The implementation of Digital Twins (DTs) represents a significant advancement for the Oil and Gas (O&G) industry. A DT virtually replicates a physical asset, enabling the monitoring, diagnosis, prediction, and optimization of its outcomes. Since failures are undesirable outcomes, investigations into potential failure modes are often integrated into the development. Traditional methods, such as Failure Modes and Effects Analysis (FMEA) and Failure Mode, Effects, and Criticality Analysis (FMECA), are widely used to identify, assess, and mitigate risks. However, there is still a lack of specific guidelines for studying potential failures in complex systems. This article introduces a framework for Failure Modes, Mechanisms, and Effects Analysis (FMMEA) as a tool for identifying and assessing failures in early DT development. Exploring failure mechanisms is highlighted as essential for effective prediction and management We also propose adjustments to FMMEA for complex, predictable systems, such as using a DPR (Detectable Priority Risk) instead of RPN (Risk Priority Number) for prioritizing risks. A comprehensive case illustrates the framework’s application in developing a DT for an intelligent completion system in a major O&G company. The approach enables mechanism-oriented failure analysis and more detailed prognostic health management, providing greater transparency in the failure identification process.

Highlights: The prevalence of Research and Development (R&D) as an inducing factor for innovation has been questioned, and studies show that low-tech companies are able to innovate. Innovation capability is essential to increase productivity and improve competitiveness. However, when formal R&D is considered as the main parameter in its evaluation, it tends to underestimate it. The extent and heterogeneity of studies on innovation in non-R&D practitioners is a relevant problem and, despite theoretical and empirical advances, it is not satisfactorily resolved yet. Goal: This article presents a reference model for innovation capability maturity in low-technology, non-R&D performing companies, oriented to guide decision makers and the institutions supporting these organizations. Design / Methodology / Approach: The method used to construct the model is based on the proposition of Ahlemann and Gastl (2007) for the construction of reference models based on empirical evidence. Results: The model is based on ten organizational dimensions and their effects on the organization's performance. Five maturity levels are defined, synthesizing the fundamental characteristics of innovation capability in non-R&D practitioners or low technology companies. Limitations of the investigation: Although the model has been tested and validated, more applications and tests may still be required. Practical implications: The model is an important tool for decision-making, at the management level, in low-tech companies that aspire to broaden their innovation capability. Originality / Value: The model has significant potential academic repercussions, in terms of the expansion of knowledge, as well as for the industry, since it can contribute to the increase of the competitive performance of companies.

Goal: This article presents an analysis of the validity and reliability of the assessment instrument used in the 2016–2017 Brazilian Innovation Award. Design/Methodology/Approach: This study used multivariate analysis techniques on the data from 2,651 companies. Two hypotheses were tested. The first (H1), related to reliability, used Pearson’s Correlation and Cronbach’s alpha. The second hypothesis (H2), related to validity, used Confirmatory factor analysis (CFA). Results: The instrument is reliable and valid and is an important mechanism for the assessment of the maturity of innovation management. Limitations of the investigation: One of the constructs can still be improved in future studies and applications, although it has demonstrated acceptable levels of reliability and validity. Practical implications: The combined use of the constructs “organizational dimensions” and “innovation outcomes” proved to be an accurate conceptual model for assessing the maturity level of innovation management in organizations. Originality/Value: The instrument is a robust diagnostic instrument and, with appropriate adaptations, it can be replicated and used in other contexts and countries, providing international comparative studies.

Innovation projects with sustainable characteristics are increasingly seen as strategic drivers for organizations to expand market share and retain customers. Yet, firms face limited resources while dealing with many potential projects. To address this challenge, an integrated framework for evaluating and ranking innovation projects using sustainability-related factors can support more consistent decision-making. Although several models for project selection exist in the literature, few provide a comprehensive approach that incorporates sustainability criteria. This study proposes a model for selecting innovation projects by explicitly considering sustainability aspects, supported by multi-criteria decision support methods. The methodological approach followed the Design Cycle method, grounded in Design Science Research. The main result is a novel, customizable model for evaluating, ranking, and managing innovation projects within a sustainability-oriented context. The model was validated through application in two high-performance organizations recognized for their innovation and sustainability practices. Additionally, this research offered reflections on how sustainability-driven innovation can be implemented in practice. Overall, the findings demonstrated that the proposed model is adaptable to different organizational realities, sectors, and sizes, enhancing the capacity to assess and understand the role of sustainability in innovation projects more effectively.