Analysis of outbound process defects at Amazon's ONT2 Fulfillment Center

Abstract

This thesis investigated two outbound process defects that result in damaged, late, missing or wrong-item shipments, leading to a poor customer experience and concessions for Amazon. Reducing these defects will improve customer satisfaction, reduce concession and re-work costs, and improve operations for last mile carriers, including Amazon Logistics. Investigation focused on two defects 1) missorted packages and 2) SLAM kickout errors. For each area, the process was mapped, data sources determined, statistical process control analysis performed and solutions implemented. For the sortation process, sort center data provides the most complete and timely view of process performance. Using this data control charts were created, providing a historical view of ONT2 process performance, enabling comparison to other sites and justifying the purchase of new equipment. A daily missort report was created and is delivered daily to ONT2 management to enable continuous tracking of sortation performance. Accurate data to track SLAM kickout defects was not readily available, so several existing metrics were used to develop a working understanding of primary defect drivers. A training gap was identified and retraining and auditing was performed. Post-implementation, the existing metrics indicated some improvement though the presence of confounding factors makes a definitive conclusion difficult. Recommendations for future work include incorporation and analysis of equipment data to create a leading indicator for missort defects. Concurrently, retrofits on the ONT2 flat sorter should be performed to reduce equipment-induced sortation problems. To reduce SLAM kickout errors, a metric should be created to track and correct human errors, and technology should be used to both reduce the need for re-processing and verify correct re-processing.