Program Overview
This program focuses on the role of data analytics in vehicle lifecycle management, covering IoT-enabled data collection, predictive maintenance, and customer insights. Designed for junior to mid-level professionals, the course combines theoretical understanding with practical applications, situational problem-solving, and real-life examples to help participants apply these insights to real-world challenges. Through interactive exercises and simulations, participants will learn to analyze and use vehicle data to enhance operational efficiency, reduce downtime, and improve customer satisfaction.
Features
- Understand the fundamentals of IoT-enabled data collection and its relevance to vehicle lifecycle management.
- Apply predictive maintenance techniques to reduce vehicle downtime and extend lifecycle performance.
- Use data analytics to derive actionable customer insights and enhance satisfaction.
- Analyze real-world datasets to identify maintenance needs and improve decision-making processes.
Target audiences
- Automotive engineers
- Fleet managers
- Data analysts
- Service managers
- Operations executives
Curriculum
- 3 Sections
- 25 Lessons
- 1 Day
Expand all sectionsCollapse all sections
- Introduction to Data Analytics in Vehicle Lifecycle Management9
- 1.1Overview of the vehicle lifecycle management (VLM) process
- 1.2The role of data analytics in VLM
- 1.3Key data sources in the automotive industry
- 1.4Understanding IoT (Internet of Things) and its significance in vehicle data collection
- 1.5Key concepts: IoT sensors, real-time data, cloud platforms, data analytics, machine learning
- 1.6How data is collected from various vehicle systems (e.g., engine diagnostics, tire pressure, fuel efficiency)
- 1.7The evolution of vehicle technology and its impact on data generation (telematics, autonomous vehicles, etc.)
- 1.8Group activity: Identifying key data points from a vehicle’s diagnostic system for analysis (IoT sensors, telematics)
- 1.9Hands-on experience with a mock IoT dashboard (using simulated data) for real-time vehicle data analysis
- Functional Use of Data Analytics for Predictive Maintenance9
- 2.1Basics of predictive maintenance and its importance in reducing downtime and increasing vehicle lifespan
- 2.2Types of predictive maintenance models: Condition-based, predictive analytics, and prescriptive analytics
- 2.3Key concepts: Failure modes, maintenance intervals, health monitoring, fault detection
- 2.4Practical applications of predictive maintenance using IoT data (e.g., tire pressure monitoring, oil change schedules, battery health)
- 2.5Tools and techniques for predictive analytics in the automotive industry (e.g., machine learning, statistical models)
- 2.6Case study: How a fleet management company successfully implemented predictive maintenance to reduce repair costs by 25% using IoT data
- 2.7Example: The role of predictive maintenance in electric vehicle (EV) battery lifespan optimization
- 2.8Group exercise: Analyzing vehicle diagnostic data to predict maintenance needs (participants will use a sample dataset to create a predictive maintenance model)
- 2.9Discussion of real-life scenarios where predictive maintenance could have prevented unexpected vehicle failures
- Using Data Analytics for Customer Insights7
- 3.1The role of customer insights in vehicle lifecycle management
- 3.2How data analytics enables customer behavior analysis and personalization
- 3.3Key concepts: Customer journey mapping, usage patterns, satisfaction metrics, customer lifetime value (CLV), vehicle performance data
- 3.4Identifying how customer data (usage patterns, service history) can drive insights for better customer experience
- 3.5Practical insights: Creating customer profiles based on driving behavior and vehicle usage
- 3.6Understanding the impact of vehicle data on customer loyalty, satisfaction, and retention
- 3.7Case study: A car manufacturer using telematics data to enhance aftersales service and improve customer retention
Instructor
