Coffee production chain management strategies using data analytics
DOI:
https://doi.org/10.26507/paper.4519Keywords:
Productivity, coffee cultivation, data analyticsAbstract
This research proposes management strategies for the coffee production chain using data analytics to improve decision-making in the management of the crop and the producer (coffee grower) as the actor of analysis; the statistical base was formed with data on production, quality, cultivated areas and number of producers of the bean in the department of Norte de Santander; the time window of the data was established from 2013-2024, with which an exploratory analysis was constructed to understand the behavior of the coffee sector in the department and the impacts on productivity and competitiveness at the national level.
With the above, the impacts that affect productivity such as geographic dispersion, planting conditions, harvest and post-harvest handling were established; with an accumulated loss of 12% by 2024; which allowed making relational crosses between different associative denominations of producers, cultivated area, available area and crop renewal or transfer; from the above, and to support the strategies, clustering techniques and advanced prediction models were used, segmenting the crops by productive conditions, cultivable area and crop operation.
Finally, the study shows the importance of the coffee-growing sector for the region's economy, proposing strategies for managing the production chain focused on the coffee grower as the actor under analysis; strategies oriented to the harvest and post-harvest of the crop are established, as well as to the management of associativity in the region in the medium term; at the same time, the statistical heat maps show coffee cultivation and renewal zones.
Author Biography
Óscar Mayorga Torres, Universidad Francisco de Paula Santander
University professor, researcher and consultant on Industry 4.0 issues applied to modern organizations and economic sectors, industrial engineer, specialist in production and logistics engineering, master in industrial engineering and PhD student in engineering.
References
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