The quality of products is a characteristic which customers consciously perceive and which can make a decisive decision about the purchase of a product. If quality is poor, poorly quantifiable risks such as loss of image or negative reporting are particularly critical. Consequently, it is extremely important for companies to ensure a high level of quality even with increasing product complexity and increasing price pressure. This can be achieved with the help of data analytics. Data analytics refers to the scientific process of extracting and analyzing data. The aim is to determine conclusions or correlations by evaluating existing data. For example, the analysis of existing warranty data can help to identify future quality problems in the field ahead of time or to identify commonly occurring damages.
The following figure shows the impact of different production lines of the same product on the service life. This can be used, for example, to discuss process optimizations or to determine clusters for field actions.
The field of data analytics is broadly diversified. The creation of reports, key figures, drilldowns and alerts provides access to and an overview of a large data volume. This is of particular interest, for example, to determine the time, background and frequency of certain events from the existing data. In addition, statistical methods can be used to identify the underlying causes of deviations or anomalies in the data volume. Forecasts and optimizations can be used to make predictions with a confidence level or to determine trends. Answering these questions with the help of data analytics offers new opportunities to control, monitor and optimize data-driven processes. As the complexity of the questions and the amount of data increase, so does the complexity of the analysis. We use a variety of methods to meet this challenge with you.
The next figure shows a scatterplot matrix which shows the correlation between various influencing factors on a measurable target value. This allows the visual recognition of patterns in the influence factors. In addition to the graphical evaluation, a mathematical weighting of the influencing factors also takes place.