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dc.contributor.advisorSouza, Marcos Leandro Hoffmann
dc.contributor.authorHerold, Lucas da Silva
dc.date.accessioned2021-03-18T12:43:16Z
dc.date.accessioned2022-09-22T19:42:00Z
dc.date.available2021-03-18T12:43:16Z
dc.date.available2022-09-22T19:42:00Z
dc.date.issued2019-11-07
dc.identifier.urihttps://hdl.handle.net/20.500.12032/63959
dc.description.abstractThe need to establish effective demand management is a challenging activity for organizations and dealing with market uncertainties is a key activity to ensure the growth and competitiveness of a company. In an era where it is possible to store large amounts of data, using advanced analytical techniques can bring useful information to decision makers. Thus, the present work was developed in a medium-sized industry, inserted in the asphalt paving market productive chain, aiming to use a demand forecasting method, using a predictive model with Machine Learning. Using the Law and Kelton method for modeling construction, a study was conducted and it was decided to use the R software and apply the Prophet algorithm to analyze the product time series and build a model for validation. Then the model was built, adjusted to simulate the actual demand of the subsequent months. The results were evaluated and interpreted, obtaining a maximum error of 2.8% and SEM of -7%. The use of Machine Learning for demand forecasting was satisfactory and can be considered as an effective tool for decision making. A critical analysis of the method and suggestions for future research concludes this study.en
dc.publisherUniversidade do Vale do Rio dos Sinospt_BR
dc.subjectPrevisão de demandapt_BR
dc.subjectDemand forecastingen
dc.titlePrevisão de vendas com machine learning: implementação do algoritmo prophet em linguagem Rpt_BR
dc.typeTCCpt_BR


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