TY - GEN
T1 - Artificial neural networks to estimate the forecast of tourism demand in Peru
AU - Ramos-Carrasco, Rogelio
AU - Galvez-Diaz, Shirley
AU - Nunez-Ponce, Victor
AU - Alvarez-Merino, Jose
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Service companies, for the most part, do not have physical inventories that allow them to cushion demand variability. The high logistics costs of each process of the company are the reflection of these differences in the forecast. For this reason, having a successful demand forecast will generate a competitive advantage in companies that take these processes with interest. In the present work it is possible to estimate the amount of tourist packages that will be sold in the next three months using ANN (artificial neural networks) that present a decrease in the error of the current situation of 9.89% which, together with a forecast management system of adequate demand, it was thus possible to reduce the logistics costs of the services company by up to 33%.
AB - Service companies, for the most part, do not have physical inventories that allow them to cushion demand variability. The high logistics costs of each process of the company are the reflection of these differences in the forecast. For this reason, having a successful demand forecast will generate a competitive advantage in companies that take these processes with interest. In the present work it is possible to estimate the amount of tourist packages that will be sold in the next three months using ANN (artificial neural networks) that present a decrease in the error of the current situation of 9.89% which, together with a forecast management system of adequate demand, it was thus possible to reduce the logistics costs of the services company by up to 33%.
KW - artificial intelligence
KW - artificial neural networks
KW - demand forecast
KW - forecast management
KW - tourism
UR - https://www.scopus.com/pages/publications/85082388067
U2 - 10.1109/SHIRCON48091.2019.9024873
DO - 10.1109/SHIRCON48091.2019.9024873
M3 - Contribución a la conferencia
AN - SCOPUS:85082388067
T3 - SHIRCON 2019 - 2019 IEEE Sciences and Humanities International Research Conference
BT - SHIRCON 2019 - 2019 IEEE Sciences and Humanities International Research Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Sciences and Humanities International Research Conference, SHIRCON 2019
Y2 - 13 November 2019 through 15 November 2019
ER -