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Abstract Paper aims This study analyzed the feasibility of the BiGRU-CNN artificial neural network as a forecasting tool for short-term electric load. This forecasting model can serve as a support tool related to decision-making by companies in the energy sector. Originality Despite a large amount of scientific research in this area, the literature still searches for more assertive forecasting models regarding short-term electric load. Thus, the BiGRU-CNN model, based on layers of BiGRU and CNN architecture networks was tested. This model was already proposed and used for other similar tasks, however, it has not been used on load forecasting. Research method The code was programmed in Python using the keras package. The forecasts of all networks were carried out 10 times until an acceptable statistical sample was reached so that future electric load values are as close as possible to reality. Main findings The best forecasting model was the proposed BiGRU-CNN network when compared to classical and some hybrid networks. Implications for theory and practice This methodology can be applied to short-term electric load forecasting problems. There is evidence that the combination of different layers of neural networks can provide more efficient forecasting results than classical networks with only one architecture.

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Abstract Paper aims The main objective of this article is to present and validate a method to integrate multiple management tools aiming for organizational excellence in medium and large enterprises. Originality There is a scarcity of methods to integrate management tools to support companies in their operations. This study aims to fill this gap, proposing a manner for companies to perform this integration. Research method The method was developed considering tools and concepts well-established in the literature, such as Lean Thinking, Six Sigma, Balanced ScoreCard, among other management tools. The mentioned method was validated through a survey with managers and directors who are experts in organizational strategies. They were carefully selected considering their professional background. Main findings The proposed method provides an alignment between strategy and execution, presenting a cyclical characteristic to be continuously reviewed method, considering market needs. The survey verified the adherence of the method and to conclude that it is a feasible alternative to reach organizational excellence. Although integration of management methods is fundamental for companies to reach organizational excellence status, this kind of guidelines is scarce in the literature. Implications for theory and practice This method can be used to increase companies’ performance and competitivity.

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Abstract Paper aims This paper studies a new Cross-Docking (CD) scheduling problem inspired by the trend of resource mutualization at CD operations where a company owning a CD platform can lease its docks to other companies. Originality This work pioneers the studies of the resource mutualization at CD operations solving at scheduling problem, where there is a set of requirements for leasing the loading and unloading docks for a period, and the company owning the docks can accept or decline each requirement. Considering that the availability of the docks is linked to scheduling the trucks at the company’s loading and unloading docks, the decision to accept the requirements must be made along with the decisions for scheduling the trucks. Research method To solve this problem, this work proposes a Mixed-Integer Linear Programming (MILP) formulation. Main findings Using a commercial solver, the MILP formulation can optimally solve 65/128 instances based on a real operation. For the remaining instances, the MILP formulation obtains an average optimized gap of 8.95%. Implications for theory and practice The MILP formulation obtains acceptable results. Moreover, we found that all the instances accepted the requirements showing that it is economically interesting to lease docks.

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Abstract Paper aims This paper studies the influence of process variation on deviation from nominal control chart performance and proposes some adjustments on the control limits to make it enable on small batches. Originality Specific methods were developed to monitor small batches, mainly due to unavailability of data for precise parameters estimation, like the deviation from nominal control charts. However, Montgomery (2014) highlights some essential aspects, such as the influence of process variation on its performance. Research method The method used was mathematical modeling and computer simulation. Main findings The results validated that there is a significant influence of the process variation on the control chart performance. It has been demonstrated that small adjustments on the control limits can make it enable on lean environments. Implications for theory and practice The main contribution is demonstrating the use of deviation from nominal control chart, through the valid control limits definition regardless of the samples size.

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Abstract Paper aims This study presents several business analytics tools that allow improving the tactical planning of the collection process for a Colombian solid-waste management company. Originality The extant literature of operations research/analytics applied to these systems focuses on facility location or vehicle routing. Tactical decisions are seldom studied in the operations research/analytics literature devoted to waste management systems. By contrast, the focus of this paper is on tactical decisions: fleet sizing, frequency assignment, route scheduling and internal resource allocation in a new waste transfer station. Research method We follow a multimethodology approach that uses mathematical programming, metaheuristics, and discrete event simulation. The models use historical information of the system, and the solution of a model are used as input data for the other models. Main findings Introducing a new waste transfer station allows an important reduction of the compactors fleet. However, to prevent a collapse in its internal operation an even operation is needed. This is achieved by rescheduling the routes to balance their arrival during the day. Additional benefits can be attained if some soft constraints are relaxed. Implications for theory and practice Practitioners looking for tactical planning tools on waste collection systems have here an example of their application and benefits. Improvements can be achieved by tactical planning without heavily disrupting decisions at the operational level.

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Abstract Paper aims Several Barriers impede the robust effectiveness of the supply-chain management implementation for the Malaysian automotive industry. The purpose of the present work is to identify these Barriers and prioritize them. Originality Originality of the present work lies in identifying the Barriers, putting them into categories and subsequently prioritize them by applying a scientific method such as Analytic Hierarchy Process (AHP).Research Research method This study has identified five main categories of Barriers through literature reviews. Fourteen practitioners who are involved with the automotive industry helped prioritise those Barriers by applying the AHP.Main Main findings Five main categories of Barriers are found to be technological, organisational, individual, strategic, and cultural barriers. Organisational barrier is the most critical barrier followed by technological and strategic barriers. Implications for theory and practice This research provides important feedback to the automotive company managers to take appropriate measures to minimise negative impacts of the barriers identified.

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Abstract Paper Aims This paper aims to develop an approach to support group decision making combining methods and tools to a holistic MCDA process. Originality Authors have been using Value-Focused Thinking (VFT) for structuring problems with different MCDA methods, but there is a lack of a process that defines a clear transition from VFT to those methods. Here we propose a process to fill this gap. Research method Rich Picture and VFT structure the problem and elicit objectives that become criteria within a decision hierarchy. Analytic Hierarchy Process (AHP) with ratings supports preference elicitation and sensitivity analysis in the judgment weights of decision-makers. Main findings VFT is effective for eliciting the decision structure to AHP; using weight distribution of stakeholders may affect the results, and the multimethodology approach developed here can deal with group decision making. Implications for theory and practice The approach developed is effective in complex environments (complex problems and multiple stakeholders) because it focuses on values and defines a process to bring those values into a multicriteria method. Furthermore, sensitivity analysis with the judgment weights of the different stakeholders may be useful in negotiation.

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Abstract Paper aims This study aims to analyze the relationship between the corporate social responsibility (CSR) strategy and competitiveness, considering the moderating effect of the governance mode on CSR actions. Originality This study sheds some light on a tendency towards the proactivity in terms of CSR of Brazilian Multinationals. They also present collaborative governance mode more frequently to conduct CSR actions. Research method In order to reach the research objective, a survey research was carried out in 144 Brazilian Multinationals and SmartPLS was used to conduct partial least square-structural equation modeling analysis. Main findings The results indicated that there is a positive relationship between CSR and competitiveness. Regarding the CSR governance mode, the adoption of different governance modes depending on the characteristics of the CSR action developed. Implications for theory and practice This paper contributes to the literature, since Latin America, especially Brazil, still lacks research that analyzes their CSR practices and establishes relationships with other organizational objectives, such as competitiveness.

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Abstract Paper aims This study aimed to analyze how the satisfaction level of public transport (PT) users is influenced by their socioeconomic characteristics. Originality The analysis of how socioeconomic variables influence the satisfaction level through the association between MANOVA and exploratory analysis is still not explored, especially in Brazil and in medium-sized cities, which represent the majority of Brazilian cities. Research method Data collection was carried out by questionnaire in a Brazilian city. The 330 users of PT evaluated their satisfaction level concerning 12 quality indicators. Statistical analyses were performed using Pearson's correlation and MANOVA. Main findings Most socioeconomic variables influenced user satisfaction in at least one indicator, gender and schooling being the most prominent. The results showed that women’s mean level of satisfaction is lower than men’s concerning indicators affected by gender. Fare was the indicator with the worst score, being influenced by users’ occupation. Implications for theory and practice The study presents practical and methodological contributions. The results provides technical and scientific subsidies for public policies and service improvement. It possible to improve each indicator according to the users’ socioeconomic characteristics, encouraging the use of PT and contributing to the urban environment sustainability.

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Abstract Paper aims Aiming to avoid an inefficient digital transformation, the present work proposes a framework that will provide companies with a strategy to implement technologies to legacy systems of maintenance. Originality Such a framework was produced through a series of strategic analyses using multicriteria decision-making (MCDM) methods. Research method These analyses are composed of three steps. First, reviewing the literature of industry 4.0 and interoperability, combining the RAMI4.0 architecture and Framework for Enterprise Interoperability (FEI). Second, by exploring technics of maturity assessments, addressing systems attributes and requirements. Third, reviewing the literature of Total Productive Maintenance (TPM) and recent maintenance technologies applications. Main findings The results confirm that such a framework can support the adequacy of legacy systems that are part of digital transformation projects. Implications for theory and practice To test the proposed framework, a multinational industrial entity belonging to the automotive sector was selected for a case study.

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Abstract Paper aims To define and evaluate the environmental performance of the implementation of reverse logistics (RL) for empty pesticide plastic containers (EPPC) in Brazil through the Campo Limpo System (CLS). Originality Discussion about the implementation of RL for the development of sustainable systems, through the identification of environmental impacts associated with RL of EPPC by means of Life Cycle Assessment (LCA). Research method CLS is described and used to quantify the potential environmental impacts of the RL chain of EPPC waste management by using the LCA methodology. The analysis comprehends the situations prior and post implementation of the CLS, it takes into account container manufacturing, transportation and end-of-life. Main findings Implementation of the CLS resulted in a reduction in nine assessed environmental impacts categories, ranging from a reduction of 79% to 26% in potential impacts. The stage leading to the largest contribution to environmental impacts is the manufacturing of EPPC. For end-of-life options, recycling proved to be the best option to lower environmental impacts. Implications for theory and practice Results show that the public policy was environmentally effective given that current activities in EPPC management led to lower environmental impacts enabling the development of a sustainable supply chain.

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Abstract Paper aims This research article addresses an innovative solution to face one of the main problems in the footwear industry, namely the high order non-fulfillment rate. Originality This research offers companies in the footwear industry the possibility of implementing Andon through a structured and planned process that allows expediting problem solving. The advantage of this system is the ability to signal the status of workstations through rapid visual identification. Moreover, in this way social distancing is also secured, in line with World Health Organization guidelines to prevent the spread of COVID-19. Research method The article presents four phases through which the case study was addressed. Planning (determination of objectives), Restructuring of the Operations Area, Improvement of the Production Line and Continuous Improvement. Main findings An Arena simulation system was used to establish a new scenario where the defective product indicator decreases by 3.13% and productivity improves by about 38%. In turn, the resulting increase in the number of orders enhances company sales and profits, as well as the ability to meet the customer demand in a timely manner. Implications for theory and practice The case study suggests that the main causes of this issue (high order non-fulfillment rate) are the increasing rate of defective goods, delays in the production process, and excessive time consumption in the movement of staff and materials. This research serves as the basis for future lines of research, as well as for other organizations with similar characteristics to implement the proposal.

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Abstract Paper aims the combination of the quality indices, a novel model called “Dynamic Growth Allocation Model (DGAM)”, Fuzzy Decision Making Theory (FDM), Analytical Hierarchy Process (AHP) and the Evolutive Particle Swarm Optimization (EPSO) is proposed. Originality the multi-objective optimization (with uncertainty) of the Argentine energy transition is not sufficiently studied. This combined methodology in this problem was not published and it had good, relatively easy and fast results. Research method the optimization indices (EROI, CO2, IC and RP), the methodology used (DGAM, FDM, AHP and EPSO) and its results are analyzed. Main findings (i) the nuclear energy allowed the renewable transition; (ii) the fossil dismantling and the investment in biomass and wind are needed; (iii) the EROI depends on the good load factor, useful life and performance. Implications for theory and practice It is sought a minimum Renewable Participation (RP) of 20% of Argentina with a sustainable energy matrix.

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Abstract Paper aims Presents a new group decision approach for circular business models (CBMs) prioritization based on circular economy (CE) principles and indicators. Originality It was not found in the literature quantitative approaches to indicate which CBMs can be prioritized towards CE implementation. Research method Fuzzy AHP is applied to calculate the weights of the CE principles. Fuzzy TOPSIS is used to rank the CBMs. A pilot application demonstrates the applicability of the proposed approach. Main findings To perform equally on all dimensions of the circularity indicators, recovery by-products have a greater impact on CE implementation. Focusing on economic indicators, Product as a Service and Recovery By-Products should receive priority. Focusing on social indicators, Product as a Service is the most recommended CBM. Implications for theory and practice It was identified the CBMs that are most suitable for performance improvement regarding CE implementation, according to the organization’s dominant CE principles.

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Abstract Paper aims Due to increasing energy prices, manufacturers have to pay more attention to the energy efficiency of their production processes. This paper aims to support manufacturers in increasing processes’ energy efficiency by using production data and applying machine learning approaches. Originality Systematic guidelines or standards for minimising the energy consumption of manufacturing processes through machine learning approaches are still lacking. This gap is addressed in this paper. Research method The paper follows a qualitative research method to understand the manufacturing processes and their challenges in improving energy efficiency. The raw data for a 5-step approach were collected in research projects with manufacturing SMEs, and information about the processes through interviews and workshops with them. Then, an analysis of currently available machine learning frameworks and their selection and implementation is conducted. Main findings The main result is a 5-step approach for increasing the energy efficiency of manufacturing processes through machine learning. Essential applications and technical challenges for data mapping, integrating, modelling, implementing, and deploying machine learning algorithms in manufacturing processes for increasing energy efficiency are presented. Implications for theory and practice The findings can guide manufacturers, researchers, and data scientists to use machine learning in practice when they intend to increase the energy efficiency of manufacturing processes.

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Abstract Paper aims This paper presents a manufacturing process model for assessing the effects on economic, social, and environmental targets, given variations on corporate strategies of production, innovation, marketing, and demand for final goods. Originality The model integrates economic, social, and environmental dimensions that are validated through three main scenarios: Business as Usual (no strategic application), Business as Investment (strategic application), and Business as Vision (changes in demand). Research method The model estimates the social, environmental, and economic performance through time based on the System Dynamics methodology. Main findings The results demonstrate the model's suitability as a decision-support tool for sustainability planning in a corporate environment. Implications for theory and practice The model facilitates the analysis of the effects of resource allocation on corporate strategy.

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Abstract Paper aims Investigate whether the results of time series models can be adjusted with the AHP method towards a more assertive forecast. Originality Considering demand forecasting as a complex decision-making situation, this research investigated the use of the AHP as a complement to traditional forecasting methods. Research method This applied research employed, as main procedures, literature review and mathematical modeling. Main findings Two models were proposed that presented satisfactory results: model I reduced the forecast error by 16% in January, 25% in February, 37% in March, 3% in April, and 7% in May; model II reduced it by 17% in January, 21% in February, 29% in March, 2% in April, and 5% in May. Implications for theory and practice We conclude that the AHP has the potential to correct the results of time series in the textile industry by allowing the incorporation of quantitative and qualitative variables.

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Abstract Paper Aims This work aims to identify the socio-environmental attributes related to the label ‘Brazilian Organic Product – BOP- perceived by consumers of organic foods. Originality This paper is pioneering in the understanding of the attributes of BOP label in Brazil by consumers. Also, is unique in exploring the dimensions “organic’, “environmental” and “social” based on Brazilian law regarding organic food and the Guidelines of the Certifiers of Organic Products in Brazil. Research method A survey was conducted with 106 consumers of organic foods and an exploratory factor analysis (EFA) was carried out. Main findings Results showed that eight dimensions of attributes (Labor Laws and Social Incentive; Social and Environmental Practices; Environmental Management; Social and Environmental Protection; Environmental Preservation; Natural Products; Animal and Social Welfare; Non-renewable resources) influence consumer’s perception about the BOP label. Implications for theory and practice For theory this study shows how consumers perceive socio environmental attributes through labels and how this perception can motivate the purchase. For practice, intended to provide insights for marketing managers to better improve information on the labels Also, provide information to public policy managers to improve policies that can reach out more consumers.

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Abstract Paper aims Analyze the relationship among interdepartmental integration (IDI), socialization, and product portfolio performance (PPP), mainly the effect of socialization’s mediation on the relationship between the other two constructs. Originality No study has related both IDI and socialization for improving PPP, with the aim of overcoming the information absence and flow in this portfolio management. This work offers an alternative when compared to past studies, which is the importance of socialization for product portfolio’s success. Research method Survey with 131 most innovative companies in Brazil. Results were provided by PLS-SEM. Main findings IDI has direct effect on socialization and no direct effect on PPP, besides the mediation of socialization is confirmed. Furthermore, IDI presents indirect effect on PPP. Implications for theory and practice Socialization’s mediation opens space for other theories that can be beneficial to the product portfolio. Furthermore, Managers can prioritize IDI’s actions to potentialize PPP through socialization.

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Abstract Paper aims This paper analyzes Command and Control (C2) Systems and identifies the level of knowledge on the subject by actors involved in disaster response operations in Brazil, besides assessing the evolution in this area since the disaster of the Serrana Region of Rio de Janeiro in 2011. Originality The concept of C2 is still little explored academically, especially in disaster management systems. This study contributes to the understanding of the dynamics of different C2 Systems operated in Brazil, as well as identifying the level of knowledge on the subject and its evolution since 2011. Research method This is an exploratory survey in which data was analyzed using descriptive statistics techniques. Main findings We conclude that the actors involved in real disaster situations have little formal training in C2. The findings indicate that the performance of a simulated exercise helps in disaster preparedness, but the participants observed deficiencies concerning C2 training besides the lack of local community involvement in the simulated exercise. Implications for theory and practice This study contributes to the disaster management literature, showing empirical evidence that the results obtained in a simulated exercise can boost the specialization of professionals in C2 systems in the area of disasters.

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Abstract Paper aims The aim was to adapt and apply a SERVQUAL scale to measure the perceived service quality of an e-learning platform provided by the United Nations for Environment Programme (UNEP) focused on sustainability issues. Originality There is a scarce number of publications using the SERVQUAL for digital services evaluation. This study contributes to filling this gap, generating new insights for the service quality literature by proposing an adaptation of the SERVQUAL scale for online sustainability courses. Research method The SERVQUAL was carried out with undergraduate and post-graduate students enrolled at a Brazilian higher education institution. A set of 21 questions was set up to evaluate the perceived service quality of the students before/after finishing the online course “Introduction to Life Cycle Thinking (LCT)” provided by UNEP. Main findings The results showed that the most important features were related to the empathy, assurance and tangibility quality dimensions with emphasis on the high quality of addressing the user doubts and the audio and video resources that helped students in the learning process. In conclusion, the perceived quality was higher than the expectations for many of the SERVQUAL dimensions. Implications for theory and practice Finally, the step-by-step methodological approach used by this paper should be adopted by other e-learning platforms to investigate quality and service management aspects. Empathy, assurance and tangibility may be seen as key components in the service quality management of e-learning platforms.

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Abstract Paper aims The aim of this study was to propose a hybrid approach to develop and prioritize the indicators which should be used to monitor the development of plastering supply chains while using lean manufacturing practices. Originality This approach proposed the integration of a Balanced Scorecard (BSC) - a multi-perspective tool for performance evaluation and strategic planning, and FITradeoff for the ranking problematic – a multicriteria method that features a robust axiomatic structure and uses partial information. Research method The results showed that the approach provided a set of indicators to assist the company’s manager to monitor and improve the company's competitiveness and sustainability. Main findings The results showed that the approach was able to provide a set of KPIs to assist the company’s manager to monitor and improve the plastering company management in terms of competitiveness and sustainability. Implications for theory and practice The results allowed evaluation of important issues for the strategic, economic, and environmental stability in complex business environments.

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Abstract Paper aims This work aims to develop a systematized approach for the reduction of medical appointment waiting lists, proposing an optimization decision-making model followed by continuous people engagement towards a systematic approach for waiting list problem-solving. Originality There are several studies related to waiting lists in healthcare contexts, however, the present study presents an innovative approach for waiting list problem-solving by proposing prescriptive decision-making models followed by continuous improvement and people engagement. Research method A research approach with the following phases was developed: system analysis, problem quantification, and development of an optimization model. After these phases, the model was applied, and the results were analysed, as contributions to a systematized model. Main findings The model was applied to the screening waiting list for orthopaedics appointments followed by the fundamental involvement of medical doctors, which made it possible to implement the optimal solution generated by the model, resulting in a reduction of 90% by 56 days in waiting time for the screening process. Implications for theory and practice This model contributes for theory and for practice as a way to deal with different scenarios for waiting list reduction in the upcoming days during and after the pandemic.

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Abstract Paper aims This study analyzes the comprehension of production engineering students about the influence of some key variables on the process performance measures in a service process. Originality This paper points out the need for educators to re-evaluate their approaches to teaching the Operations Management (OM) principles related to process flow measures. Research method This study used scenario-based role-playing experiments with 2×2×2 between-subject factorial design with three independent variables (variability of activities, capacity utilization, and resource pooling) and four dependent variables related to key internal process performance measures (Flow Time, Overall Quality of service, Quality of service employees, and Queue Size). The sample was composed of 178 undergraduate production engineering students from a large university in Brazil from various institution units. Main findings These results show that students perceived the use of resource pooling as an impactful practice. However, the students did not correctly identify the effects of increasing resource utilization and the variability on flow time and queue size when activities are pooled. Implications for theory and practice The teaching of basic concepts of OM requires the support of computational tools. Undergraduate courses that contemplate subjects in the field of OM should work more intensely on simulation-based learning.

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Abstract Paper aims Frequently, researchers try to find a better way to allocate assets in order to have maximum return and low variability in a portfolio as diverse as possible. This paper aims to apply D-Optimal Design in the context of Mixture Design and portfolio optimization to efficiently select the runs of the proposed experimental design. Originality A new approach to find the optimal weights that maximize the returns and minimize the risk using D-Optimal Design was used. A multi-response optimization problem considering returns, variability and entropy as functions of the weights was proposed. However, as there is a significant correlation between the objective functions, a Factor Analysis combined with FMSE to dimensionality reduction was used. Research method All the steps for both stages of the methodology applied in this paper are presented below: select real time series; predict one step ahead; generate a D-Optimal mixture design; apply weights and generate mathematical models; solve the optimization problem. Main findings Using the desirability method, the optimal values were determined, obtaining approximately 79% for the compound desirability function. The proposed method presented a 16.80% higher return with a 4.98% higher risk exposure if compared against Naïve method. Implications for theory and practice The proposed methodology can be applied to any portfolio optimization study. Mixture Design studies have already been proposed for modeling portfolio optimization problems. However, the D-Optimal Design proved to be adequate, which represents less computational effort.

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Abstract Paper aims This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series. Originality This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient. Research method Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.

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Abstract Paper aims This paper aims to create propositions that would be a guide the future construction of an industrial policy capable of rebuilding the Brazilian automotive industry (IAB) through the creation of demand by scrappage policy. Originality This paper is based on more than 470 IAB representatives’ answers, C-Level/Senior Executives, converting experience and empiric perspectives in scientific knowledge. Research method Hypothetical-Deductive research were conducted applying survey as procedure and Delphi Method as technique, using electronic tools to collect data, combining quantitative and qualitative approaches in statistical data analysis. Main findings Were identified a consensus among IAB representatives and literature evidences about scrappage policy as an engine regarding sustenance of automotive industry and the need for rules that guide national competitiveness key factors. Implications for theory and practice The paper theoretical and practice implications are provide subsidies to future sectorial policies based on empirical experience of more than 470 IAB representatives.

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Abstract Paper aims This research aims to analyze the primary studies published in recent years focusing on defect detection or classification in manufacturing and extract information about frequently used data mining (DM) methods, their accuracy, strengths, and limitations. Originality Industrial production is now undergoing a dynamic transformation in the context of Industry 4.0, where implementation of data mining is a frequently discussed topic, and such an overall summary is missing. Research method In this study, the PRISMA-driven systematic literature review is combined with the approach defined by Kitchenham (2004). Main findings The most frequently used data mining methods for defect detection are Bayesian network (BN) and Support vector machine (SVM). Besides previously mentioned methods, the Decision trees (DT) and Clustering are often used for defect classification. Neural Networks (NN) use is common for both defect detection and classification. DT, together with the Genetic algorithm (GA) and SVM, achieved the highest average accuracy. Recently, authors often combine different DM methods, and also methods for data dimensionality reduction are often used. Implications for theory and practice This study contributes to the quality management literature by extending a summary of recently used DM methods for defect detection and classification. This summary can help researchers choose a suitable method and build models for achieving its research purpose.

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Abstract Paper aims Propose digital skills that industrial engineers must possess to face the industry 4.0 challenges, through the integration of technological strategies and people in complex processes that transform factories, cities, and organizations into smart and flexible ones. Originality Due to the lack of clarity in the existing literature, this research aims to reframe the role of industrial engineers to face the industry evolution. Research method A systematic review of the publications made between 2016 and 2021 in indexed databases was carried out. After filtering, the selected articles were analyzed and compared to answer the aim of the work. Main findings There is no consensus on the digital skills required for industrial engineers to face the industry 4.0 challenges. Implications for theory and practice The technological strategies, pillars of industry 4.0, are in constant change, forcing us to rethink future research on the digital skills demanded by industrial engineering professionals.

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Abstract Paper aims The present study aims to identify the limitations of the artifacts used in the decision-making process in the adoption of energy efficiency measures in productive systems, using non-intensive energy companies as a delimitation. Originality Identifies authors and connections, the relationships between them and how these interactions contribute to the advancement of knowledge on the subject. Regarding energy efficiency, studies show that the real investment in initiatives in the industrial sector is below the full potential and that the artifacts used in the decision-making process have severe limitations when used in a complex and dynamic context. Research method In this paper a systematic literature review was conducted from the Literature-Grounded Theory. Additionally, social network analysis was used. Main findings It concludes that the approaches are limited to technical and financial factors and does not consider a systemic and dynamic understanding of different internal and external variables to the organization. Implications for theory and practice The contribution of this study is that it identifies the initiatives that help in the process of decision-making for the adoption of energy efficiency measures in productive systems. Specifically, the focus of this study is on non-intensive energy companies. Scientific articles published in the main databases of management were selected.

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Abstract Paper aims This paper aims to develop a proper maintenance policy directly related to defining critical components for ensuring a high level of safety and high-level in-service quality for all hydro generator units. Originality An innovative integrated tool that contributes to ensuing assertiveness in decision-making to determine the critical components is presented in this study. Specifically, hydro-generator unit type Kaplan belonging to a Brazilian Hydroelectric power plant is used as an application case to highlight the choice of the most suitable maintenance policy in light of the proposed approach. The selection of the case study is based on the fact that hydroelectric power plants are the basis of the Brazilian energy matrix, accounting for 75% of the demand in the country. Therefore, the need to maintain hydroelectric plants' availability and operational reliability is clear not to compromise the continuity and conformity (quality) of the electrical energy supply. Research method Seven multi-criteria decision-making methods were applied in addition to two methods for deciding weight (Critic Method and Entropy) have been compared to determine the critical components of the hydro-generator. To investigate the robustness of the classification of the applied Multi-Criteria Decision Making approaches, a sensitivity analysis was performed based on the weight change of each decision criterion. Main findings As a main result, the Entropy- Multi-Attribute Utility Theory model is proposed as the best approach to guarantee the selection of critical components for the Brazilian hydroelectric power plant case study. The validation sensitivity analysis by critical Fuzzy K-means groups guarantees that it is a robust tool for decision-making. Implications for theory and practice Ensuring the availability and reliability of hydroelectric plants can be achieved by employing appropriate maintenance policies that reduce the likelihood of failure or even eliminate its root causes, preventing failure from occurring. Consequently, a robust tool for decision-making regarding the Kaplan hydro generator's critical components' monitoring was developed.

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Abstract Paper aims To provide guidelines on current state of the literature on aspects related to the implementation of Industry 4.0 (I4.0) for operations flexibility in emerging economies, and derived from this, propose a conceptual framework for the analysis of the main topics studied. Originality Based on previous literature reviews on related topics (I4.0 and flexibility), we recognized an opportunity to focus on developing countries which until now has not been documented. Research method We carried out a systematic review of the literature together with a topic cluster analysis. Main findings The study revealed that there is a perceived consensus on how Industry 4.0 technologies can impact flexibility in emerging countries. Cloud computing is by far the most adopted technology despite the fact that there is a wide concern about security issues. The use of other technologies and their impact appears to be incipient. Implications for theory and practice The study presented here can be used as a starting point for new directions of research in terms of adoption of these technologies and new applications developed and/or customized to the realities of emerging countries.

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Abstract Paper aims Diagnostic analysis of PLM (Product Lifecycle Management) based on Industry 4.0 requirements supported by the AHP (Analytic Hierarchy Process) method. Originality The literature review found no research papers on PLM Maturity Models linked to requirements of Industry 4.0, attesting to the originality of the research and the topic proposed by the study. Research method The analysis is performed using an assessment framework that organizes and lists I4.0 attributes and PLM categories, supported by a multicriteria diagnostic assessment model based on the AHP method. The case study used in the research is an automotive industry located in the city of Curitiba, Brazil. Main findings A diagnostic analysis of the PLM categories and attributes of the I4.0 was performed, and the company’s overall levels of maturity were observed. Implication for theory and practice The results obtained from the diagnostic analysis indicate that the company maturity is Level 2, analyses were also undertaken with respect to PLM categories and Industry 4.0 attributes. Proposals for improvements were made with the objective of increasing the level of maturity and reducing gaps and problems identified in the diagnostic assessment.

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Abstract Paper aims The FMEA method (Failure Mode and Effects Analysis) has difficulty in prioritizing actions, verifying agreements among group participants and assessing uncertainty. This paper overcomes these shortcomings. The proposal applies FMEA in traditional format, establishes priorities for improvement actions, updates them periodically with better criteria than the traditional ones and values group agreement. Originality Other contributions claim to change the method, but this is not accepted by the organizations that apply it. In addition, they propose individual approaches, when the FMEA is essentially group-based. In contrast, this proposal establishes priorities without altering the basic requirements of the FMEA. Research method An action-research approach is used. Main findings Ease of application, improved group learning and commitment to action plans are confirmed. Implications for theory and practice A new group decision-making method is applied and a flexible solution is proposed, adaptable to very diverse problems.

Resumo em Inglês:

Abstract Paper aims This research develops an expert system for interoperable data acquirement and information enrichment in the manufacturing lines of a healthcare product, ensuring the correct data and information sharing and supporting the decision-making process. Originality The research contributes to creating and developing a novel method of knowledge representation that systematises the data collection and creates semantic relationships that allow the analysis of the productive performance of healthcare product manufacturing through semantic rules and inference engines. Research method The Interoperable Data Extraction and Information Enrichment system 4.0 (IDEIEs 4.0) was developed using an ontological approach and experimentally applied in an implantable Vascular Access Catheterindicated production process, which involves a machining controlling process. Main findings The developed system application pointed out the reduction of human mistakes in the data collecting, errors in the production control and data loss due to the digital automatic and interoperable collection process that brings precision in data collection and security in their storage. Implications for theory and practice The solution presented here can be used as a starting point for new directions of research to support the decision-making process with extra and formalised information, improving product quality, flexibilities the manufacturing process and reducing the time wasted.