Optimizing the inbound process with a machine learning model | by Coupang Engineering | Coupang Engineering Blog | Medium (opens in new tab)

Coupang optimized its fulfillment center inbound process by implementing a machine learning model to predict the exact number of delivery trucks and dock slots required for vendor shipments. By moving away from manual estimates, the system minimizes resource waste from over-allocation while preventing processing delays caused by under-prediction. This automated approach ensures that the limited capacity of fulfillment center docks is utilized with maximum efficiency.

The Challenges of Dock Slot Allocation

• Fulfillment centers operate with a fixed number of hourly "slots," representing the time and space a single truck occupies at a dock to unload goods.
• Inaccurate slot forecasting creates a binary risk: under-prediction leads to logistical bottlenecks and delivery delays, while over-prediction results in idle docks and wasted operational overhead.
• The diversity of vendor behaviors and product types makes manual estimation of truck requirements highly inconsistent across the supply chain.

Predictive Modeling and Feature Engineering

• Coupang utilized years of historical logistics data to extract features influencing truck counts, including product dimensions, categories, and vendor-specific shipment patterns.
• The system employs the LightGBM algorithm, a gradient-boosting framework selected for its high performance and ability to handle large-scale tabular logistics data.
• Hyperparameter tuning is managed via Bayesian optimization, which efficiently searches the parameter space to minimize prediction error.
• The model accounts for the inherent trade-off between under-prediction and over-prediction, prioritizing a balance that maintains high throughput without straining labor resources.

System Integration and Real-time Processing

• The trained ML model is integrated directly into the inbound reservation system, providing vendors with an immediate prediction of required slots during the request process.
• By automating the truck-count calculation, the system removes the burden of estimation from vendors and ensures consistency across different fulfillment centers.
• This integration allows Coupang to dynamically adjust its dock capacity planning based on real-time data rather than static, historical averages.

To maximize logistics efficiency, organizations should leverage granular product data and historical vendor behavior to automate capacity planning. Integrating predictive models directly into the reservation workflow ensures that data-driven insights are applied at the point of action, reducing human error and resource waste.