Information Management for The Projection of Productive Capacities Articulated to Export Scenarios

Abstract: Information in various markets constitutes the primary basis for making the right decisions in a modern and globalized world. Therefore, opportunities grow based on the availability of data and how the data is structured to obtain information that supports decision-making processes, Ogrean (2018) and Neubert (2018), and even more so when business dynamics revolve around satisfying the demand for the products or services offered, Jacobs and Chase (2009). This article proposes the analysis of Colombian green coffee, as an alternative for strategic nodes that lack information on export scenarios, through operational research techniques, addressing the behavior of export sales for medium and long-term projections for management. business. The analysis is developed through Markov Chains, time series analysis techniques and mathematical functions, so a complementary approach is used to obtain predictions in future scenarios such as analysis of sales levels related to market shares.Choi et al (2018), indicate that one of the important applications of information management is in the field of demand forecasts, becoming one of the common alternatives in prediction for data series over time. The data is taken from Statistics of the National Federation of Coffee Growers (FNC), from 2016 to 2019 for export of Colombian green coffee, FNC (2021). Merkuryeba (2019) proposes procedures between techniques that allow a comprehensive approach to forecasts and where the methods complement each other, it is through the use of the methodology in Markov chain models (Kiral and Uzun 2017), plus the methodology of the time series analysis and mathematical functions (Stevenson et al 2015), which with a complementary approach, can reach a more detailed and comprehensive level of analysis for the statement about the future of the variable of interest: exportation market sales for Colombian green coffee.The results showed that Markov chains were very useful in long-term analysis for exports forecasting and their analysis by market segmentation, for this the demands level is classified according to the technique of Pareto. Another important contribution to the Markov chain in information management corresponds to the analysis disaggregated by export sales in demand levels associated to productions levels for aggregate plans.Complementarily, for the alternative of times series analysis; we start from the analysis of the demand, where a seasonal behavior of coffee green demand is detected. Rockwell and Davis (2016) and Stevenson et al (2015), establish a procedure for estimating and eliminating seasonal components by using the seasonal index. Additionally, Weller and Crone (2012) and Lau et al (2018), recommend two common alternatives to measure forecast error and make decisions to select the technique more adequate for information management: mean absolute deviation (MAD) and mean absolute percentage error (MAPE), finally, the result of the three techniques developed: moving average, exponential smoothing, and weighted moving average, the simple exponential smoothing, optimized through MAPE minimization is the selected technique, with which short and medium-term forecasts are defined.This study contributes directly to information management in the context of the exports of Colombian green coffee, as well as in academic settings in relation to research processes in data series under the configuration of big data. In this sense, it was demonstrated that the behavior of sales, segmented by demand levels, can be transformed into estimates of future behavior that establishes an orienting mapping of information management with respect to the possible level of production to energize the supply chain. Finally, the methodological scheme under an epistemological perspective supported by technical decisions, represent an academic contribution of great relevance for information management, where is recommended to use the time series techniques for short and medium-term forecasts, while Markov chains for the prediction and analysis of the sales structure in medium to long term forecasts, supported by median predictions through the use of mathematical functions.

Keywords: Information Management, Productive Capacities, Projection, Markov Chains, Time Series.

DOI: 10.54941/ahfe100925

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