From design to delivery: The role of artificial intelligence in the automotive industry

Automotive manufacturers are embracing AI technology to gain a competitive advantage in the market. Gartner predicts that by 2026, 75% of large enterprises will have adopted some form of robotics in their warehouse operations. Additionally, 25% of manufacturers will have transitioned by then to a microservices architecture to enable differentiated AI and analytics capabilities. 

From a purely strategic standpoint, smart manufacturing makes sense. AI and predictive analytics allow you to detect data outliers using IoT capabilities, perform root cause analysis in seconds, avoid accidents and downtime before they happen, optimize supply chain processes, and augment customer experience for increased conversions and brand loyalty.

So how are the benefits of predictive analytics and AI achieved across the automotive value chain? By modernizing legacy technology with industrial Internet of Things (IIoT) systems and a robust analytics Platform, manufacturers can gain real-time insight into factory floor, supply chain and customer experience processes.

In this blog, we’ll explore the role of AI in automotive manufacturing throughout the value chain and how it is driving innovations in this dynamic industry.

In today’s globalized world, it is imperative that vehicle manufacturing value chains continue to function regardless of extenuating circumstances such as equipment failure, extreme weather conditions, geopolitical events, pandemics, or any other cause of potential disruption. Any turmoil at a vehicle manufacturing site in one country runs the risk of creating a ripple effect and having a negative impact on a global scale. This is where machine learning (ML) and artificial intelligence (AI) come into play.

Predictive and real-time data analytics are among the top applications of AI in the automotive industry. They help to ensure maximum production output, eliminate workplace inefficiencies and hazards, and empower manufacturers to prepare for and respond efficiently to changing market demands.

For example:

• Predictive maintenance algorithms monitor equipment/machinery data and predict timely maintenance schedules to avoid any unplanned production downtime.
• Anomaly detection models are trained to identify outliers in data that indicate machinery faults, detect where the faults are, and even provide relevant fixes or alert machine operators to attend to the issue before major damage occurs.
• Visual inspection tools  in the form of IoT sensors, smart robots, and cameras can identify product faults and assembly line inefficiencies to optimize quality control and speed of production.
• On the supply chain and commercial side of manufacturing operations, AI analytics can help optimize product demand planning, prevent product stock-outs, enable real-time inventory visibility, and manage product delivery and logistics more efficiently.
• GenAI-powered design tools enable a spectrum of product design capabilities for rapid prototyping, customization, restyling, and product visualization.

As such, manufacturers can focus on their core business strategy and begin to invest in further business innovation.

Stalled or broken down equipment is a disaster in the making for any automotive manufacturer. It’s not just about losing money: sometimes, these setbacks can also pose a serious risk to the workers operating the shop floor. Add the cost of product quality defects and car assembly line disruptions to that scenario and you quickly realize the overwhelming value that automated quality control tools can bring to the factory floor. 

Through the use of Industry 4.0 tools for anomaly detection, predictive maintenance and visual quality control, manufacturers are better able to:

• Monitor equipment health;
• Establish timely equipment maintenance schedules;
• Detect machinery/product anomalies and automate fixes or alert staff;
• Conduct root cause analysis to identify where faults are located; and
• Identify product quality defects and car assembly line inefficiencies.

💡Read more:

• Building a Visual Quality Control solution in Google Cloud using Vertex AI
• Building a Predictive Maintenance Solution Using AWS AutoML and No-code Tools
• Anomaly detection in industrial IoT data using Google Vertex AI: A reference notebook
• Loss Prevention with AI-powered IoT analytics platform on AWS
• Building an IoT Platform in GCP: A Starter Kit
• Anomaly Detection in Industrial Applications: Solution Design Methodology
• Detecting anomalies in high-dimensional IoT data using hierarchical decomposition and one-class learning

Smart manufacturing can drastically simplify inventory planning across your supply chain networks. Managing numerous suppliers and distributors can quickly become a cumbersome process if the right system isn’t in place. AI tools for supply chain optimization can help to improve:

• supplier network and inventory management;
• warehouse management; and
• logistics and transportation.

Predictive analytics, combinatorial optimization, and simulation algorithms can help determine changes in consumer demand and supplier network capacity, allowing manufacturers to plan ahead concerning production schedules and obtaining any necessary raw materials. It also optimizes inventory flow, safety stock levels, and production costs. The training data used to provide this insight is gleaned from various factors such as the manufacturer’s historical sales data, financial data, supplier data, weather/social/political events, market forecasts, third-party platforms such as social media, and so on.

💡Read more:

• Reimagining resilience: supply chain optimization for smart manufacturing
• Optimization of order and inventory sourcing decisions in supply chains with multiple nodes, carriers, shipment options, and products
• Inventory allocation optimization: A Dataiku starter kit
• How explainable AI helped reduce warehouse order picking time by ¼
• Multi-agent deep reinforcement learning for multi-echelon supply chain optimization
• Supply Chain and Inventory
• A rapid response to COVID-19 supply chain and market shocks: Emerge from the crisis stronger

In today’s digitally connected world, the benefits of AI in the automotive industry extend beyond enhancing manufacturers’ capabilities; they can also contribute to enriching digital customer experiences. As part of their smart manufacturing modernization efforts, many automotive companies are shifting their go-to-market strategies from B2B only to a blend of B2B and D2C sales to drive revenue, maximize profits and gain greater control of their brand. 

To facilitate this new go-to-market model, manufacturers must continually improve their operations and technology platforms to support digital marketing and personalized customer experience. How are they doing it? Industrial e-commerce sales optimized with AI-driven customer experience capabilities.

Using AI-powered solutions to provide real-time inventory visibility, multiple fulfillment options, accurate delivery timelines, and automated delivery notifications enriches the customer experience and greatly minimizes the risk of churn. Moreover, AI algorithms can detect real-time changes in business metrics, such as unexpected drops or spikes in traffic, conversion rates, session durations, and so on, enabling manufacturers to quickly react to any arising issues and respond to customer demands.

Automotive companies looking to stay on top of equipment and assembly line productivity, supply chain capacity, and customer experience personalization are turning to AI to take smart manufacturing to a whole new level – from the factory floor right through to the customer’s door.

Interested in expanding your smart manufacturing strategy? Get in touch with us to start learning more!