IAP-GmbH

Intelligent Analytics Projects

General Advanced Analytics

Our services include detecting simple patterns and taking into consideration essential variables. Advanced analytics is exemplary reflected in the logo of IAP-GmbH, which shows the separation of two distinctly different processes represented as hills (
Gaussian Mixture Model). The separation. for example, allows a division of products into defective and functional. Such analysis can, of course, identify more than two processes or states: The figure below represents a published analysis of the income of the Germans, and it depicts four data generating processes. The representation implies the existence of four different groups of people in Germany, which may be interpreted as social classes.

Proof of Concepts and Prototype Construction

We are able to extract knowledge from data and present it in a way that is comprehensible using the so-called databionic methods borrowed from nature. Successful prototype developments for conditional monitoring of IoT devices have already been carried out, whereby defective devices were directly recognizable.
The illustration shows a typical project process in a proof-of-concept (PoC). The client defines a concrete goal (in the figure, the grouping of data or cluster analysis).

Predictive Maintenance

Utilizing intelligent data analysis, our client can plan maintenance by foresight. For this purpose, it is necessary to identify the data patterns of a malfunction beforehand. In the next step, we use pattern recognition to predict malfunctions on devices during their lifetime or predict defective devices during production.

Automated Forecasts in Supply Chain Management

Machine learning methods developed through research enable the automated forecasting of product sales. The outcome clearly beats the quality of standard statistical software. This allows the supply chain to be optimized, freeing up the capital tied up in storage costs, and stored products. In contrast to our service, “General Forecasting using Artificial Intelligence”, these forecasting approaches can be applied to a large number of time series. A forecast (red) against the time series (black) is shown in the figure.

General Forecasting using Artificial Intelligence

Our expertise enables work/training/vacation scheduling of call center staff 24 hours, seven days, or one year in advance, based on historical data and publicly available data (e.g., weather). The workforce management system is based on artificial intelligence and has a forecasting quality of over 90%, which beats all published statistical systems. The AI is automatically capable of deriving the number of employees required per time unit (hours, days, weeks, months) from the number of processes (incoming calls). The procedure can be used for any application with the restriction to one target time series.

Human in the loop for explainable Artificial Intelligence

Visualization of High-dimensional Data and Cluster Analysis

With the latest neural networks and swarm intelligence approaches, the relationships of many features can be displayed as a 3D landscape and even printed out. Attached is the so-called cluster analysis which divides leukemia, cancer of the blood cells, into five types using genetic data consisting of 8000 features. Each point represents a patient. The valleys represent groups of similar patients and the mountains the differences between patients. This method, of course, can also be applied to customers. For example, customer discounts can be optimized through a strategic cluster analysis by identifying similarities beforehand. The applications are so widespread that, based on their quarterly financial reports, we have been able to group companies in terms of recommendations (see ESANN 2019) for purchase or sale of shares.