ETA (Estimated Time of Arrival) and Delivery Time Estimation in Smart Warehouse Management

2026-06-27 · Source: Data Science on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Logistics Optimization · Depth: Intermediate, long

Summary

This analysis details an ETA (Estimated Time of Arrival) and delivery time estimation model designed for smart warehouse management, extending beyond basic distance calculations. The proposed architecture evaluates total delivery time from order creation to customer receipt, integrating variables like order processing, product picking-packing, shipment waiting, route, traffic, stock, and operational intensity. It outlines a three-stage model flow: Operational Data, Prediction Engine, and Delivery Window, presenting the output as a time range rather than a single minute value. The article also examines industry approaches from Google Maps Platform, Uber's DeepETA, DoorDash's long-tail delivery modeling, and Amazon's SPEEDY framework for dynamic delivery windows. It further discusses various model approaches, including Gradient Boosting and component-based models, alongside academic evaluation metrics such as MAE, RMSE, Prediction Bias, and Window Coverage Rate, emphasizing the need for robust training, testing, and continuous live monitoring.

Key takeaway

For MLOps Engineers or Data Scientists building last-mile delivery ETA systems, you must integrate warehouse operational data with route predictions. Relying solely on route services is insufficient; your model needs to account for processing, picking, packing, and waiting times. Implement a hybrid approach, starting with a baseline, and present delivery estimates as dynamic windows. Continuously monitor metrics like MAE, RMSE, and window coverage to ensure accuracy and adapt to real-world operational shifts.

Key insights

Accurate last-mile ETA demands a hybrid model integrating route prediction with diverse warehouse operational data.

Principles

• Total ETA combines route time with all operational steps.
• Present ETA as a delivery window, not a single value.
• Model performance requires continuous live monitoring.

Method

Collect operational data (order, stock, route, traffic), process it via a prediction engine (rules, stats, ML), then present the output as a lower and upper delivery time window.

In practice

• Implement Gradient Boosting for tabular operational data.
• Start with a simple reference model as a benchmark.
• Monitor MAE, RMSE, Bias, and Window Coverage Rate.

Topics

• ETA Estimation
• Last-Mile Delivery
• Warehouse Management Systems
• Machine Learning Models
• Delivery Window Prediction
• Model Performance Monitoring

Code references

• miracozturk17/eta-ve-teslimat-suresi-tahmini

Best for: Machine Learning Engineer, Data Scientist, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Science on Medium.