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Football is significant in the global economic context. However, the same significance is not identified in the value added to the chain, as the elements that make it up act in a disintegrated and independent manner with undesired effects. Consequently, it is necessary to structure the undesired effects to seek the basic causes that sustain this problematic situation. Thus, this paper aims to use a CRT to structure the undesired effects and basic causes that limit the positive economic impacts of the south Brazilian football value chain.

A semi-structured questionnaire was applied with questions about the existing constraints. Based on the answers in the interviews, the current reality tree was elaborated, premised on identifying the basic causes that sustain the undesirable effects detected.

Among the main results, it was identified that management problems of the traditional leaders of football clubs and federations are considered the main reason why south of Brazil football cannot fully exploit its economic potential.

It is evidenced that the paper shows the undesired effect that has the most impact on the development of this chain, and it is important to propose improvements to its root causes, aiming at greater efficacy of the resulting actions.

The purpose of this paper is to investigate the relations in the Marketing/Operations interface through the analysis of data of the operational reality of a Brazilian company with a low technological intensity. The study aims to quantify and determine the impacts of marketing decisions on delivery performance and on flexibility of the operations area.

A longitudinal case study was conducted and the variables used in the model were derived from established theories and were evaluated with artificial neural networks. The case of a food manufacturing company was selected to reflect the relations in the marketing/operations interface of a low technological intensity enterprise.

The results show that the decisions on Place/Channel, Price and Product dimensions of marketing exert a significant impact on flexibility and delivery performance of the operation area.

The findings of the case study cannot be generalised and the outcomes are specific to just one firm. However, the approach lends itself to replication, particularly within low technological intensity companies.

Prior studies have focussed on coordination among functional areas as marketing and operations at higher levels of abstraction. The study contemplate empirical propositions through the data analysis of a company with a low technological intensity that can be used to improve managers' decisions and alignment in the Marketing/Operation Interface.

The data monetization market is valued at $1.5 billion, with an expected annual growth rate of 25%. This growth presents significant opportunities for companies to expand their revenue streams. However, many companies struggle to extract value from their data due to existing challenges and need for more knowledge. While existing studies describe and classify dimensions of the phenomenon, there is a need to explore causality relations that can help the structuring of data monetization processes. This study aimed to support the structuring of the data monetization process.

Proposing causality relations is important to explore the data monetization phenomenon. Therefore, empirical knowledge about data monetization was organized into design patterns using the context-intervention-mechanism-outcome (CIMO) logic. The effectiveness of these patterns was then assessed through an exploratory case study conducted at a leading Brazilian academic institution where data monetization is central to its business model.

The study yields six design patterns that address various aspects such as data pricing, data-driven business models and best practices for data monetization. Additionally, it presents a comprehensive understanding of the data monetization process through a value-added chain framework.

The findings contribute to the advancement of knowledge in the field, the proposition of causality, and offer valuable insights into organizations that wish to structure their resources and capabilities and leverage data.

The purpose of the present study is to identify and measure economically the losses related to inventory management in an oil refinery.

An exploratory case study was conducted to search for a better understanding of a phenomenon and its implications.

The results obtained based on the case study suggest the need to observe this phenomenon in other contexts and take managerial actions that will eliminate waste as one of the forms of generating value for the company. The results were related to each other and to the traditional metrics of inventory management and can be used as a base to improve mathematical and computational models for production planning and scheduling and also performance indicators.

No studies have been found that attempt to measure the economic losses derived from inventory management and their capacity in this environment.

The fashion sector is complex. It involves multiple actors with distinct and potentially conflicting interests, forming a value ecosystem. Thus, knowing the interested parties and belonging to the fashion sector may be a means to promote technological innovation, such as products with wearables. The purpose of this paper to identify the participants of the fashion ecosystem from the perspective of wearable technologies and develop a conceptual model.

The present work aims to identify the participants (actors) and develop a conceptual model of the fashion ecosystem from the perspective of wearable technologies. The systematic literature review is the recommended method to qualitatively analyze documents and identify the interested parties (actors) in the fashion sector in order to design the proposed conceptual model.

From the studies, the conceptual model of the fashion value ecosystem was designed, and the wearable product was considered its core business. The studies identified addressed ecosystems of fashion value in general but not specific to wearable products and their relations with other complementary industries.

The model was designed using secondary data only. Its validation is relevant through interviews with experts.

In terms of relevance, when conducting a systematic literature review, there were no studies that included wearable technologies in the fashion ecosystems discussed and their relations with other industries. The topic of wearables is an emerging subject that needs further research aiming to insert this technology in productive sectors.

The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.

Drum-Buffer-Rope (DBR) is a method to adjust production flows, synchronize the release of materials and enable a process of focused improvement in production systems. Literature on DBR applications in engineer-to-order (ETO) production systems, where customers participate in product design decisions and, consequently, in the way production is planned and executed, is rare. However, the interest in improving production management in ETO systems has received attention from the scientific and business communities. The goal of this research was to evaluate the implementation of DBR in an ETO productive system, critically analyzing the necessary adaptations for its use.

This research was conducted through a case study in a company that manufactures electronic equipment, known as avionics, in the aerospace sector.

In this context, the contribution of this study consists of evaluation of the implementation of DBR in an ETO productive system, describing the implementation and the necessary adaptations of the DBR to the ETO productive system explored, comparing it with the DBR theoretical proposals and Simplified Drum-Buffer-Rope (S-DBR) methods.

The study contributes to knowledge by expanding the field of the DBR application to make it more precise, and by applying the theory of constraints, in a general manner, to this type of productive environment.

The purpose of this paper is to longitudinally assess the technical efficiency and productivity, considering investment projects and technological change, in a second-generation petrochemical company.

The study uses data envelopment analysis (DEA) together with the Malmquist index to measure efficiency during the analysis periods. The working method consists of four main phases, namely development of the conceptual model, construction of the mathematical model, application of model to the case, and analysis of the results. The study utilizes a quantitative approach with descriptive goals seeking to evaluate the impacts of technical changes on the operational efficiency and productivity of the production process.

The use of DEA associated with the Malmquist index proved to be viable for analyzing a single company and identifying efficiency improvements, as well as the impacts of the learning process and the implementation of improvement projects. However, the results of the improvement projects and learning process were not representative and had no statistical significance on the actual change in efficiency of the company during the periods analyzed. For the case in question, the learning process and continuous improvement were not observed during all study periods.

The proposition that the improvement projects and investments implemented increased the efficiency of the company was rejected. Hence, with this work, it was possible to determine that the company unnecessarily invested resources in projects to increase efficiency. Furthermore, the company could have explored more internal resources before making significant investments in increased efficiency.

As for the value of this research in the theoretical and academic scope, this paper advances knowledge on the application of DEA because it proposes to establish an internal reference benchmarking for comparison. The literature contains few studies that analyze organizations using continuous processes, such as petrochemical processes, in longitudinal studies as a function of time, especially with the use of DEA.

The purpose of this paper is to present a method for assisting micro and small companies of the industrial sector with the adoption of Lean practices.

The paper outlines the method construction steps, which used a design science research approach.

This research led to the structuring of a method for implementing Lean Manufacturing tools in micro and small companies of the industrial sector. The developed method contributed to the knowledge in Lean Manufacturing by systematizing its tools in a heuristic approach that can be applied to an operation using overall equipment effectiveness (OEE) as a guiding indicator.

This method can be used to guide the implementation of Lean tools in SMEs industries.

The originality of this paper lies in the adoption of an operation-focused approach only (rather than an approach that begins with the mapping of an entire process) and the use of OEE as the basis for prioritization of improvements to be performed and operational control.

The purpose of this paper is to analyze the productive efficiency and the best operational practices in an armaments manufacturer.

A longitudinal case study is performed using data envelopment analysis (DEA). Using DEA, an assessment of six years in the company manufacturing process is conducted. The research aims at developing an internal benchmark on three production lines of the company.

The results show that only one of the three analyzed production lines increased efficiency over time. With this result, the most efficient production line may be used as a reference in relation to the best operational practices of the company. Moreover, it was found that the current indicators to evaluate efficiency are insufficient and may lead to wrong management decisions.

This research could allow a larger understanding of the factors that really contribute to increased operational efficiency. This is due to internal benchmark assist in the identification of the best practices. The identification of best practices can contribute to enhance the efficiency of inefficient operations without the need for external comparisons.

DEA contributes due to its robustness, for the evaluation of productive efficiency. One of the contributions of this study is to identify opportunities for improvement in key components of the operation through targets, internal benchmarking and robust assessment of productive efficiency.