Brazilian Journal of Operations & Production Management https://bjopm.org.br/bjopm <div align="justify"> <div class="gmail_default"><span style="color: #000000; font-family: 'comic sans ms', sans-serif;">The Brazilian Journal of Operations &amp; Production Management (BJO&amp;PM), ISSN (Online): 2237-8960, is an international and open-access journal that providing a platform for publishing applied researches. As an open access journal, articles in BJO&amp;PM will always be freely available online and readily accessible. This means that your work will be recognized and can be searched in Google Scholar, Sumários.org, Diadorim and Web of Knowledge. The journal is dedicated towards dissemination of knowledge related to the advancement in scientific research.</span></div> <div class="gmail_default"> </div> <div class="gmail_default"><span style="color: #000000; font-family: 'comic sans ms', sans-serif;">BJO&amp;PM promote and disseminate the knowledge by publishing original research findings, review articles and short communications in the broad field of Engineering. The BJO&amp;PM welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published approximately after acceptance. All articles published in BJO&amp;PM will be peer-reviewed. </span></div> <div class="gmail_default"> </div> <div class="gmail_default">Brazilian Journal of Operations and Production Management (ISSN (Online): 2237-8960) is a Journal of <a href="http://www.abepro.org.br/indexsub.asp?ss=40" target="_blank" rel="noopener"> ABEPRO - Brazilian Association of Production Engineering</a>. BJO&amp;PM mission is to provide an internationally respected stream for original and relevant research.</div> <div class="gmail_default"> </div> <div class="gmail_default"><span style="color: #000000; font-family: 'comic sans ms', sans-serif;">Only articles in English are considered for submission and publication.</span></div> </div> Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) en-US Brazilian Journal of Operations & Production Management 2237-8960 <p>Authors who publish with this journal agree to the following terms:</p> <p>- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" target="_new">Creative Commons Attribution License</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</p> <p>- Authors must have a written permission from any third-party materials used in the article, such as figures and graphics. The permission must explicitly allow authors to use the materials. The permission should be submitted with the article, as a supplementary file.</p> <p>- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</p> <p>- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after BJO&amp;PM publishes it (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</p> The role of digital supply chain practices in enhancing firm performance https://bjopm.org.br/bjopm/article/view/2493 <p class="RESUMOBJO"><strong><span lang="EN-US">Purpose</span></strong><span lang="EN-US">: This study examines the impact of digital supply chain practices (DSCP) on firm performance (FP) in the context of Bangladesh's manufacturing sector, with a focus on supply chain responsiveness (SCR) and customer development (CD) as mediating factors.</span></p> <p class="RESUMOBJO"><strong><span lang="EN-US">Design/methodology/approach</span></strong><span lang="EN-US">: A quantitative research design was employed, utilizing partial least squares structural equation modeling (PLS-SEM) to analyze survey data collected from 438 participants representing manufacturing firms in Bangladesh. The model evaluates the direct effects of DSCP on FP, as well as the mediating roles of SCR and CD.</span></p> <p class="RESUMOBJO"><strong><span lang="EN-US">Findings</span></strong><span lang="EN-US">: Results indicate that DSCP positively and significantly impacts FP, with SCR and CD serving as effective mediators in this relationship. The study demonstrates that digital integration in supply chains enhances responsiveness and customer engagement, ultimately leading to improved organizational performance.</span></p> <p class="RESUMOBJO"><span lang="EN-US">Through increased operational efficiency, streamlined logistics, and improved decision-making, the results show that Digital Supply Chain Practices (DSCP) are essential for boosting company performance. By empowering businesses to quickly adjust to market needs, minimize interruptions, and maximize resource usage, DSCP improves supply chain responsiveness and, in the end, increases agility and competitive advantage. Additionally, by enhancing engagement, trust, and service quality, DSCP improves customer development, which supports long-term business success and sustainability in Bangladesh's manufacturing industry.</span></p> <p class="RESUMOBJO"><strong><span lang="EN-US">Research limitations/implications</span></strong><span lang="EN-US">: The study is limited by its focus on the manufacturing sector in Bangladesh, which may affect the generalizability of findings to other industries or regions. Future studies should examine additional variables, such as organizational culture and market conditions, that may influence DSCP's effectiveness.</span></p> <p class="RESUMOBJO"><strong><span lang="EN-US">Practical implications</span></strong><span lang="EN-US">: The findings provide actionable insights for managers seeking to enhance performance through digital supply chain integration. Investing in digital tools can improve responsiveness and customer satisfaction, driving organizational success in competitive markets.</span></p> <p class="RESUMOBJO"><strong><span lang="EN-US">Social implications</span></strong><span lang="EN-US">: This study underscores the potential of digital transformation to improve supply chain efficiency, fostering economic growth and sustainability in emerging economies like Bangladesh.</span></p> <p class="RESUMOBJO"><strong><span lang="EN-US">Originality/value</span></strong><span lang="EN-US">: This research contributes to the limited literature on digital supply chains in developing economies, offering empirical evidence of their role in improving firm performance.</span></p> Tahsina Khan Md Mehedi Hasan Emon Copyright (c) 2025 Tahsina Khan, Md Mehedi Hasan Emon http://creativecommons.org/licenses/by/4.0 2025-07-17 2025-07-17 22 2 2493 2493 10.14488/BJOPM.2493.2025 A reactive hybrid product-driven system for rescheduling in a manufacturing planning https://bjopm.org.br/bjopm/article/view/2570 <p class="RESUMOBJO"><span lang="EN-US"><strong>Goal</strong>: </span><span lang="EN-US" style="font-weight: normal;">This research aims to develop a novel scheduling model integrating the intelligent product paradigm of a product-driven system (PDS) with the shifting bottleneck heuristic (SBH) to enhance rescheduling efficiency and adaptability in dynamic job shop scheduling problems with disruptions (JSSP-D).</span></p> <p class="RESUMOBJO"><span lang="EN-US"><strong>Design / Methodology / Approach</strong>: </span><span lang="EN-US" style="font-weight: normal;">The model employs agent-based modeling, where products act as autonomous agents in rescheduling decisions. Simulations covered 151 scenarios across 14 benchmark instances of machine failures, with production time increases of 100%, 200%, and 300%. The model’s performance was evaluated on its ability to minimize makespan deterioration and maintain efficiency under different disturbance levels.</span></p> <p class="RESUMOBJO"><span lang="EN-US"><strong>Results</strong>: </span><span lang="EN-US" style="font-weight: normal;">The PDS-SBH model effectively reduced production efficiency gaps, achieving an average makespan reduction of 7.81%, with peaks of 36.06%. Higher disturbance levels allowed for better rescheduling outcomes, albeit with increased variability. The model’s adaptability provided solutions comparable or superior to stable scheduling benchmarks.</span></p> <p class="RESUMOBJO"><span lang="EN-US"><strong>Limitations of the investigation</strong>: </span><span lang="EN-US" style="font-weight: normal;">The study used 14 benchmark instances and focused solely on machine failures, limiting generalizability to other disruptions like resource shortages or order changes. Despite these constraints, the 151 scenarios and rigorous analysis strengthen result reliability.</span></p> <p class="RESUMOBJO"><span lang="EN-US"><strong>Practical implications</strong>: </span><span lang="EN-US" style="font-weight: normal;">The PDS-SBH model offers a robust approach for real-time schedule adjustments, maintaining operational continuity, and optimizing resource use. It provides practical insights for decision support systems and policy development in dynamic manufacturing.</span></p> <p class="RESUMOBJO"><span lang="EN-US"><strong>Originality / Value</strong>: </span><span lang="EN-US" style="font-weight: normal;">This study pioneers a hybrid approach combining intelligent product paradigms with SBH. It advances JSSP-D research by presenting a resilient, adaptive framework for dynamic scheduling, significantly contributing to manufacturing efficiency and robustness.</span></p> Patricio Sáez Bustos Victor Parada Daza Copyright (c) 2025 Patricio Sáez Bustos, Victor Parada http://creativecommons.org/licenses/by/4.0 2025-07-17 2025-07-17 22 2 2570 2570 10.14488/BJOPM.2570.2025 Enhancing inventory accuracy in dairy industries https://bjopm.org.br/bjopm/article/view/2439 <p><strong>Goal:</strong> The goal is to analyze the application of Lean Six Sigma and DMAIC in the inventory management of a dairy industry, aiming to optimize inventory accuracy, reduce picking errors, and enhance OTIF performance, thereby improving overall operational efficiency.</p> <p><strong>Design / Methodology / Approach:</strong> This applied and descriptive research adopts an action research strategy, with researchers actively participating in the project's execution. Lean Six Sigma principles and the DMAIC framework were systematically applied to inventory, picking, and supply processes. Statistical validation was conducted using the Z-test for two proportions to confirm the effectiveness of the improvements implemented.</p> <p><strong>Limitations of the investigation: </strong>The study focuses on a case analysis in a dairy processing environment; however, the structured methodology adopted allows replication in industries characterized by operational complexity, perishability, or high inventory turnover.</p> <p><strong>Practical implications:</strong> The study offers valuable insights for logistics, procurement, and operations professionals. The implemented methodologies led to a 36.63% reduction in inventory discrepancies, resulting in cost savings of R$124,540.56. Additionally, significant improvements were achieved in controlling picking errors and enhancing OTIF performance, underscoring the critical role of structured continuous improvement methodologies in inventory management. </p> <p><strong>Originality / Value:</strong> This research addresses a gap in applied studies on inventory management in the dairy sector, demonstrating the successful application of Lean Six Sigma and DMAIC to achieve substantial operational improvements. The study presents a replicable model for other industries seeking to optimize inventory control, minimize operational errors, and elevate supply chain performance.</p> Francisco Tiago Araújo Barbosa Maria Silene Alexandre Leite Mauro Sergio Carneiro Mascarenhas Rogerio Santana Peruchi Copyright (c) 2025 Francisco Tiago Araújo Barbosa, Rogerio Santana Peruchi, Maria Silene Alexandre Leite, Mauro Sergio Mascarenhas http://creativecommons.org/licenses/by/4.0 2025-07-17 2025-07-17 22 2 2439 2439 10.14488/BJOPM.2439.2025