Analysis statistical neutrosophic on the diagnosis of a System of Internal Control in Inventories. Case of study: Distribuidora Mc cellular world

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Fanny Maricela Pozo Hernández
Jorge Hernán Almeida Blacio
Oscar Rodrigo Aldaz Bombón

Abstract

The present investigation article carries out an analysis diagnosis neutrosophic to know if the companies dedicated for sale more big of telephones and teams of communication of Sacred Domingo, legally constituted in the superintendence of companies, Values and Insurance apply the system I SEW with its conceptual mark as measure of internal control of their inventories and to compare them with the Distribuidora MC Cellular World. The used methodology was based by means of a mixed paradigm (qualitative - quantitative) with qualitative tendency and a type of non experimental design of traverse character, applying the documental technique on the information of the companies and the survey to the legal representatives, it is obtained as a result that all the business ignore the methodology of the system I SEW I and their components as the nonexistence of an ethics code, the lack of evaluating the risks to their objectives, nonexistence of a manual of procedures, limited communication and information and the scarce pursuit to the personnel's functions, concluding that the Distribuidora MC cellular World in spite of not integrating to a control organism like the other ones, it has the same problems on their inventories, concluding in the impact that you/they have the inventories in the commercial companies about its liquidity for its inadequate control.

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Analysis statistical neutrosophic on the diagnosis of a System of Internal Control in Inventories. Case of study: Distribuidora Mc cellular world. (2023). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 26, 21-30. https://fs.unm.edu/NCML2/index.php/112/article/view/299
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How to Cite

Analysis statistical neutrosophic on the diagnosis of a System of Internal Control in Inventories. Case of study: Distribuidora Mc cellular world. (2023). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 26, 21-30. https://fs.unm.edu/NCML2/index.php/112/article/view/299

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