Data Science and Artificial Intelligence

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.