Optimizing Logistics Inbound Process Using (opens in new tab)

Coupang has implemented a machine learning-based prediction system to optimize its logistics inbound process by accurately forecasting the number of trucks required for product deliveries. By analyzing historical logistics data and vendor characteristics, the system minimizes resource waste at fulfillment center docks and prevents operational delays caused by slot shortages. This data-driven approach ensures that limited dock slots are allocated efficiently, improving overall supply chain speed and reliability.

Challenges in Inbound Logistics

• Fulfillment centers operate with a fixed number of "docks" for unloading and specific time "slots" assigned to each truck.
• Inaccurate predictions create a resource dilemma: under-estimating slots causes unloading delays and backlogs, while over-estimating leads to idle docks and wasted capacity.
• The goal was to move beyond manual estimation to an automated system that balances vendor requirements with actual facility throughput.

Feature Engineering and Data Collection

• The team performed Exploratory Data Analysis (EDA) on approximately 800,000 instances of inbound data collected over two years.
• In-depth interviews with domain experts and logistics managers were conducted to identify hidden patterns and qualitative factors that influence truck requirements.
• Final feature sets were refined through feature engineering, focusing on vendor-specific behaviors and the physical characteristics of the products being delivered.

LightGBM Implementation and Optimization

• The LightGBM algorithm was selected due to its high performance with large datasets and its efficiency in handling categorical features.
• The model utilizes a leaf-wise tree growth strategy, which allows for faster training speeds and lower loss compared to traditional level-wise growth algorithms.
• Hyperparameters were optimized using Bayesian Optimization, a method that finds the most effective model configurations more efficiently than traditional grid search methods.
• The trained model is integrated directly into the booking system, providing real-time truck quantity recommendations to vendors during the application process.

Operational Trade-offs and Results

• The system must navigate the trade-off between under-prediction (which risks logistical bottlenecks) and over-prediction (which risks resource waste).
• By automating the prediction of necessary slots, Coupang has reduced the manual workload for vendors and improved the accuracy of fulfillment center scheduling.
• This optimization allows for more products to be processed in a shorter time frame, directly contributing to faster delivery times for the end customer.

By replacing manual estimates with a LightGBM-based predictive model, Coupang has successfully synchronized vendor deliveries with fulfillment center capacity. This technical shift not only maximizes dock utilization but also builds a more resilient and scalable inbound supply chain.