Beyond Traditional Metrics: A Novel Framework for Managing R&D Project Performance in the Petroleum Industry

Research and Development (R&D) represents a strategic pillar of the petroleum industry, where technological innovation drives competitiveness, and the transition toward sustainable and cleaner energy systems. However, measuring the performance of R&D projects remains a complex challenge because their outcomes are often intangible, uncertain, and multidimensional. Traditional Key Performance Indicators (KPIs)—such as cost, time, and number of deliverables—provide only a partial view of effectiveness. R&D performance assessment must therefore consider the intrinsic nature of the activity. Reverse engineering emphasizes replication and adaptation of existing technologies, while innovation-driven R&D seeks to create novel solutions. Accordingly, the selection of performance indicators must differ across these categories. To avoid biased evaluation, the framework integrates B. Roy’s (1996) Multi-Criteria Decision Analysis (MCDA) approach, enabling prioritization of criteria aligned with each project’s objectives and complexity (Martinsuo et al., 2022). Moreover, in R&D environments, traditional indicators such as cost and time act as strategic signals rather than mere management metrics. Cost data guide managerial decisions on partnerships, external funding, and open innovation when internal resources are limited. Similarly, adherence to schedule directly influences technological relevance—delays may result in obsolescence, missed market windows, or loss of first-mover advantage (Tsinopoulos & Al-Zu’bi, 2023). To move beyond simple cost and time metrics, this study revisits the meaning of “performance” in R&D and explores multi-dimensional evaluation tools capable of capturing both tangible and intangible value creation, by integrating five novel dimensions: knowledge creation and diffusion, innovation velocity, dynamic strategic alignment, team and organizational health, and resilience under uncertainty. Beyond its conceptual formulation, the framework has been numerically applied to a portfolio of 10 ongoing R&D projects spanning renewable energy, digitalization of upstream processes, advanced materials, and industrial decarbonization. Each project was scored on a standardized 0–10 scale across the five dimensions, allowing for fine-grained benchmarking and identification of strengths and gaps. For example, Projects 3 and 7 achieved high innovation velocity scores (≥ 9) but lagged in resilience metrics (< 5), indicating exposure to external risks. Conversely, Projects 5 and 9 showed strong knowledge diffusion and team health (scores of 8–10) but slower strategic alignment (< 6). The analysis demonstrates how the proposed framework can generate actionable dashboards for managers, enabling more balanced resource allocation, improved project selection, and proactive mitigation of weaknesses. Applications in industry, academia, and public R&D contexts are also explored, illustrating how this systemic, ecosystem-aware approach moves performance management beyond a narrow project-level perspective to a dynamic, portfolio-wide view. The results provide both theoretical contributions and practical tools for R&D managers seeking to measure and enhance the multidimensional value of their projects.