Renan Silva Santos

Purpose: This research develops an integrated technological system combining multicriteria decision-making (MCDM) methods and machine learning (ML) techniques to support industrial maintenance in Industry 4.0 and digital transformation. The system supports strategic asset maintenance decisions, and its application has been validated at the Brazilian Mint (Casa da Moeda do Brasil). Design/methodology/approach: A structured case study was conducted using qualitative and quantitative data from interviews, focus groups, direct observations and document analysis. Guided by an Operations–Maintenance fit lens, MCDM methods (AHP-AIP, MOORA, MULTIMOORA and Borda Count) and forecasting techniques (ARIMA, ANN) were implemented in a Python-based decision support system (DSS) to prioritize assets for predictive maintenance and forecast corrective maintenance needs. Findings: The integration of MCDM and ML improved decision-making in asset maintenance by addressing complex data challenges. The developed system has enhanced the accuracy of maintenance decisions and is currently operational at the Brazilian Mint. The fit analysis shows that the DSS delivers the highest value under contingencies, clarifying when and where the tool is transferable beyond this specific organization. Originality/value: This study contributes to the Maintenance 4.0 literature by (1) extending Contingency Theory by formalizing how digital maintenance DSSs depend on the alignment among asset criticality, information-processing requirements and analytical capabilities; (2) introducing an operations–maintenance fit perspective that explains how machine learning forecasting and MCDM jointly support the reconciliation of production throughput objectives with reliability and availability goals and (3) presenting a transparent, transferable and benchmark-ready DSS architecture, validated within a real industrial environment, that integrates four MCDM methods and two forecasting techniques to assess key performance metrics, such as overall equipment effectiveness, for cross-firm comparison.

Innovation policy in Europe is steered by a single headline number: gross expenditure on research and development as a share of gross domestic product. The 3%-of-GDP target has anchored European Union strategy for two decades, and R&D intensity remains the backbone of the scoreboards that benchmark national performance and help justify the allocation of funds. Yet R&D sits inside a narrow accounting boundary that leaves out most modern intangible investment: organizational capital, design, brand, and training. We ask whether the indicator that drives policy is a faithful proxy for national knowledge investment and, if it is not, in which directions it misjudges which economies. We use the Global INTAN-Invest database, whose asset detail separates intangibles inside the national-accounts boundary (R&D, software, other intellectual property) from those outside it, together with Eurostat R&D data, and we compare each European economy’s rank on the R&D target with its rank on full Corrado–Hulten–Sichel intangible intensity, year by year from 2010 to 2024. R&D intensity is positively but imperfectly related to knowledge investment (Spearman’s ρ = 0.53 across the EU in 2019, rising to 0.70 once the Irish and Luxembourg multinational hubs are removed). The residual is not noise. The same economies are over-credited and under-credited year after year, and a stochastic acceptability analysis over the entire space of admissible asset weights shows that the reordering is weight-invariant and survives the addition of 15% estimation noise to the modelled assets. Austria is the limiting case: third in the Union on R&D intensity, sixteenth of twenty-four on total intangibles, over-credited in every year of the panel and under 99% of weightings. The pattern is not unique to Europe: extended to a global pool of economies using UNESCO R&D data, the same biased-but-informative relationship holds (ρ ≈ 0.71), with Japan the clearest over-credited case (over-credited under 99% of asset weightings) and the United States under-credited. We read the R&D-intensity target as biased-but-informative: informative about effort, biased in directions that can be named. We then draw out what that means for indicator-based innovation policy.Anonymized for review; author identity, affiliations, acknowledgements, funding, and the declaration of generative-AI use are provided on a separate title page.

Digital transformation (DT) has become a strategic imperative for sustaining competitiveness in global supply chains. This study situates DT within the frameworks of Dynamic Capabilities Theory (DCT) and Structural Contingency Theory (SCT) to explain how leadership, culture, and institutional contexts shape adoption pathways in Brazil and Germany. Using a sequential mixed-methods approach, it combines a tertiary literature review with expert elicitation and Interpretive Structural Modelling (ISM), supported by DEMATEL and MICMAC analyses, to uncover hierarchical relationships among barriers and foundational technologies—Big Data Analytics (BDA), the Internet of Things (IoT), and cloud computing. The results reveal distinct causal structures: in Germany, workforce deficits and economic-risk perceptions act as root barriers that constrain managerial and cultural adaptation; in Brazil, executive sponsorship drives workforce capability and analytics development, activating subsequent IoT and cloud adoption. Across both contexts, BDA consistently emerges as the foundational enabler, indicating a layered sequence of capability accumulation. The findings demonstrate that effective digital transformation depends on leadership-enabled alignment between organisational structure and environmental contingencies. This study contributes a comparative framework linking DCT’s dynamic routines with SCT’s structural fit, providing theoretical, methodological, and policy insights for context-sensitive digitalisation strategies.

The emergence of Industry 5.0 (I5.0) underscores the importance of integrating sustainability and human‐centricity into organizations. This requires combining corporate social responsibility (CSR) and circular economy (CE) within sustainable supply chain management (SSCM). However, the interplay between CSR, CE, and SSCM remains underexplored, especially in the context of I5.0. Therefore, this article proposes a framework for integrating CSR and CE principles within SSCM, serving as a transition to I5.0. The framework is derived from a comprehensive scoping review and experts' input, with a backdrop of the business legitimacy theory. It comprises several key constructs, including the supply chain structure and promoters, organized around four pillars representing the components of CE and embracing the triple bottom line dimensions. With 12 guidelines, the framework stands as a practical tool for organizations aiming to seamlessly integrate CSR and CE within SSCM, facilitating the transition to I5.0 and promoting a sustainable, socially responsible ethos.

This study fills a crucial gap in the research on Micro, Small, and Medium Enterprises (MSMEs), with the main objective of proposing a framework to support the integration of Industry 4.0 (I4.0) and sustainability into the supply chains of MSMEs. This integration is accomplished by people, process, and technology (PPT) mechanisms, all while adopting the triple bottom line (TBL) perspective. A mixed-method approach was employed for this study, which included a scoping review of 147 publications, expert panels, focus groups, and a survey with 55 valid responses. The findings identified 32 key indicators linked to the primary barriers and enablers of I4.0 and sustainability integration (S-I4.0) in MSMEs’ supply chains. This study also highlighted the dominant barriers and enablers within the PPT mechanisms and TBL dimensions, and their causal relationships and influences. The results were synthesized into a novel S-I4.0 framework, separately applied to Micro and Small Enterprises (MSEs) and Medium Enterprises (MEs) due to company size-related variations. This study offers valuable insights for academics and provides practical guidelines to assist MSMEs in integrating I4.0 and sustainability into their supply chains. An important contribution concerns to the need to treat MSEs and MEs differently. These research results provide relevant and novel guidance for MSEs and MEs to accelerate the S-I4.0 adoption process, with an immediate impact on their supply chains.

Goal: This study aims to use artificial intelligence, specifically a random forest model, to predict the annual corrosion rate on FPSO offshore platforms in the oil and gas industry. Corrosion is a significant cause of equipment failure, leading to costly replacements. The random forest model, a machine learning technique, was developed using climatic and other relevant data to forecast corrosion trends based on selected variables. Design/methodology/approach: The methodology involved four steps: identifying influential factors affecting corrosion, selecting factors based on reliability and accessibility of measurements, applying the machine learning model to predict annual corrosion progression, and comparing the random forest model with other ML models. Results - The results showed that the random forest regression model successfully predicted corrosion rates, indicating an average yearly increase of 2.43% on the analyzed platforms. The main factors influencing this increase were wind speed, percentage of measured corrosion, and platform operating time. Regions with higher incidence of these factors are likely to experience higher corrosion rates, necessitating more frequent maintenance. Limitations of the investigation - The research sample consisted exclusively of 4 platforms located in the offshore region of Rio de Janeiro, Brazil. Thus, the results obtained must be interpreted as representative of these platforms and respective climate conditions. Practical implications – The use of data science tools to improve corrosion management allows managers to have knowledge of which areas has a greater or lesser tendency to corrode, helping to prioritize maintenance activities over time. Originality/value - This study aims to fill gaps regarding the use of random forest techniques for regression focused on predicting the rate of increase in corrosion. It offers a novel approach to assist decision-making in maintenance planning, providing insights into influential corrosion factors and facilitating more effective painting plans to preserve industrial unit integrity.