Logistics and transportation companies track ETA (estimated time of arrival), which is a key metric for their business. Their downstream supply chain activities are planned based on this metric. However, delays often occur, and the ETA might differ from the product’s or shipment’s actual time of arrival (ATA), for instance due to shipping distance or carrier-related or weather-related issues. This impacts the entire supply chain, in many instances reducing productivity and increasing waste and inefficiencies. Predicting the exact day a product arrives to a customer is challenging because it depends on various factors such as order type, carrier, origin, and distance.
Analysts working in the logistics and transportation industry have domain expertise and knowledge of shipping and logistics attributes. However, they need to be able to generate accurate shipment ETA forecasts for efficient business operations. They need an intuitive, easy-to-use, no-code capability to create machine learning (ML) models for predicting

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