The importance and amount of data has increased significantly over the past years. One of the biggest challenges in the automotive industry is the intelligent evaluation and use of large amounts of data.
Analytical approaches are taking an increasingly important role in making decisions to improve forecast accuracy. In addition, due to the current discrepancy between planned and additional costs, there is a need for optimization in the planning process.
Predictive analytics is a comprehensive analysis of historical data that examines “what is likely to happen.” Algorithms are used to predict further development. For example, customer behavior and possible sales per customer can be forecasted and direct measures can be made.
The interplay of different analytics tools enables the prediction of future developments. In this way, for example, an expensive assembly breakdown can be anticipated and prevented with countermeasures. In addition, the focus is on the processing and integration of data from different business areas, since only a holistic view reveals the cross-links between individual areas. For example, after-sales data can be used to identify the wear of components and eliminate weak points in product development.
The classical data analysis is complemented by the prediction of events. On this basis, future developments can be identified at an early stage, and meaningful patterns and dependencies can be identified quickly and easily. Predictive Analytics already represents a decisive competitive advantage for experts.
Typical challenges in predictive analytics are:
BearingPoint has already successfully completed numerous predictive analytics projects and has extensive process and implementation experience in the automotive industry.
An overview of the large flow of data is necessary. For an effective evaluation and use of the data, clear visualization and preparation is paramount.
With the help of visual analytics, business users can extract knowledge from data and intuitively discover trends and developments. The aim of visual analytics methods is to gain insights from extremely large and complex data sets and to make them easily understandable. The approach combines the strengths of automatic data analysis with human abilities to quickly capture patterns or trends visually. For example, within the framework of warranty management, possible causes of engine damage per series can be processed using the visual analytics tools. The visualization provides deeper insights and new findings in cause analysis to develop the fastest possible countermeasures.
The visualization of large amounts of data provides unexpected insights and the following advantages:
The next step in data and analytics is the opening of data science methods for the business user. Many analytics tools have developed a very high level of maturity with immense automation. This also allows untrained users without any IT knowledge, such as engineers or a parts list manager, to independently design ad hoc analyses and experiments. In addition to self-service BI, where own reports can be created, statistical models are used in self-service analytics to identify new patterns. This democratization will make it possible to rapidly react to changes in priorities. For repetitive tasks, business users can flexibly reshape certain data and find quick answers to questions.
How BearingPoint creates value: