Decision Making for Knowledge Management in the Tequila Sector: A Fuzzy Logic Model




Agriculture Knowledge Management, Fuzzy logic, Bayes network, Decision making, Machine learning, Tequila sector, Mexico, low-tech


Knowledge management creates value for organizations, allowing them to be more innovative, productive, and competitive if that knowledge is used appropriately. Through this management, vital information is created and disseminated systematically and efficiently, and at the same time, knowledge learning is adopted, transformed, shared, and applied. This research analyzes decision-making for knowledge management in a mature low-tech sector, such as Tequila in Mexico. At the same time, it generates a predictive model of knowledge management that allows innovation in this sector by combining knowledge of modern technologies and ancestral knowledge in manufacturing the product, along with providing support to public policymakers and decision-makers to support small producers and rural communities. The methodological strategy used is an expert system through fuzzy logic, starting from a data set based on the patterns found in a Bayes network. The results show that the most relevant variables in decision-making for knowledge management in the Tequila sector are modern technologies, ancestral knowledge, and the Denomination of Origin. Under the above, it could be inferred that the ancestral knowledge variable is the most influential in achieving high values in managing knowledge management -the output variable preserving the value of a product with a designation of origin.