Bibliographic citations
Martinez, M., Soto, C. (2020). Propuesta de mejora utilizando mejora del método de picking, pronósticos y un sistema kanban para la reducción de niveles de devoluciones de productos por deterioro en almacén de condición enfriada en una compañía de carnes [Trabajo de Suficiencia Profesional, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/667581
Martinez, M., Soto, C. Propuesta de mejora utilizando mejora del método de picking, pronósticos y un sistema kanban para la reducción de niveles de devoluciones de productos por deterioro en almacén de condición enfriada en una compañía de carnes [Trabajo de Suficiencia Profesional]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2020. http://hdl.handle.net/10757/667581
@misc{renati/1294034,
title = "Propuesta de mejora utilizando mejora del método de picking, pronósticos y un sistema kanban para la reducción de niveles de devoluciones de productos por deterioro en almacén de condición enfriada en una compañía de carnes",
author = "Soto Donayre, Christian Alexander",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2020"
}
The management of perishable products in the cold chain is a real challenge for companies that sell these products. This is since its short useful life requires more precise demand estimates to avoid overstock and during inventory management, the conservation of the product must be considered as the main priority (Pal & Kant, 2019). It was identified that most of the investigations focused on the cold chain did not apply Lean tools and that many of them focused on the development of monitoring techniques for the cold condition during the transport of the product, but not on its handling within the companies. The work presented, has as a case study a meat marketer from Peru, which imports meat products, and which currently registers high levels of order returns for its refrigerated products due to the deterioration of their condition. The application of the following three tools and/or methodologies will be proposed: Improvement of the Picking method, Forecasts for demand management and a Kanban system, which will allow an adequate dispatch of the product, reduce the current forecast error and implement an agile system that reduces the times of the product out of the refrigerated warehouse respectively (La Scalia et al., 2019). The validation of the model is carried out through the ARENA simulation software version 14.0 applied in the case study. The results obtained show that the reduction of 84% of the time of the product out of warehouse was obtained. In addition, economic losses due to product deterioration were reduced by 83%.
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