Uma arquitetura de referência para o processamento distribuído de stream de dados em soluções analíticas de near real-time

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Virtual Enterprise Process Management: An Application to Industrial Maintenance

Part 2: Behaviour and Coordination

Estágio TAP/Megasys - Data Warehouse & Reporting

Mestrado em Gestão de Sistemas de Informação

Hotel Vila Galé Lagos

Dissertação de Mestrado, Direção e Gestão Hoteleira, Escola Superior de Gestão, Hotelaria e Turismo, Universidade do Algarve, 2016

Inteligencia de negocios aplicando la metodología RFM a las cuentas de los Socios de la COAC Jardín Azuayo

El presente proyecto:Inteligencia de negocios, aplicando la metodología RFM a las cuentas de los socios de la COAC Jardín Azuayo, se desarrolla sobre la necesidad de la institución de contar con herramientas eficientes y eficaces para la toma de decisiones y conocimiento del socio. Primero, se determina la importancia de construir una herramienta de Inteligencia de Negocios dentro de Jardín Azuayo que permita obtener información clara y concisa en tiempo real para la toma de decisiones. Segundo, se continúa con el desarrollo de metodologías para la gestión del valor del socio a través del conocimiento de sus necesidades analizando la información histórica de su última transacción realizada, la frecuencia con la que acude para acceder a los servicios que ofrece la Cooperativa y el monto promedio por transacción. Finalmente, al combinar la herramienta de Inteligencia de Negocios para la obtención de información y la aplicación de metodologías para el conocimiento del socio, se ha podido plantear dos estrategias básicas para la afianzar la fidelización del socio con la Cooperativa.

Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

Reingegnerizzazione di unSistema Informativo: analisi,diagnosi e prototipazione

L'obiettivo di ciascuna azienda privata, piccola o grande che sia, è quello di ottenere utili attraverso la commercializzazione di beni o servizi. Per raggiungere ciò, la base da cui si parte è sempre una corretta organizzazione della struttura e dei processi aziendali. Questi ultimi, per poter raggiungere i risultati attesi, hanno bisogno costantemente di informazioni. All'interno di un'impresa la parte che si occupa della gestione di informazioni e processi viene chiamata Sistema Informativo (SI). Questo progetto di tesi nasce dall'esigenza di un'azienda privata in ambito utility di analizzare il proprio Sistema Informativo con il duplice scopo di effettuare una diagnosi dell'attuale e progettare una possibile soluzione ottimale. Andando nello specifico, il progetto è stato suddiviso in due parti: la prima comprende tutta la fase di analisi del SI con la relativa diagnosi, mentre la seconda, ben più verticale, tratta la progettazione e prototipazione di un Data Mart per la gestione delle informazioni all'interno dell'azienda.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Business development in Siemens global shared services delivery center Portugal

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Guincho surf center business plan viability: Case study

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Increasing sales volume in mass business through inbound contact center

Field lab: Consulting lab

Iowa State Center Business Office, June 30, 2002

State University Audit Report

Iowa State Center Business Office, Iowa State University of Science and Technology, Independent Auditor's Reports Financial Statements, June 30, 2003 & 2004

State University Audit Report