Bibliographic citations
Rivas, J., Castillo, N. (2020). Plataforma tecnológica para el seguimiento de transporte logístico de alimentos en la cadena de frío [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/653134
Rivas, J., Castillo, N. Plataforma tecnológica para el seguimiento de transporte logístico de alimentos en la cadena de frío [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2020. http://hdl.handle.net/10757/653134
@misc{renati/1288177,
title = "Plataforma tecnológica para el seguimiento de transporte logístico de alimentos en la cadena de frío",
author = "Castillo Talexio, Nora Victoria",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2020"
}
Technological advances in the logistics field have allowed people and organizations to develop more efficient ways of carrying out their operations; however, there is a lack of these technologies for the logistics of a cold chain, which has the objective of preserving the quality of a perishable product throughout the transportation process to its final destination. Large quantities of shipments from this industry are made daily, but the lack of efficient systems for monitoring and traceability in this process causes up to a third of food to be wasted before consumption. This causes losses for organizations in terms of labor, resources, energy, among others. Por this reason, this project will focus on the development of a technological platform to optimize the cold chain transport process. The platform consists of 8 parts: 1. Input elements; 2. Devices; 3. Channels; 4. Processing; 5. Validation; 6. Data visualization; 7. Exit and 8. Support. The technologies used are Blockchain integrated with Internet of Things (IoT) for capturing temperature data in real time. As a result of the implementation of the platform, 3 improvement scenarios are obtained as a result of constant control of the food that is being transported. In a pessimistic scenario, 4% of total losses are decreased; in a moderate scenario, 12% of losses would decrease; and finally, in the most optimal scenario, a 16% loss reduction would be achieved.
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