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Authors
Advisor(s)
Abstract(s)
Ao longo do tempo, as organizações, e suas cadeias de abastecimento, foram-se adaptando às
constantes mudanças globais, abraçando cada vez mais as tecnologias digitais como fonte de
otimização, eficiência operacional e de vantagem competitiva.
A Transformação Digital, pela utilização de tecnologias inovadoras permitem automatizar
processos de fonte manual e repetitiva de maneira a otimização os processos operacionais como
promover a redução do erro humano.
Deste modo, esta dissertação, recorre à Transformação Digital de forma a desenvolver um modelo
de apoio à decisão (“artifact”), com recurso a programação em Python, de forma a otimizar os
atuais processos manuais de consulta de 62 tabelas Excel e cálculos associados, procurando
eficiência dos processos de transporte da organização X, sendo pretendido avaliar o modelo em
questão quanto à sua precisão de resultados obtidos, como também a obtenção de poupanças
monetárias e temporais através da utilização do presente modelo versus realização do processo via
manual.
Para a sua constituição, optou-se por selecionar uma metodologia de investigação Design Science
Research Methodology (DSRM), de maneira a favorecer a estruturação e organização da
metodologia deste trabalho.
Os resultados deste projeto foram bastante positivos sendo possível obter uma precisão de 93,01%
na obtenção dos tempos recolhidos pelo “artifact”. Por outro lado, com a criação de três distintos
cenários (pessimista, realista e otimista), foi possível obter resultados satisfatórios em cada um dos
casos, resultando em poupanças monetárias anuais sempre superiores a 6 000€ e eficiências
temporais acima de 85%.
Em suma, apesar das pequenas limitações existentes, este projeto foi bem conseguido, sendo
possível aplicar a Transformação Digital ao processo de otimização de transportes em questão, e
concluir que a implementação deste modelo de apoio à decisão promove tanto vantagens
temporais como monetárias significativas para organização.
Over time, organizations, and its supply chains, have adapted to constant global changes, increasingly embracing digital technologies as a source of optimization, operational efficiency and competitive advantage. Consequently, these technologies, associated with Digital Transformation, make it possible to automate manual and repetitive processes in order to optimize operational processes and reduce human error. Therefore, this thesis uses Digital Transformation in order to develop a decision support model (“artifact”), using Python programming, in order to optimize the current manual processes of consulting 62 excel tables and associated calculations, looking for efficiency in the transport processes of organization X, with the aim of evaluating the model in question in terms of the accuracy of the results obtained, as well as obtaining monetary and time savings through the use of this model versus carrying out the process manually. For its constitution, the decision was made to select a Design Science Research methodology (DSRM), in order to facilitate the structuring and organization of the methodology of this work. The results of this project were very positive and it was possible to obtain an accuracy of 93.01% in obtaining the times collected by “artifact”. On the other hand, by creating three different scenarios (pessimistic, realistic and optimistic), it was possible to obtain satisfactory results in each case, resulting in annual monetary savings of over €6 000 and time efficiencies of over 85%. In short, despite the minor limitations, this project has been successful and it has been possible to apply Digital Transformation to the transport optimization process in question, and to conclude that the implementation of this decision support model promotes both significant temporal and monetary advantages for the organization.
Over time, organizations, and its supply chains, have adapted to constant global changes, increasingly embracing digital technologies as a source of optimization, operational efficiency and competitive advantage. Consequently, these technologies, associated with Digital Transformation, make it possible to automate manual and repetitive processes in order to optimize operational processes and reduce human error. Therefore, this thesis uses Digital Transformation in order to develop a decision support model (“artifact”), using Python programming, in order to optimize the current manual processes of consulting 62 excel tables and associated calculations, looking for efficiency in the transport processes of organization X, with the aim of evaluating the model in question in terms of the accuracy of the results obtained, as well as obtaining monetary and time savings through the use of this model versus carrying out the process manually. For its constitution, the decision was made to select a Design Science Research methodology (DSRM), in order to facilitate the structuring and organization of the methodology of this work. The results of this project were very positive and it was possible to obtain an accuracy of 93.01% in obtaining the times collected by “artifact”. On the other hand, by creating three different scenarios (pessimistic, realistic and optimistic), it was possible to obtain satisfactory results in each case, resulting in annual monetary savings of over €6 000 and time efficiencies of over 85%. In short, despite the minor limitations, this project has been successful and it has been possible to apply Digital Transformation to the transport optimization process in question, and to conclude that the implementation of this decision support model promotes both significant temporal and monetary advantages for the organization.
Description
Keywords
Supply chains Digital transformation Industry 4.0 Process digitization Decision support model Automation Cadeias de abastecimento Transformação digital Indústria 4.0 Digitalização de processos Automação Modelo de apoio à decisão