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.
MathIT.Retail uses powerful Machine Learning algorithms (time series, trees, random forest, SVM, regression, neural networks, ensembles…) to make demand predictions with the necessary granularity.
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, MathIT.Retail is an important part of your solution.
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