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A dimensão ambiental no contexto da estratégia de operações de montadoras do ABC paulista
(2016) Scur G.; Heinz G.
A high-speed shaft supported by magnetic bearings applied to energy systems
(2017) Rosa F.C.; Lima F.; Fumagalli M.A.
© 2016, The Brazilian Society of Mechanical Sciences and Engineering.This paper presents the construction of a mechanical shaft supported by active magnetic bearings (AMB), reproducing a prototype of a turbogenerator. In turbogenerators, the shaft is supported by two radial and one axial magnetic bearings. At the center of the shaft was inserted a rotor of an induction machine, which will operate as motor as well as generator, and at the shaft extremity is coupled a small turbine. The mechanical modeling and assembly are presented in detail. After that the control system of the AMB is introduced. The high-speed AMB-supported shaft reduces the length of the system as well as losses and increases the system efficiency. The validation of the high-speed shaft supported by AMB allows the energy generation, in the future, provided by the asynchronous generator mounted upon the shaft. Prime mover, such as gas, will be considered to provide the mechanical power. Finally, for injecting the energy into the grid, an indirect field-oriented control (IFOC) was used for high performance of the generator side. On the grid side, the voltage-oriented control (VOC) is applied to guarantee the high quality of the current injected into the grid.
A LQG/LTR Based Robust Control Applied to a Steel Strip Coiling System
(2015) Lima F.; De Oliveira A.A.; De Aguiar M.L.; De Almeida Monteiro J.R.B.
© 2015 IEEE.This work presents the modelling, analysis and control of a steel strip coiling system using twin roll direct casting, in a plant placed at Technological Research Institute of Sao Paulo. The characteristics of the process are first introduced. After that the mathematical modelling of the coiling system and its parametric changes were addressed. The control system was first implemented using a PID controller since this kind of control is highly used in the industry. A proposal of introducing an ultrasonic sensor in the plant, to measure the coiling radius, is suggested and the simulation results are presented. Finally, a robust compensator using the LQG/LTR method was designed for improving the control system performance. The simulations were carried out using Matlab/Simulink software. The real plant provided the parameters to the simulations.
A machine learning approach applied to energy prediction in job shop environments
(2018) PEREIRA, M. S.; Fabio Lima
© 2018 IEEE.Energy efficiency has become a great challenge for manufacturing companies. Although it is possible to improve efficiency applying new and more efficient machines, decision makers tend to look for some less expensive alternatives. In this context, the adoption of more efficient strategies during the production planning can allow the reduction in energy consumption and associated emissions. Furthermore, the current reality of manufacturing companies, brought by Industry 4.0 concepts, requires more flexibility of production systems, thus, increasing complexity for machine rescheduling without compromising sustainable requirements. In this paper, we propose a method to predict total energy consumption in job shop systems applying machine learning techniques. Different schedules may result in different consumption rates. However, there is a nonlinear relationship between these targets. Therefore, an Artificial Neural Network (ANN) is applied for a quick estimation of total energy consumption. In order to validate the model, computational experiments, using digital manufacturing software tools, are performed on different job shop configurations to show the efficiency of the proposed model.
Academic research published in the management & production journal (G&P, 1999-2010): Trends and future research directions A produção acadêmica publicada na revista Gestão & Produção de 1999 a 2010: Tendências e direções para pesquisas futuras
(2012-01-05) BORTOLLOSSI, L. N.; Mauro Sampaio
This study is a review and evaluation of studies published in a Brazilian journal of management & production (G&P) from 1999 to 2010 in order to investigate the diversity of articles in terms of the purposes of research, data collection approaches, and data analysis techniques. The research articles were classified based on their primary purpose (theory building, application, and concepts), data collection approach (case study, qualitative research, archival research, survey-based research, laboratory research, and field research), data analysis technique (descriptive statistics, various multivariate statistical techniques, and mathematical modeling), and operations topics (strategy, quality, and supply chain management). The numbers of articles published increased substantially between 1999 to 2010. The focus of the articles on the manufacturing industry published in this journal changed considerably, and more recently the gap between the number of manufacturing and service-focused articles on production and operations management (POM) has reduced. While previous articles published in the G&P journal were based on conceptual descriptions, our results indicate that the journal has published articles based on theory and application. Case study is the most widely used method for data collection. Directions for future research in operations management were also discussed.
Adoção da Tecnologia da Informação: Análise da assimilação de um Portal de Fornecedores com foco na integração da cadeia de suprimentos
(2016) MATTOS, C. A.;DE MATTOS, CLÁUDIA A.;DE MATTOS, CLAUDIA APARECIDA;DE MATTOS, C.A.;DE MATTOS, CLAUDIA;MATTOS, CLAUDIA APARECIDA DE;MATTOS, CLAUDIA A.; LAURINDO, F. J. B.
Adoption of digital technologies for asset management in construction projects
(2022-07-05) ATTENCIA, G.; Claudia Mattos
© 2022 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Smart asset management can provide a framework for validating and improving asset performance, as well as collecting and incorporating reliable asset information into the decision-making process. In this context, this study analyzes the adoption of technology in the management of intelligent assets through multiple case studies, the technologies used, and the factors that possibly affect the level of technology adoption in asset management and a new method for operating. Technology-Organization-Environment framework and Technology Acceptance Model were used to analyze the main factors influencing technology adoption in intelligent asset management. The contribution of the study lies in the integration of asset management concepts and technology adoption. This integration allows structuring a theoretical framework for technology adoption by identifying the adoption factors in each phase of the asset management cycle in construction projects.
After sale service in the auotomobile industry: anlalysis of trends and customer loyalty
(2010) NOGUEIRA NETO, M.S.; OLIVEIRA, H.H.; SACOMANO, J.B.; LIMA, J. L. A.
Agile Manufacturing: the 21st century competitive strategy e Postponement Manufacturing in European Supply Chain: a triangular approach
(2003) SAMPAIO, M.
Algoritmos de Tecnologia de Grupo para Projetos de Células de Manufatura
(2006) MASSOTE, A. A.
ALINHAMENTO DO PMO À GESTÃO ORGANIZACIONAL: ESTUDO DOS ELEMENTOS DO PMO SOB AS DIMENSÕES ESTRATÉGICA, TÁTICA E OPERACIONAL
(2016) CRUZ, C; SCUR, Gabriela;SCUR, GABRIELA
Allocation of Improvement Strategies in a Flow Shop with Two Capacity Constrained Resources
(2020-07-05) Marcel Utiyama; CORREIA, FERNANDA CAVEIRO; Dario Alliprandini
The main goal of this paper is to identify the best strategy to allocate improvement programs in a flow shop with two capacity constrained resources. We collected data from a manufacturing company, and we used them to conduct a simulation with the System Dynamics-Factory Physics model. The main variables considered were the mean time between failures and the mean time to repair, and lead time was the output. Strategies used are mainly focused, distributed, and hybrid. Findings show the effect of the allocation of different improvement strategies on lead time reduction. We indicated that for the flow shop analyzed the focused strategies delivered the best results. When it is not possible to perform a large investment in the capacity-constrained resource, companies should use hybrid approaches. An important contribution is a hybrid approach, which is a new way of directing efforts of improvements. Results showed that both strategies, hybrid time to repair improvement and hybrid time between failures improvement, delivered better results compared to the respective distributed strategies.
An alternative for improving setup times and time between failures aiming at manufacturing lead time reduction
(2021-10-05) Marcel Utiyama; GODINHO FILHO, M.; OPRIME, P. C.
© 2021, German Academic Society for Production Engineering (WGP).The main goal of this study is to propose an alternative approach to improve manufacturing setup time and the time between failures. This approach focuses on eliminating the outliers concerning manufacturing setup times and time between failures. With this aim, we performed simulations to compare the effect on the lead time of both improvement strategies with the traditional strategies of reducing the mean or variability. The simulation results indicate that for low variability, the best strategy is to focus on the mean setup time and time between failures and the worst-case strategy emerges as an excellent practical alternative. For environments with moderate variability, investing in eliminating the worst-case setup time and time between failures is suggested as the best option. Both options presented results like the strategies of improving the mean and variability and it is generally achieved with a lower cost and easier implementation. Therefore, the results of the present study indicate that the worst-case strategy is an important improvement option and can be adopted by companies in practice, deserving further investigation in manufacturing and operations-management studies.
An empirical analysis of a neural network model for the time series forecasting of different industrial segments
(2015) ZOUCAS, F. A. M.; BELFIORE, P.
Copyright © 2015 Inderscience Enterprises Ltd.This paper aims to propose a neural network model for forecasting the production time series of 11 different industries in Brazil. The data was collected from Brazilian Institute of Geography and Statistics (IBGE). Firstly, we study different networks topologies that have been implemented in the literature in recent years, such as perceptron, linear networks, multi-layer perceptron (MLP), probabilistic network, Hopfield model, Kohonen model, time delay neural network (TDNN), Elman and Jordan network, in addition to the backpropagation and Levenberg-Marquadt algorithms. Studying the behaviour of these time series and the main characteristics of the each network topology, we conclude that the TDNN with multi-layer perceptron is the best to estimate the production time series of 11 industrial segments. The neural network was then applied considering two different strategies of structural model. We conclude that the neural network model proposed was effective for forecasting production time series in these industries.
An exploratory study on emerging technologies applied to logistics 4.0 Um estudo exploratório sobre tecnologias emergentes aplicadas à logística 4.0
(2020-09-05) CORREA, J. S.; Mauro Sampaio; BARROS, R. DE. C.
© 2020 Brazilian Institute for Information in Science and Technology. All rights reserved.The concept of Logistics 4.0 works closely to that of Industry 4.0. While Industry 4.0 proposes a disruptive change in manufacturing, Logistics 4.0 advocates a transformation in the way organizations buy, manufacture, sell, and deliver products. The objective of this paper is to identify, in Brazilian companies, the degree of interest in the investment in six emerging technologies applicable to logistics, according to scientific literature, as well as to identify the current perception of data quality of these companies. To achieve these objectives, an online survey was conducted. The research showed that the technologies that most interest Brazilian companies are Internet of Things (IoT) and cloud computing, both with 82% of investment intention. The two technologies that least interested companies are crowdsourcing and 3D printing, both with 68% investment disinterest among respondents.
An exploratory study on the returns management process in an online retailer
(2017) Ana Carolina de Araujo; Érica Mayumi Matsuoka; July Ellen Ung; MASSOTE, A. A.; SAMPAIO, M.
An exploratory study on the returns management process in an online retailer
(2018) de Araujo A.C.; Matsuoka E.M.; Ung J.E.; Massote A.; Sampaio M.
© 2017 Informa UK Limited, trading as Taylor & Francis Group.The returns management process is gaining increasing importance both in academia and among supply chain professionals, not only as a tool in the practice of sustainability, but also for its strategic role in many economic sectors. Several studies show how efficiently returns management can reduce costs and provide a competitive advantage to companies. Likewise, electronic commerce, or e-commerce, has shown significant growth in many countries and, due to its very nature, it presents much higher return rates than traditional trade. However, despite the importance of returns management operation for electronic commerce, few studies explore this theme in the literature. The aim of this paper is to describe the process of returns management of the largest online retailer of the Brazilian market, showing its evolution and analysing its performance, as well as identifying its main shortcomings. We conclude with suggestions for improvements and the implementation of a more efficient system.
Análise da relação entre a prática do lean e o desempenho de prazo de projetos em organizações representativas de pesquisa clínica
(2017) Maldonado, C.; Lopes, A.P.V.B.V.; Carvalho, M.M.
Análise da relação entre Tecnologia da Informação, Práticas de Lean Manufacturing e Desempenho Operacional
(2019-10-05) TESTA FILHO, NELSON; Claudia Mattos; Dario Alliprandini
O principal objetivo deste estudo foi examinar como as empresas estão aplicando as práticas de manufatura enxuta (LM) combinadas com as características da Tecnologia da Informação (TI), a interação entre TI e LM e os resultados obtidos em termos de desempenho operacional. A aplicação da TI e os princípios da manufatura enxuta foram vistos por alguns autores como mutuamente exclusivos, mas há outros estudos indicando que ambas as abordagens são cada vez mais interdependentes. Assim, há necessidade de estudos complementares analisando essa inter-relação de recursos de TI e práticas Lean. Esta pesquisa foi desenvolvida com uma abordagem qualitativa aplicada através do método multicaso. O estudo contribui para a teoria, fornecendo uma indicação de quais propriedades de TI trazem efeitos significativos para a implementação de práticas Lean; O estudo também oferece várias recomendações para gerentes de implantação enxuta em suas organizações.
Analysis of artificial intelligence techniques applied to thermographic inspection for automatic detection of electrical problems
(2019) OLIVATTI, Y.; PENTEADO, C.; Plinio Thomaz Aquino Junior; Rodrigo Maia
© 2018 IEEE.Electrical energy is among the most important resources used nowadays, in the modern world. Despite this, there is a considerable waste of much of what is spent on power generation, since some of the energy generated does not reach the final consumer. One of the scopes addressed by smart cities is the optimal use of the basic resources available to the population, such as electricity. Thermographic inspection is a technique that allows non-invasive sensing. Through it, it is possible to analyze components and electrical equipment by capturing the temperature of its surface. With the aim of finding electrical faults and reducing losses, this work studies the use of thermography, computational algorithms and artificial intelligence techniques to automatically detect electrical problems.

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