The problem of not analyzing historical data to predict sales and make decisions about the maximum and minimum values that we want to have for each reference either in the central warehouse or in each of our points of sale can end up in a breakage of stock, loss of sales, impoverishment of the service levels that we offer to our customers and, of course, in our final accounts.
This helps us to optimize our production chain and/or orders to suppliers, to anticipate the future, to detect changes in trends or other types of patterns, to make better decisions, to minimize reverse and cross logistics between stores and, ultimately, to be more efficient.
oga.Retail uses powerful Machine Learning algorithms (time series, trees, random forest, SVM, regression, neural networks, ensembles…) to make demand predictions with the necessary granularity.
Profesionales en Inteligencia Artificial y en Investigación Operativa, en la aplicación práctica, sobre escenarios reales de alta complejidad, del estado del arte en las técnicas de optimización de procesos.
Expertos en la logística del transporte, con un elevado registro de proyectos en organizaciones de transporte y distribución.
Apasionados por resolver problemas operativos complejos y aportar el máximo valor de negocio a organizaciones logísticas desde la aplicación de estas capacidades.
Hiperpersonalización: no hay dos negocios iguales, ni dos operativas iguales. La flexibilidad de capturar y modelar las diferencias nos permite maximizar el valor aportado.
Amplio registro de proyectos de alto valor para grandes empresas nacionales e internacionales de alta demanda.
Valores diferenciales: conocimiento experto, innovación e inversión constante en actualización científico-técnica, flexibilidad, agilidad y adaptabilidad.
If you identify inefficiencies or points of improvement in the planning or execution of your stock operations and you need the best tool to transform your business by exploiting the full value of your own data, oga.Retail is an important part of your solution.
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