What is Data Science?
Data science is a specialist area which uses statistical methods and modern technologies to gain insights from data. In particular, data scientists are currently using methods from the field of ML (Machine Learning) and AI (Artificial Intelligence) to obtain information from and make decisions based on a large amount of data. We have been supporting companies for many years in the collection, transfer, storage and refinement of their data.
Reviving data
Transferring data
Storing data
Refining data
Added value through data refinement
The use of machine learning methods has proven that they offer significant added value. With our data scientists, we put a special focus on the scientific analysis of data sets. The goal is to enable you to gain even greater added value from your data.
Cost-effective design of processes and production
To make your production more economical, topics such as predictive maintenance and anomaly detection are of great importance.
In addition, it is possible to use historical data to optimize current processes and thus make them more efficient. The past is the key to the future: the variety of your data offers many opportunities.
Predictive Maintenance: Optimization of maintenance costs
Make your production more efficient!
How? With predictive maintenance. This method allows you to optimize the maintenance of machines and systems in production. Because if you only maintain your machines when it is probably necessary, you reduce the costs of unnecessary maintenance and at the same time increase production efficiency by reducing unnecessary maintenance.
In many production plants, maintenance is carried out preventively. This means that maintenance is carried out in time windows defined by runtime or fixed intervals. In some productions, no maintenance windows are set at all. A failure of a machine then leads to an unplanned production stop. In this case, the machine is maintained reactively.
Every line or part of a line in a production has an inherent residual service life, which decreases with increasing use. Predictive maintenance is about estimating the remaining service life based on existing data. If the remaining duration is known, material can be ordered early for maintenance, and thus maintenance can be carried out with minimal disruption to production. In extreme cases, hazards for employees can also be avoided if a malfunction would jeopardize safety.
The challenge: An unplanned failure of a machine or system should be prevented, and at the same time maintenance should only be carried out when necessary. However, there is no one-size-fits-all solution for this. Every production is different and the requirements for the solution are therefore different.
The MEGLA solution:
Benefit from our MEGLA solution, based on the scientifically sound and proven CRISP-DM method*. Our goal is to develop a model for you that meets your specific requirements so that it can ultimately be integrated into your production. We proceed strategically, step by step:
Interview 01
Interview 01
The interview with you is a central part of our approach. To achieve the best outcome for you, our data science experts need to understand the problem and the business case. Therefore, together with you, we develop an understanding of your data and the underlying problem. We are convinced that a good understanding of the initial situation is the basic prerequisite for an optimal solution. In this interview, we discuss all the details from the business case to the technical implementation.
Analysis 02
Analysis 02
In the analysis phase, we examine the data you have recorded so far. For example, we will answer the following questions: What is the data quality? What correlations can be found in the data? What insights can be gained? Is the data meaningful?
The purpose of this phase is to find out whether your previous data is sufficient to answer the questions. If this is not the case, we are happy to develop concepts or strategies to improve the quality of your database.
Development 03
Development 03
With a good database, our data scientists can start developing predictive models based on machine learning algorithms. This makes it possible to make initial ad-hoc predictions about whether a plant will fail. However, these models are not yet usable in production. For you to gain real added value, the models must first be integrated into your production.
Production 04
Production 04
A model that makes reliable predictions finally becomes a product which we integrate into your production. We coordinate this step closely with you so that you can use the predictions optimally for your greater added value.
As a result, the underlying database will change in production. Therefore, it makes sense to check the prediction model at certain intervals and, if necessary, optimize it.
Find out more now!