Supply chain forecasting model using neural networks
Overstock affects the supply chain, making it vulnerable and generating costs due to obsolescence. Up to now, it is needed to implement tools to forecast production demand with impact in supply chain inventory levels in order to know the reorder point and fulfill the client requirements. In this wor...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | México |
| Institución: | UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO |
| Repositorio: | PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI |
| Idioma: | español |
| OAI Identifier: | oai:repository.uaeh.edu.mx:article/11482 |
| Acceso en línea: | https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/11482 |
| Access Level: | acceso abierto |
| Palabra clave: | forecast demand supply chain neural networks simulation pronóstico demanda cadena de suministro redes neuronales simulación |
| Sumario: | Overstock affects the supply chain, making it vulnerable and generating costs due to obsolescence. Up to now, it is needed to implement tools to forecast production demand with impact in supply chain inventory levels in order to know the reorder point and fulfill the client requirements. In this work, a simulation model of a supply chain was developed, considering four links (Supplier, Production, Distribution and Retailer) with their respective warehouses. Data concerned to inventory level and response time obtained from the simulation model were used for training of 100 different artificial neural network (ANNs) configurations, to identify the one with the best prediction for inventory levels. The ANN with the best performance (r2 = 0,99408, RMSE = 1,44217) contains 12 neurons in the input layer, 70 neurons in the first hidden layer, 60 in the second hidden layer and 4 neurons in the output layer. |
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