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.
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.
Abstract Paper aims Introduce a new statistical test to verify whether two small samples of variable classified into multiple categories are drawn from the same population. This problem can be represented by a contingency table of order (m x 2). Originality We do not have adequate asymptotic texts to treat this issue in all instantiation of the problem, and exact methods require substantial computational effort and specialized algorithms. The proposed test covers this gap. Research method It can be classified within design science research. The result, as well as the research process, meets the guidelines of that research method. Main findings Computational experiments show that the proposed test has similar effectiveness to the exact test, even when dealing with sparse data contingency tables and small values of m. Furthermore, examples show that it can work well in cases where the chi-square test, its numerous variations, and even in situation where the more recently developed methods fail. Implications for theory and practice This type of decision problem has received significant attention in the literature because it represents many real-life situations. The test proposed is as useful for small samples as Chi-square is for larger samples.
Abstract Paper aims This study aims to explore the relationship between Total Quality Management (TQM), Technology Management (TM), and sustainable performance in manufacturing. Originality The originality of this study lies in its integrated approach, combining TQM and TM to assess their collective impact on Corporate Sustainability Performance (CSP). While prior research has examined these concepts separately, this study provides a comprehensive framework that highlights their synergies in driving sustainability. Research method This study employs a mixed-methods approach, combining surveys and expert interviews. The quantitative phase assesses TQM and TM practices' impact on sustainability in manufacturing firms, while qualitative interviews provide deeper insights into key success factors, challenges, and mechanisms driving the adoption of these strategies. Main findings The findings indicate that TQM and TM collectively enhance CSP by improving operational efficiency, reducing waste and emissions, fostering sustainable innovation, and promoting a culture of continuous improvement and employee involvement. These findings highlight the need to integrate quality management and technology for sustainability goals. Implications for theory and practice Theoretically, this study enriches the understanding of how TQM and TM interact to drive sustainable performance. Practically, it provides organizations with actionable strategies to align quality management and technology for long-term sustainability.
Abstract Paper aims Businesses are facing a challenge to transform their supply chain operations and contribute to sustainable development through circular economy practices. Whilst the transformation takes time and requires financial resources, many businesses like small and medium sized enterprises (SMEs) in developing world are struggling to survive and need to set priorities. This work aims to understand whether and how business survivability (SRV) and supply chain finance (SCF) can drive circular economy practices (CEP) amongst SMEs in an emerging country. Originality We contribute by offering a novel perspective of need hierarchy and showing that SRV is a critical driving factor of CE adoption amongst SMEs in the developing world. Research method We survey 515 SMEs from a diverse range of industries in Indonesia, one of the largest emerging economies in the world, and analyse the data using Partial Least Squares Structural Equation Modeling (PLS-SEM). Main findings This research shows that SRV is a critical driving factor of CE adoption amongst SMEs in Indonesia. Whilst higher SRV could increase chances to access SCF, non-financial support from SC actors is needed to make the financing effective and to motivate SMEs to adopt CEP. Without SCF, SMEs could adopt CEP when their business performs well. Otherwise, their focus is on surviving their business operations. Implication for theory and practice Our research challenges current research in developed countries showing that CE practices could affect SME performance and business survival. We provide evidence on the opposite argument and extend the applicability of Maslow’s hierarchy of needs theory in the business context.
Abstract Paper aims This study aims to assess the network efficiency of the bar and profile rolling process in a steel manufacturing company using Network DEA (NDEA). Originality This research presents the first application of NDEA in combination with internal benchmarking, illustrating its feasibility in supporting significant performance improvements. Research method A case study was conducted in a steel plant to analyze the overall network efficiency. Main findings The average efficiency was 41.99%, with minimum and maximum values of 15.12% and 99.23%, respectively. Internal benchmarking revealed that the third stage of the rolling process negatively affected overall efficiency. Additionally, critical incidents influencing performance were identified, with 66.67% occurring in the fourth quarter each year. Implications for theory and practice Combining NDEA with internal benchmarking enables a continuous improvement framework, allowing the company to monitor and adjust operations for enhanced efficiency.
Abstract Paper aims This paper investigates how variability in certification requirements, organizational capabilities, and traceability technologies influences halal traceability system readiness in the Indonesian cosmetics industry. Guided by Institutional Theory, which explains how external regulatory pressures shape organizational behavior, and the Resource-Based View (RBV), which highlights the role of internal capabilities, this study aims to identify the determinants crucial for ensuring halal traceability system readiness amid regulatory diversity and supply chain complexity. Originality This study fills a significant research gap by focusing on halal traceability in the cosmetics sector, an area predominantly studied in the food industry. It offers an integrated conceptual framework that links institutional Theory and RBV to enhance traceability readiness. Research method Qualitative analysis was conducted using semi-structured interviews with key stakeholders, including regulatory bodies, industry representatives, and material suppliers. Data was analyzed systematically with ATLAS.ti 23, ensuring reliability through inter-coder agreement. Main findings Institutional pressures influence traceability readiness, with organizational capabilities and traceability technologies playing critical roles in addressing compliance challenges. Key factors include adaptive compliance, organizational responsiveness, and the traceability integration of material tracking, regulatory systems, and compliance Implications for theory and practice As a qualitative and exploratory study, the findings are context-specific and not intended for statistical generalization. They extend Institutional Theory and RBV by demonstrating how regulatory pressures and internal capabilities shape halal traceability readiness. The study provides actionable strategies for policymakers to harmonize certification systems and for industry stakeholders to enhance training, adopt adaptive compliance, and integrate traceability technologies to support halal system integrity.
Abstract Paper aims This study investigates the influence of digital transformation, technology use, and business management on the digital maturity of real estate agencies in Peru. We strive to provide an empirical model to assess digitalization's role in enhancing operational efficiency within the real estate sector. Originality This is the first study to systematically quantify digital maturity levels in Peruvian real estate agencies through structural equation modeling. Our research bridges a critical gap by linking digitalization practices with tangible business outcomes in emerging markets. Research method The study employed a quantitative methodology, gathering data from 116 real estate agents and analyzing it using partial least squares structural equation modeling (PLS-SEM). It also utilizes robust statistical techniques, including confirmatory factor analysis and model fit indices, to validate constructs and ensure reliability. Main findings Digital transformation significantly enhances business management practices and the digital maturity level, while digital networking showed no direct impact. Technology use emerges as a pivotal driver of digital transformation, emphasizing its foundational role in real estate innovation. Implications for theory and practice The findings highlight a novel framework for real estate professionals to benchmark digital maturity and optimize digital investments. By outlining key enablers of digital transformation, this study provides actionable insights for policymakers aiming to foster innovation in the real estate sector.
Abstract Paper aims This study aimed to identify the main risks present in Food Supply Chains (FSCs) and the strategies employed to mitigate them through a Systematic Literature Review (SLR). Originality The originality of this research lies in the systematization of risks and mitigation strategies from an extensive international body of literature, providing an integrated and updated perspective on the challenges facing FSCs. Research method The review used PRISMA and Joanna Briggs Institute (JBI) protocols. Sources included the Scopus, Web of Science, and Google Scholar databases, along with grey literature. Main findings The study identified fourteen key risks, including disruptions, forecasting failures, and operational, environmental, logistical, and intellectual property risks. Mitigation strategies were grouped into proactive, reactive, and concurrent approaches, and involved technologies such as IoT, blockchain, and big data, as well as practices like supplier diversification, traceability, and sustainability. Implications for theory and practice Findings support managerial and policy decision-making and contribute to building more resilient, efficient, and secure FSCs. They also highlight the need for further research tailored to the Brazilian context and reinforce the importance of digital and sustainable strategies in supply chain risk management.
Abstract Paper aims The main objective is to determine which Industry 4.0 (I4.0) technologies significantly impact the scale efficiency of 3PLs’ (Third Party Logistics). Originality This paper provides a significant academic contribution given that it is the first quantitative research endeavor to evaluate the influence of I4.0 applications on productivity within the Brazilian 3PL industry. Research method A two-stage Data Envelopment Analysis (DEA) model was adopted. The first stage of the DEA enabled the measurement of 3PL efficiency, and the second stage (Bootstrap Truncated Regression) allowed us to explore the relationship between efficiency and the I4.0 technologies. Secondary data from Revista Tecnologística provided the inputs, outputs, and contextual variables for this analysis. Main findings In the first stage of the analysis, a high average technical inefficiency was identified, suggesting managerial failures to efficiently use available resources. However, 3PLs demonstrated low-scale inefficiency, operating close to the optimal production scale. In the second stage, the contextual variables Drones, Big Data, and Business Intelligence were positively significant, while Internet of Things technology was negatively significant. Implications for theory and practice Our study enhances 3PL efficiency literature by applying DEA, considering contextual aspects, and exploring the adoption challenges of I4.0 technologies in emerging economies.
Abstract Paper aims This paper explores the integration of Industry 4.0 digital technologies and the Theory of Constraints (TOC) in manufacturing systems, focusing on their reciprocal enhancement to drive operational improvements. Originality It pioneers an examination of how TOC’s principles can effectively guide the adoption of I4.0 innovations while also showing how emerging technologies can extend the practical applications of TOC in operations management. Research method A systematic literature review was conducted following the PRISMA protocol, ensuring a comprehensive synthesis of existing studies at the intersection of TOC and I4.0. Main findings The review identifies three key elements: (i) the critical role of systems analysis in understanding manufacturing constraints, (ii) the effective implementation of I4.0 strategies guided by TOC principles, and (iii) the support provided by advanced technologies—such as artificial intelligence, digital twins, and RFID—in enhancing TOC applications. Implications for theory and practice The study offers a robust theoretical framework that bridges traditional operations management with modern digital strategies. Practically, it provides actionable insights for managers seeking to optimize technology adoption and operational efficiency in manufacturing, ultimately paving the way for future research on integrated digital and constraint-based management systems.
Abstract Paper aims This study seeks to investigate the accuracy of machine learning algorithms for estimation of the effort required for software development in the manufacturing sector to identify the most effective algorithms according to the nature and complexity of the data and the number of available attributes. Originality This work distinguishes itself from other studies in the field of effort prediction by utilizing a data repository that consists exclusively of projects from the manufacturing sector. This approach ensures that the specific characteristics of manufacturing projects are reflected in the predictions, addressing a gap in the existing literature. Another notable contribution of this study is the comparative analysis of various machine learning algorithms assessed under different dimensionality scenarios (three and five variables). Although this factor is crucial for enhancing effort estimation accuracy, it has received limited attention in the literature. Research method The investigated techniques in this work were (i) Support Vector Regression, (ii) Gradient Boosting Machines (GBM), (iii) eXtreme Gradient Boosting (XGBoost), (iv) Random Forest (RF), (v) Extreme Learning Machine (ELM); and (vi) Linear Regression (LR). Performance measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) were used to compare the results achieved by each model, considering a dataset of 230 records originating from various countries. Main findings The comparison among machine learning models revealed significant performance variations depending on the number of variables and the evaluation metrics adopted. GBM stood out for its robustness in complex scenarios, while SVR achieved the lowest mean absolute error. ELM, in turn, proved effective with fewer variables but showed sensitivity to outliers and less stability in more complex contexts. Among all the techniques evaluated, XGB yielded the worst performance across all parameters. Implications for theory and practice This study contributes by applying these models to the manufacturing sector and comparing scenarios with three and five variables. The results support a more informed selection of models based on project complexity and data dimensionality. The more research conducted in this area, the stronger the theoretical and practical conclusions can be drawn.
Abstract Paper aims This article introduces a novel hybrid methodology in order to optimise dual-axis photovoltaic tracking systems in three Argentinian provinces by combining artificial intelligence, swarm intelligence and the productive chain. It identifies the most suitable strategy by balancing fixed-panel worst-case scenarios with continuous-tracking best-case scenarios and incorporating the decision makers’ preferences. Originality Firstly, the novel research methods listed below combine mathematical modelling and graphical analysis, and highlighting their complementarity and distinct contributions. Secondly, theoretical, methodological and practical gaps are identified and addressed in Argentina and other under-explored regions. This offers decision-makers a viable interim solution. Research method Firstly, it involves the novel mathematical modelling, simulation, optimisation, comparison of dual-axis solar tracking in fixed and mobile cases using multi-criteria techniques, while also validating across provinces and extreme scenarios. Secondly, it consists of a novel hybrid multi-criteria optimisation model combining particle swarm optimisation with constriction factor and a fuzzy-guided feedback metaheuristic system. It is for dynamic boundary-reflected constraints, the Analytic Hierarchy Process, and radial basis function neural networks. Thirdly, this survey is based on data obtained through the present line of research, including government and meteorological station data, manufacturer data and independent research. Main findings This methodology improves energy efficiency by 10–27% and economic performance by 40–110% compared to fixed panels, depending on regional and technical conditions. Implications for theory and practice This novel, scalable hybrid methodology combines the aforementioned research methods (theory) with support for decision-making in the planning of renewable energy projects in constrained economies (practice).
Abstract Paper aims This study aims to identify the main risks affecting food supply chains (FSCs) in Brazil, analyze their interrelationships, and propose mitigation strategies. Originality This research presents a structured approach to modeling the interdependencies among FSC risks, offering original insights with practical applications for risk managers in the Brazilian food sector. Research method The methodology consists of three phases: (1) an Exploratory Literature Review (ELR) to identify and categorize FSC risks; (2) a survey with industry experts to collect qualitative data on the interrelationships among the identified risks; and (3) the application of Interpretive Structural Modeling (ISM) and the Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis to explore the relationships among risks and propose mitigation strategies. Main findings Ten key risks were identified and ranked. Natural risks and macro-level risks emerged as the most influential, while demand, supply, and operational risks were found to be more dependent on these factors. The ISM model illustrated the interconnections among the risks, and the MICMAC analysis classified them according to their driving and dependence power. Implications for theory and practice The findings support the development of targeted mitigation strategies, strengthening risk resilience and decision-making within FSCs. Additionally, the study contributes to academic discourse by integrating structural analysis into food supply chain risk management.
Abstract Paper aims This study explores sustainability adoption in Indonesian construction firms by (a) describing current levels of the three sustainability pillars, (b) analyzing associations between key variables influencing sustainability performance, and (c) providing managerial insights and recommendations for improving sustainable construction practices in Indonesia. Originality It extends the literature by proposing and testing a theoretical model that explains the interaction between sustainability attitudes, practices, and performance, tailored to the Indonesian construction context. Research method A cross-sectional, self-administered survey targeted Indonesian construction firms, achieving a 22.8% response rate with 104 usable responses. Moderation analysis evaluated the association of ‘sustainability attitudes’ and ‘sustainability performance’ with ‘management practices’ as the moderating variable. Main findings Management practices partially moderate the association between sustainability attitudes and performance. Firms prioritize compliance-driven environmental sustainability, internal stakeholder well-being, and short-term economic benefits but lack strategic vision and sustainability teams. Implications for theory and practice The study contributes to the theoretical understanding of sustainability performance in construction by extending the Attitude–Behavior (A–B) framework to a firm-level context. It also addresses practical gaps in sustainability practices among firms in emerging economies. Findings highlight Indonesian construction firms’ priorities and challenges, guiding intervention strategies such as policy reforms, market incentives, and capacity-building programs.
Abstract Paper aims This study characterizes Purchasing and Supply Management (PSM) projects in the context of Industry 4.0 (I4.0) at a Brazilian steel company, using the reference architecture model industry 4.0 (RAMI4.0) as a basis. Originality The article adapts RAMI4.0 to the PSM context and empirically evaluates the model, promoting digitalization practices in PSM. Research method An in-depth case study was conducted using participant observation, document analysis, individual interviews, and focus groups with PSM professionals. Main findings By adapting the life cycle (dimension) to the stages of PSM deployment, this study establishes a framework for monitoring and optimizing activities throughout the PSM process, offering valuable insights. Implications for theory and practice The customization and evaluation of the RAMI4.0 model for PSM can serve as a reference framework for companies seeking to drive the digital transformation of the PSM function. This model offers a strategic perspective on how to integrate Industry 4.0 technologies across various organizational levels, with the goal of promoting digital PSM.
