Strategy of variable data analyses to characterize mipymes

Authors

  • Valery José Lancheros Suárez Universidad de Córdoba
  • Ángel León Gonzáles Ariza Universidad del Norte

Keywords:

Variable methods, characterization, mipymes, sector foods, diagnosis

Abstract

This article contains a strategy of variable dataanalyses to characterize Mipymes, was applied inthe nutritional sector of the city of Montería, butit can be used in other economic sectors in whichit is desired to know its characteristics. One of thebenefits of this application, is to be able to comparethe companies with its counterparts at national andinternational level, to have arguments that allow todetermine their permanence in the time in spite offacing the great challenges that are approached inthe matter of commercial strategies as Free TradeAgreement with other countries and the otherpolicies that the Colombian State has projected.Osorio, B. (2009) [1].The nutritional industry was selected (Code CIIUD150000) as object of study of this investigation76 AVANCES Investigación en Ingeniería 14 (2011)by their importance and representativeness, sincein the data base of the Chamber of Commerce ofMontería, appear 12497 companies registered inthe department of Cordoba, of which 6307 haveaddress in the city of Montería and a good numberof them is related to this sector.The departure point of this work is a study of thediffusion degree and implementation of practicalstandards related to organizational Systems ofManagement in the Mipymes of the nutritionalSector of Montería, that presents a diagnosis ofthe state of the companies of this sector from theoptics of the quality, where the collected data wereprocessed through descriptive Statistical methods,reason why it was considered important to developone second part corresponding to the applicationof the variable data analysis where InferencialStatistics was used. Sánchez, J. (2007) [2].

Downloads

Download data is not yet available.

References

1. Osorio, B. (2009). Caracterización de las micro,pequeñas y medianas empresas en el Departamento deCórdoba. Montería: Ediciones Universidad del Sinú,pp. 8-60.

2. Sánchez C., J.J. (2007). “Algunas aproximacionesal problema de financiamiento de las Pymes enColombia”, en: Scientia et Technica, Año XIII, No.34, mayo, pp. 321-322.

3. Lancheros, Valery; Hernández, Helman y Robles,Juana (2008). “Sistemas de Gestión en el sectorde elaboración de productos alimenticios y debebidas”, en: Revista Ingeniería, Universidad DistritalFrancisco José de Caldas, Vol. 13, No. 2.

4. Figueras, S. (2000). “Introduccion al AnalisisMultivariante. de Estadística”, en: http://www.5campus.com/leccion/anamul. Consultadoel 12 de octubre de 2009.

5. Gurrea, M. T. (s.f.). “UOC, Análisis decomponentes principales, Proyecto e-Math”,en: http://www.uoc.edu/in3/emath/docs/Componentes_principales.pdf. Secretaria deEstado de Educación y Universidades MECD.Consultado en octubre de 2009.

6. Olmos, S., & Di Renzo, M. (2004). “INTA”, en:http://www.inta.gov.ar/ediciones/2004/biotec/parte6_cap1.pdf. Consultado en octubre de 2009.

7. Peña, Daniel (2002). Análisis de datos multivariados.Madrid - España: Editorial MacGraw Hill, pp. 133-158.

8. Romero, R.V. (2003). Javeriana. Recuperadoen enero de 2010, de Capítulo 10: analisis decomglomerados (clúster): http://tic.javeriana.edu.co/apps/Manuales/R/CursodeestadisticaconR.pdf

9. Pérez, Cesar (2004). Técnicas de análisis multivariantede datos. Aplicaciones con SPSS. Madrid - España:Editorial Pearson educación, pp. 121-154.10. Dallas, Johnson (2000). Métodos multivariadosaplicados al análisis de datos. México: ThomsonEditores S.A., pp. 93-396.

Downloads

Published

2011-06-01

How to Cite

Lancheros Suárez, V. J., & Gonzáles Ariza, Ángel L. (2011). Strategy of variable data analyses to characterize mipymes. Avances: Investigación En Ingeniería, 8(1), 75-87. https://revistas.unilibre.edu.co/index.php/avances/article/view/2606