Data Science and Artificial Intelligence

Decisions based on data instead of intuition

Data science is a synergy of data, analytical methods, domain knowledge and engineering-based solutions to obtain desired outcome for process optimization resulting in increased process yield, decreased operational downtimes, improved logistics, or creating benchmark solutions with state-of-the-art efficiencies and high impact results.

We use data science to leverage artificial intelligence techniques such as machine learning to make your data work for you for coherent decision making. Gain your first insights from the processing and visualisation of process and machine data or even reinvent whole business processes – the possibilities of data science are as wide-ranging as your use cases.

Start your Data Science project now

Whether you have a concrete vision, perhaps have started your first data science project, or have not yet been involved with it, like our software solutions, we tailor our workshops to suit your requirements and domain. This way, we ensure that we add value with modest efforts right from the start.

Solutions that optimise your outcome and processes

Time Series Forecasting

Whenever you’ve got data or observations recorded at regular time intervals, you’re looking at time series data. Time Series are all about looking at data over time to forecast or predict what will happen in the next time period, based on patterns or re-occuring trends from previous periods.

Many machine learning algorithms can be used to forecast various future observations in your business. For example, you could predict the number of future orders or product sales over a certain period of time. Once you have this knowledge, you can take action to be as prepared as possible by, for example, increasing production or hiring more staff for that period to meet the higher demand or even increase sales to boost your profits.

Predictive Quality

Simply put, the secret behind predictive quality lies in identifying correlating process parameters from production data indicating the quality of the product. This makes it possible to determine which parameters and configurations influences the quality of the product and how to tweak them to increase the product quality. This not only helps you to make reliable predictions on the quality of products but also to detect the root cause of the problems if they occur.

As a result, predictive quality leads to

  • lower production, material and inspection costs, as scrap, rework and throughput times are reduced
  • increased turnover, as higher margins are achieved
  • greater planning reliability, as accurate production figures can be predicted reliably

Predictive Maintenance

In factories, unplanned machine breakdowns or production downtimes have a direct impact on plant operations, thus costing money and time. Predicitve Maintenance focuses on these issues before they even happen by drawing upon huge sets of historical data together with sensors monitoring real-time values like vibration, temperature or pressure. If these measured values drop below their optimal operating efficiency, notifications are automatically triggered and a service or part replacement can be scheduled proactively. It is not only useful for a company’s own production, but also for the goods it manufactures and sells, such as motor vehicles or wind turbines.

  • Best maintenance timing
    Service only when required, minimal impact on the production line, maximised machine availability
  • Improved machine performance
    Improved performance through continuous data analysis, increased machine lifetime
  • Higher profitability
    Reduced failures & downtimes, increased plant safety, cost- and resource-optimised production

Recommendation Systems

Recommendation systems are data-based algorithms that give users valuable recommendations about items, products or actions that are of interest to them. In combination with predictive maintenance, these can be automated recommendations about which technician is best to take care of the impending machine failure or which spare parts are best to use. But also outside of the production environment, the number of use cases for recommendation systems is almost infinite, such as recommendations about similar documents, comparable calculations or videos that one might also like. The advantage here is to enable users to perform their tasks more quickly, comprehensibly and with a high degree of certainty, since decisions are data-based.

The XITASO Difference

XITASO combines high-end software engineering and data science, thus our customers receive the preparation, visualisation and analysis of their data as well as the technical testing and implementation of their individual use cases from one single source.

From simply creating an overview of one’s own data all the way to automation, we approach data science projects in an agile manner, step by step, as far as it makes sense for our customers.

Generating benefits at every level

The XITASO Data Science Value Pyramid

Even though people usually talk about artificial intelligence when it comes to data science, this is actually only the ultimate discipline – the top of the pyramid, so to speak. But often, one’s own goal can be achieved not only with AI, but much before that. At XITASO, we define five levels of data science projects – data overview, live reporting, prediction, recommendation and action – and each level delivers you direct added value.

Are you interested in a project or do you have any other questions?

Your contact person

Bernd Schächterle

Tel. +49 821 885882-58