## 2 State of the Art
### Artificial Intelligence in Science and Industry  
Several prevalent AI techniques are being used in supply chain management (SCM). For example, predictive analytics involves using historical data and machine learning algorithms to forecast future demand, inventory levels, and supply chain disruptions. This helps with optimizing inventory management and improving demand forecasting accuracy. Machine learning (ML) techniques are used for various SCM tasks such as demand forecasting, route optimization, and quality control. ML models can analyze large datasets to identify patterns and to make data-driven decisions. Computer vision technologies can be applied to automate quality control processes by inspecting products and identifying defects. They can also help with inventory tracking using visual data. Blockchain technology can be used for enhanced transparency and traceability in supply chains. It helps to verify the authenticity of products and to ensure that goods are not tampered with during transit. AI can be used to assess supplier risk by analyzing various factors like financial stability, geopolitical events, and natural disasters, allowing organizations to proactively manage supply chain disruptions. AI techniques can be used to optimize inventory levels by considering factors like demand variability, lead times, and carrying costs, ensuring that the right amount of stock is available without excess. Figure 5: Decision tree model estimation. AI can analyze vast amounts of data to monitor and measure environmental and social sustainability metrics across a supply chain. This includes tracking carbon emissions, water usage, waste generation, and labor conditions. By providing real-time insights, AI helps organizations to identify areas where sustainability goals are not being met and to take corrective actions. AI can predict and identify potential environmental and social risks in a supply chain, such as natural disasters, regulatory changes, and labor issues. This early warning system allows companies to proactively address these risks, reduce disruptions, and ensure the well-being of workers and the environment. AI plays a crucial role in promoting sustainability in supply chains by providing data-driven insights, optimizing processes, ensuring transparency, and helping organizations to make informed decisions. By addressing environmental and social challenges, AI contributes to the creation of more resilient and responsible supply chains that are better equipped to handle the threats posed by climate change, resource scarcity, and social issues. Using predictive maintenance with AI methods to manage vehicle components in a supply chain can offer several key benefits and implications for reducing disruptions, costs, and waste while ensuring sustainability:  
**Reduced Downtime and Disruptions. ** Predictive maintenance helps in identifying potential issues with vehicle components before they fail. This proactive approach minimizes unexpected breakdowns and downtime, ensuring that goods are delivered on time. **Improved Resource Allocation. ** Organizations can allocate resources more efficiently by focusing on components that require immediate attention. This ensures that maintenance efforts are directed where they are most needed. **Reduced Waste. ** By minimizing the need for premature component replacements, predictive maintenance reduces waste, including discarded vehicle parts, which is environmentally harmful. **Resource Conservation. ** Extending the lifespan of vehicle components reduces the need for raw materials and energy required for manufacturing new components, thus contributing to resource conservation. **Emissions Reduction. ** Predictive maintenance reduces the frequency of vehicle breakdowns and the associated emissions from idling vehicles waiting for repairs or towing services. **Resilience and Risk Mitigation. ** Predictive maintenance helps to ensure that vehicles are in optimal condition, reducing the risk of disruptions due to breakdowns or accidents.