Big data y cadenas de suministros un binomio complejo para américa latina

Raúl Rodríguez-Luna; Margareth Mercado-Pérez; Mariana Escobar-Borja

doi:10.15649/2346030X.825

Authors

Raúl Rodríguez-Luna Universidad Cooperativa de Colombia https://orcid.org/0000-0002-8718-2681
Margareth Mercado-Pérez Universidad Cooperativa de Colombia
Mariana Escobar-Borja Universidad Cooperativa de Colombia

DOI:

https://doi.org/10.15649/2346030X.825

Keywords:

bug data, neural networks, supply chains, logit models

Abstract

The objective of the work was to predict the use of Big data tools in decisión making processes in palm oil supply chains in Latin America, based on a set of predictor variables. It was a study of a quantitative type, of a correlational level, for this, discrete choice models were used, since we were interested in knowing the influence that a series of variables had on a response variable. For this, a survey type instrument was carried out with 30 items that involved 84 managers of the chain applied between February 2019 and June 2020. Among the main findings, it is w rth noting that the companies in the chain that had and used Big Data in the process decision-makers managed to optimize it, in terms of minimizing errors in marketing procedures. Regarding the type of personnel training, those companies that incorporated resources with specialized training in Data Science, handling of Big Data tools and analytics, contributed significantly to the Marketing and commercialization processes. Regarding the subcontracting of personnel to carry out technical activities, there was a reduction in human resources in the different companies studied, however, in the commercial of the chain, the companies that offered greater freedom for subcontracting during the period studied tended to increase the use of Big Data and therefore e-commerce. In conclusion, it is considered that the use of predictive models incorporated in the chain make it possible to prevent failures in decisions and project them, but it will be difficult to incorporate them into the decision- making processes in the medium term, given the high costs of investment, explained by additional costs in training and learning. However, in those companies where a greater number of qualified workers in big data are located, it is more feasible that new innovations and generation of value are produced for customers, finally with this work it is intended to make an empirical contribution to the generation of value from Big Data

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