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O crescimento da análise de dados é uma das principais forças do processo de criação de valor nas empresas. Esse processo consiste em analisar dados "parados" nas empresas, transformando-os em informação com valor para o processo de tomada de decisão. Os modelos de regressão são um método de análise preditiva, permitindo extrair insights dos dados e fazer previsões.
O objetivo deste estudo, desenvolvido na Administração dos Portos do Douro, Leixões e Viana do Castelo, S.A. (APDL), foi identificar os principais determinantes externos das toneladas de carga movimentadas no porto de Leixões.
Os dados obtidos da APDL, assim como os coletados externamente, foram utilizados para estimar quatro modelos de regressão. O primeiro modelo é mais geral, enquanto os restantes visam identificar os principais determinantes dos movimentos de carga no porto de Leixões para cada tipo de carga: contentorizada, granéis líquidos e “outros”.
Os resultados mostram que o ano do movimento, o mês do movimento, o tipo de carga, as toneladas movimentadas em Lisboa, as toneladas movimentadas em Sines, as toneladas movimentadas em Aveiro, o PIB trimestral e a existência ou não de greves são os determinantes mais importantes dos movimentos de carga no porto de Leixões.
Growing data analytics is one of the driving forces of companies' value-enhancement process. Data analytics consists of analysing data that is "stalled" in companies, transforming them into valuable information for the decision-making process. Regression models are a method of predictive analytics, allowing to extract insights from the data and to produce forecasts. The aim of this study, developed in the Administration of Douro, Leixões and Viana do Castelo Ports, S.A. (APDL), was to identify the main external determinants of cargo tonnage in the port of Leixões. The data obtained from APDL, as well as collected externally, were used to estimate four regression models. The first is a general model, while the others were aimed at identifying the main determinants of the cargo movements in the port of Leixões for each type of cargo: containerized, liquid bulk and “others”. The results show that the year of the movement, month of the movement, cargo type, tonnage moved in Lisbon, tonnage moved in Sines, tonnage moved in Aveiro, quarterly GDP and the existence or absence of strikes are the main determinants of cargo movements in port of Leixões.
Growing data analytics is one of the driving forces of companies' value-enhancement process. Data analytics consists of analysing data that is "stalled" in companies, transforming them into valuable information for the decision-making process. Regression models are a method of predictive analytics, allowing to extract insights from the data and to produce forecasts. The aim of this study, developed in the Administration of Douro, Leixões and Viana do Castelo Ports, S.A. (APDL), was to identify the main external determinants of cargo tonnage in the port of Leixões. The data obtained from APDL, as well as collected externally, were used to estimate four regression models. The first is a general model, while the others were aimed at identifying the main determinants of the cargo movements in the port of Leixões for each type of cargo: containerized, liquid bulk and “others”. The results show that the year of the movement, month of the movement, cargo type, tonnage moved in Lisbon, tonnage moved in Sines, tonnage moved in Aveiro, quarterly GDP and the existence or absence of strikes are the main determinants of cargo movements in port of Leixões.
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Portos Análise de regressão Carga Ports Regression analysis Cargo