Business Intelligence Model for Decision Making in a MYPE in the Construction Sector

Authors

  • Bravo Huivin, Elizabeth kristina
  • Barrantes Tapia, Piero A.
  • Amayo Contreras, Claudia D.
  • Florian Castillo, Odar R.
  • LLaque-Fernández, Grant Ilich

DOI:

https://doi.org/10.18687/LACCEI2023.1.1.428

Keywords:

Business intelligence, Decision making, Processes, MSE, Construction company.

Abstract

The objective of the research is to design a business intelligence model for the decision-making process in a company in the construction sector. The type of research is applied, descriptive, qualitative. The population is made up of 7 areas of the company and 20 workers. Interviews and surveys validated by expert judgment will be applied. As a result, it was obtained that the business intelligence system significantly improved the quality of the information for the 4 areas in which it would be implemented, in addition, meeting time was reduced by 90%. The information collected was analyzed, and it was concluded that the design of BIS in the company has gone from being a cost to a competitive advantage, this being a necessary resource to meet the objectives of the organizations since it reduces time, improves the quality of information, and ensures optimal organizational decision-making. The estimated cost of the investment amounted to S/. 12920.

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Published

2023-07-27

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Section

Articles

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

LACCEI retains copyright of all published articles under the terms of its copyright transfer agreement. As the copyright holder, LACCEI distributes the articles to the public under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

How to Cite

Bravo Huivin, Elizabeth kristina, Barrantes Tapia, Piero A., Amayo Contreras, Claudia D., Florian Castillo, Odar R., & LLaque-Fernández, Grant Ilich. (2023). Business Intelligence Model for Decision Making in a MYPE in the Construction Sector. LACCEI, 1(8). https://doi.org/10.18687/LACCEI2023.1.1.428

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