Analysis of profitability through with the generation of lscenarios from a hybrid method between artificial neural network and monte carlo simulation

Título traducido de la contribución: Análisis de rentabilidad a través de la generación de escenarios a partir de un método híbrido entre red neural artificial y simulación de monte carlo.

J. Bermeo, H. Castillo, S. Serrano, D. Arce, H. Bermeo

Producción científica: Contribución a una conferenciaDocumento

Resumen

Business Intelligence analyze existing data, to create knowledge about environment, in this paper, the accounting and operating information is analyzed to generate L-scenarios from hybrid method between ANN and Monte Carlo Simulation (MCS), then analyze the profitability in a Collection center of Raw milk. Every scenario is generated into analysis period, and has information about purchases, sales, cost of goods, sales price, operative cost and opportunity cost, then the cash flow, Net Present Value NPV and Modified Internal Rate of Return MIRR is calculated in order to evaluate the profitability of each scenario. The statistics (with a 95% of confidence) shows that MIRR has a confidence interval between 18,8% and applying an expected rate of return of 20% results in the average NPV is positive, so it implies the project is profitable. Furthermore, the opportunity cost analysis suggests proposes to increase the plant size.
Título traducido de la contribuciónAnálisis de rentabilidad a través de la generación de escenarios a partir de un método híbrido entre red neural artificial y simulación de monte carlo.
Idioma originalInglés
Páginas239-247
Número de páginas9
EstadoPublicada - 1 ene. 2017
Evento29th European Modeling and Simulation Symposium, EMSS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017 -
Duración: 1 ene. 2017 → …

Conferencia

Conferencia29th European Modeling and Simulation Symposium, EMSS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017
Período1/01/17 → …

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