Improving Perfect Orders at a Freight Transportation SME through Process Management, Autonomous Maintenance, Working Methodology and Demand Forecast

German Bazanchafloque, Fernando Sinchi-Perez, Iliana MacAssi-Jauregui

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Small and Medium-sized Enterprises (SME) in the transportation industry represent 91.5% of the existing companies in Peru. This paper discusses low efficiency rates of the Perfect Order since this value is 41.93% below the optimal value of 90% for these companies. The proposed solution model combines the Autonomous Maintenance, Forecasting Method, Working Methodology, and Process Management tools. During the Literature Review, an information gap was found with respect to studies focusing on this problem, as well as other existing problems in the industry. In addition, this paper combines four tools against other authors who have only combined two. The validation of this model is conducted by simulating the implementation of these four tools. The results reveal a 15% increase in Perfect Order efficiency, as well as an 8.95% reduction in costs.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2022 8th International Conference on Information Management, ICIM 2022
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas176-181
Número de páginas6
ISBN (versión digital)9781665451741
DOI
EstadoPublicada - 2022
Evento8th International Conference on Information Management, ICIM 2022 - Cambridge, Reino Unido
Duración: 25 mar. 202227 mar. 2022

Serie de la publicación

NombreProceedings - 2022 8th International Conference on Information Management, ICIM 2022

Conferencia

Conferencia8th International Conference on Information Management, ICIM 2022
País/TerritorioReino Unido
CiudadCambridge
Período25/03/2227/03/22

Huella

Profundice en los temas de investigación de 'Improving Perfect Orders at a Freight Transportation SME through Process Management, Autonomous Maintenance, Working Methodology and Demand Forecast'. En conjunto forman una huella única.

Citar esto