What are the Prediction Techniques?

The capacity to anticipate to future changes and uncertainties of the business is essential to guarantee success. The aim of Prediction Techniques is to minimise these uncertainties, generating useful forecasts to:

Anticipate customer demand

Anticipate the demand from customers at different sales points, allowing for factors under control, such as the price for sale, and external factors, such as calendars or weather conditions.

Calculate electricity generation needs

Provide forecasts of electricity generated from renewable and not easily manageable energy, such as wind or solar energy.

Predict employee performance

Predict the performance of the personnel, for instance, the sales that a Sales team will make according to the data gathered on their recruitment processes.

Prediction Techniques may give us estimations on all these unknown variables, which contributes to a better planning of the business.

How do they work?

As alike automatic classification procedures, the ones used for Prediction Techniques are grounded on the scientific discipline known as supervised automatic learning and similar ones, such as the study of temporal series. These techniques analyse historical data of the measures that need to be predicted and, using statistical techniques, relate them to external factors that may influence them. Therefore, a predictive model is built to estimate the most probable values of these measures in the near future.

In the way of classification systems, a regression model is a living solution, which can be reloaded constantly with new data as they are obtained, thus readjusting to future trends or unexpected circumstances automatically.

Likewise, with the aim of integrating the prediction model into the business process more easily, the model can generate variability indexes or confidence intervals on the basis of predictions, thus informing not only about the most probable value but also about the volatility expected.

This allows implementing business rules based on these predictions, for instance, supplying sales points with more stock whether a high demand or high volatility are expected.

Benefits and value of IIC Prediction Techniques solutions

Advantages of the Prediction Techniques:

Reduction of the uncertainty

Reduction of the uncertainty at business level elaborating predictions of the customers demand, sales or other relevant measures.

Setting-up of more solid business rules

Setting-up of more solid business rules based on the reliability and precision of the statistical models.

High speed of response

High speed of response, besides updating the measures of predictions whenever new data arise.

Guaranteed efficiency of IIC Prediction Techniques solutions

Over 25 years of experience at the IIC vouch for our experience developing predictive models and high-value solutions, generated in fields such as energy prediction and demand prediction in the electric sector.

IIC has a team of highly qualified professionals in machine learning who combine their activity at the Institute with works associated to research, teaching and collaboration with universities, thus keeping up-to-date with the latest technologies. This knowledge and practical experience in the different Prediction Techniques allows us to develop customised solutions which fulfil the response times and efficiency required for each individual project.

Prediction techniques allow us to anticipate to changes and uncertainties of the business, an essential aspect to guarantee success.