Abstract: The Supply Chain Operations Reference (SCOR) model is widely acknowledged as an international benchmark for assessing, contrasting, and enhancing supply chain performance. In 2022, the Association for Supply Chain Management (ASCM) launched the SCOR Digital Standard (SCOR DS), marking a major advance toward integrating digital technologies into supply chain management. This paper presents a systematic literature review (2022–2025) on SCOR DS implementation across industries. We used the PRISMA method to review 25 peer-reviewed articles and identify benefits, challenges, and research opportunities. The findings revealed that SCOR-DS, supported by technologies including artificial intelligence, big data analytics, the Internet of Things, and blockchain, enhances supply chain performance through improved reliability, responsiveness, and cost efficiency. However, challenges persist, including high implementation costs, data governance issues, lack of skilled workforce, and limited adoption among small and medium-sized enterprises. The review highlights diverse methodological approaches, demonstrating the applicability of SCOR-DS in industries such as manufacturing, healthcare, logistics, telecommunications, and food supply chains. Future research opportunities include the creation of standardized digital performance indicators, enhancement of AI-driven decision-making in SCOR processes, and investigation of sustainable, human-centric supply chains aligned with Industry 5.0. Overall, this study highlights SCOR DS as a practical framework and research agenda connecting digital transformation theory with supply chain practice.
Abstract This study was conducted within the scope of a technological transition project in I4.0, implemented in a supplier of technology and services company from the industrial sector in Portugal. In this context, we aim to understand how workers can be actively involved in technological transitions processes to ensure that the specificities of their work are recognized, and their voice is acknowledged. Using a mixed-methods approach (e.g., work observations, interviews, questionnaire), the research followed three technology use-cases at different stages, involving the point of view of the company’s development engineering and digitalization team, direct team leaders, and workers whose tasks or functions were to be impacted by the technological changes in the use-cases. Complementarily, other stakeholders (HR, OHS, Production and Logistics managers, Quality manager), and workers also participated. Findings reveal that workers’ participation is still often absent or delayed - impacting workers’ leeway, collaboration, learning, meaning of work, and work recognition. Individual, collective, and organizational resources were identified as leverage points for action. A participatory and evidence-based framework was developed to support human-centered decisions, enabling the reflection and action of stakeholders that lead technological transition processes, promoting decent, sustainable work through the integration of workers’ voices throughout these processes.
Abstract This article empirically investigates the implementation of Lean principles through specific tools and practices in service operations, with a focus on car dealerships. A multiple case study design was adopted to examine the Sales and After-Sales operations at two Brazilian dealerships, purposely selected to observe exemplary applications of lean concepts and methods in different service domains. As findings of the study, the research demonstrates that the application of Lean principles in dealerships can be effectively assessed by examining the integration of lean thinking into daily operations, organizational routines, and employee behaviors. Furthermore, the deployment of Lean principles is shown to occur through the consistent use of specific lean tools and practices, which act as operational enablers of the lean philosophy and support the development of continuous improvement capabilities in both Sales and After-Sales processes. Despite the close relationship between car dealerships and manufacturers, the spread of lean practices in this context remains limited. The cases analyzed offer practical insights and references for companies planning to adopt or improve lean service systems.
Abstract Electricity costs challenge the competitiveness of the supermarket retail sector. This study analyzed the technical and economic feasibility of three strategies in supermarkets in Santa Catarina, Brazil: the Free Electricity Market (FEM), battery energy storage systems (BESS) for peak shaving, and zero-grid photovoltaics. Based on real data from 2024 and a 15-year financial model (LCOE and payback), the analysis took into account Brazilian Law No. 15,235/2025. The results indicated that the FEM with incentivized energy offers the most immediate return, reducing costs by 25% to 28%. Photovoltaic generation proved to be a robust investment, with a payback period of 4.5 to 5.5 years. In contrast, BESS was found to be economically unfeasible under current conditions due to high CAPEX, with a payback period exceeding 8 years. It is concluded that contract management and solar generation are the investment priorities, while energy storage depends on future cost reduction.
Abstract Supplier selection in project environments is a complex multicriteria decision-making problem. Uncertainty, qualitative judgments, and limited data availability make the adoption of robust and flexible decision-support approaches essential. In this context, this study proposes a decision model that integrates Hesitant Fuzzy Linguistic Term Sets (HFLTS) with the Quality Function Deployment (QFD) method to support the selection of project suppliers. The model allows decision makers to express hesitant evaluations using comparative linguistic expressions and considers the difficulty of data collection when defining evaluation criteria. A pilot application was conducted in an energy sector company, involving three decision makers and three supplier alternatives evaluated across 11 criteria. The results of the application indicate that delivery capacity and cost were identified as the most heavily weighted criteria. Sensitivity analysis tests suggest the robustness of the supplier ranking. The proposed approach contributes to the literature by integrating hesitation modeling, criteria structuring, and data availability considerations into a unified decision framework.
