ABSTRACT. Opening session

ABSTRACT. On March 1, 2024 the International Resource Panel officially launched the Global Resources Outlook (GRO) 2024 at the United Nations Environment Assembly (UNEA-6) in Nairobi, Kenya UNEP headquarter. It is now clear that the role of the sustainable production research became foremost important in the coming years of the remainder of the Sustainable Development Goals (SDG), given that only 15% were achieved since 2015. This talk will focus on nine policy implications of the GRO 2024, and correlates the production (and consumption) patterns to the triple planet crisis; namely: climate change, biodiversity, and pollution. Implications indirectly call for attention in addressing eco-efficiency, productivity, quality utility function, optimal value chain, improved logistics, AI, Industry 4.0 and Industry 5.0, and stronger connectivity of ethical engineering service to quality of life, equity and well-being. The findings of the GRO2024 has many policy options that aligns well with a good performance of production system. Thus, production researchers have a major role to play in the transition to a sustainable future.

ABSTRACT. Multi-faceted systems of the future will entail complex logic and reasoning with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized This is also true for globally integrated manufacturing systems of today and the very near future. They are mission based many objective complex adaptive systems with dynamically changing meta-architecture. Manufacturing systems solutions is a system of systems solution that needs the creation of meta-architectures integrated with digital engineering capabilities. In this talk the possible use of Large Natural Language Models like ChatGPT and Deep Learning AI Algorithms will be discussed based on the current experiences gained in health care from the development of a prototype type digital twin that generates meta-architecture real time for each organ donated that uses digital engineering threads to assess translatability of the donated organ using deep learning AI models and negotiating with transplant surgeons

ABSTRACT. Computer vision is a key technology and fundamental within the scope of Industry 4.0. Through the combination of cameras with hardware and specific algorithms for image processing, a wide range of possible solutions and applications help industries, such as automation of quality inspections, object identification, process control, and dimensional measurements, among countless others. This technology is constantly evolving, so every day, new solutions emerge mainly to reduce downtime, maximise effective production, guarantee the quality of the product, and reduce costs such as labour. In this context, this article aims to propose an innovative solution based on the newest machine vision technologies to increase efficiency, reduce waste such as the generation of defects and the unnecessary use of raw materials, as well as the need for constant monitoring of the operation in the process of cutting and creasing for carton packs. The solution, through the installation of two sets of cameras from the Keyence brand, will have as its main objective to monitor and measure in all packages two of the main variables of the process, correlating them and sending the result to the main control system of the machine, which in turn, it will automatically and in real-time correct the process parameters, which today are done through sample measurements and corrected from time to time by the operator, creating a high dependence on human interaction. The results exceeded expectations, making it a global project that helped the whole corporation, increasing profitability and optimising process resources to a level never achieved.

ABSTRACT. This paper presents an innovative approach to multi-robot path planning within Robotic Compact Storage and Retrieval Systems (RCSRS) by integrating Mixed-Integer Linear Programming (MILP) with the A* algorithm. Facing the challenge of optimizing paths in densely populated, dynamic environments, we propose a solution that combines the comprehensive optimization capabilities of MILP with the heuristic-driven efficiency of the A* algorithm. Through detailed mathematical modeling and algorithmic strategies, our approach addresses critical issues such as collision avoidance and computational efficiency. Simulated experiments in a typical RCSRS environment demonstrate the effectiveness of our method, showing significant improvements in pathfinding optimality and computational time. This study contributes to the fields of logistics, warehousing automation, and robotics by offering a scalable and efficient solution for the complex problem of multi-robot path planning, paving the way for future research in the optimization and practical application of autonomous robotic systems in compact storage spaces.

ABSTRACT. In the context of Industry 4.0, marked by significant technological advancements, there is a profound transformation in the landscape of work. At the core of this evo-lution lies the critical role of competencies, particularly in the domain of mainte-nance, which emerges as a vital component in navigating the transition to more au-tonomous and technologically advanced work environments. This article explores the intricate relationship between maintenance and competencies within the frame-work of Industry 4.0, underscoring their essential role in shaping the workforce and operational methodologies. Building upon an ontology developed for work in the 4.0 era, we conducted a literature review and crafted a competency management system tailored for maintenance to further advance this approach, resulting in an ontology with a maintenance focus. To establish this ontology's connection to maintenance and competencies, we applied the PROMETHEE II method, a multicriteria decision support tool, to analyze the interaction between maintenance pillars, maintenance indicators, and ontology competency concepts. Through dialogues with maintenance experts, we devised two matrices that scrutinize maintenance pillars and critical indicators concerning competencies. The findings underscore the importance of continuously exploring how critical maintenance indicators impact competencies, thus contributing to the discourse on enhancing employability and competence in Industry 4.0.

ABSTRACT. Product Life Cycle Management (PLM) is the process of managing the entire life cycle of a product, from its conception through design and manufacturing to service and disposal. In order to validate the product life cycle classification ranking method, the Multi-Objective Optimization by Ratio Analysis (MOORA) method was applied. This method allowed us to analyze which stages of PLM were the most relevant, based on a ranking scale, and which ones showed different results in terms of the importance of various criteria. This demonstrated the significance of each item within each phase of PLM, which can vary depending on specific factors to be considered by the organization. Thus, the combination of the traditional PLM concept and the application of the MOORA method enables organizations to understand the dynamics of their products more comprehensively, making strategic decisions more effective, based on various criteria and specific goals. The results revealed that the PLM phase leading the final ranking was the introduction phase. This finding validates the applicability of the method, allowing for the identification of growth and financial return criteria as highlights. Study limitations may arise from market dynamics, the definition of weights for decision criteria, which are independent and may vary from one organization to another, and the ability of PLM to accurately predict a product's performance over time. The study's contribution and originality lie in the integration of PLM management and the MOORA method, which together allow for the rapid, standardized, and precise classification of criteria. Although the greatest challenges are market complexity and fierce competition, organizations can overcome obstacles and stand out uniquely in their product management strategies by applying them to other problem contexts. This can be achieved by conducting new comparisons and evaluations, using different parameters and significant criteria, as well as experimenting with other Multiple-Criteria Decision-Making (MCDM) methods.

ABSTRACT. Resilience refers to a system's ability to recover from disruptions, which can significantly impact manufacturing supply chains, at any time. This paper aims to perform a bibliometric analysis of resilience in manufacturing-supply chain management. The methodology employs the PRISMA method to search document data, followed by data analysis utilizing the Biblioshiny tool. The findings include scientific production, influential elements such as the most relevant resources and authors, the most cited papers worldwide, and countries with the most scientific output. Furthermore, a visualization of the key words in the form of word clouds, trend topics, conceptual structures, and thematic analysis is provided.

ABSTRACT. This workshop aims to present the state-of-the-art of the IOF industrial ontology suite in the context of various industries (discrete manufacturing, process industries, batch manufacturing, biomanufacturing). We will address current standard efforts and identify semantic gaps in those standards that the use of ontologies could help overcome. For discrete manufacturing, we will explain intelligent manufacturing planning approaches toward the digital twin of product, process, and system. An overview of knowledge-based approaches will be followed by an introduction to production planning ontologies for selected manufacturing domains. We will demonstrate the application of ontology in resource capability evaluation, process sequencing, and material order processing. Batch processes manufacture finite quantities of materials using ordered sets of activities over finite periods of time. The finite amount of material produced at the end of a given batch process is referred to as a batch. The initial aim of the ANSI/ISA-88 standard was to address batch process control issues in a systematic way; however, it obtained the major objective of decoupling information regarding equipment, product recipes, and control strategies. To achieve this goal, knowledge is organized along three different perspectives: the physical model, the process model, and the procedural control one. Currently, many batch manufacturers run their operations by observing the ISA-88 standard. Though widely adopted in industry (specialty chemicals, pharmaceuticals, food products, etc.), the ANSI/ISA-88 standard has a series of semantic drawbacks that will be highlighted in this presentation. ANSI/ISA-95 model is another well-accepted industrial standard whose aim is the seamless exchange of information between levels 3 and 4 of the Purdue hierarchical model. An analysis of this standard also uncovers some semantic problems, which will be pointed out. Unfortunately, a joint analysis of the ISA-88 and ISA-95 standards reveals that if both are adopted in a given industrial setting, serious gaps, overlaps, and semantic problems might arise, which will be summarized in this presentation. The workshop will conclude with a presentation about the Industrial Ontology Foundry (IOF) effort to develop reference ontologies for various industrial domains, such as production planning, supply chain, maintenance, product design, systems engineering, and others. The hub-and-spokes approach based on the Basic Foundation Ontology (BFO) provides a well-grounded ontological foundation and strong reasoning mechanisms to provide for the consistency of ontology terms within and across various domains. The current results in developing the IOF-Core ontology as basis for other reference ontologies will be presented. Presentations will be accompanied by use cases from a few domains under the IOF umbrella.

ABSTRACT. Countless studies have demonstrated the need for improved information sharing; without proper information sharing, the supply network is less competitive, which suggests that participants’ trust is low. Both factors in-dicate that the supply network exhibits low resilience. A novel approach, based on incentivizing information sharing and punishing non-sharing, is conceptualized to increase resilience during supply network negotiation. This new model attempts to reduce hidden information and increase the willingness of participants to share private information by weighting the cost contributors’ operation costs and desired participants’ welfare. This paper establishes an agent-based collaborative negotiation framework with a first simulation. The simulation analyzing the impact of information shar-ing was conducted for 100 participants. Two scenarios were tested against a baseline: (1) all participants follow a neutral bidding strategy, and (2) a mi-nority of aggressive or conservative bidders are introduced to the simula-tion. The simulation shows that information sharing is more beneficial to the participants than non-information sharing. In the second part of this re-search, we expand this work to a case study that implements the framework in a real-world setting: venue parking. A collaboration between the venue and parking lot owner is introduced, which includes information sharing and is designed to streamline the venue visitor’s experience. Additionally, it is expected to increase parking revenue and decrease venue costs by re-ducing overall inefficiencies through aligning incentives.

ABSTRACT. In the era of digital transformation in the industry, the increasing adoption of Cyber-Physical Systems (CPS) and Digital Twins (DT) is transforming traditional approaches, enabling the creation of virtual representations of the physical world. Supply Chain Management (SCM) stands out as a promising area for the application of these innovative technologies, although its implementation is challenging, requiring detailed evaluative approaches. Multicriteria Decision Analysis (MCDA) emerge as a promising approach to deal with the complexities involved in decision-making processes, considering both objective and subjective criteria. This work, focused on Industry 4.0 (I4.0), aims to develop an evaluation model for the implementation of the DT concept in SCM, with an emphasis on Enterprise Interoperability (EI) perspectives as a lens on challenges and barriers. Analyzing scientific and gray literature, project reports, and drawing on collaborative interactions, the research encompasses the relationship between I4.0 reference architectures and DT models, considering the perspective of interoperability to comprehend the implementability of the DT concept in the SC under SCOR model dimensions. The MCDA PROMETHEE II method is employed for the assessment dimensions. The project provides a diagnostic perspective that highlights the integration between EI, DT, and the Reference Architecture Model Industrie 4.0 (RAMI 4.0) model to optimize Supply Chain (SC) in I4.0. The MCDA approach facilitates strategic decision-making, addressing dynamic challenges and seeking operational efficiency in SCM.

ABSTRACT. This paper presents a systematic literature review aimed to identify key drivers and barriers associated with the implementation of Industry 4.0 technologies in fresh food supply chains across emerging countries. The transformative potential of Industry 4.0 holds promises enhanced efficiency and sustainability in these chains, but its adoption presents unique challenges in emerging countries. This review collected and synthesized the latest research, showing perspectives on the factors influencing the success or failure of Industry 4.0 technology implementations in fresh food supply chains. The insights derived from this review can offer valuable guidance for professionals, researchers, and policymakers seeking to strengthen fresh food supply chains in emerging economies through the integration of Industry 4.0 technologies with the local idiosyncrasies. Some of our findings show that the barriers range from financial constraints and a lack of guidance for entrepreneurs to organizational complexities and regulatory challenges in implementing technologies like blockchain and IoT in agricultural supply chains.

ABSTRACT. Originating in Germany, "Industry 4.0" emerged as a catalyst for technological progress in the industrial landscape, garnering global attention. This led to widespread exploration and adoption of Industry 4.0 technologies by various nations. A decade later, the European Commission introduced "Industry 5.0", emphasizing value creation over pure technological advancement. This sparked discussions about the coexistence of these two industrial transformations. Integrating new technologies and complex products significantly impacts the business sector, which is crucial for economic development. Critics note Industry 4.0's focus on digitalization and emerging technologies, which paved the way for Industry 5.0, emphasizing the importance of the workforce. Notable advancements in Industry 4.0 include Artificial Intelligence, the Internet of Things, and Cyber-Physical Systems, reducing human involvement in decision-making processes. On the other hand, Industry 5.0 emphasizes human-centric methods by introducing ideas like hyper-customization and predictive maintenance. The construction of a human-centered, sustainable, and comprehensive value generation model is the main goal of the transition to Industry 5.0. Nonetheless, the intricacies of digitalization pose noteworthy obstacles, especially for small and medium-sized businesses (SMEs) who do not possess the financial means to undertake comprehensive digital projects. This paper explores advancements in Industry 4.0 and 5.0, with concerns including technical integration and data protection. The resilience of Industry 5.0 supports hyper-individualization. This study also explores the drivers and facilitators of these evolving paradigms, providing a structured framework to understand the technologies, challenges, and potential solutions associated with Industry 4.0.

ABSTRACT. Digital servitization is a pivotal research trend reshaping the manufacturing sector, leveraging Industry 4.0 technologies for a transition from product-centric to sophisticated service-centric business models. As the field of digital servitization matures, development of more context-specific maturity models that investigates the optimal combination of traditional and digital dimensions are needed. This study investigates a framework for a digital servitization maturity model, utilizing an exclusive dataset from the European Manufacturing Survey, comprising 1,496 industrial firms from Germany and Serbia. The study identifies five levels of digital servitization maturity (Pure manufacturer, Traditional servitization, Outcome based business model, Digital servitization, and Digitalized PSS business model), emphasizing the need for a nuanced understanding of how companies navigate traditional and digital dimensions. Surprising disparities emerge, particularly in traditional servitization levels, suggesting a unique trajectory for Serbian firms. The paradoxical dynamic prompts exploration into whether digitization acts as a catalyst, propelling Serbian manufacturers swiftly into servitization upon deciding to transition.

ABSTRACT. New product development (NPD) is a collaborative and cognitive process to bring a new product or service. NPD must meet customers' needs while achieving business objectives such as growth, profitability, and competitive advantage. The stages of NPD include formatting the opportunity, identification, conception and generation of ideas, design, development, and implementation. Knowledge management encompasses aspects like fostering the creation and exchange of ideas among multidisciplinary teams, retaining, and preserving accumulated technical knowledge, and capitalising on continuous learning opportunities. For this purpose, tools, platforms, and the application of management practices are necessary so that all produced knowledge can be employed in accelerating the development of new products. Therefore, this article explores an analysis dealing with the intersection between collaborative engineering, knowledge management, and digital technologies for new product development. Initially, aspects of collaborative engineering will be discussed. Then, how collaborative engineering interconnects with knowledge management will be examined, revealing synergies that enhance the creation, sharing, and effective application of knowledge throughout the product life cycle. The paper will also address the role of emerging technologies in this dynamic context and their transformative influence on how product development teams collaborate and manage knowledge. Therefore, the article will outline how this convergence of these three elements can accelerate the conceptual development stage of new products.

ABSTRACT. The integration of Artificial Intelligence (AI) technologies in industrial maintenance engineering is revolutionizing the management and upkeep of assets. This article presents the development of an intelligent dashboard, leveraging generative AI-based chatbots to intuitively and interactively display complex maintenance data. Designed to be trained with extensive databases from a company specialized in asset management, the tool is capable of identifying patterns, forecasting maintenance requirements, and recommending proactive measures. This work introduces an analytical instrument that simplifies the visualization of crucial maintenance indicators and enables specialists to tailor the dashboard to the specific demands of each operational environment. A case study utilizing current data showcases the tool's contribution to enhancing asset management efficiency and fostering sustainable maintenance practices, in line with the advancements of Industry 4.0. Furthermore, this study delves into the role of digital twins, sparking a discourse on the boundaries of the work developed within this area of knowledge.

ABSTRACT. In the past years, artificial intelligence has been the subject of much discus-sion and debate, both in the academic world and in popular culture. Going from seeing AI as a competitor for humans and potential existential threat, it is now time to discover the useful applications of AI in world of practical applications, which in the current case focuses on production processes and systems. The current paper uses a qualitative approach to discover and assess the possibilities of customizing AI through the process known as “alignment” into a concrete tool that can be used by engineers and managers confronted with the everyday challenges of the manufacturing industry. Based on expert surveys and requirements engineering tools, the benefits and pitfalls of this approach are analyzed, and conclusions are drawn regarding implementation scenarios from the perspectives of technical, economic and social criteria, which can be used as guidelines for future development.

ABSTRACT. The integration of AI solutions within companies holds significant transformative potential, particularly within sectors such as manufacturing. However, successful AI implementation requires adept orchestration of resources, with boundary spanners playing a pivotal role in bridging boundaries between different groups in organizations. Despite their importance, the role of boundary spanners in facilitating AI adoption remains underexplored. This paper aims to address this gap by empirically examining key boundary-spanning activities, as-sessing the relevance of various boundary-spanning roles, and analyzing the interplay be-tween different levels of AI capabilities and boundary-spanning skills in organizations. Drawing upon empirical data obtained from 215 representatives of German companies, our study underscores the criticality of all relevant boundary-spanning activities in the context of AI implementation. Furthermore, certain roles, notably project managers, emerge as widely endorsed and suitable boundary spanners, while others such as employee representa-tives and HR personnel exhibit comparatively less relevance. Our findings indicate that companies with higher AI capabilities exhibit markedly superior levels of boundary-spanning skills compared to their counterparts. Similarly, our regression analysis demon-strates that the extent of current AI utilization significantly influences the availability of boundary-spanning skills in organizations. Interestingly, companies operating within the manufacturing industry display notably lower levels of boundary-spanning skills when juxtaposed against those from other sectors. In sum, our study empirically underscores the significant role and thus the imperative of investing in boundary spanning to augment AI adoption processes, particularly within manufacturing companies.

ABSTRACT. In many electronics manufacturing processes, quality control of the cable assembly is conducted manually by a human operator. However, given that the sizes of cables are thin and the different product groups include complex color combinations, determining whether the correct colored cable is assembled to the right port is challenging for a human operator. With recent developments, automated visual quality control systems in the manufacturing industry can accurately detect errors and lower costs. These technologies utilize deep learning models, which usually demonstrate higher prediction performance when trained through comprehensive datasets encompassing images collected under various working conditions ranging from simple to complex cases. As preparing extensive datasets entails high costs and time, open-source datasets related to the subject with real-life images are helpful to enrich the image dataset. This study uses an industry data set and an open-source repository to explore the prediction performance of You Only Look Once version 8 with a Segmentation (YOLOv8Seg) object detection model for contributing to quality control processes in cable assembly in the electronic manufacturing sector. The preliminary findings of the study demonstrate that YOLOv8Seg shows superior performance in detecting different colored cables in the electronics assembly with a mean average precision above 92%.

ABSTRACT. Recent rapid development of machine learning and artificial intelligence (ML/AI) have led to the development of fundamental questions for humans: how are we to understand and trust those more and more complex software systems. From ML approach of a data-based black box and earlier developments of knowledge and/or ontology based expert systems, the need to be able to explain ML/AI procedures has been identified and research area of explainable/responsible AI has been born. This paper applies the methodology for evaluation of AI systems for explainability and transparency on a use case of a traditional rule-based system for intelligent manufacturing planning. The methodology from the literature that addresses four components of explainability (namely, aspects, contexts, addressees, and explainers) has been utilized for the purpose of the case study. Overview of the methodology is briefly discussed in the paper before the analysis for the case study is performed. The analysis focuses on transparency of several aspects of explainability, like purpose, inputs, data, processing, and outputs with identification of which aspects are given by the systems, and which are still under the purview of humans. The illustration of transparent rule-based procedures for process selection and process sequencing in the planning system have been provided. The paper can serve as a guideline or template for performing explainability studies of classical and modern AI systems.

ABSTRACT. This research uses bibliometric analysis to investigate the concept of human-robot collaboration in advanced manufacturing systems (HRCIAMS). The PRISMA method is used as a document search technique. The main facts, scientific production, most relevant resources, most relevant authors, most widely referenced publications, relevant affiliations, and countries with scientific output are all included. Further-more, visual representations of keyword analysis are supplied, including word cloud, trend topics, conceptual structures, and thematic progression. The authors explain the key elements that establish human-robot collaboration (HRC) as a major concept of industry4.0, as well as the characteristics that will most likely make it the primary topic of the next, industry5.0. Research themes in advanced manufacturing systems include digital twins, industry 4.0, industry 5.0, artificial intelligence, safety, smart manufacturing, operator 4.0, machine learning, augmented reality, and virtual reality, all of which can improve human-robot collaboration. The paper's findings can serve as an overview and source of insight for future related research.

ABSTRACT. Multi-agent path finding (MAPF) has been widely used for path routing of multiple robots with applications in industries like automated warehouses. MAPF consists of finding a route for each robot while avoiding collisions. Many algorithms have been deployed to address this problem, including A* algorithm. Also, multi-robot task allocation (MRTA) plays a crucial role in the efficiency of the proposed algorithm. To deal with this problem, we present a heuristic algorithm for item pickups among all the robots. In the proposed algorithm, we use a conflict avoidance method to avoid collisions between robots in the system. Regarding the workstation assignment for each robot, we develop a model called the minimum-time-based assignment rule (MTAR) to minimize the working time of the robots. The proposed algorithm shows significant reduction in time consumption and is able to find efficient task assignment between different robots.

ABSTRACT. Collaborative automation, which combines humans and robots in a shared workspace, has emerged as a promising approach to address the increasing complexity of tasks and the need for greater efficiency in manufacturing ac-tivities. This article explores the current state of the art, motivations, and potential benefits of collaborative automation, drawing on insights from re-cent research in three different directions: collaborative robotics, collabora-tive digital management, and cyber-augmented human-robot collaboration. In addition, this article aims to provide a basis of understanding between the research and industry implementation of collaborative automation. By examining how complementary strengths of humans and robots are lever-aged, the study aims to unlock future possibilities for automation in in hu-man-robot manufacturing systems, leading to greater efficiency, flexibility, and operational safety.

ABSTRACT. Abstract: Introduction: Human augmentation technologies, such as virtual reality (VR), augmented reality (AR), mixed reality (MR), digital twins, collaborative robots, and exoskeletons, have stimulated scientific research, unlocking new possibilities for exploration, data analysis, and human-machine interactions. These technologies provide novel means to visualize data, simulate scenarios, improve collaboration, and enhance human capabilities that can shape the future of work. However, addressing the associated costs, technical barriers, and user difficulties is essential to fully harness their potential for various industrial applications. This study aims to critically review the literature to assess the feasibility and impact of human augmentation technologies and to propose future research directions. Methods: Google Scholar databases were used to review human augmentation studies from January 2015 to January 2024. Eligible studies were evaluated using criteria focused on subjective evaluation, quantitative performance, internal validity, and generalizability. Results: The initial literature search identified 60 publications. After excluding duplicates, 32 records remained. In total, 17 studies were identified as potentially eligible based on an abstract review. Following a full-text review of these 17 studies, only 9 met the core criteria for inclusion. The reviewed studies demonstrate that human augmentation technologies reduce job completion time and eliminate periods of inactivity for robots in collaborative situations, indicating increased and efficient productivity. VR has proven to be an effective tool in training, improving the learning process while maintaining performance requirements. Conclusion: More research is necessary to understand the impact of human augmentation technologies on mental and physical health across different age groups, particularly regarding their development, benefits, and drawbacks in relation to safe integration and optimization. Future research should also focus on including larger and more diverse participant cohorts and assessing the long-term impacts of these technologies in various professional settings to enhance their practicality and the breadth of their application.

ABSTRACT. The adoption of technology for enhancing efficiency has become a wide-spread practice among companies. In recent years, the digital transformation movement has played a pivotal role in this effort, making substantial contributions. One of the fundamental pillars of digital transformation is digitalization, a strategy whose goal is to automate analog and manual activities. In this context, a widely adopted solution is known as Robotic Process Automation (RPA), which refers to software robots automating repetitive tasks to increase efficiency and reduce costs. However, a very difficult task is prioritizing because multiple variables must be considered. Inevitably, poor choices can lead to stress and inefficiency. In this article, a strategy is presented to overcome this issue: first employing Multi-Criteria Decision Making (MCDM), a methodology used to evaluate and prioritize alternatives in the presence of multiple criteria. Secondly, by Process Mining (PM), which involves analyzing event data recorded during business processes to gain insights into performance, compliance, and efficiency. Therefore, this approach involves the use of MCDM techniques for prioritization, combined with process mining to identify and assess the feasibility of automation with RPA, the application of which will be demonstrated in a case study. The findings provide valuable guidance for process practitioners seeking to leverage advanced technologies for process optimization.

ABSTRACT. The global challenge of waste management has become prominent due to the increasing waste production, surpassing available disposal space. Simultaneously, the need to adopt renewable energy sources in response to the global energy crisis and climate change has intensified. In addressing this challenge, Waste-to-Energy (WTE) emerges as a solution, involving power generation and alternative fuel through waste treatment. This study adopts a portfolio approach to investigate WTE, identifying recommended technologies for effective implementation. Through a systematic literature review using the ProKnow-C methodology, we focused on papers published in English between 2018 and 2022, primarily from journals and conference proceedings on Science Direct and Scopus. From an initial pool of 2,214 papers (Scopus) and 1,364 papers (Science Direct), 53 were selected for thorough analysis, and 23 were included in the final portfolio due to their approach of the theme. Eight WTE technologies were identified: Pyrolysis, Incineration, Combustion, Gasification, Thermochemical Hydrolysis, Anaerobic Digestion, Fluidized Bed Combustion, and Composting. Incineration, known for its energy efficiency and significant waste volume reduction, was the most used technology. Pyrolysis, versatile and applicable to various waste types, ranked second, followed by gasification, which reduces greenhouse gas emissions. However, challenges, such as greenhouse gas emissions, highlight the environmental impact variability of WTE technologies. Despite drawbacks, WTE technologies persist as viable waste management and energy production alternatives, emphasizing the need for future studies to assess technology-specific criteria for precise application selection.

ABSTRACT. Copper mining is experiencing problems of maintaining the business over time because the grades are increasingly lower due to the movement of more material to obtain the same amount of metallic copper. On one hand, this extra work at deposits of copper oxides implies more waste generation and utilization of capital resources leading to increased costs and CO2 emissions. By the other hand, in addition to current uses, new technologies are demanding more copper for electromobility solutions and renewable energy generation, as a response to the problem of climate change, which means increasing demand of this material in a scenario of depletion of non-renewable resources. Due to properties such as Workability, Electrical/Heat Conductivity, Durability, Anti-Bacterial, and Recyclability, copper will continue as a key material for new industries and products, which calls for an effective strategy to increased productivity and care of this non-renewable resource. Circular Economy (CE) is one effective strategy for tackling this problem in mining industry, helping not only to resource conservation but to reduce resource spending and contain costs. The CE’s Ten-R practices (10R) are applicable to copper industry but its incorporation require decision making models in order to select the most suitable actions in accordance to the realities of each organization.

The paper presents the results of a Systematic Literature Review (SLR) linking CE with copper industry as to identify the main research and applications and their contribution to sustainable production systems. Of these, 10R practices are selected to build a multi-criteria decision analysis (MCDA) model which will prioritize the actions that will have more impacts in the achievement of sustainable production goals.

The model is solved with Promethee technique and is applied to a case study in copper hydrometallurgy processes, composed of unit operations of mining, loading, crushing, conveyor belt, leaching, solvent extraction and electrodeposition. The aim is to contribute to the company's triple bottom line by applying 10R practices for various generated wastes so that positively impact the sustainability of copper mining business. Finally, with the support of the principles of circular economy, a proposal is prepared to prioritize actions in unit operations, producing a ranking of most favorable alternatives to invest in.

ABSTRACT. This article deals with the functioning of the free energy market in Brazil and around the world in relation to the means of energy production used, seeking to show how the energy market regulates and encourages the use of renewable energy. The objective is to synthesize the understanding of the current scenario of energy commercialization and present the main regulatory aspects of each means of energy generation, in order to develop a base of knowledge to subsequently improve the performance of systems and machines belonging to the electrical area, thus bringing a scientific contribution to the Electricity Generation, Transmission and Distribution market - GTD. Noticeably, the global energy demand has increased, and due to the high emission of gases that contribute to global warming in the energy generation process, it is necessary that the generation of renewable energy increases to reduce the effects caused by the burning of fossil fuels. Therefore, it's essencial to understand the market's regulatory requirements for the distribution of renewable energy, its challenges and opportunities and the influence of consumers on this distribution. Hence, 15 base articles were chosen through a systematic literature review process, as well as visiting the regulatory framework in different countries (developed and developing). The aim was to synthesize the aforementioned themes, focusing on the differences between the regulatory requirements for wind and solar distribution, as well as onshore and offshore wind. It was noticed that Brazil stands out in policies to encourage renewable energy, since there is a considerable discount on transmission and distribution tariffs for this type of generation. Nevertheless, there are developed countries, such as the United States, that still need to intensify investment in clean energy, as the burning of fossil fuels still predominates in their energy matrix.

ABSTRACT. The transition to biobased materials from reliance on fossil resources is pivotal in establishing a sustainable biobased economy. However, sustainability assessment methods and tools face limitations in adequately evaluating biobased products due to their renewable nature and complex value chains. This study uses a scoping review to explore the potential of Industry 4.0 (I4.0) technologies to address these limitations in the sustainability assessment of biobased product manufacturing. Additionally, published articles on Industry 5.0 (I5.0) technologies were re-viewed to highlight the integration of human creativity with intelligent technologies as well as opportunities for tailoring sustainability assessment methods and tools to address the unique challenges of biobased product manufacturing. By harnessing the capabilities of I4.0, real-time data exchange and predictive modeling can be used to create and assess the sustainability profile of biobased products. Establishing transparent, decentralized systems powered by Internet of Things (IoT) technologies will be crucial to enhancing sustainability assessment. Robust frameworks can be developed leveraging cognitive computing capabilities and collaborative approaches of I5.0, rendering sustainability assessment methods and tools more adaptive, accurate, reliable, and holistic in supporting the sustainable growth of the biobased industry, ensuring its economic competitiveness against alternative fossil-based product industries.