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Campo DCValorLengua/Idioma
dc.contributor.advisorParra González, Ezra Federico
dc.contributor.advisorMaciel Arellano, María Del Rocío
dc.contributor.advisorLarios Rosillo, Víctor Manuel
dc.contributor.authorToribio Nava, José Luis
dc.date.accessioned2023-06-18T21:43:05Z-
dc.date.available2023-06-18T21:43:05Z-
dc.date.issued2022-07-11
dc.identifier.urihttps://wdg.biblio.udg.mx
dc.identifier.urihttps://hdl.handle.net/20.500.12104/92380-
dc.description.abstractIn current time, the process of making decisions in companies through the data is an important task to be competitive Worldwide. One of the most important areas inside companies is the Procurement department. This area usually has a huge amount of contracts due to, the hight number of agreement with suppliers, that may make business with them. To analyze contracts for making the purchase decisions or verify the contracts to identify any risk usually the purchase process is a manual and exhausting process that may take a lot of time to the contract analysts. These exhaustive and manual process can be reduce through implementing Artificial intelligence (AI) approaches. The AI has many branches of study; however, in the current project, in this work we will focus on Machine learning (ML) techniques, where we present a proposal to develop and implement a model training that includes ML techniques to identify risk, and according to this approach we are able to make the right decisions through Business Intelligence (BI).
dc.description.tableofcontentsI. Introduction 1.1 Real World Problem 1.1.1 Solution Strategy 1.1.2 Hypotesis 1.2 Research Problem 1.2.1 Research Questions 1.3 Document Structure II.State of the art 2.1 Information Technology on Business 2.2 Information Technology on Procurement 2.3 Artificial Intelligence on Procurement 2.4 Natural Language Processing on Procurement 2.5 Importance of Natural Language Processing to reduce costs on Business 2.6 Intelligent Workflows based on Natural Language Processing on Procurement 2.7 Business Intelligence as result of Intelligent Workflows implementation 2.8 Business Intelligence as Digital Transformation enabler 2.9 Summary III. Proposal 3.1 Machine Learning model for managing risk on Procurement Contracts 3.2 Foundational Methodology for Data Science 3.3 Architecture 3.4 Conclusion IV. Implementation and Results 4.1 Introduction 4.2 Snippets Extraction 4.3 Watson Knowledge Studio annotation 4.4 Export and consume the model on Cloud Pak For Data 4.5 Risk model logic implementation 4.6 Conclusion V. Conclusions and future work 5.1 Conclusion and contributions 5.2 Future Work 5.3 Contributions REFERENCES
dc.formatapplication/PDF
dc.language.isoeng
dc.publisherBiblioteca Digital wdg.biblio
dc.publisherUniversidad de Guadalajara
dc.rights.urihttps://www.riudg.udg.mx/info/politicas.jsp
dc.subjectMachine Learning
dc.subjectArtificial Intelligence
dc.subjectBusiness Intelligence.
dc.titleMachine Learning model for managing risk on Procurement Contracts
dc.typeTesis de Maestría
dc.rights.holderUniversidad de Guadalajara
dc.rights.holderToribio Nava, José Luis
dc.coverageZAPOPAN, JALISCO
dc.type.conacytmasterThesis
dc.degree.nameMAESTRIA EN CIENCIA DE LOS DATOS
dc.degree.departmentCUCEA
dc.degree.grantorUniversidad de Guadalajara
dc.rights.accessopenAccess
dc.degree.creatorMAESTRO EN CIENCIA DE LOS DATOS
dc.contributor.directorSalazar Linares, Pablo
dc.contributor.codirectorCalzada Orihuela, Gustavo
Aparece en las colecciones:CUCEA

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