Bibliographic citations
Veliz, P., (2020). Mejora en el proceso de recepción de camiones para un depósito minero utilizando la metodología Lean Six Sigma [Trabajo de Suficiencia Profesional, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/653446
Veliz, P., Mejora en el proceso de recepción de camiones para un depósito minero utilizando la metodología Lean Six Sigma [Trabajo de Suficiencia Profesional]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2020. http://hdl.handle.net/10757/653446
@misc{sunedu/3660284,
title = "Mejora en el proceso de recepción de camiones para un depósito minero utilizando la metodología Lean Six Sigma",
author = "Veliz Yañez, Pedro Yunior",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2020"
}
The company under study, dedicated to the integral logistics of mining concentrates has as one of its main processes the reception of trucks with concentration of Cu, Pb and Zn of the different miners in Peru whose daily demand is 170 trucks for which it has set out to serve it in a period of 10 hours whose indicator of the process is 17 trucks/hour, but currently an indicator of 15 trucks/hour has been handled, this results in overruns such as overtime and resource utilization which amount to an annual cost of S/. 638,655; for this reason, they intend to implement two tools of the Lean Six Sigma methodology. The diagnosis revealed that the problem of non-compliance with indicator lies in the inefficiency and effectiveness of the receiving process, in which the total time of trucks throughout the receiving process circuit is high on average 2.4 hours; the causal analysis found root causes such as disordered areas, congestion in sampling area and discharge zone, after which it is proposed the deployment of the methodology's own tools such as the implementation of 5S that seeks to generate culture of order and cleanliness with and the improvement of the flow of trucks between stages with the standardization of processes. When deploying, it is estimated to reduce average truck time from 2.4 hours to 1.7 hours and optimize resources and reduce over-costs.
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