Conference Agenda

Optimization challenges in industrial engineering, particularly in economic order quantity (EOQ) and materials requirement planning (MRP), have traditionally been complex. This research addresses critical limitations in existing production and inventory management models by addressing recent computational advancements. We propose a comprehensive approach to resolving large-scale industrial engineering optimization problems by integrating high-level programming languages and advanced optimization tools. The study focuses on developing a generic Python-based optimization algorithm using a reference optimization model and Gurobi solver, with primary contributions including: (i) systematic exploration of optimization methods in industrial engineering; (ii) development of a flexible, scalable optimization approach; (iii) demonstration of computational techniques' potential in solving complex production planning challenges. By bridging theoretical optimization models with practical implementation, this research offers a cost-effective solution that extends beyond traditional limitations of economic order quantity and production lot sizing methodologies.

Lean Manufacturing (LM) is a methodology that uses various tools to optimize production processes and reduce waste in various industries. The objective of this work is to apply these principles to minimize waste in the productive field, through the analysis of articles indexed in Scopus published in the last five years. From a systematic literature review, 183 Scopus documents were obtained, which were discarded through duplicate articles, studies excluded for not using the methodology or applying it and finally not complying with the PIOC questions, thus achieving 50 documents in the end. To this end, four selection criteria were considered: articles that cover the problem of waste, only documents that are articles, that are studies that have applied the methodology, while only being in Spanish and English and a range of 5 years old (2020-2024), thus reducing it to 50 articles. The analysis of the documents obtained allowed us to identify the main tools such as Single Minute Exchange Die (SMED), 5S, Kaizen, Value Stream Mapping (VSM), and Just-In-Time (JIT), which reduce operational waste, improve delivery times and increase efficiency in key sectors. For example, reductions of 45% in set-up times and 70% in process cycles were achieved. In addition, the integration of Lean Six Sigma (LSS) has increased productivity by up to 80% and significantly reduced defects. This study reinforces Lean Manufacturing's ability to transform production processes towards more sustainable and competitive operations.

This systematic review analyzes the influence of ergonomics on work performance in the manufacturing sector. Ergonomics, which emerged in the 1940s, has established itself as a fundamental discipline for adapting work environments to human capabilities. Its correct implementation not only prevents musculoskeletal injuries, but also increases efficiency and improves work excellence and employee well-being. From detailed research in sources such as Scopus, 36 relevant studies published between 2014 and 2024 were included, which provide solid evidence on how ergonomic interventions can positively transform working conditions. The findings indicate that poor application of ergonomic principles can lead to health conditions, such as muscle discomfort and high levels of stress, which negatively impact work efficiency. It was identified that most studies focus on large manufacturing industries, where lower back and upper extremity injuries are common. To analyze performance in the work environment, the most commonly used tools were questionnaires and performance records, which have proven to be effective in detecting ergonomic risks. In addition, the relevance of applying ergonomic adjustments based on scientific studies to improve working conditions is emphasized. In short, a correct ergonomics management not only favors the health of workers, but also strengthens the profitability and sustainability of companies in the manufacturing sector.

This research article explores the impact of applying Lean Manufacturing methodologies (5S, VSM, SMED, Kaizen, TPM) on the optimization of industrial processes within the Peruvian food industry. A mixed research approach was employed, combining quantitative and qualitative methods to assess the effectiveness of these methodologies in improving productivity, reducing costs, and promoting sustainable practices. The results, illustrated through tables and figures, reveal significant improvements in operational efficiency and waste reduction. A gap is identified between the potential of Lean Manufacturing and its actual implementation, attributed to organizational and technological barriers. Finally, specific recommendations are presented to promote the adoption of these methodologies in the sector, considering both technological and organizational aspects.

This research article explores the application of Big Data-based predictive analytics to optimize inventory management in the manufacturing sector. Through a survey applied to industrial and systems engineering students, knowledge, experience and perceptions regarding the use of Big Data were analyzed. The results reveal a gap between interest in Big Data and practical experience, highlighting the need to improve training and access to educational resources. The study concludes that proper implementation of Big Data improves demand forecasting accuracy, reducing operating costs and increasing customer satisfaction. Recommendations are presented to facilitate the adoption of these technologies in the sector.

The era of digital transformation has led organizations to adopt significant changes due to the growing importance of data. Business intelligence (BI) plays a pivotal role in facilitating data analysis for sound decision making. The present systematic review aims to analyze the current status of the influence of business intelligence on business decision processes. For this purpose, 26 original articles published in the Scopus, ScienceDirect and Scielo databases between January 2019 and June 2024 were examined. Also, a bibliometric analysis was performed using VOSviewer software. The main results showed that business intelligence has generated great benefits for organizations, such as process optimization, increased financial performance and better comprehensive visibility of data. However, major barriers to implementation were also identified, such as the ability to integrate data with other systems and technical skills, as well as high initial costs. It is concluded that the integration of business intelligence tools contributes to making more informed and accurate decisions in organizations, which has an impact on improving their organizational performance and in turn allows them to maintain their competitive advantage.