Fernando Acebes

This article provides a conceptual comparison of predictive, agile and hybrid approaches for managing research, development and innovation (R & D & I) projects, with explicit attention to differences between low-TRL research projects and high-TRL innovation and transfer projects. Building on the R&D&I life cycle proposed by Carranza Ruiz de Loizaga, the paper develops a phase-dependent hybrid framework that integrates practices from PMBOK, P3.express, Scrum and Lean Startup to address a central tension in R & D & I management: enabling rapid learning under high uncertainty while ensuring governance, traceability and accountability to sponsors and regulators.The contribution is threefold. First, the article clarifies the differential value of predictive and agile approaches according to project phase and key attributes, including technological maturity (TRL), regulatory criticality, stakeholder complexity and time pressure. Predictive standards are shown to be particularly effective in phases where formalisation, quality assurance and configuration control are critical, notably during initiation, planning and transfer. By contrast, agile approaches provide greater value during exploration, prototyping and validation, where short feedback cycles and incremental experimentation reduce technical and market uncertainty.Second, the paper operationalises hybridisation through an explicit decision architecture that combines strategic decision gates with tactical iterative cycles. Decision gates define transparent entry and exit criteria related to technical maturity, desirability, economic viability and residual risk, while iterative cycles (e.g., sprints) are used to generate the evidence required to support each decision. This “gates plus iterations” logic helps prevent the confusion of activity with progress and aligns resource commitments with validated learning.Third, the article proposes a dual-metric system that complements traditional performance indicators (cost, schedule and quality) with learning-oriented metrics such as validated hypotheses, evidence quality and residual risk. This dual logic provides a shared evaluation framework for project teams, sponsors and industrial partners. Lightweight documentation practices, inspired by P3.express, are incorporated to preserve decision traceability without constraining experimentation.The paper concludes by discussing implications for R&D&I project governance and portfolio management. The proposed framework is conceptual and requires empirical validation across sectors, TRL levels and types of university–industry collaboration.

Vehicle development projects are increasingly exigent in a highly competitive automotive world. The profitability of those projects can only be assured by seeking optimisations in the way they are performed. Project managers and their teams require support from tools and technologies that can deliver better results. In this context, blockchain technology is proposed to play a key role in vehicle projects, particularly in applications such as parts and prototype management. It will ensure the traceability, immutability, and decentralisation of the information, and it will also lead to shorter negotiations, which will contribute to keeping the project milestones on time. This research presents the details of a real case for a prototype management request in a vehicle project. It proposes how blockchain technology can be applied to improve the traditional approach to handling this type of request, yielding several benefits that will be highlighted.

Several techniques have been proposed to prioritize risks identified in projects. In academia and business, the most standard use is the probability-impact matrix, also known as the risk matrix. Although widespread, this tool has specific limitations, as demonstrated in numerous studies. This article introduces a quantitative method based on Monte Carlo simulation to enhance project risk prioritization. Our proposed method quantifies the impact of each risk on both duration and total cost objectives, enabling us to determine each risk’s relative importance based on these values. In contrast to previous work, this paper proposes a methodology that integrates the effects of risk interactions and the structure of the project-defining network. We conducted two simulation studies, which are detailed in this article. The first study uses fictitious projects to examine the influence of project network structure on the effectiveness of the risk matrix, compared with our proposed methodology. In the second study, we apply our method to two distinct real-world projects and compare these findings with results from simulations of fictitious projects. The conclusions indicate that the traditional risk matrix method produces results that diverge from those generated by our proposed approach, which quantifies impacts. Moreover, it was observed that the risk matrix provides more reliable results when a project’s structure approximates a serial configuration. However, its accuracy in risk prioritization declines for projects with a more parallel structure.

The risk matrix is a commonly used tool to assess, visualise and analyse risks. It consists of a two-dimensional graphical representation of the probability (“likelihood” or “frequency”) of an outcome occurring and the impact (“severity” or “consequences”) that the outcome would have if it were to occur. Each of the two dimensions of the matrix is further divided into various levels of probability, usually located on the ordinate axis of the matrix, and diverse levels of impact, located on the abscissa axis. The result of each cell, formed by interconnecting a given probability level with the corresponding impact level, is associated with a risk level (urgency, priority, or management action).The risk matrix (or probability-impact matrix) has been widely implemented as a decision-support tool in both the public and private sectors in areas such as well-integrity risk, drilling hazard management, climatic change responses, or health and safety. Using these matrices to set priorities and guide resource allocation has been established as a recommendation within various international standards and industry guidelines. It is easy to construct and simple to apply compared to other risk assessment methodologies. Also, they provide a clear framework for the systematic review of risks, practically justify risk ranking and prioritisation, and keep stakeholders informed visually and attractively, among other reasons.Despite this, recent studies have shown that the risk matrix has shortcomings and limitations, making authors question its functionality and accuracy. Taking these limitations into account, in this article, we propose a quantitative method based on Monte Carlo simulation for prioritising the risks of a project, avoiding the use of the probability-impact matrix. To conduct the simulation, we will have the probability and impact data of the risks identified in the project and use ‘MCSimulRisk’ as a simulation tool. We will be able to quantitatively determine the impact of each risk on the cost objective and total duration of the project. The magnitude of the resulting impact will allow us to determine a quantitative prioritisation of the risks identified in the project.On the other hand, after estimating the probability and impact of the project’s risks, we have connected the qualitative risk analysis with the quantitative analysis by quantitatively prioritising the identified risks. In addition, we have managed to quantify the impact of each risk on the cost and duration objectives. We observed existing risks that significantly impact the project’s cost, even though they were initially identified for their impact on the project duration.As a result of applying the proposed method, we obtain a list of prioritised risks according to their impact on the duration objective and another list, different from the previous one, with the prioritised risks on the impact on the project’s cost. The order is different; thus, we can discriminate on which risk to act according to which is the most crucial objective in our project and the impact each risk has on the project.

The project managers who deal with risk management are often faced with the difficult task of determining the relative importance of the various sources of risk that affect the project. This prioritisation is crucial to direct management efforts to ensure higher project profitability. Risk matrices are widely recognised tools by academics and practitioners in various sectors to assess and rank risks according to their likelihood of occurrence and impact on project objectives. However, the existing literature highlights several limitations to use the risk matrix. In response to the weaknesses of its use, this paper proposes a novel approach for prioritising project risks. Monte Carlo Simulation (MCS) is used to perform a quantitative prioritisation of risks with the simulation software MCSimulRisk. Together with the definition of project activities, the simulation includes the identified risks by modelling their probability and impact on cost and duration. With this novel methodology, a quantitative assessment of the impact of each risk is provided, as measured by the effect that it would have on project duration and its total cost. This allows the differentiation of critical risks according to their impact on project duration, which may differ if cost is taken as a priority objective. This proposal is interesting for project managers because they will, on the one hand, know the absolute impact of each risk on their project duration and cost objectives and, on the other hand, be able to discriminate the impacts of each risk independently on the duration objective and the cost objective.