Bibliographic citations
Barrenechea, O., Mendieta, A. (2019). Modelo tecnológico de Data Analytics para los procesos de la cadena de abastecimiento para pymes [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/639533
Barrenechea, O., Mendieta, A. Modelo tecnológico de Data Analytics para los procesos de la cadena de abastecimiento para pymes [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2019. http://hdl.handle.net/10757/639533
@misc{renati/1286774,
title = "Modelo tecnológico de Data Analytics para los procesos de la cadena de abastecimiento para pymes",
author = "Mendieta Retuerto, Aaron Andres",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2019"
}
This business intelligence project has as a general objective to implement a Data Analytics Technology Model for the supply chain processes for SMEs for strengthening decision making. For its development, it mainly collected information of the company identifying its business requirements. We also used the Kimball and PMBOK methodology for the development of project deliverables and artifacts, and Pentaho Business Analytics 7.1 was selected as the main tool for the implementation of our solution. Our proposal aims to improve and increase the efficiency in the Purchasing, Inventories and Sales processes and thus obtain real-time responses such as reducing the time and costs that are used when managing the internal operations of the business, improving customer service, as well as Measure the performance of personnel and processes in the supply chain. In this way, we used a business intelligence methodology that supports the development of our solution, which we propose management indicators, KPI`s, Dashboards, reporting and graphs dynamically. In order to validate our Data Analytics solution, the platform was deployed accordingly, and we began to evaluate the results over time obtaining the following results and conclusions of our solution based on the times and costs: Help to control the different processes in the supply chain from specific metrics or KPIs. Support on the decision-making to obtain reliable and effective results based on BI best practices and tools. The time and cost that was used to analyze the information recorded in the system was reduced.
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