Industrial Engineering and Operations Management

This study reports the eco-efficiency quantification of urban public transportation in Brazilian states. The modal focused on is urban bus. A composite indicator was specified to meet the premises of Sustainable Consumption and Production (SC&P) in the urban planning domain. This paper can be considered applied research that considers the Benefit of the doubt (BoD) approach in Data Envelopment Analysis (DEA) models. DEA is non-parametric a technique recommended by the Organization for Economic Co-operation and Development (OECD) for performance assessments using composite indicators (CI). The scope of this research encompasses 26 capitals of Brazilian states and the capital of the Federal District. This paper also reports the framework of Key Performance Indicators (KPIs) reflecting urban transportation system in Brazil. The results using data of 2019 showed that only the Brazilian Federal District was considered eco-efficient whilst more economically developed and populous states showed undesirable levels of CO2 emissions (e.g. Sao Paulo and Rio de Janeiro).

To provide a safe and fair-value health service that ensures quality, hospitals must provide efficient processes, trained and committed personnel, appropriate technology and a strategic platform which integrates these aspects effectively. At present, a broad set of tools and methodologies are available, associated to the reconfiguration of processes for enhancing efficiency and enabling excellence and sustainability. Of these, the most noteworthy are Lean and Six-Sigma methodologies. A literature review was performed covering the implementation of these methodologies in health services over the last 5 years. The aim was to determine the current approach in this sector and propose guidelines aligned with the future challenges and the needs of healthcare managers. The influence of team management strategies in the final project outcomes has also been addressed representing a novelty.

With global development, companies need to seek opportunities for improvement and act accordingly to ensure the optimization of industrial production processes. Companies also try to ensure the quality of the final product, reducing process costs to increase the company’s sustainability. Many industries are going through restructuring processes and new challenges are emerging.The present investigation respects to the operations of an industrial unit, which produces rubberized cork agglomerates. Cork industry invests in search for new products, increasing its competitiveness in the market, expanding its range of products and promoting a better flow of this limited natural resource. This work identifies the causes behind some problems in terms of the final product quality, allowing the creation of corrective actions and opportunities for improvement in terms of product quality, working conditions and product line optimization. It is known that linear programming can make a strong contribution to the optimization of processes within companies. This work presents the application of linear programming as a tool through which it was possible to optimize the discharge process of chemicals from the production line, reorganizing the locker arrangement, and obtaining a reduction in the time spent in production operations of certain articles.

In Brazil, the accelerated growth of solar photovoltaic energy through Distributed Generation (DG) conducts National Electric Energy Agency (ANEEL) to revise the Electric Energy Compensation System to balance the electricity grid costs DG participants and non-participants. For this, ANEEL proposed five Alternatives with the entry of grid electricity use costs (TUSD and TE) for DG participants. This article aims to analyze TUSD’s Alternatives’ impact on the economic viability of PV systems in Brazil in three scenarios: residential, commercial, and industrial. We verify that the new legislation significantly impacts the residential investor. From Alternative 0 to Alternative 5, the Payback Time can take approximately three times in years for the residential class, from 4.22 to 13.07 years. TUSD Alternatives’ impact for the commercial and industrial class occurs with less intensity since the proportion that TUSD rates have compared to the level of electricity consumption is modest.

Energy management is a concerning subject due to the rising energy prices and environmental concerns. In this scenario, the maturity model measures an organization’s competences with benchmarking actions and performance improvement. Energy Management Maturity Models (EMMM) are already discussed in the literature. Still, few works focus on the classification of the existing models to identify gaps. This article aims to identify the main EMMM of the literature and propose a classification scheme aiming to identify gaps in the construction of future EMMM.To achieve the goal, a literature review used an adaptation of the ProKnow-C method and snowball. Eleven maturity models were analyzed, eight directly related to energy management and three not directly related to energy management but with contributions to the research. The classification focused on the description of the type of the model, model structure, number of maturity levels, methodology of analysis, reference to international standards, mode of assessment, results of the evaluation, and guide to improvement. Results showed that most of the models are descriptive, which means they analyzed the current situation. Still, with no guide for improvement, here is also a preference for utilizing five maturity levels. ISO 50001 appears as an international reference standard, which is explicable by the area of study, which is energy. As for the presentation of the results, graphics and visual tools are common to present the results. Since the classification of the maturity models has resulted from a literature review, this research is limited by the period, keywords, and area of study. For future works, it is suggested that the expansion of the research area to identify relationships between EMMM and other maturity models is also offered the construction of a new EMMM, which fulfills the gaps identified in this research.

Companies have realized that certain customers, despite consuming on a large scale, are not profitable and demand great service efforts. Adopting methods that make it possible to differentiate customers according to their share of the company’s results contributes to improving the management process. The present study aims to present the use of the Cost of Serving (CTS) methodology in the analysis of customer profitability and its use as a commercial strategy tool, demonstrating how we can apply it in a consumer portfolio of a Liquefied Petroleum Gas distributor (LPG) in the state of Rio de Janeiro. The work discusses how to calculate the CTS of each client, adopting concepts derived from the activity costing method and its impact on the contribution margin. Criteria for customer segmentation are discussed, and all these concepts support managerial decisions considering the market forces present in the sector it operates. The literature review supports the empirical study and is based on data obtained from the LPG company. The CTS analysis seeks to improve management efficiency, reduce costs and increase the company’s profitability. The conclusions offer an instrument for the management of the company’s portfolio and address logistical, commercial, financial and competitive aspects that affect the LPG distribution market.

This paper aims to offer a classification of frameworks, as well as a critical reading of gaps in Sustainable Supply Chain Management (SSCM) published from 2014 to 2022. SSCM leads companies to produce with minimum environmental impact. Besides, this production becomes economically feasible and socially responsible. Many authors present frameworks joining the SSCM subjects, following three dimensions of Triple Bottom Line (TBL). The research had three search phases in Scopus and Web of Science databases. After the definition of the research question and the strings, some filters were applied to select papers for the sample. An analysis of titles and abstracts reduces the number of papers. Within the papers, some gaps emerged. They are related to sectors, countries, the partners in a supply chain, information and communication technologies implementation, suppliers’ assessment, Circular Economy in SSCM, social subjects, and risk. The economic dimension appears in a higher number of articles than other dimensions. Most frameworks integrate the three dimensions of Triple Bottom Line (TBL). Most of the papers that join the TBL dimensions in their frameworks were published from 2018 to 2022, and less than 10% of selected articles presented new dimensions.

Today’s VUCA (Volatile, Uncertain, Complex and Ambiguous) world and the continuous search for new products and services requires companies to pursuing excellence and continuous improvement. This paper aims to describe a lean implementation project developed in a Portuguese cutlery SME. Action-research was adopted as research method. The production processes of the most representative products (spoons, forks and knives) were analyzed with adequate tools (e.g. VSM – value stream mapping – and indicators monitoring). The main problems identified were excess of inventory (of both finished products and WIP – work-in-process), and consequent long throughput times and low productivity. The root causes for those problems were investigated. Then, improvement proposals, involving the creation of a system to monitor and control production orders, the implementation of daily kaizen meetings, the redefinition of safety stocks and reorder point of finished products and the promotion of training and creation of work instructions regarding equipment setups, were developed and implemented to mitigate the identified problems. The implementations had quite positive impacts such as WIP reduction of 255 K€, 38.5% decrease in finished products stock, throughput time reduction from 17.3 to 4.6 days for spoons and forks, and from 12.4 to 6.8 days in the case of knives, and, finally, an overall productivity enhancement of 36.5%.

Digital transformation has allowed advances in several areas, as it promotes the adoption of sophisticated digital technologies aimed at improving processes and reducing costs. Digital transformation technologies have the potential to encourage improvements and innovations in all types of institutions, both public and private. Unlike the evolution of private organizations, the public sector lacks adaptation to these innovations. Thus, to reap the benefits of digital transformation, the public sector needs to be more agile and more flexible and adaptive. Therefore, this study aims to raise contributions from enabling technologies for digital transformation in the public sector. We carried out a systematic review of the literature using the ProKnow-C method. Through content analysis, the focus of this article, the technologies were pointed out where and how they are being used in this sector, in addition to some benefits and difficulties of implementation. The survey results from the 18 articles in the bibliographic portfolio indicated that Europe and Asia have the highest utilization rate, especially the United Kingdom. In addition, Blockchain is the technology with the most significant applications. On the other hand, the Internet of Things, widely used in the private sector, had few appearances. However, public agencies in several countries have improved using these technologies despite the nascent maturity.

This paper provides estimates of the impact of the COVID-19 outbreak on Brazilian Ethanol sales. To this end, weekly data on Ethanol sales volumes are analyzed through an ITS SARIMA model and a counterfactual analysis covering the 2019–2020. We find that the real effect of COVID-19 was a reduction above 77.97% in Brazil after the first COVID-19 death, in March 2020, and still a decrease of about 50.15% at the end of 2020. The empirical evidence confirms that the impact of the pandemic crisis, the counterfactual analysis allows estimating the real effect of COVID-19 is on average 3.76% greater than the observed against an index date reference. These results suggest that ethanol sales in Brazil were more affected than only when comparing previous results to the effects of the pandemic.

The Lean Six Sigma approach has been driving several organizations worldwide through quality and performance improvement. Its implementation process is complex and success depends, besides several factors, on the organization’s current maturity level. Lean Maturity Models (LMM) can reveal guidelines to conduct organizations during the implementation process, ensuring its continuous improvement. In this context, this paper presents a Scoping Review to synthesize which are the Maturity Models (MM) already developed and understand their contributions to the successful implementation of lean approaches. The results reveal a diversity of MM being developed to assist these implementations. Most of them are hybrid models and are highly generalizable. In general, these artifacts are used to assess and indicate to organizations their lean maturity level and the points to be improved. The synthesis of existing MM in the literature presented in this paper can be a starting point for creating or adapting new models. Moreover, organizational managers can verify the existence of any replicable model to assist them in lean maturity.

The health system is quite complex because its process includes medicines, supplies, but patients either. Information about them involves privacy, registration, storage and analysis. Besides, equipment, medicines and supplies need a storage organization because its absence leads to high costs and waste of resources. With the intention of reducing these problems, some information technologies (IT), such as Radio Frequency Identification (RFID), improve material traceability, information recording, and increased accuracy. Information technologies (IT) demand evaluation, considering that they can be underestimated in a company, due to their high costs. Some stakeholders just consider the IT cost before the decision of implementing them. Hence, the absence of a complete evaluation leads the company to implement these technologies without any analysis. However, this evaluation is not simple and fast because it requires a structure supported by some attributes. In this context, the article aims to evaluate RFID in two hospitals, focusing on major operations, by a set of attributes. For this purpose, there was a literature review with an initial survey of articles in the main database. Then, there was the literature review, the structuring of the questionnaire, semi-structured interviews with two case studies, ending with a counterpoint between theory and field findings. After the case studies, the authors noticed that RFID increased profitability and inventory accuracy. On the other hand, there are factors to improve in the studied sample, such as system complexity, lack of technology knowledge, interference, return on investment, deployment cost, and employee training.

This article aims to provide a qualitative-quantitative theoretical basis for the elaboration of an assertive Supply Chain Management (SCM) project that maximizes the performance of a Brazilian system operating Reverse Logistics that collects and separates waste from urban centers and processes it through recycling and/or thermochemical conversion. For this purpose, exploratory research of bibliographic content was conducted on data from the generation, destination, processing, and recovery of plastic Municipal Solid Waste (MSW), in addition to the physical characteristics of this type of waste, plant infrastructure, socioeconomic profile of the urban population and urban development policies that manage materials. The quali-quantitative information of consolidated literature helped in the preparation of the survey – to be answered by professionals of this market segment – whose answers will be complementary to the literature on the mathematical approach by Structural Equation Modelling using the Partial Least Square method in the design of high-precision actions for the Brazilian need for plastic waste management. All this in a business model that generates employment and income, and produces electricity, among other benefits, in the conception of sustainability promoted by the Circular Economy. To complement the methodology used in this work, successful cases in the recovery of materials in addition to future trends in the generation of plastic waste, are also presented as some of the research results.

Due to the constant need to establish methods in the supplier selection process to avoid delays, reworks, cost increases and stakeholder discontent in projects in the Oil and Gas (O&G) sector, it is necessary to evaluate multi-criteria models and tools to prioritize both the industry regulations and strict technical specifications. This prioritization provides robustness in the construction of procurement contracts as well as effectiveness in the analysis of the technical proposal by engineering. The present study presents the use of the QFD (Quality Function Development) model in an O&G project in Bahia to build a validation system for high pressure fluid tests, simulating the pre-salt layer. The study aimed to prioritize the requirements to be met by suppliers of Service, Material, Equipment and Instruments from the design phase to the construction and delivery of the project. As a main result, the use of the model provided the integration between different disciplines in the group decision-making process, quantification of the team’s judgments in numbers, understanding of the main risks between project requirements and establishing a technical proposal validation plan considering the requirements resulting priorities, enabling greater assertiveness for acquisitions and technical validations throughout the project life cycle.

The amount of waste related to the improper disposal of medicines by the end consumer increases propositionally to the pharmaceutical industry’s growth. Although many studies propose public policies that encourage the environmentally proper disposal of medicines, the domestic waste remains the main form of disposal. In this context, there are opportunities for research to understand consumer behavior and, based on this, generate insights to make the medicines chain more sustainable. This study aims to investigate medicine disposal behavior of consumers in Southern Brazil. A quantitative research was conducted through the application of a survey with a non-probabilistic sample of 204 consumers. Data were analyzed using descriptive statistics through frequency analysis. The results show that most consumers discard end-of-use medicines in the household waste, sink or toilet. Consumers say they have not received information or guidelines for environmentally proper disposal. Based on the findings, insights are provided to stimulate pro-environmental consumer behavior and to the design of circular business models in the pharmaceutical supply chain.

In the fourth industrial revolution, the digital network is the basis of smart manufacturing systems. In work environments 4.0, the operators’ role is drastically changed. There is increasing utilisation of innovative devices, and new technologies have changed work activities into more cognitive than physical tasks. According to scientific studies in the new industrial era, the operators’ skill to process the information related to a single task plays a crucial role in improving the manufacturing systems’ effectiveness. The methods available in scientific literature to assess the operator’s performance are mainly focused on the cognitive and physical efforts required by the task. In other words, they depend on tasks complexity and neglect human behaviour over time and the workers’ abilities. Therefore, an evaluation including the skills and properness of a specific operator to perform an assigned task needs more investigation. Consistent with this research gap, the paper aims to develop an information-based theoretical model allowing to estimate an operator’s performance index to accomplish an assembly job by evaluating both the tasks’ complexity and the operator’s skill.The model is applied to an automotive company to test and evaluate the potential applications of the methodology that go beyond the case study developed. The results proved the effectiveness of the model in estimating the operators’ performance, providing a job schedule based on task complexity and workers’ abilities.

The transportation sector is essential for today’s global economy, as it tan-gents a wide range of issues such as mobility, urban planning, and economic development. Understanding the performance of vehicles is fundamental for the Brazilian economy since millions of passengers are carried by public transport every day, and this sector represents a significant share of the national GDP. Although in literature, there is a range of suitable approaches for efficiency analysis, the fourth industrial revolution has leveraged the way of acquiring data (e.g., via digital technologies), bringing the need for more advanced data analytics models to explore and process the data beforehand, as well as dealing with uncertainty. In this sense, this paper aims to provide a novel approach to assessing the efficiency of public transport vehicles by combining fuzzy clustering and Data Envelopment Analysis models in a real case study with data from embedded sensors in buses in Rio de Janeiro. A more robust integrated approach for evaluating operational efficiency can assist decision-makers and consumers in better comprehending the relationship between energy (fuel) consumption and bus efficiencies. This could enable the authorities and public transport management departments to develop appropriate policies and strategies and to reconstruct certain features of the inefficient routes, thereby increasing the operational efficiency of land transportation, reducing mobility costs, and even decreasing the carbon footprint.

Today’s world is living in the age of digital transformation, the so-called Industry 4.0, in which technological advances have revolutionized the decision-making process in supply chain management. In this domain, inventory management can represent 50% of all organizational costs, and still a challenging task to keep the trade-off between maintaining inventory levels as low as possible, meeting clients’ demands, and maintaining satisfactory service levels. Forecasting the MRO inventory demand is even a more difficult task. To address this problem, machine learning (ML) applications, which deal well with nonlinear data, can predict irregular and intermittent demand with better accuracy than traditional approaches. This study employed the Support Vector Machine model to predict maintenance parts demand in a railroad logistic operator case study. This technique can deal with the nonlinear data encompassed by demand variations, avoid overfitting, and produce very accurate classifiers. Results indicated a considerable improvement in the demand forecast performance of the selected SKUs. This model can enhance the reliability of the purchasing and stock maintenance process and generate financial gains by reducing the need for large volumes of safety stock and greater assertiveness in meeting internal demands. It also contributes by showing a real case with an ML approach to predict inventory demands.

Assuming the great importance of the stock for organizations, we idealize a solution applicable on a large scale, making possible its implementation without the need to change the organizational structure or implement training with the staff. Based on the Problem Based Learning methodology, the present study proposed an alternative solution for a general logistics problem concerning inventory accounting. On a theoretical basis, a brief review of the literature relating to logistic management is explored, presenting the main points of inventory problems and technological models practiced. In this context, is proposed an alternative technology based on smart glasses devices, integrating hardware and software based on cloud technology in search of making feasible work as support in the management of stocks. The proposal results were positive, demonstrating the viability within the real world, not being limited only to the metaphysical. Therefore, society can benefit by optimizing its resources and avoiding unnecessary waste and expense.

The development of new products leads companies to consider not only aspects related to the time and cost of producing the product, but also to implement innovative and technological features. Therefore, achieving all of these goals requires an efficient and systematic way of managing the product design and development process. In this context, this work presents the implementation of some basic concepts for the design and development of technological products, specifically a case study of the design of a charpy-type impact testing machine, with electronic components to automate it. To this end, a mechatronic product development methodological approach was implemented. This approach proved to be flexible for applications in different types of products with technological components and to facilitate the monitoring of the development process of a product as well as the fulfillment of user demands. The results also evidenced the improvements when applying a mechatronic product orientation in the optimization of a traditionally mechanical product.

The regulation for reverse logistics of household electrical and electronic products and their components published in 2020 raised the research gap as the absence of a detailed characterization which demonstrates the progress of the planned phases and their goals. Therefore, this work aims to analyze the advances in the agenda of structuring and implementing the reverse logistics for electronic products in the collective model. In the end, it presents the structured knowledge of the evolution and potential of this model to comply with legal requirements. The analysis was based on secondary data collected on the institutional pages of the companies which manages the collective model, not limiting the period, with data triangulation in the social networks of communication with society to obtain data intertextuality. Despite the challenge of waste management in a continental country like Brazil, this research showed a significant advance in operationalizing the reverse logistics system for electronic products, with industrial sector of electronic equipment joining at collective model and investing in infrastructure for reverse logistics. The study suggests to the entrepreneurs a market to act both in support of the logistics business, meet legal requirements, and set up recycling companies in regions that are lagging behind. Suggestions to fully meet the demands are simplification and tax incentives for the recycle industry and establish a relationship between government spheres to reduce tax differences between regions and adapt the system’s operation to the reality of each location.

The increasing demand and the limitation of resources in health organizations raise the need for more efficient management. With demand higher than the capacity, patient queues increase, causing hospitals to need flexible management mechanisms to deal with the existing lists. In this context, adaptations of principles and methods of Industrial Engineering and Management, such as Lean Thinking and Heijunka, naturally arise. These adaptations contribute to the improvement of performance in health processes. This work aims to reduce the average resolution times of surgical cases by application of Heijunka to level the surgical production between different specialties of a large Brazilian hospital. With the application of Heijunka principles, the organization obtained an estimated mean reduction in the resolution time of elective surgeries from 17 to 10 months. In addition to the equity obtained between the queues of the specialties, reduction of costs with materials, consultations, and preoperative examinations, there are also estimated cost savings. In this context, Heijunka has advantages related to the leveling of surgical production, greater equity in resolution times, and serves as a tool for planning the distribution of operating rooms.

The new coronavirus pandemic has brought changes in the lives of the world population. The perception of the need for social isolation became a determining factor for creating new work models with known characteristics, such as the home office and mixed work (face-to-face and telework). There was a significant increase in illnesses caused by abrupt introduction of these models in the context to pandemic. There was a strong relationship of the working model with 4 variables: area of work (p = 0.000), work equipment used during quarantine (p = 0.000), illnesses found during the pandemic (p = 0.001), problems reported during the pandemic like sleep, stress, etc. (p = 0.010) and future expectations regarding the work performed and the pandemic (p = 0.022). There were also relationships between illnesses before the pandemic and those perceived reported during the pandemic period studied (p = 0.000) and the sleep profile before and during (p = 0.010). The study results added physical and psychological aspects, which have as premises the worker’s perception of the dynamics of companies, as well as their social structures and conditions of work execution. These events interfered with the quality of work of the interviewees and may lead to further studies in the clinical, ergometric or productivity areas. The results presented can help companies make more informed decisions when changing work processes and guide workers in labor operations.

This work aims to present the elements related to production and logistics that companies have used in the last four years and that can lead managers to a correct decision in times of uncertainty based on the literature review. During the pandemic caused by COVID-19, industrial companies in the automotive sector had to adapt to the sharp drop in demand, varying negatively by up to 40% from March to November 2020. As of December 2020, demand increased again, recovering the fall from the pandemic’s beginning. Thus, a supply chain did not have enough time to recover and meet that speed. Even with international air freight, there is still a halt in automaker’s production lines in the automotive sector. Illustrating that context, we studied a case in a company that operates in Brazil, which produces automotive components for a manufacturer of heavy cargo and passenger vehicles. The company was impacted by the COVID-19 pandemic due to its low level, heightened by its suppliers’ delays. The results indicated the need for the company to be prepared for the increase in costs and the search for strategies that minimize the negative impacts still caused by the pandemic context.

After grocery retail experienced a radical change with the emergence of the Internet and the use of dot-com in electronic commerce, this sector is once again in a gradual and accelerated process of adaptation and implementation of disruptive technologies. These technologies are the result of automation processes and connectivity implemented by Industry 4.0 tools and recently by the new regulations that brought the impact generated by the COVID-19 pandemic. This research paper uses a systematic literature review, this work was carried out from 50 scientific papers collected in the last 10 years (from 2012 to 2021) and that portray the planning of prototypes and implementation of disruptive technologies in grocery retail. The results gather information from technologies such as the Internet of Things, Artificial Intelligence, Machine Learning, Blockchain, Virtual Reality, Augmented Reality, use of Drones, Big Data, Robotics and Cloud Computing, and their use in the grocery retail industry in specific areas such as Logistics, Warehousing, Customer Service and Assistance, Freight Transport, Supply Chain and Supplier Management.

There are currently large amounts of public databases on COVID-19 patients. Among them the FAPESP COVID-19 Data Sharing/BR has gathered laboratory tests and hospitalization data from large health centers in the São Paulo metropolitan area. This paper uses part of such a repository to assemble a set of networks of positive COVID-19 patients according to the similarity of their age and laboratory tests results. Next, popular complex network metrics such as clustering coefficient and average path length are extracted from such networks and compared to the expected values observed for classical networks. Similarities of the clustering coefficient values with those of Watts-Strogatz networks were observed, although there are no sustainable characteristics of Small World networks. There are also similarities to scale-free networks, such as high-degree variance and the presence of hubs of nodes. In addition, a partition of the networks based on the modularity measure using the Fast Greedy algorithm is obtained and analyzed. An structure with four clusters and modularity values greater than zero was found, indicating that the network has some community structure.

This study aims to describe the perceptions and perspectives regarding the University-Industry collaboration of a spin-off developed by the technology park of a Federal University in Brazil. Initially, a literature review was conducted to identify facilitating aspects and barriers in UIC, and then a case study was conducted. This way, by means of an interview supported by a semistructured questionnaire, the facilitating aspects and barriers that they observe and experience during this process of development, collaboration, and technology transfer were addressed, as well as the positive and negative points of this process. In addition, the spin-offs perspectives related to the future of the contributions between universities and firms were analyzed. As a result of the case study, the need to further study and deepen the perspective of a new developing company, with the purpose of creating ways to eliminate obstacles so that the collaboration process occurs in a more fluid manner, thus providing greater technological development between the parties.

Business environments are becoming increasingly complex and uncertain, and this requires companies to formulate strategies to adapt to changes. In this sense, it is necessary for organizations to use tools that allow them to develop and monitor the strategy, ensuring that the organization’s goal is achieved. Considering this context, this article aims to present a framework to integrate throughput accounting and the focusing process of the Theory of Constraints in the planning and monitoring of an organization’s Objectives and Key Results. To develop this research, an adaptation of Design Science Research was used since the research method is oriented towards the development of artifacts. As a result, the GOAL-OKR is presented, and each step of the framework is detailed. In addition, the GOAL-OKR is evaluated in a business environment through application in a technology company and its results are presented. Finally, an overview of the framework is explained, demonstrating future opportunities and limitations of this work.

The covid-19 pandemic accelerated many digital transformations in Brazil and the world, and one of them was online shopping, which already had an upward trend in revenue and grew even more from 2020 onwards due to the social isolation actions as a way as how to contain the spread of coronavirus. This above-expected increase directly impacted the value and time delivery of online purchases. It is relevant, in this moment of advanced vaccination and return of presidential economic activities, to study whether the dynamics of online shopping tend to remain constant. To characterize the online purchase behavior of Brazilian consumers throughout the pandemic and their intentions of online shopping in the post-pandemic future, as well as their perceptions about the shipping prices and delivery time, questionnaires, were applied to e-shoppers from Brazil. Information was collected regarding the profile of the sample, the isolation situation in the face of the pandemic, the online purchase behavior during the period, and their perceptions about the freight changes. The results indicate that part of the changes in online shopping that occurred during the period of the covid-19 pandemic should remain in the post-pandemic scenario. They also indicate that there is a perception that the shipping prices has increased and that the delivery time has decreased during this period.

The COVID-19 pandemic has affected virtually every human activity over the past 2 years. This paper examines how the COVID-19 pandemic interfered with the business processes in Brazil’s public vocational and higher education institution. Throughout the pandemic, the Organization forced the enactment of the paper-recorded processes in a virtual implementation. To unveil how the referred paper-recorded processes subset got executed during the pandemic, we conduct a process mining on the company’s information system. The process mining data shows various indications of task merging, precluding, and duration modifications. The analysis of 4231 instances of administrative processes between 2019 and 2021 showed a reduction in duration times and the number of tasks.

Research on Circular Business Models (CBM) supports companies in changing the business paradigm from linear to circular. The field of research on business models for CE is in a phase of maturation and consolidation, as several publications on the CBM topic have grown exponentially in the databases. However, little research has been carried out in Brazil to investigate how companies are applying EC principles in their business practices, what business models are adopted, and the value achieved. Thus, this study aims to contribute to knowledge about the adoption of CBM in Brazil, identifying the models adopted and which enabling elements are inducing and facilitating the transition. This research is exploratory, based on qualitative data, having as a research method the study of multiple cases carried out in five large manufacturing companies installed in an industrial park in northern Brazil, which are introducing the principles of CE in the form of doing business and achieving economic and environmental results. The survey results show that the companies surveyed are diversifying their business model towards models that slow down or close the production loop in a hybrid way (linear and circular), revealing that the creation of ecological value has been greater than the economic value. The role of several enabling elements was observed, but innovation is one of the main enablers for circularity and the creation of more sustainable organizations.

Among the problems caused by the pandemic of the new coronavirus (SARS-COV-2), besides the irreparable loss of loved ones and the damage to global health caused by the disease, the restrictions imposed and the economic losses incurred by them stood out. Given the sudden changes imposed on the routine of society and companies, many businesses went bankrupt, while the other survivors needed to adapt quickly, resulting in a routine based on home office, e-commerce and distance learning. The educational sector was strongly affected by these restrictions, as well as the assets linked to it, as highlighted by the cumulative annual drop of 22% of the IFIX (index of Real Estate Investment Funds), witnessed by investors during the arrival and spread of the pandemic in Brazilian territory. Thus, the work was based on the prospective analysis of scenarios through the Momentum method, producing three possible future scenarios for the recovery of the educational REITs (Pessimistic, Optimistic, and Trend), with the help of three financial planning specialists. At the end of this study, it was possible to configure the scenarios: “The recovery of education REITs” as optimistic scenario, “Challenges of education REITs” as trend scenario, and “The crisis of education REITs” as Pessimistic scenario.

Variable selection has become important in some research areas such as data science and analytics, in which datasets with hundreds or even thousands of variables are what is available for model development. Among the objectives of variable selection, also called feature selection, is to improve the predictive capacity of input variables, as well as to provide faster and cost-effective predictors. A greater number of variables theoretically allow more information to be stored, but in practice this rarely happens due to the greater possibility of noise and redundancy in real-world data. Similarly, working with large data sets implies greater computational capacity and the principle of parsimony is no longer fulfilled. In this article, we use different methods for selecting variables, such as Gini, R2, LASSO, and LARS to select variables in the context of predicting the probability of default of a mortgage loan database. A systematic way for variable selection is used taking advantage of the characteristics of the different selection methods.