Digital 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.

Why Artificial Intelligence? Why now?

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:

  • Data Engineering:
    • Creation of digital connectivity to industry 4.0 and smart devices.
    • Collecting, preparing and storing data in different formats and systems (Hadhoop, Kafka, Splunk).
  • Data Science:
    • Analysis and definition of relevant data and information using modern tools and algorithms (R, Python, AI, Machine Learning, Deep Learning).
    • Definition of recommendations for action in cooperation with industry and process experts.
    • Target-group specific visualization in reporting tools (BOARD, Tableau, Qlik Sense, SAP BI).
  • Data security
  • Data quality and consistency

Why BearingPoint?

BearingPoint is your suitable partner in Predictive Analytics:

  • We have successfully completed many predictive analytics projects in the automotive industry.
  • We combine specific process know-how along the entire automotive value chain with the use of modern data science technology.
  • We cover the entire service range from data acquisition and analysis to business management recommendations.
  • With our BearingPoint Predictive Workbench, all modern data science tools are available.

Below you will find a selection of use cases in which our Artificial Intelligence solutions have successfully identified and recovered potentials.

  • 1. Use Case Navigator - Identification of use cases with the highest benefit from AI

    Our ready-to-be-delivered concept quickly identifies use cases with the highest benefit from Artificial Intelligence.

    Problem Description: Situation & Pain Points

    • Many companies lack the expertise which tools and methods should be applied to analyze their data in order to get the most out of it
    • Many use cases are unsuccessful due to incorrect data selection and missing know-how in the area of Artificial Intelligence

    Project Results: Our Approach

    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:

    • Increased understanding of the fields of application for Artificial Intelligence
    • Data quality and data availability checks as well as tool and method recommendation
    • Identify well-founded and targeted use cases that have the potential to be implemented as an Artificial Intelligence project in the company

    Customer Benefits: Measurable and Sustainable

    • Detailed Artificial Intelligence use cases are provided
    • Individual and personalized tool and methodology recommendation
    • Increased understanding of the fields of application for Artificial Intelligence
  • 2. Factory Navigator - Sales forecast optimization

    We optimize sales forecasting for new cars using hierarchical, combined forecasting methods.

    Problem Description: Situation & Pain Points

    • Demand structures differ strongly among products and regions
    • Lack of quantitative consideration of temporary demand shocks and structural breaks
    • External indicators (e.g. tax-law changes) are difficult to be quantified

    Project Results: Our Approach

    The Predictive Analytics module of the Factory Navigator was used to improve the sales forecast with a focus of slow-moving products:

    • Creation of consistent forecasting results across all levels of demand planning
    • Optimization of forecast accuracy using demand patterns, shocks, structural breaks and external indicators
    • Quantitative planning of complex events and new product launches

    Customer Benefits: Measurable and Sustainable

    • Sophisticated decision support for sales forecasting
    • Highly accurate forecast results due to consideration of external events and effects
    • Consistent sales forecast across all products and regions without contradictory individual forecasts
  • 3. Quality Navigator - Improvement of quality and warranty with the help of AI

    An Eco system is provided to develop a customer-specific early warning system for quality and warranty issues.

    Problem Description: Situation & Pain Points

    • Unforeseen quality and warranty issues
    • Missing comprehensive root cause analysis for quality problems
    • Missing cost and provision forecasts

    Project Results: Our Approach

    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:

    • Early warning system for quality and warranty issues
    • Integrated data model for aftersales, warranty, vehicle and quality data
    • Implementation of predictive analytics models to reduce costs throughout the product quality lifecycle

    Customer Benefits: Measurable and Sustainable

    • Shorter detection-to-correction cycles
    • Less warranty cases and improved vehicle quality
    • Lower costs by improved parts and provision forecasting
  • 4. Battery Navigator - Smart battery management

    Analysis of battery data in vehicles to create transparency and prevent failures.

    Problem Description: Situation & Pain Points

    • Commercial vehicles in particular fail due to deep discharging of the installed batteries
    • High costs related with vehicle breakdowns in spite of avoidable replacements with low unit costs
    • Internally, there is insufficient transparency concerning failure reasons

    Project Results: Our Approach

    The Battery Navigator contains our entire experience to cover our customer’s challenges in the area of smart battery management:

    • Exploration of influencing factors and correlations that worsen the health and life expectancy of batteries
    • Accurate prediction of the battery’s state of health
    • Enabling of proactive maintenance by forecasting of the remaining battery lifetime and occurrence probability

    Customer Benefits: Measurable and Sustainable

    • Long-term cost savings by eliminating the effects of a damage-dependent, reactive maintenance strategy
    • Improved capacity utilization of the vehicles by less unexpected battery failures
    • Increased overall customer satisfaction level and transparency on the assessment of customer complaints & gestures of goodwil
  • 5. Predictive Maintenance - Optimization of production line uptime

    Predicting spot welding failures in vehicle production has enormous potential to prevent production line downtime.

    Problem Description: Situation & Pain Points

    • Welding robots fail unforeseen and interrupt production
    • Highly inconsistent and erroneous data records
    • Serious errors in documentation and data recording

    Project Results: Our Approach

    Machine learning algorithms enable prediction of malfunctions and proactive maintenance of welding robots:

    • Proactive maintenance to prevent failures in the future
    • Quality improvement through specific recommendations for the structured documentation of incidents
    • Preparation and enrichment of existing data sources for the prediction of incidents

    Customer Benefits: Measurable and Sustainable

    • Avoidance of a malfunction during robot maintenance operations
    • Process-related improvement possibilities for the documentation of incidents
    • Prediction of failures in the previous shift enables the reduction of overcapacities
  • 6. AI for Supply Chain Management - Realtime AI-controlled material flow

    Artificial Intelligence is used to optimize material flow in logistics.

    Problem Description: Situation & Pain Points

    • The manual control of material disposition is highly complex and labor-intensive
    • Parameters are subjected to high uncertainty and require continuously adjustment
    • In-house and inbound processes are very interdependent, even small effects can lead to production disruptions

    Project Results: Our Approach

    The real-time Digital Process Twin enables the prevention of missing parts by integrating process data:

    • Integrated control instrument in real time
    • Integration of process data to avoid missing parts
    • Automated optimization of control parameters

    Customer Benefits: Measurable and Sustainable

    • Prevention of production disruptions through timely intervention
    • Reduced effort, lower inventory and fewer errors in logistics
    • AI continuously monitors the material flow and adjusts demand settings

Would you like more information?

If you want to get more information about this subject please get in touch with our experts who would be pleased to hear from you.

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