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AI-based predictive maintenance uses a variety of data from IoT sensors, weather, vehicles, and more to determine which components should be replaced. AI-based supply chain optimization can utilize a variety of factors including environmental and mobility data to predict optimal resource needs. AI models can look for patterns in data that indicate failure modes for specific components or make better predictions of the equipment lifespan.

AI-enhanced transportation optimization leverages route info, weather data, fuel cost, and more to arrive at optimal routes and faster deliveries. This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed.

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