Machine Learning in Business Intelligence 4.0: Cost Control in a Destination Hotel.

Fulgencio Sánchez Torres; Iván González; Cosmin C. Dobrescu

doi:10.9781/ijimai.2022.02.008

Authors

Fulgencio Sánchez Torres University of Alicante
Iván González University of Castilla-La Mancha
Cosmin C. Dobrescu University of Castilla-La Mancha

DOI:

https://doi.org/10.9781/ijimai.2022.02.008

Keywords:

Business Analysis With Expert Assessment, Business Intelligence, Candidate Product, ICT Ecosystem, Machine Learning

Abstract

Cost control is a recurring problem in companies where studies have provided different solutions. The main objective of this research is to propose and validate an alternative to cost control using data science to support decision-making using the business intelligence 4.0 paradigm. The work uses Machine Learning (ML) to support decision-making in company cost-control management. Specifically, we used the ability of hierarchical agglomerative clustering (HAC) algorithms to generate clusters and suggest possible candidate products that could be substituted for other, more cost-effective ones. These candidate products were analyzed by a panel of company experts, facilitating decisions based on business costs. We needed to analyze and modify the company's ecosystem and its associated variables to obtain an adequate data warehouse during the study, which was developed over three years and validated HAC as a support to decision-making in cost control.

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