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Abstract Paper aims The study aims to provide actions and research directions in the area of supply chain collaboration and sustainability, with a focus on the effect of supply chain risk management. Originality The findings provide a theoretical bridge between supply chain collaboration literature and sustainability theory by demonstrating that collaboration fosters sustainable performance through risk management, and emphasizes collaboration as a sustainability driver. Research method The study employed a stratified sampling technique with a total of 288 completed questionnaires were received from seven manufacturing firms, using a 5-point Likert scale. The sample was those practitioners who are exposed to supply chain management and sustainability. Main findings The results find that supply chain collaboration positively influences sustainability, and that information sharing, resource sharing, collaborative communication, and joint knowledge creation—as components of supply chain collaboration—also positively influence sustainability. Implications for theory and practice The research adds to the body of knowledge by integrating collaboration as a critical enabler of effective risk management. For practitioners, the study highlights that sustainability should not be viewed separately from risk management. Managers should integrate collaborative risk management practices to achieve sustainability.

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Abstract Paper aims This study aims to address a manufacturing scheduling problem where jobs are processed in the same sequence across multiple machines, focusing on minimising unproductive time, including machine idle time and job waiting time. Originality The research fills a gap in the literature by extensively comparing metrics and heuristic algorithms for the permutational flow shop scheduling problem, introducing four tailored metrics and evaluating their performance against classic rules. Research method The study employed computational experiments with 720 instances from four benchmarks, varying the number of jobs and machines. Performance was assessed based on percentage success and failure, average relative deviation, and computational time. An insertion heuristic algorithm was implemented to improve scheduling solutions. Main findings The Longest Last Front Idle Time (LLFIT) heuristic outperformed traditional methods, including the NEH heuristic, consistently ranking in the top three across all metrics. LLFIT demonstrated superior performance in minimising core and front idle times as well as core waiting time. Implications for theory and practice The LLFIT heuristic offers an efficient and reliable scheduling solution for practitioners in production and operations management, advancing both theoretical understanding and practical applications in minimising unproductive time in flow shop scheduling.

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Abstract Paper aims This study aims to propose minimum ergonomics course content based on the current situation in top-tier production engineering programs in Brazil. Originality This is a groundbreaking study that brings together two fields of production engineering: engineering education and ergonomics. The findings contribute to the theoretical foundation of ergonomics by demonstrating a standard for the characteristics of teaching plans. Research method A methodological procedure composed of five steps was developed: defining the research problem, identifying universities, investigating the ergonomics course, analyzing data, and proposing a teaching plan. An investigation in online databases of undergraduate production engineering programs aimed to identify course content, teaching strategies, and professors’ qualifications. Main findings This study evaluated 88 courses, and the results showed a trend of programs with only one course with 60 class hours, focusing on general definitions, physical ergonomics, and Ergonomic Work Analysis. Ergonomics courses mainly use traditional teaching approaches, and professors have diverse educational backgrounds, but most have some production engineering in their curriculum. Implications for theory and practice This study provides two standardized plans for teaching ergonomics, as well as an updated overview of how the course is currently taught in production engineering programs.

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Abstract Paper aims This paper proposes a conceptual model for a circular product service system (PSS-C) in the Brazilian coffee capsule chain, integrating requirements engineering and circular economy principles. Originality The research introduces a novel approach by employing the Requirements for Sustainable Product-Service Systems (R-PSS) method, combining tools from requirements engineering, design, innovation, and sustainability to address key challenges in the lifecycle of coffee capsules. Research method The study was conducted as a case study in a Brazilian startup specializing in managing packaging waste for a coffee manufacturer. It utilizes the R-PSS method to propose strategies for improving circularity in the capsule chain. Main findings The PSS-C conceptual model prioritizes strategies such as reducing material usage, implementing remanufacturing processes, and fostering consumer education for proper disposal. It emphasizes the critical role of consumers in achieving capsule circularity, including initiatives to expand collection points and promote environmentally responsible behaviors. Implications for theory and practice The findings contribute to the theoretical discourse on circular business models by demonstrating how the integration of product and service requirements can generate environmental value. Practically, the study offers insights into sustainable development, providing a foundation for further research in both theoretical and applied contexts.

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Abstract Paper aims This research investigates faculty perceptions of the contributions and ideal design of Teaching and Learning Centers (TLCs) to promote Competency-oriented Learning (CoL) in engineering education. Originality The study addresses a significant research gap: the limited understanding of TLCs in engineering education, particularly in the Brazilian context, where such centers are still emerging in public higher education. By exploring engineering faculty perspectives, it establishes a baseline framework for implementing TLCs in institutions without existing models. Research method A survey-based approach guided by two research questions on TLC contributions and design. A questionnaire was administered to 415 professors at a leading Brazilian engineering school, yielding 67 valid responses (16.1%). Main findings The findings highlight challenges in course planning, competency assessment, and teaching practices, including limited alignment with competency goals and weak dialogue between faculty and program coordination. Respondents view TLCs as key to improving teaching methodologies, supporting pedagogical development, and promoting systematic skill assessment. Their effectiveness depends on clear objectives, institutional support, and strategic alignment. Implications for theory and practice This study contributes to theoretical discussions on pedagogical support structures and offers practical insights into TLC implementation. It underscores the importance of engaging faculty and students for successful TLC adoption.

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Abstract Paper aims This paper examines how computer vision (CV) technologies are being integrated into the Quality 4.0 framework, addressing the gap between conceptual discussions and industrial implementation. Originality Unlike previous studies based on simulated environments or theoretical frameworks, this research draws on 202 proofs of concept (POCs) developed with real industrial demands. The dataset provides a rare empirical perspective on how CV technologies are adopted. Research method The study employs a mixed-method approach combining descriptive statistics, trend identification, and correspondence analysis to identify technical and contextual patterns across the POCs. Main findings The analysis reveals challenges in CV adoption, including the need for customization to sector-specific requirements and environmental characteristics. Simultaneously, it identifies opportunities in automated inspection, predictive maintenance, and real-time decision-making. The increasing use of classification, segmentation, and object detection techniques indicates a progression toward greater technical maturity. Implications for theory and practice The findings extend Quality 4.0 research by providing empirical evidence of the technological and organizational conditions shaping CV implementation. For practitioners, they offer actionable insights on aligning CV deployment with infrastructure and production constraints. Overall, this study provides the first large-scale empirical mapping of CV implementation, demonstrating its role as an enabler of data-driven quality management.

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Abstract Paper aims Addressing the critical need for integrated improvement methodologies in construction operations, this study proposes a theoretical framework integrating DMAIC methodology, ISO 31000:2018 risk management, and Green Lean Six Sigma for enhanced construction operations management. Originality This proposes the first systematic theoretical integration of DMAIC, ISO 31000, and GLSS specifically for construction operations management, establishing theoret ical foundation for future empirical research and practical implementation. Research method A systematic literature review following PRISMA 2020 guidelines analyzed 127 peer-reviewed publications (2020-2025) from Scopus, Web of Science, and ScienceDirect. Thematic analysis employed Braun & Clarke’s six-phase framework with systematic coding procedures and intercoder reliability assessment (Cohen’s kappa=0.82). Framework consistency was validated through methodological triangulation. Main findings This study proposes integration opportunities through process alignment, tool compatibility, and performance measurement harmonization. A five-phase framework systematically maps connections between methodologies while preserving individual strengths. Analysis of 48 safety performance indicators illustrates how integrated approaches could address construction challenges more effectively than individual implementations. Implications for theory and practice Framework advances operations management knowledge by demonstrating systematic methodology integration possibilities. Construction organizations can enhance operational efficiency through coordinated quality improvement, risk management, and sustainability initiatives, achieving comprehensive operational excellence while leveraging individual methodology strengths.

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Abstract Paper aims This paper aims to support decision-makers of the public slaughterhouse of Sumé in the preliminary selection of machinery for animal slaughter operations based on quantitative technical and economic criteria derived from equipment specifications. Originality The originality of this study lies in the integrated application of the Modified Preference Selection Index (MPSI) and the Weighted Aggregated Sum Product Assessment (WASPAS) methods to a real-world public slaughterhouse context. The study contributes by using MPSI to generate data-driven criterion weights and WASPAS to rank machinery alternatives in a transparent selection framework. Research method The research adopts a multicriteria decision-making (MCDM) approach, employing the MPSI method to determine criterion weights and the WASPAS method to rank machinery alternatives using quantitative technical and economic data obtained from manufacturers’ manuals, catalogues, and technical sheets, with contextual validation provided by a slaughterhouse inspector. Main findings The integrated MPSI-WASPAS model identified a favourable set of machinery alternatives with superior overall performance across the evaluated technical and economic criteria. Implications for theory and practice The study reinforces the applicability of hybrid MCDM methods in public infrastructure contexts and provides a transparent decision-support framework for preliminary equipment screening in public slaughterhouse projects.

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Abstract Paper aims This research maps the evolution of intelligent omnichannel fulfillment models via a Systematic Literature Review (SLR), addressing the gap in literature which lacks a comprehensive synthesis of these models and their critical trade-offs. Originality The study provides an evolutionary framework that organizes the field's progression. Its primary contribution is charting this trajectory as a response to the evolving trade-off between cost efficiency and operational responsiveness. Research method A PRISMA-guided Sistematic Literature Review was conducted using Scopus and Web of Science databases, covering publications from database’s inception to November 2025. An initial 1,975 papers were screened, resulting in a final in-depth qualitative analysis of 33 core articles. Main findings The analysis reveals the evolutionary trajectory for fulfillment models. The field progresses from (1) operational efficiency in warehousing and transportation via heuristics to (2) inventory positioning strategies under uncertainty using stochastic models, and culminating in (3) Artificial Intelligence (AI)-driven approaches focused on supply chain resilience and lead-time compression. This progression is shown to be a direct response to the limitations of each preceding paradigm, proposing approaches that prioritize both scalability and responsiveness. Implications for theory and practice For researchers, this study offers a consolidated state-of-the-art framework and a structured research agenda focused on AI-based solutions, operational realism, and new decentralized decision structures. For practitioners, it serves as a guide to select appropriate models based on operational complexity and strategic goals, identifying AI as the key enabler for the next generation of adaptive fulfillment systems.

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Abstract Paper aims This study synthesizes evidence on how Industry 4.0 digital technologies support Triple Bottom Line (TBL) outcomes in agroindustries and proposes a decision-support framework to guide technology selection and investment sequencing. Originality The study integrates dispersed findings into a capability-bundle logic, explaining how technology sets generate economic, environmental, and social outcomes under agroindustrial constraints such as perishability, seasonality, traceability, and supply-chain complexity. Research method A Systematic Literature Review was conducted following Tranfield, Denyer, and Smart (2003), structured into planning, conducting, and reporting phases, and reported according to PRISMA 2020. Searches in Scopus combined terms related to Industry 4.0, agroindustry/agribusiness, and TBL. After filtering journal articles in English, removing duplicates, screening titles, abstracts, and keywords, and assessing full texts, 24 studies were retained and coded through structured content analysis. Main findings The most recurrent technologies were IoT and Big Data Analytics, followed by AI, blockchain, cloud computing, advanced sensors, IIoT, autonomous robots, cyber-physical systems, and machine-to-machine communication. These technologies operate as capability bundles that enhance visibility, forecasting, optimization, efficiency, transparency, and trust, improving Profit, Planet, and People outcomes. Implications for theory and practice The framework supports technology prioritization, investment roadmaps, and future research on complementarities, trade-offs, and implementation conditions.

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Abstract Paper aims This paper aims to analyze how different sources of uncertainty interact and influence decision making across the early phases of the Product Development Process, with particular attention to their implications for manufacturing readiness and project performance. Originality The study advances project management and product development research by integrating a phase based perspective of uncertainty with a causal modeling approach, offering a structured understanding of how uncertainty dynamics evolve rather than treating uncertainties as isolated or static factors. Research method The research adopts the Decision Making Trial and Evaluation Laboratory method, supported by expert interviews, to model cause and effect relationships among eight uncertainty dimensions across the informational, conceptual, and detailed design phases of product development. Main findings The results indicate that technological and competitive uncertainties are the most influential factors in the informational phase, exerting strong mutual influence. In the conceptual phase, commercial uncertainty emerges as the most affected dimension, reflecting its dependence on strategic, technological, and market related factors. In the detailed phase, uncertainty intensifies, with commercial, technological, and resource uncertainties showing the highest levels of interdependence and overall importance. Implications for theory and practice The study contributes theoretically by demonstrating the value of a dynamic, phase sensitive view of uncertainty in product development projects. For practitioners, the findings highlight the need for adaptive, phase specific uncertainty management strategies that support better decision making, reduce late stage risks, and improve alignment between product development and manufacturing execution. These insights support more resilient projects and sustained competitive advantage.