Using "Analytics" techniques, we can use the data and business behavior reflected in them to establish predictive sequences that would allow us to make decisions in advance according to the requirements of the organization. These models are based on the similar historical behavior of the data and the displacement of endogenous and exogenous environmental conditions that may condition the value of the data for a particular situation.
Our goal is to produce predictive models using Data Science-related logic which can simulate past data and best predict the near future under similar environmental conditions. This technique can be used in topics related to demand processes or resource planning among others.
Our experience in this process forces us to use cutting-edge technology under a neural-associative approach (Qlik), this type of technology allows processing large amounts of data (Big Data) under global parameters of efficiency and effectiveness. In the event that our client possesses this type of technology, not necessarily the same, we try to make the most of that situation and complement it with our solution platform.
To ensure the quality of our services, we have internationally certified professionals, who, we guarantee, are an integral part of the consulting team.
Frequently Asked Questions
What is the level of precision of such models?
The accuracy of the models will largely depend on two factors: 1. quantity and quality of the historical information and 2. correct modeling of the data by the Data Scientist. Normally, these models can hover at an accuracy above 85%.
What have been the areas of greatest demand in this type of model?
This technique can be used for many areas, but most of the requirements received are related to sales forecasts, demand planning, inventories and applications additional to Scoring models.
