ABSTRACT. Industries are grappling with a multitude of challenges, including soaring energy prices, the impacts of climate change, and increasing societal pressures. Consequently, they are reevaluating their strategies for energy management. Energy management not only facilitates improved energy efficiency – meaning less energy is used to maintain the same level of production – but it also has the potential to bolster a company’s environmental credentials and reputation. Enhancing energy efficiency aligns with the objectives outlined in the 7th Sustainable Development Goal.

In this context, digital technologies and AI are being deployed within factories to optimize their energy systems. This involves analyzing vast quantities of energy data generated by machines and processes, as well as real-time monitoring of systems to enable swift intervention when necessary. This presentation will provide insights into the current landscape of energy management practices in industrial settings, alongside an exploration of the digital technologies and AI underpinning these efforts and their practical applications. Additionally, the talk will delve into future research trends in this field and showcase real-life case studies from companies that have implemented effective energy management strategies.

ABSTRACT. While Industry 4.0 has gained significant attention due to its focus on process optimization, as well as its ability to enable novel value propositions and business models (through e.g., servitization), there has been a noticeable oversight regarding the crucial role played by human resources within manufacturing systems. Addressing this gap, a human-centric Industry 5.0 perspective has emerged in the literature, driven by the necessity to leverage the distinct competences and creativity of humans and integrate it seamlessly with powerful, smart, and precise machinery, including collaborative robotics, AI solutions, etc. However, despite the potential of Industry 5.0 strategies, there is insufficient empirical evidence on how the synergy between competent human resources and emerging technologies can foster the innovativeness and competitiveness of manufacturing companies.

This keynote presentation starts with Industry 5.0’s human-centric organizational approach and its operationalization, emphasizing the integration of human intelligence, creativity, and skills with emerging digital technologies such as artificial intelligence, intelligent robotics, and cyber physical systems. Additionally, the talk introduces the European Manufacturing Survey as a relevant and representative dataset for researching the modernization of manufacturing companies. Second, the keynote elaborates on three empirical studies examining the effects of human-centric I5.0 orientation on the innovation, servitization, and sustainability performances of manufacturing companies. These studies provide valuable insights into the impacts of complementing human competences and emerging technologies in manufacturing companies. The presentation closes with offering a forward-looking perspective, proposing avenues for future research in this evolving domain.

ABSTRACT. The DoD faces extraordinary constraints as it attempts to navigate a rapidly changing world. Efforts to address the dynamics of a “great power competition” are constantly impacted by large forces such as a shrinking industrial base, an insufficiently digital-ready workforce, sustainment of aging systems, and pressures to embrace digital transformation across the enterprise. The implications for defense production are numerous and challenging: how do we bring new capability to the battle in a fraction of the traditional timespan? Are we prepared to surge with a shrinking industrial base? This presentation will introduce a few of the many challenges facing the department, and highlight ways the Air Force Research Laboratory’s Manufacturing Technology Program plans, collaborates, and invests to facilitate a future where production remains a powerful differentiator.

ABSTRACT. Problems in manufacturing systems such as identifying the number of manufacturing cells open to process jobs and scheduling jobs on each cell, must be solved efficiently. This paper presents an innovative method for optimizing job scheduling in manufacturing cells using the Simulated Annealing approach. While this approach is not always optimal, it is tailored for such problems and offers more practical solutions with computational efficiency. In this paper, a two-phase analysis of job shop scheduling is conducted with the aim of minimizing the number of open cells in a cellular manufacturing system and ensuring the lowest job completion time using Simulated Annealing. An illustrative example is carried out to show how the developed approach tackled the optimization scheduling problem in manufacturing cells and yielded minimum total completion time while maintaining the minimum number of cells.

ABSTRACT. According to researchers and reports in the field, approximately 90% of startups do not thrive, but this is not attributed to a single factor. Irrelevant or unviable ideas, complex and inadequate business models, as well as the need for sufficient human, financial, physical, and technological resources - in other words, failure is the result of a combination of factors. To better understand these challenges, the study relies on configurational theory, the startup lifecycle, focusing on technology-based startups in the Industry 4.0 to examine combinations of factors that lead to startup success. 120 technology-based startups operating with Industry 4.0 technologies in a globally recognized innovation ecosystem located in the Southern region of Brazil were studied. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), eight distinct configurations were identified that can lead startups to the market.

ABSTRACT. The technological revolution has played a fundamental role in the continuous evolution of industrial sectors, and the logistics area is no exception. The rise of Logistics 4.0 represents a significant milestone, characterized by the strategic integration of advanced technologies to improve operational efficiency. This emerging paradigm has generated considerable interest, reflected in the increase in publications and discussions on the topic in recent years. In this context, we seek to identify and analyze the main applications already documented in the scientific literature pertaining to this specific area, searching for an answer to the following questions: What are the work technologies that are used for Logistics 4.0? How are companies preparing for the transition to Logistics 4.0? To contribute to a clearer and deeper understanding of the implementation of the various technologies in the context of Logistics 4.0, we conducted a systematic literature review, using the Proknow-C method to classify the relevant articles and then performing a content analysis. In the end, we provide a more comprehensive understanding of the practical implications and potential of the technologies associated with Logistics 4.0, establishing key aspects to be observed when considering the implementation of this concept by the application of technologies such as advanced analytics and IoT, in particular.

ABSTRACT. This study explores the operations strategies of an electricity distribution company, focusing on the effectiveness and efficiency of these practices. Energy distribution is a crucial pillar in any country's infrastructure, directly influencing economic development and social welfare. The research uses a qualitative and exploratory approach, including a detailed case study in an energy company. The article highlights the importance of innovation and adaptation in a highly regulated and technically complex sector. The effective implementation of operations strategies responds to the growing demands of maintenance and operation, ensuring reliability and sustainability in energy supply. The study reveals that the alignment between theory and practice is crucial to the success of operational strategies, reflecting a balance between academic knowledge and the practical demands of the sector.

ABSTRACT. A Multi-objective Linear Programming model is presented for the selection of raw materials and the creation of a product of non-meat origin for a Colombian company. The main objective of the model is to maximize the flavor score of the final mixture and minimize the cost of the selected raw materials. Based on the re-sults of the proposed model, a prototype of the product was developed, and sen-sory tests were carried out using the sensory profile methodology. The experi-mental results show that the proposed model can generate a mixture of raw mate-rials that meets the nutritional and sensory objectives desired for a product of non-meat origin. However, we recognize the need to continue adjusting the model to adapt it to consumer preferences and ensure greater market acceptance.

ABSTRACT. The Seru Production System is gaining increasing recognition as a production innovation renowned for its flexibility, speed, cost-effectiveness, and environmentally friendly features, which surpass those of lean production methods. This study aims to compare the Seru Production System with its foundational systems, the Toyota Production System and mass production, across various performance indicators. In this study, industry experts' opinions are consulted to compare production systems based on criteria established within social, economic, environmental, and operational dimensions. A decision structure is formulated by incorporating diverse perspectives from managers in the automotive, electronics, and textile industries, utilizing a fuzzy logic approach. The resulting decision matrix is analyzed using the Preference Selection Index method. The findings of the study revealed that the operational dimension emerge as the most influential factor in comparing production systems. Furthermore, the Seru Production System consistently obtains higher score values across different industries compared to other production systems, affirming its superiority in various contexts.

ABSTRACT. Graduate medical education is expected to teach clinical knowledge and skills, but more recently, also expected to teach topics in systems thinking and quality improve-ment. We sought to determine the best path for teaching these topics to medical residents while also developing faculty knowledge on the same topics. Family medicine residents were trained to complete six root cause analysis (RCA) projects on hospital readmissions during Spring and Summer of 2018. These RCA projects were then reviewed for systematic process failures. This initiative has resulted in several outcomes (1) medical faculty were trained in root cause analysis and techniques for teaching RCA to students; (2) medical residents were trained in quality improvement and root cause analysis techniques by leading RCA projects; (3) the hospital gains knowledge on systematic problems in chronic readmissions cases. Formal collaboration between medical faculty and business faculty can benefit both faculty and students. In addition, the results of RCA projects can lead to improvement efforts at the hospital.

ABSTRACT. In the context of Industry 5.0, the transformation in how work is conducted is notably driven by technological advancements, necessitating a shift towards the enhancement and requalification of the workforce to embrace these changes. This article highlights the integral role of competencies in navigating the transition to more autonomous and technologically advanced work environments. The conver-gence of Lean methodologies with Industry 4.0 technologies facilitates a symbi-otic relationship where Lean principles aid in the efficient implementation of Industry 4.0, and conversely, Industry 4.0 technologies enhance Lean processes. Based on an ontology created about work in the 4.0 era, a systematic literature review was conducted to expand it, leading to the identification and thorough evaluation of 67 articles. With the intention of connecting this ontology to Lean, this research utilizes the PROMETHEE II method, a multi-criteria decision sup-port tool, to analyze the interaction between Lean tools, critical implementation factors, and competencies. Through discussions with Lean experts, two matrices were developed, analyzing Lean tools and critical factors in relation to competen-cies. The findings suggest a continuous need to explore how critical Lean factors affect competencies, contributing to the discourse on enhancing employability and competence in the context of Industry 5.0.

ABSTRACT. Pandemic situations can cause significant harm to everyday life and may have a profoundly negative impact on various industries, with the healthcare sector being the most affected. The optimal allocation of resources in a pandemic is pivotal; poor allocation can lead to more deaths, escalate the spread of the virus, and overwhelm healthcare. The demand and resource requirements in the pandemic continuously change with shifting epicenters. During the recent COVID pandemic, there was a significant surge in research devoted to the responsiveness of medical facilities and personnel to a high increase in a number of cases and patients. This paper presents a systematic literature review of resource and patient allocation during a pandemic using SLR methodology. This research will identify the methodologies used in solving this problem dynamically, highlighting key findings from comprehensive research. The process of collecting the papers for review and identification of crucial keywords in collecting the papers will be reported. Understanding the methodologies and key findings in the pandemic resource allocation problem is important as the pandemic is a global problem and will likely recur in the future.

ABSTRACT. The 7th Sustainable Development Goal of the United Nations targets a twofold increase in energy efficiency improvement rates. However, the current global energy crisis from the Ukraine-Russia conflict adversely affects the manufacturing sector, the primary contributor to rising global electricity demand. In response, companies use energy management (EM) strategies to enhance efficiency and cut costs. Integrating technology information systems is a vital step in EM implementation, with digital technologies (DT) playing a key role in coordinating activities. This paper explores the relationship between energy management and digital technologies, delving into the technologies utilized for EM issues and their appli-cations. Conducting a systematic literature review using Scopus, Science Direct, and Web of Science databases, the study focused on energy and Industry 4.0 keywords over the past five years (2018-2023). Of 1,117 initially identified publications, 417 remained after removing duplicates, and only 40 papers met the inclusion criteria. The research identified 14 digital technologies, categorized into eight groups based on Industry 4.0 design principles. These categories were also used to classify the application of digital technologies in EM, aiding in determining the appropriate DT investments for specific applications. The study's limitation lies in its reliance on a literature review, and the proposed categorization awaits validation in real-case scenarios, paving the way for future research

ABSTRACT. This study undertakes a meticulous analysis of the mediating role played by Industry 4.0 in the relationship between the more abstract and technical aspects of Lean philosophy, with the aim of assessing its concrete impact on company performance. Comprised of a representative sample of 270 manufacturing firms, employing the Technology Acceptance Model (TAM) as the underlying theoretical framework, the investigation delves into the intricate interplay between the elements constituting Industry 4.0 and Lean philosophy, utilizing statistical techniques such as confirmatory factor analysis, ordinary least squares regression (OLS), and bootstrapping. The results obtained substantiate the fundamental significance of Industry 4.0 as an effective mediator, enabling companies deeply ingrained in the Lean philosophy to enhance their Just-in-Time operations, Total Productive Maintenance, and Total Quality Management, resulting in a substantial improvement in their overall performance. Additionally, it underscores the significant influence of active leadership, employees, and customers in embracing the Lean culture, thus accentuating the recognition of the value of Industry 4.0. Consequently, the seamless integration of these technologies with the technical aspects of Lean tools emerges as a paramount factor in enhancing organizational performance. This study enriches the body of knowledge by emphasizing the premise that the adoption of Lean philosophy precedes the integration of Industry 4.0 technologies and offers invaluable practical insights for managers seeking to optimize their operational processes. It addresses a critical gap in existing literature by exploring the conditions under which the synergy between Lean and Industry 4.0 yields remarkable performance outcomes for organizations.

ABSTRACT. Nowadays, topics related to Digital Twin (DT) are gaining interested mainly because its benefits of operation improvements and reducing of costs, since, in the digital replica, simulations can be performed, which indirectly helps users to test scenarios and collect results before real implementation. One of the techniques that supports this task relates to the ability to discover the process that is being executed. In this context, Process Mining (PM) can be considered a helpful tool to discover and map the real path and behavior of the process. Nevertheless, the standard instantiation of PM only supports one kind of case, e.g., a case-focused (traces) analysis. However, in a smart manufacturing environment, processes are dynamic and could allow the production of more than one final and/or intermediate goods simultaneously (i.e., a parallel execution). In this context, different kinds of process mining rise in favor of this solution, which consists of an analysis focused on Object-Centric Process Mining (OCPM). This technique allows for forward-looking and data-driven solutions, both requirements of smart manufacturing and Industry 4.0 guidelines. Thus, the current study was developed considering the smart manufacturing environment, which is considered one of the most relevant and interesting areas to apply such concepts of process mining and digital twin techniques, as well as analysis and data-driven decisions for processes.

ABSTRACT. Companies are struggling to thrive in the evolving industry, marked by the Industry 4.0 revolution, driven by technologies such as the Internet of Things, Artificial Intelligence, Cloud Computing, Big Data, among others. The integration of these innovations is challenging due to internal and external barriers, such as budget constraints, employee resistance, technical skills gaps, and traditional organizational culture. The primary objective of the study is to analyze the barriers that companies face during the deployment of Industry 4.0 technologies and how these barriers impact the expected outcomes. It uses secondary data from a survey conducted by the Confederation of National Industry (CNI) with 1,691 companies to establish a connection between these barriers and the Brazilian industrial landscape. The study provides insights into the challenges that hinder the progress of Industry 4.0 and informs strategic decision-making in the context of the Four Smarts: Smart Products, Smart Manufacturing, Smart Supply Chain, and Smart Working. It emphasizes the importance of overcoming barriers for a successful adoption of Industry 4.0 and highlights the relevance of the TOE framework in analyzing these issues.

ABSTRACT. Quality 4.0 is a trend gaining strength as a promising instrument in industries for better control and quality assurance of processes and products. It is based on the application of different Industry 4.0 technological tools, such as process virtualization, the Internet of Things (IoT), and intelligent data analysis. It allows quality problems to be identified in advance, in addition to better correlating problems to their root causes. The scientific literature on the topic has grown, especially in the last three years, with works from different approaches and perspectives, which develop the concept and show different applications. However, there is a lack of organization of this literature that consolidates these contributions. This work, therefore, aims to present a systematic literature review (RSL) on the topic of Quality 4.0, showing the different meanings of this concept, the technologies applied and areas of application, results obtained, limitations and difficulties encountered, and steps of its implementation. Proknow-C was used as a method for creating the bibliographic repository, with searches being carried out in the Scopus and Web of Science reference databases. In the end, in addition to the analysis of the previously mentioned points, a framework is developed and presented that can be used as a guide for the application of the Quality 4.0 concept in industrial companies.

ABSTRACT. In recent years, the use of smart agriculture has significantly gained popularity, with applications and solutions to automatically maintain and monitor critical variables such as humidity and temperature in farms throughout the world. However, its use in the flower export industry in emerging countries such as Colombia and Ecuador are still in its infancy. This paper presents insights on how smart agriculture technologies can be applied to the industry of export flowers. This paper focuses on existing applications used to monitor five standard key factors in flower crops. These variables are water, light, fertilization, plant health, and temperature. Our conclusion is that the use of smart agriculture in the flower industry can be applied

ABSTRACT. This study responds to the increasing market demand for manufacturers to provide reliable information about the longevity of their products. Manufacturers are particularly interested in the 100p-th percentile of a product's lifetime distribution. Degradation tests are vital for this, as they offer insights into the product's lifespan under various conditions over time. We propose a novel optimization method for designing a bivariate degradation test based on gamma processes, where the variables are dependent. We test various copula functions and select the best copula using the Akaike Information Criterion. This method optimizes the number of samples to be tested, the frequency of measurements, and the number of measurements, all while considering the constraints of experimental costs. This strategic approach ensures effective and efficient reliability prediction, catering to both the technical needs of engineering systems and the market demands for product reliability information. Finally, we test our model on a numerical example.

ABSTRACT. This contribution focuses on the estimation of travel and service times in last mile delivery (LMD) problems, which concern the last stretch of supply chains associated with consumer products. These times, which are affected by a multiplicity of factors in urban settings, play a critical role in Intelligent Transportation Systems associated with Smart Cities. In addition, they are employed as input data in VRPTW (Vehicle Routing Problem with Time Windows) type of problems that take place in urban areas. The correct estimation of travel and service times becomes crucial in the effective solution of different LMD problems. The estimation task is tackled in this work by means of non-parametric, data-driven, machine learning (ML) methods, such as Random Forest and XGBoost. Raw data coming from different datasets, which were supplied by a company that provides GPS fleet tracking services in Argentina, has been employed. It can be concluded that is possible to address the problem by means of a trip-based approach that mainly uses truck GPS data. However, the quality of the results heavily depends on the characteristics of the input data. This is why efforts were devoted to data pre-processing activities (anonymization, cleaning, analysis, datasets integration, etc.) and feature engineering tasks, which led to the creation of two high-quality training datasets to be used by the ML approaches. This work provides details on these data curation and feature engineering tasks.

ABSTRACT. Combinatorial optimization problems (COP) are multifaceted set of problems with discrete decision variables and determinate exploration space. Typically for optimality, combinatorial optimization problems require exponential time to be solved. Hence COPs are generally classified as NP-hard class of optimization problems to be solved. With the recent advances in computational intelligence Metaheuristic (MH) algorithms are vastly used for solving COPs. In our recent investigation when applying the metaheuristic algorithm (non-dominated sorting genetic algorithm-II) to the benchmark (Taillard - 1993) flowshop problems, we identified the exceptional application of Machine learning (ML) algorithms for parameters selection in Metaheuristics. In this paper we attempted to leverage several ML algorithms for the selection of genetic operators of MH for benchmarks problems. We considered the energy efficient multi-objective flowshop scheduling problem in minimization of flowtime (FT) and energy consumption (EC) as multi-objective criteria in the selection of best parameters of MH. Further we observed that ML algorithm-based parameters selection enhanced the MH performance in solution identification in lesser number of iterations.

ABSTRACT. This study presents a comparative analysis of two multi-objective optimization algorithms, NSGA-II and DEoptim, in the context of portfolio optimization. The research evaluates the performance of these algorithms based on key portfolio indicators such as return, risk, Sharpe ratio, diversification, and computational efficiency. The findings offer valuable insights into the trade-offs between computational speed and portfolio performance, highlighting the significance of algorithm selection in financial optimization scenarios.

ABSTRACT. The product development process is becoming increasingly critical to the com-petitiveness of industries due to the growing internationalization of markets and the increase in the diversity and variety of products. Industries are increasingly looking for methods and tools to help locate waste and take action to eliminate it. This article aims to deal with concepts and presentations of the applicability of the VSM (Value Stream Mapping) tool on a production line that produces copper molded coils to order, manufactured for the stators of medium and high voltage hydroelectric power station generators. Using a case study methodology applied in an industry in the southwestern region of Paraná (Brazil), with the tool's com-petence in implementing the principles of lean production in operations, it showed that making maps of the current and future state in the language standardized by the Lean philosophy can be applied in the development process of this product. After analyzing the maps and the spaghetti diagram, it was possible to identify points of waste and operations that do not add value. Among the most important results were better visual management, a 20% reduction in lead time, elimination of bottlenecks, intermediate stocks and optimization of the production process.

ABSTRACT. Unexpected equipment failures in production lines pose substantial financial risks, jeopardize worker safety, and diminish overall productivity. Leverag-ing process mining (PM) for insights from event data, this paper introduces an innovative approach that integrates process mining with the Fuzzy TOPSIS method to enhance predictive maintenance decision-making. The proposed solution is specifically applied to vibration sensor data within an automotive manufacturing line. Fuzzy TOPSIS prioritizes measure based on temperature and axial, horizontal, and vertical vibration criteria. This method enables the early identification of potential fault, thereby mitigating down-time, reducing repair time, and minimizing the adverse impact on production. This integrative approach PM – FTOPSIS (Process Mining – Fuzzy TOPSIS) holds promise for proactive and efficient maintenance strategies in industrial settings.

ABSTRACT. Brazilian agribusiness is one of the largest in the world, being re- sponsible for 25% of Brazil's GDP, with production expected to increase by 70% in 30 years. For this reason, investments in new technologies, such as busi- ness analysis, are necessary. Business Analytics (BA), which has become in- creasingly crucial in the context of agribusiness, providing essential data for more accurate decisions, promoting an analytical understanding of the market, and improving companies' production processes. An exploratory literature review (REL) was carried out based on a nine-step Unified Framework, using topic modeling to highlight the main research in- volving BA applications in Brazilian agribusiness. The search was carried out in the Scopus database, obtaining 154 articles to identify key topics. Subse- quently, the most relevant articles were selected for elaborating and deepening the research on the application of Business Analytics (BA) in Agribusiness. Four key topics were obtained to prospect Business Analytics applications with an emphasis on agribusiness, giving special focus to agribusiness companies in Brazil, obtaining the subjects i-performance, ii-data-management, iii-innovation and iv-sustainability, representing the current profile of Brazilian agribusiness and Worldwide.

ABSTRACT. Abstract. The term ESG has been occupying a prominent role in corporate stra-tegic plans, aiming to go beyond setting social-environmental goals, increase the organization’s efficiency, profitability gain, and market positioning. Thus, this ar-ticle aims to propose the application of a framework based on the approach of implementing practices related to environmental, social and governance issues (ESG) in a medium-sized road transport company. Therefore, the goal is to help companies make the transition to a more sustainable business model. The current article seeks to demonstrate the applicability of sustainable ESG operations through an interventionist approach, creating a model of ESG indicators – Envi-ronmental, Social and Governance. To achieve this objective, the company’s ESG materiality is identified, allowing for the evaluation of actions and implementa-tions in the organizational process. For the creation of the ESG indicators model, a qualitative method of action research (interventionist) will be used, as it sought to solve organizational problems through the application of ESG indicators in the company. A framework will be applied for modeling ESG materiality, granting a definition aligned with the company’s strategic goals, developing its ESG mod-els. With that being said, this article, besides the academic contribution to the lit-erature that abords the thematic, corroborates the replication of an indicator framework to the transports sector scenario, specifically a freight carrier, in the three ESG dimensions: environmental, social, and governance. The present framework aims to identify the ESG materiality of a company under study and create indicators capable of contribute both management and decision-making processes, by obtaining materiality and deriving the ESG indicators. The data was collected in the field and the results obtained were validated in dialogue with the industry managers under analysis. In this sense, the thesis argues that, with the elaboration of the proposed study, improvement actions will be provided for the practice of ESG in the organization studied and companies in the freight transport sector.

ABSTRACT. In the year 2020, a series of restrictive measures and social distancing were implemented worldwide due to the global outbreak of COVID-19. These measures had a significant impact on work models, leading to the need to find alternative solutions for teams. One of the solutions found was the formation of distributed virtual teams, which experienced a significant increase during this period and are now considered the answer to many organizational issues in the pandemic scenario. Studies on the performance of these teams have provided different perspectives to understand how to assess and measure their performance. The objective of this study was to identify factors that hinder or facilitate the performance of distributed virtual teams. Through a systematic literature review, 48 articles addressing "factors that hinder or facilitate the performance of distributed virtual teams" were identified. Factors such as leadership, effective communication, cohesion, trust, technology, emotions, geographic dispersion, spoken language, culture, and training were found. These factors were identified and grouped. This research represented a contribution to the literature by providing a comprehensive list of factors that affect or contribute to better performance, which can be used by distributed teams as an evaluation tool.

ABSTRACT. In the dynamic and highly competitive environment of Digital Transformation, where new solutions and technologies continually evolve, the growing demand from customers drives companies to continuously update their business models in pursuit of innovation. A large number of companies are classified as small and medium-sized enterprises (SMEs), and it is precisely these companies that face major challenges during the journey of Digital Transformation, including significant resource constraints, knowledge limitation, and a shortage of qualified workforce. Despite the extensive literature on Digital Transformation in large corporations, the application of these approaches in SMEs is limited. This article aims to fill this gap by highlighting the characteristics and specific constraints faced by small and medium-sized enterprises when undertaking Digital Transformation projects. The adopted methodology included a review of existing literature and direct interviews with managers of SMEs. The study emphasizes the aspects and barriers faced by SMEs, offering strategic guidance for managers. The results identify challenges and provide a deeper understanding of constraints related to resources, human capital, and specialized knowledge, offering practical guidance for the success of Digital Transformation in SMEs.