A Machine Learning Approach to Predict Bin Defects in E-commerce Fulfillment Operations

Bin location is the smallest possible unit inside a fulfillment center building where a product is stored to pick customer orders. Inventory in each bin inside a modern fulfillment center is tracked by the warehouse management system. Inventory discrepancies between inventory records in the warehouse management system and on hand inventory in the bin are referred to as bin defects. Bin defects in e-commerce fulfillment centers pose significant challenges, impacting operational efficiency, customer satisfaction, legal compliance, and overall profitability. This paper presents a comprehensive predictive model leveraging machine learning techniques to anticipate bin defects within fulfillment centers. The study involves the analysis of historical data primarily encompassing item attributes, location attributes, and any actions that might change the current state of a bin. The proposed model in this paper has been trained, tested, and implemented in an enterprise environment, and it can be easily leveraged by any e-commerce fulfillment centers to optimize their inventory control strategies. Promising predictive capabilities demonstrated by the model substantiate the model's effectiveness in preemptively identifying defective bins that can severely impact order fulfillment process. A successful integration of this model into organization's broader inventory management strategy will enable fulfillment centers to proactively implement preventive measures, reducing the occurrence of defects, minimizing inventory losses, reducing labor costs, and optimizing operational workflows. Further implications of this research extend to streamlining quality control processes and fostering a proactive approach toward mitigating inventory defects in fulfillment centers.