Análisis del proceso productivo en la fabricación de muebles en la ciudad de Cuenca y propuesta de un plan de optimización del área de producción de la fábrica Vitae Muebles. Periodo 2015

El presente trabajo de titulación fue elaborado en la Fábrica Vitae Muebles en el área de producción donde se realizó el estudio de la filosofía empresarial. Se analizaron las herramientas de Lean Manufacturing como: Justo a tiempo, 5 S, Kaizen, Kanban y Poke Yoke lo que permitió decidir que la mejor alternativa para el área de producción de la fábrica es la metodología 5 S. Se efectuó un análisis de la producción de muebles en la ciudad de Cuenca, además se realizó una comparación de los niveles de producción con empresas que tienen mayor trayectoria en el mercado nacional. Para describir el proceso concerniente a la fabricación de muebles se realizaron visitas diarias durante un período de tiempo de 2 meses y para su respectivo análisis se realizó un check–list lo que permitió comprobar las dificultades que se presentan en las diferentes secciones de producción donde se encontraron problemas relacionados con la acumulación de materiales, desorganización, falta de limpieza e indisciplina. . Se elaboró la propuesta del plan de optimización usando los 5 pilares fundamentales que comprende Seiri (clasificación), Seiton (organización), Seiso (limpieza), Seiketsu (estandarización), Shitsuke (disciplina), los cuales brindarán beneficios en obtener una mejor imagen en la planta de trabajo, cambios de hábitos en los operarios e incremento de su seguridad laboral. Por último se realizaron las conclusiones y recomendaciones de este trabajo de titulación.

Lean e sua aplicabilidade na indústria da construção

Nos tempos que correm a gestão de topo das empresas tem como desafio adaptar-se às exigências do mercado, tendo em conta a crise atravessada nos diversos setores. O lean mais do que uma ferramenta, é uma filosofia de vida que visa a excelência. Esta filosofia contém dois pilares importantes, a melhoria contínua e o respeito pelas pessoas. A melhoria contínua pretendida visa a obtenção do valor esperado e definido perante um desafio. O lean é uma filosofia de gestão que nasce após a segunda guerra mundial caracterizado como sistema de produção enxuto que visa eliminação de desperdícios. Algumas empresas do setor da construção após verificar os resultados satisfatórios noutras áreas, impuseram a implementação da filosofia lean nas suas organizações. Esta implementação passa por adotar ferramentas que visam a eliminação de desperdícios, produção puxada e a flexibilidade de processos. A construção adotou ferramentas tais como: just in time, definição de valor, definição de desperdício, fluxo contínuo, task time, sistema puxado, poka-yoke, kanban, heijunka (nivelamento de produção), seis sigmas, last planner, kaizen, fluxo de valor, 5S`s. A presente tese pretende clarificar algumas destas ferramentas e conceitos, aproximando-os da área da construção. Inclui-se um questionário a um grupo de trabalhadores de uma empresa de construção que está na fase inicial de implementação do lean. O questionário visou entender em que fase se encontra a implementação e a importância dada ao tema pelos trabalhadores. Foram analisados alguns casos práticos onde a gestão da empresa decidiu implementar o lean.

Lean e sua aplicabilidade na indústria da construção

Nos tempos que correm a gestão de topo das empresas tem como desafio adaptar-se às exigências do mercado, tendo em conta a crise atravessada nos diversos setores. O lean mais do que uma ferramenta, é uma filosofia de vida que visa a excelência. Esta filosofia contém dois pilares importantes, a melhoria contínua e o respeito pelas pessoas. A melhoria contínua pretendida visa a obtenção do valor esperado e definido perante um desafio. O lean é uma filosofia de gestão que nasce após a segunda guerra mundial caracterizado como sistema de produção enxuto que visa eliminação de desperdícios. Algumas empresas do setor da construção após verificar os resultados satisfatórios noutras áreas, impuseram a implementação da filosofia lean nas suas organizações. Esta implementação passa por adotar ferramentas que visam a eliminação de desperdícios, produção puxada e a flexibilidade de processos. A construção adotou ferramentas tais como: just in time, definição de valor, definição de desperdício, fluxo contínuo, task time, sistema puxado, poka-yoke, kanban, heijunka (nivelamento de produção), seis sigmas, last planner, kaizen, fluxo de valor, 5S`s. A presente tese pretende clarificar algumas destas ferramentas e conceitos, aproximando-os da área da construção. Inclui-se um questionário a um grupo de trabalhadores de uma empresa de construção que está na fase inicial de implementação do lean. O questionário visou entender em que fase se encontra a implementação e a importância dada ao tema pelos trabalhadores. Foram analisados alguns casos práticos onde a gestão da empresa decidiu implementar o lean.

Bonnita

Bonnita es una empresa que nace en el año 2015 en el mes de marzo, su actividad económica se clasifica bajo el código CIIU 4751, comercio al por menor de productos textiles en establecimientos especializados, actualmente cuenta con un establecimiento de comercio ubicado en el barrio Kennedy de la ciudad de Bogotá, la inversión realizada para esta empresa se realizo en su totalidad con recursos propios, los resultados obtenidos hasta el momento han llenado la expectativas de los inversionistas y en el mes de agosto del presente año se abrirá una nueva tienda que mejorara las utilidades de la empresa. Las tiendas Bonnita enfocan todo su modelo de negocio a crear una experiencia de compra para su segmento objetivo que son las mujeres entre los 18 y 40 años de edad, de estrato 2 – 3 medio - bajo, por medio de estrategias de merchandising propias, que generan una actitud diferente y mas impulsiva dentro de la tienda, utilizando elementos sensoriales y psicológicos que el consumidor no percibe pero influye en su decisión y manera de comprar, haciendo de esta más rentable para las tiendas Bonnita. El segmento objetivo es grande en la ciudad de Bogotá, los competidores más representativos del mercado emplean modelos de negocio diferentes y no existe un líder en este segmento ya que muchas de estas empresas diversifican con respecto al género al que enfocan sus esfuerzos, Bonnita se dirige únicamente a la mujer, la rotación de sus productos es rápida debido al manejo de inventarios justo a tiempo, lo que permite ser competitivos frente al precio y innovación en los productos.

Estrategia del Grupo Volkswagen de México para la reducción del impacto ambiental generado por sus procesos productivos

El siguiente escrito pretende mostrar las estrategias que el Grupo Volkswagen de México implementó para reducir el impacto que generan sus dos fábricas en México al medio ambiente. La primera de ellas, y la más grande en cuanto a infraestructura, recurso humano, complejidad y productividad entre ambas, se ubica en el Estado de Puebla, en su capital Puebla. Se dedica a la fabricación de varios modelos de vehículos para el mercado interno y mundial. La segunda fábrica se ubica en la ciudad de Silao en el Estado de Guanajuato, esta fábrica se dedica principalmente a la fabricación de motores, los cuales posteriormente son distribuidos a distintos complejos productivos alrededor del mundo para allí ensamblarse en carrocerías. En primera instancia se describe la estrategia general con los debidos alcances establecidos desde la dirección de la empresa. Posteriormente se prosigue a desglosar cada una de las sub-estrategias, explicándolas y relacionándolas en contexto, tomando de esta manera, paso a paso, como se ha construido una campaña que ha logrado adquirir una identidad propia ampliamente reconocida tanto por la fuerza laboral de la compañía e inclusive por los agentes del mercado en el que se encuentran. Finalmente se reúnen datos numéricos relevantes en cuanto a la reducción del impacto ambiental que por último se ven reflejado en un ahorro económico para la empresa.

Gestión de la cadena de suministro de Bimbo

El presente trabajo es un informe final sobre los procesos de producción, logística y distribución de la empresa Bimbo. El informe está conformado por aspectos teóricos y prácticos, de esta manera, brinda información clara y concreta sobre los antecedentes más importantes de esta organización y su contribución a la productividad del país. La información presentada ha sido recolectada mediante una revisión teórica y una visita a sus instalaciones en Veracruz, México. De esta manera, se han estudiado sus procesos y logística de producción y cómo contribuyen en la eficiencia y calidad de la empresa; así mismo, son fundamentales para diagnosticar los aspectos a mejorar de la entidad. Por otro lado, gracias a la visita, se logró desarrollar habilidades administrativas que hasta el momento solo estaban en teoría. Llegar a un contexto real y con problemáticas evidentes permitió consolidar el aprendizaje que se obtuvo durante años en la carrera para plasmarlos en la labor. Así mismo, el detallar las instalaciones de Bimbo permitió alcanzar una mayor perspectiva con respecto a los aspectos importantes dentro del panorama organizacional; tales como la producción, la gestión y mejora de los procesos de la compañía. Esto se logró realizando labores correspondientes a la inspección, observación, revisión de logística y el análisis de los procesos productivos. El trabajo práctico indudablemente sirvió para acrecentar los conocimientos obtenidos en la universidad.

Nuevas tendencias en la Supply Management en Colombia y México

La Misión empresarial a México 2015, tiene como propósito brindar a los estudiantes espacios para el crecimiento profesional, mediante la aplicación de los conceptos y los modelos estudiados en las diferentes visitas empresariales que se realizaron. Se pretende conocer cuáles son las nuevas tendencias en el manejo y administración de la cadena de abastecimiento de las empresas, de manera que estos conceptos puedan ser aplicados en diferentes tipos de empresas. Se realizó una investigación de cada una de las empresas que fueron visitadas realizando un análisis de un punto clave en cada una, por ejemplo, se describe la política “Think Blue” empleada por el grupo Volkswagen para el éxito y reconocimiento empresarial en la logística verde, y se analizan los principales procesos de producción de la planta productora del grupo Modelo, entre otras. Por otra parte, se analiza el modelo de gestión logística para pequeñas y medianas empresas, desarrollado en la Universidad Popular Autónoma del Estado de Puebla, el cual puede ser usado como base para el progreso y fortalecimiento de las pymes en nuestro país.

La influencia de la cultura nacional en el desempeño logístico de los 123 primeros países en el logistics performance index (LPI)

En esta tesis se presenta la influencia que tiene la Cultura Nacional en el desempeño Logístico de una Nación, en especial de los 123 primeros países evaluados por el Índice de Desempeño Logístico (LPI) realizado por el Banco Mundial. Este estudio se realiza a partir de los seis ítems evaluados en el LPI y de las seis Dimensiones Culturales de Hofstede, para relacionar estas calificaciones se empleará el programa estadístico SPSS Statistical Package for the Social Sciences obteniendo como resultado posibles correlaciones para probar la hipótesis del efecto de la cultura en el desempeño logístico y establecer patrones de comportamiento en los mejores países de cada correlación. De acuerdo a los resultados obtenidos de estas correlaciones, se encontró que si existe una relación entre la cultura nacional y el desempeño logístico de un país, al igual que las dimensiones culturales más influyentes para obtener dichos resultados y se identificaron los países que cuentan con condiciones sobresalientes que facilitan tener altas posiciones en el LPI.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Making decisions with the future in mind: Developmental and comparative identification of mental time travel

Mechanisms that produce behavior which increase future survival chances provide an adaptive advantage. The flexibility of human behavior is at least partly the result of one such mechanism, our ability to travel mentally in time and entertain potential future scenarios. We can study mental time travel in children using language. Current results suggest that key developments occur between the ages of three to five. However, linguistic performance can be misleading as language itself is developing. We therefore advocate the use of methodologies that focus on future-oriented action. Mental time travel required profound changes in humans' motivational system, so that current behavior could be directed to secure not just present, but individually anticipated future needs. Such behavior should be distinguishable from behavior based on current drives, or on other mechanisms. We propose an experimental paradigm that provides subjects with an opportunity to act now to satisfy a need not currently experienced. This approach may be used to assess mental time travel in nonhuman animals. We conclude by describing a preliminary study employing an adaptation of this paradigm for children. (c) 2005 Elsevier Inc. All rights reserved.

Time trends and predictors of mortality from ill-defined causes in old age: 9 year folllow-up of the Bambuí cohort study (Brazil)

The study objective was to examine differentials in time trends and predictors of deaths assigned to symptoms, signs and ill-defined conditions in comparison with other ill-defined conditions (ill-defined cardiovascular diseases, cancer and injury) in a population-based cohort study. Of 1,606 baseline participants aged 60 years and over, 524 died during 9-year follow-up and were included in this study. Deaths coded to "symptoms" declined by 77% in the period from 1997-1999 to 2003-2005. Deaths coded to other ill-defined conditions remained unchanged. The calendar period 2003-2005 (RR = 0.25; 95%CI: 0.09-0.70) and in-hospital deaths (RR = 0.16; 95%CI: 0.08-0.34) were independently associated with "symptoms", but not with other ill-defined conditions. Baseline socio-demographic characteristics and chronic diseases were not predictors of these outcomes. International and national agencies have focused on the reduction of deaths assigned to "symptoms" to improve the registration of vital statistics, while other ill-defined conditions have received little attention. Our data provide evidence supporting the need to redress this situation.

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.

Conditioning training and retrieval increase phospholipase A(2) activity in the cerebral cortex of rats

In rats, phospholipase A(2) (PLA(2)) activity was found to be increased in the hippocampus immediately after training and retrieval of a contextual fear conditioning paradigm (step-down inhibitory avoidance [IA] task). In the present study we investigated whether PLA(2) is also activated in the cerebral cortex of rats in association with contextual fear learning and retrieval. We observed that IA training induces a rapid (immediately after training) and long-lasting (3 h after training) activation of PLA(2) in both frontal and parietal cortices. However, immediately after retrieval (measured 24 h after training), PLA(2) activity was increased just in the parietal cortex. These findings suggest that PLA(2) activity is differentially required in the frontal and parietal cortices for the mechanisms of contextual learning and retrieval. Because reduced brain PLA(2) activity has been reported in Alzheimer disease, our results suggest that stimulation of PLA(2) activity may offer new treatment strategies for this disease.

A traffic differentiation add-on to the IEEE 802.15.4 protocol: implementation and experimental validation over a real-time operating system

The IEEE 802.15.4 is the most widespread used protocol for Wireless Sensor Networks (WSNs) and it is being used as a baseline for several higher layer protocols such as ZigBee, 6LoWPAN or WirelessHART. Its MAC (Medium Access Control) supports both contention-free (CFP, based on the reservation of guaranteed time-slots GTS) and contention based (CAP, ruled by CSMA/CA) access, when operating in beacon-enabled mode. Thus, it enables the differentiation between real-time and best-effort traffic. However, some WSN applications and higher layer protocols may strongly benefit from the possibility of supporting more traffic classes. This happens, for instance, for dense WSNs used in time-sensitive industrial applications. In this context, we propose to differentiate traffic classes within the CAP, enabling lower transmission delays and higher success probability to timecritical messages, such as for event detection, GTS reservation and network management. Building upon a previously proposed methodology (TRADIF), in this paper we outline its implementation and experimental validation over a real-time operating system. Importantly, TRADIF is fully backward compatible with the IEEE 802.15.4 standard, enabling to create different traffic classes just by tuning some MAC parameters.

Diabetes Mellitus as a Risk Factor in Glaucoma's Physiopathology and Surgical Survival Time: A Literature Review

Glaucoma is a multifactorial condition under serious influence of many risk factors. The role of diabetes mellitus (DM) in glaucoma etiology or progression remains inconclusive. Although, the diabetic patients have different healing mechanism comparing to the general population and it has a possible-negative role on surgical outcomes. This review article attempts to analyze the association of both diseases, glaucoma and DM, before and after the surgery. The epidemiological studies, based mainly in population prevalence analyzes, have shown opposite outcomes in time and even in the most recent articles also the association remains inconclusive. On the contrary, the experimental models based on animal induced chronic hyperglycemia have shown an important association of both diseases, explained by common neurodegenerative mechanisms. Diabetic patients have a different wound healing process in the eye viz-a-viz other organs. The healing process is more and it results in lower surgical survival time, higher intraocular pressure (IOP) levels and, therefore, these patients usually need more medication to lower the IOP. Both randomized and nonrandomized retrospective and experimental molecular studies have shown the association between DM and glaucoma. Further studies are needed to get better explanations about outcomes on more recent surgical procedures and with the exponential use of antifibrotics. How to cite this article: Costa L, Cunha JP, Amado D, Pinto LA, Ferreira J. Diabetes Mellitus as a Risk Factor in Glaucoma's Physiopathology and Surgical Survival Time: A Literature Review.