations, enhance safety, and optimize decision-making processes. Despite these significant advancements, the underlying tools, technologies, and frameworks for developing DTs in O&G applications remain non-standardized and unfamiliar to many O&G practitioners, highlighting the need for a systematic literature review (SLR) on the topic. Thus, this paper offers an SLR of the existing literature on DT development for O&G from 2018 onwards, utilizing Scopus and Web of Science Core Collection. We provide a comprehensive overview of this field, demonstrate how it is evolving, and highlight standard practices and research opportunities in the area. We perform broad classifications of the 98 studies, categorizing the DTs by their development methodologies, implementation objectives, data acquisition, asset digital development, data integration and preprocessing, data analysis and modeling, evaluation and validation, and deployment tools. We also include a bibliometric analysis of the selected papers, highlighting trends and key contributors. Given the increasing number of new DT developments in O&G and the many new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the development of DTs for O&G.

Many companies around the world relay on R&D to better their chances for profit and permanence in a dynamic market. To keep up with ongoing changes, many ideas arise and some transform into projects. Since resources are limited, organizations seek to select only the most suitable projects to meet their objectives. This is an old practice. However, project portfolio characteristics have changed. The portfolio objectives of today go beyond profit: strategy, environment and society have also become important, along with many other decision criteria. Computational power has also been enhanced, making multi-data decision approaches more feasible, even for small-profit organizations. In the last half century, many authors have proposed multicriteria decision making (MCDM) methods for project portfolio selection (PPS) for Research and Development (R&D). Nevertheless, only a few paid due importance to the criteria used, which would be a central issue on any multicriteria decision. Thus, in order to contribute to the R&D PPS field of study, this work investigates the hypothesis that the criteria used in R&D PPS can be selected in an uncertain environment, according to their influence and importance. To do so, we propose a novel MCDM approach for criteria selection, that integrates Fuzzy-based DEMATEL (Decision Making Trial and Evaluation Laboratory) and Fuzzy-AHP (Analytic Hierarchy Process) Extend Analysis methods. We are also presenting a case study for a representative electrical-public Brazilian R&D organization, that from 2008 to 2018 managed R&D project portfolios valued at around US$ 1.2 billion. The results reflect current Brazilian concerns on the prioritized criteria, such as environmental, social and technical criteria, especially on the face of the disaster occurrences that took place on the media on the last years. In an overall manner, the results provide guidance on the topic and facilitate knowledge accumulation and creation concerning the criteria selection process in MCDM-based R&D PPS.

Goal: The present work aims to improve the project portfolio selection processes under uncertainty of pharmaceutical companies by integrating two Multicriteria Decision Making (MCDM) methods in a fuzzy environment: the AHP and the VIKOR methods. Design / Methodology / Approach: We employed normative axiomatic modeling as the methodology for our work. The MCDM are subjective methods representing decision-makers preferences and assisting the project portfolio selection process. Thus, the model is founded on integrating the fuzzy-AHP and fuzzy-VIKOR methods; the first one determines the importance of the company's strategy and the second for elaborating the project ranking. We validated this methodology in a Brazilian subsidiary of one of the biggest pharmaceutical enterprises worldwide. Results: The developed model considers the evaluators' scores' indecision, enhancing the project portfolio selection process and optimizing its decision-making. Limitations of the investigation: The proposed method does not verify the correlation, interdependence, or cannibalization between the criteria and the projects, which are common limitations to MCDM subjective approaches. In addition, resource constraints are not considered and scheduling routines. Practical implications: The pharmaceutical market is heated by the increase in life expectancy, greater access to medications, and the most recurrent disease outbreaks, resulting in greater competitiveness, increasing the need for companies to seek greater strategic projects selection efficiency. Originality / Value: A well-structured and assertive project portfolio selection becomes extremely important for the pharmaceutical market to keep the company competitive in the market, which explains the importance of this work.

Globalization stimulated a new era of Global Software Development (GSD), followed by the gig economy (GE) phenomenon, which jointly caused considerable transformations in software development markets, mainly after the recent supply chain disruptions. The cultural and geographic barriers have compelled numerous organizations to devise comprehensive digital technologies to overcome this situation. Likewise, the rising unemployment rates led the workforce into short-term contracts or to the on-demand market known as the gig economy. Together with the enhancement in global software development, the organizations found a direction to restore their activities. However, when organizations are immersed in fast-paced environments, selecting skilled professionals is difficult and risky, especially with a lack of qualified professionals. This article identifies the criteria for hiring professionals in the GSD or GE context and proposes a novel approach to clustering them. To do so, we collected the criteria from a broad subject through a systematic literature review, then applied natural language processing with the SBERT algorithm to get the sentence embeddings. Further, we cluster the criteria by applying the k -means algorithm. After that, we innovatively and responsively grouped the clusters formed by repeating the SBERT and k -means algorithms and created its mind map. Our findings disclosed 319 criteria and 6 cluster groups comprising a mind map hierarchical structure. Consequently, these outcomes have pedagogical implications to assist specialists from education institutions in designing new course domains. Such as, it can be helpful to practitioners to assist in hiring professional processes in the GSD or GE context.