Implementation Of Machine Learning and BPM To Increase Profitability in A Travel Agency

Guillermo Huapalla; Aiko Makino; Edilberto Miguel Avalos Ortecho

doi:10.11159/icmie25.128

Implementation Of Machine Learning and BPM To Increase Profitability in A Travel Agency

Guillermo Huapalla
, Aiko Makino
, Edilberto Miguel Avalos Ortecho

Carrera de Ingeniería Industrial

Producción científica: Capítulo del libro/informe/acta de congreso › Articulo (Contribución a conferencia) › revisión exhaustiva

Resumen

This research addresses the low accuracy in monthly tourist projections for a travel agency, with a Mean Absolute Percentage
Error (MAPE) of 29.49%, resulting in an opportunity cost of $3.9 million in the travel package purchasing process. A solution was
implemented using Machine Learning (ML) techniques, Support Vector Regression - Seasonal Autoregressive Integrated Moving
Average with Exogenous Regressors (SVR-SARIMAX), and Business Process Management (BPM) to reduce the MAPE and improve
the purchasing process by redesigning and adding a negotiation activity. The ML model was trained on historical data of international
tourist arrivals from 2013 to 2023, and 2023 purchase records. Using Python and Arena software, the MAPE was reduced to 18.18%,
with key performance indicators improving by 16%, reaching 19,907 customers, $20.7 million in revenue, and $1.8 million in opportunity
cost. Furthermore, a conversion rate over 90% was achieved after implementing process improvements, demonstrating the effectiveness
of ML and BPM in resource allocation and process enhancement.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 11th World Congress on Mechanical, Chemical, and Material Engineering, MCM 2025
Editores	Huihe Qiu, Yuwen Zhang, Marcello Iasiello
Páginas	128-1
Número de páginas	8
DOI	https://doi.org/10.11159/icmie25.128
Estado	Publicada - 12 ago. 2025

Serie de la publicación

Nombre	Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering
ISSN (versión digital)	2369-8136

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10.11159/icmie25.128Licencia: Sin especificar

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Citar esto

Huapalla, G., Makino, A., & Avalos Ortecho, E. M. (2025). Implementation Of Machine Learning and BPM To Increase Profitability in A Travel Agency. En H. Qiu, Y. Zhang, & M. Iasiello (Eds.), Proceedings of the 11th World Congress on Mechanical, Chemical, and Material Engineering, MCM 2025 (pp. 128-1). Artículo 128 (Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering). https://doi.org/10.11159/icmie25.128

Huapalla, Guillermo ; Makino, Aiko ; Avalos Ortecho, Edilberto Miguel. / Implementation Of Machine Learning and BPM To Increase Profitability in A Travel Agency. Proceedings of the 11th World Congress on Mechanical, Chemical, and Material Engineering, MCM 2025. editor / Huihe Qiu ; Yuwen Zhang ; Marcello Iasiello. 2025. pp. 128-1 (Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering).

@inproceedings{0cc56771471049038e0ca3a0de636a6e,

title = "Implementation Of Machine Learning and BPM To Increase Profitability in A Travel Agency",

abstract = "This research addresses the low accuracy in monthly tourist projections for a travel agency, with a Mean Absolute Percentage Error (MAPE) of 29.49\%, resulting in an opportunity cost of \$3.9 million in the travel package purchasing process. A solution was implemented using Machine Learning (ML) techniques, Support Vector Regression - Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SVR-SARIMAX), and Business Process Management (BPM) to reduce the MAPE and improve the purchasing process by redesigning and adding a negotiation activity. The ML model was trained on historical data of international tourist arrivals from 2013 to 2023, and 2023 purchase records. Using Python and Arena software, the MAPE was reduced to 18.18\%, with key performance indicators improving by 16\%, reaching 19,907 customers, \$20.7 million in revenue, and \$1.8 million in opportunity cost. Furthermore, a conversion rate over 90\% was achieved after implementing process improvements, demonstrating the effectiveness of ML and BPM in resource allocation and process enhancement.",

keywords = "Business Process Management (BPM), Machine Learning (ML), Support Vector Regression-Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SVR-SARIMAX), tourism",

author = "Guillermo Huapalla and Aiko Makino and \{Avalos Ortecho\}, \{Edilberto Miguel\}",

year = "2025",

month = aug,

day = "12",

doi = "10.11159/icmie25.128",

language = "Ingl{\'e}s",

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Huapalla, G, Makino, A & Avalos Ortecho, EM 2025, Implementation Of Machine Learning and BPM To Increase Profitability in A Travel Agency. En H Qiu, Y Zhang & M Iasiello (eds.), Proceedings of the 11th World Congress on Mechanical, Chemical, and Material Engineering, MCM 2025., 128, Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering, pp. 128-1. https://doi.org/10.11159/icmie25.128

Implementation Of Machine Learning and BPM To Increase Profitability in A Travel Agency. / Huapalla, Guillermo; Makino, Aiko; Avalos Ortecho, Edilberto Miguel.
Proceedings of the 11th World Congress on Mechanical, Chemical, and Material Engineering, MCM 2025. ed. / Huihe Qiu; Yuwen Zhang; Marcello Iasiello. 2025. p. 128-1 128 (Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering).

Producción científica: Capítulo del libro/informe/acta de congreso › Articulo (Contribución a conferencia) › revisión exhaustiva

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N2 - This research addresses the low accuracy in monthly tourist projections for a travel agency, with a Mean Absolute Percentage Error (MAPE) of 29.49%, resulting in an opportunity cost of $3.9 million in the travel package purchasing process. A solution was implemented using Machine Learning (ML) techniques, Support Vector Regression - Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SVR-SARIMAX), and Business Process Management (BPM) to reduce the MAPE and improve the purchasing process by redesigning and adding a negotiation activity. The ML model was trained on historical data of international tourist arrivals from 2013 to 2023, and 2023 purchase records. Using Python and Arena software, the MAPE was reduced to 18.18%, with key performance indicators improving by 16%, reaching 19,907 customers, $20.7 million in revenue, and $1.8 million in opportunity cost. Furthermore, a conversion rate over 90% was achieved after implementing process improvements, demonstrating the effectiveness of ML and BPM in resource allocation and process enhancement.

AB - This research addresses the low accuracy in monthly tourist projections for a travel agency, with a Mean Absolute Percentage Error (MAPE) of 29.49%, resulting in an opportunity cost of $3.9 million in the travel package purchasing process. A solution was implemented using Machine Learning (ML) techniques, Support Vector Regression - Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SVR-SARIMAX), and Business Process Management (BPM) to reduce the MAPE and improve the purchasing process by redesigning and adding a negotiation activity. The ML model was trained on historical data of international tourist arrivals from 2013 to 2023, and 2023 purchase records. Using Python and Arena software, the MAPE was reduced to 18.18%, with key performance indicators improving by 16%, reaching 19,907 customers, $20.7 million in revenue, and $1.8 million in opportunity cost. Furthermore, a conversion rate over 90% was achieved after implementing process improvements, demonstrating the effectiveness of ML and BPM in resource allocation and process enhancement.

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Huapalla G, Makino A, Avalos Ortecho EM. Implementation Of Machine Learning and BPM To Increase Profitability in A Travel Agency. En Qiu H, Zhang Y, Iasiello M, editores, Proceedings of the 11th World Congress on Mechanical, Chemical, and Material Engineering, MCM 2025. 2025. p. 128-1. 128. (Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering). doi: 10.11159/icmie25.128