Abstract
Poor quality means that specified requirements are not met, be they safety, performance, regulatory standards, among others, resulting in additional costs for the company. The aim of this article is to study the costs related to extra freight in Logistics, delve into existing data in search of the main causes and apply these Quality management tools in A3 format, with a view to reducing extra freight costs. For the analysis, the Pareto model was chosen, using the 80/20 rule, where 80% of the results are generated by 20% of the causes, to identify the main types of causes that generated the extra freight and with the 2022 Extra Freight database controlled by Logistics, a first Pareto was built with the faults identified. The implementation of the A1 report with pareto data analysis at the first and second levels proved to be effective in directing the efforts of the areas involved to solve the causes with the greatest impact in a structured and effective manner, since the A1 tool aims to remedy the root cause, measuring the results by operational indicators, OPIS, with a view to improving the performance of the key indicators, KPIS.
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