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The market is constantly changing, requiring adaptation by companies to remain competitive. Many companies have already tried to achieve this goal by using the Lean Philosophy, and now aim to adopt Industry 4.0 practices, such as digitalization. However, there are several studies that report difficulties in adopting these practices, requiring for their success a dedicated and well-defined strategy. The present project arises in this context and aims to propose mechanisms to initiate/accelerate the digital transformation of companies in Lean industrial environments. As results, besides the framework to support the journey in this domain - a roadmap based on Lean and BPM - a technological tool to help companies throughout the digital transformation was also developed and proposed. Thus, theoretically this study contributes to increase knowledge in an emerging area, while guiding business managers in adopting the new digital paradigm from a practical perspective.

The objective of this study was to apply simulation and genetic algorithms for the economic and environmental optimization of the reverse network (manufacturers, waste managers, and recyclers in Sao Paulo, Brazil) of waste from electrical and electronic equipment (WEEE) to promote the circular economy. For the economic evaluation, the reduction in fuel, drivers, insurance, depreciation, maintenance, and charges was considered. For the environmental evaluation, the impact of abiotic, biotic, water, land, air, and greenhouse gases was measured. It was concluded that the optimized structure of the WEEE reverse chains for Sao Paulo, Brazil provided a reduction in the number of collections, thus making the most of cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Therefore, the proposed approach is replicable in organizational practice, which is mainly required to meet the 2030 agenda of reducing the carbon footprint generated by transport in large cities. Thus, this study can guide companies in structuring the reverse WEEE chains in Sao Paulo, Brazil, and other states and countries for economic and environmental optimization, which is an aspect of great relevance considering the exponential generation of WEEE.

This study addressed the development of a pilot plant for pyrolysis of scrap tires to obtain carbon black and other byproducts. The work was motivated by the goal of contributing to the development and dissemination of knowledge about existing technologies that allow modern economies to transform waste into valuable products, by documenting and discussing an empirical application in Brazil. Thispaper describes the development of a market for steel scrap, pyrolytic oil and carbon black products obtained from a vacuum pyrolysis process. The research work was conducted in Brazil, and was guided by the twofold purpose of reducing the environmental impacts, while gaining economical sustainability. Modern economies increasingly need to devise strategies to address energy generation while preserving natural ecosystems. These strategies include leveraging the use of renewable energy sources. Acknowledging that scrap tires hold an enormous potential as a sustainable energy option, this study aimed to contribute to the development and maturity of eco-friendly processing approaches to realize its full potential. The work involved a preliminary phase concerned with the operation of vacuum pyrolysis of scrap tires at a laboratorial scale, followed by the design of the pilot plant that operated for 10 years, at the time of the study, with a 100 kg/h batch flow. Results show that the yield of the pyrolysis process was 41% pyrolytic oil, 38% carbon black, 12% gas, and 8.9% steel scrap, with a calorific value of 36 MJ/kg per tire. The carbon black was composed of 90% carbon, and the pyrolytic oil was composed of 66% gasoline and 33% other oils, which have higher quality and can be commercialized with a potential profit over 3 million dollars/year.

The scientific literature reveals that there is a gap oriented towards empirical study of the relationship between the Internet of Things (IoT) and sustainability in manufacturing industries. This paper aims to fill this gap by proposing a new conceptual model (CM) for evaluating the effectiveness of IoT technologies in relation to their orientation towards socio-environmental sustainability and the circular economy approach. The research methodology for developing the CM follows the PRISMA protocol, and the data are obtained from the Web of Science (WoS) and Elsevier Scopus databases, focusing on the relationship between IoT and sustainable manufacturing. The PRISMA methodology results in six articles whose statements contribute to the development of the CM. The statements are identified, categorized and organized from the selected articles and divided into dimensions, namely: IoT technology and environmental and social context. The CM incorporates these dimensions and their constructs and indicators to support the assessment of the effectiveness of IoT technologies in relation to socio-environmental sustainability and the circular economy approach. The result of this study is a CM whose objective is to guide organizations in the use of IoT technologies applied to the production and supply chain, in order to create advances in the field of sustainability and the circular economy. The CM will be validated and applied in a manufacturing industry in the next publication. The paper contributes to management practices as it explores the knowledge of performance measurement and evaluation in the context of IoT, sustainability and the circular economy approach.

This study proposes a field study, based on a literature review, about the applications and impacts of Industry 4.0 (I4.0) in the biopharmaceutical sector. The world is facing a new industrial revolution and the central idea is the integration between the virtual and the real world through elements that will allow for a greater degree of automation and digitization of processes. The production of medicines via biological processes is a booming domain in the pharmaceutical sector, that involves extraordinary technological challenges. The fieldwork, carried out between August 2019 and February 2020, involved semi-structured interviews with managers of pharmaceutical companies and specialists in the I4.0 theme. The interviews allowed for the identification of trends and key benefits and barriers for implementing I4.0 in the biopharmaceutical sector. While the perceptions were considerably diversified, benefits in productivity, competitiveness and quality ranked among the most scored items. The main barriers, highlighted by the interviewees, refer to the need to break organizational cultural standards, the regulatory requirements, the lack of organizational strategies for implementation, and the lack of qualified professionals. This work offers a contribution to the biopharmaceutical sector and reinforces the imminent need for companies to adapt to this new reality.

Artificial intelligence (AI) has been increasingly adopted in service production systems [...]

This study investigated the integration of computer-aided design (CAD) and additive manufacturing (AM) in prototype production, particularly in the automotive industry. It explores how these technologies redefine prototyping practices, with a focus on design flexibility, material efficiency, and production speed. Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, this study encompasses a systematic review of 28 scholarly articles. It undertakes a comprehensive analysis to identify key themes, trends, and gaps in the existing research on CAD and AM integration in automotive prototyping. This study revealed the significant advantages of CAD and AM in prototype manufacturing, including improved design capabilities, efficient material usage, and the creation of complex geometries. It also addresses ongoing challenges, such as technology integration costs, scalability, and sustainability. Furthermore, this study foresees future developments by focusing on enhancing CAD and AM technologies to meet evolving market demands and optimize performance. This study makes a unique contribution to the literature by providing a detailed overview of the integration of CAD and AM in the context of automotive prototyping. This study incorporates valuable insights into the current practices and challenges and future prospects, potentially leading to more advanced, sustainable, and customer-oriented prototyping methods in the automotive sector.

Purpose – This study aims to address the management of reverse flows in the context of service supply chains. The study builds on the characteristics of services production reported in literature to: identify diverse types of reverse flows in services supply chains, discuss key issues associated to the management of reverse service flows and suggest directions for research for developing the knowledge for management of reverse flows in service contexts. Design/methodology/approach – This study first provides an overview of the theoretical background which supports the identification and the characterization of the flows, and the reverse flows, involved in service production. A short summary of each paper accepted in this special issue is also provided to give readers an overview of the various issues around reverse exchanges in service supply chains that authors have attempted to address. Findings – In this study, the authors identify distinct types of reverse flows in services production building on the analysis of the characteristics of service production and delivery reported in the literature. Our discussion highlights the fact that service supply chains can be quite diverse in the type of exchanges of inputs and outputs that take place between customers and providers, showing that often there can be substantial flows of items to return. In particular, and differently from manufacturing contexts, the authors highlight that in service supply chains, providers might need to handle bi-directional reverse flows. Research limitations/implications – The lack of research on reverse service supply chains is, to a great extent, a consequence of dominant paradigms which often identify the absence of physical product flows as a key distinguishing feature of service supply chains, and therefore lead to the misbelief that in services there is nothing to return. This special issue therefore aims to clarify this misunderstanding through the limited selection of eight papers that address various issues around reverse exchanges in service supply chains. Originality/value – While theoretical and empirical research in supply chain is abundant, management of reverse exchanges in service supply chain is sparse. In this special issue we aim to provide the first contribution to understand how the characteristics of service production raise new issues for the management of reverse flows in service supply chains, and to foster the development of adequate management strategies.

Industry 4.0 technologies offer significant opportunities to enhance sustainable production and circular economy practices in the face of challenges arising from climate change. Considering the growing interest in this field, the literature review exposed that, particularly in the case of the Internet of Things (IoT), there is a need for empirical assessments of the impact of this technology on sustainability and circularity. This paper presents the validation process of an original assessment tool that evaluates IoT’s alignment with the socioenvironmental and circular context of manufacturing organizations and their supply chains. Emphasis is placed on the construct titled “IoT Technology Expectations”. After systematically conducting a literature review, this study employed the Delphi method in conjunction with statistical analyses to refine or formulate new indicators or statements based on expert consensus, validating the proposed assessment tool. The findings of this research contribute to management practices by providing an instrument to assess the current stance of top management and other key managers (production, project and supply chain) on IoT use in manufacturing operations or supply chains from a socioenvironmental and circular perspective. The instrument serves as a starting point for exploring IoT’s potential in circular economy practices. Academically, it provides a detailed explanation of the Delphi method and its application and outcomes.

In the context of Industry 4.0, managing large amounts of data is essential to ensure informed decision-making in intelligent production environments. It enables, for example, predictive maintenance, which is essential for anticipating and identifying causes of failures in machines and equipment, optimizing processes, and promoting proactive management of human, financial, and material resources. However, generating accurate information for decision-making requires adopting suitable data preprocessing and analysis techniques. This study explores the identification of machine failures based on synthetic industrial data. Initially, we applied the feature selection techniques Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (mRMR), Neighborhood Component Analysis (NCA), and Denoising Autoencoder (DAE) to the collected data and compared their results. In the sequence, a comparison among three widely known machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron neural network (MLP), was conducted, with and without considering feature selection. The results showed that PCA and RF were superior to the other techniques, allowing the classification of failures with rates of 0.98, 0.97, and 0.98 for the accuracy, precision, and recall metrics, respectively. Thus, this work contributes by solving an industrial problem and detailing techniques to identify the most relevant variables and machine learning algorithms for predicting machine failures that negatively impact production planning. The findings provided by this study can assist industries in giving preference to employing sensors and collecting data that can contribute more effectively to machine failure predictions.