Digital analytics
The automotive industry is entering a new age of analytics!
The automotive industry is entering a new age of analytics!
In BearingPoint’s "Big Data in Automotive" study among 120 decision-makers surveyed from automobile manufacturers and suppliers , 94% of respondents attribute great importance to Big Data & Analytics. At the same time, only 7% of companies are fully using Big Data & Analytics. The scope of application covers all business areas.
Without Big Data Analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway.
- Geoffrey Moore, American organizational theorist, management consultant and author
The importance and size of data has increased significantly in recent years. One of the biggest challenges in the automotive industry is the intelligent evaluation and use of large amounts of data in the business environment and in vehicles. Artificial Intelligence makes it possible to forecast future developments at an early stage and to identify meaningful patterns.
The focus is on processing and unifying data from different business divisions: only a holistic view shows the cross connections between business areas. That was reflected among the BearingPoint study participants, with three out of four of the opinion that current developments should not be missed and that Big Data & Analytics should be introduced.
Artificial Intelligence is about predicting the future, in contrast to conventional descriptive analysis which concentrates on the comprehensive analysis of historical data. Using state-of-the-art algorithms such as Machine Learning or Artificial Intelligence, complex patterns in huge amounts of data can be recognized in order to make statements about future events. Prescriptive analytics goes one step further, giving direct recommendations for action, such as real-time offering and pricing.
For example, our unique Quality Navigator enables our customers to increase quality along the entire product development process using artificial intelligence and machine learning:
Quality Navigator – A new age of quality analytics
In addition, Predictive Analytics typically faces the following challenges that require expertise in Big Data & Analytics:
BearingPoint is your suitable partner in Predictive Analytics:
Below you will find a selection of use cases in which our Artificial Intelligence solutions have successfully identified and recovered potentials.
Our ready-to-be-delivered concept quickly identifies use cases with the highest benefit from Artificial Intelligence.
The AI-Use Case Navigator approach identifies the most valuable use cases by verifying them using a business driven as-is analysis and data driven validation workshops:
We optimize sales forecasting for new cars using hierarchical, combined forecasting methods.
The Predictive Analytics module of the Factory Navigator was used to improve the sales forecast with a focus of slow-moving products:
An Eco system is provided to develop a customer-specific early warning system for quality and warranty issues.
The Quality Navigator uses our Predictive Analytics Workbench to cover the entire product lifecycle and solve quality and warranty problems by applying machine learning algorithms:
Analysis of battery data in vehicles to create transparency and prevent failures.
The Battery Navigator contains our entire experience to cover our customer’s challenges in the area of smart battery management:
Predicting spot welding failures in vehicle production has enormous potential to prevent production line downtime.
Machine learning algorithms enable prediction of malfunctions and proactive maintenance of welding robots:
Artificial Intelligence is used to optimize material flow in logistics.
The real-time Digital Process Twin enables the prevention of missing parts by integrating process data:
