Yushu Tong

Owned end-to-end warehouse operations across inbound, slotting, outbound, and returns for a lean ~6,000 sq ft facility supporting 3PL clients and multi-channel resale (Amazon, Walmart, eBay), fulfilling ~300–450 orders per day and ~700–1,000 pick/pack actions across high-SN-sensitivity items. Designed and implemented an LPN-based slotting framework for the top ~300 high-velocity SKUs (covering ~70–75% of outbound volume), reducing average pick/search time from ~4 minutes to ~1.8 minutes per pick and significantly improving inventory traceability. Lowered outbound error rate from ~1.3% to <0.3% by introducing scan-based verification and serial-number controls, strengthening auditability for high-value electronics shipments. Reduced new-hire ramp-up time from ~1–2 weeks to ~2–3 days by standardizing putaway, picking, and returns SOPs, decreasing dependency on tribal knowledge in a part-time-heavy workforce. Diagnosed and resolved ~5–7 weekly WMS-to-marketplace exceptions (inventory sync issues, tracking failures, order status mismatches), cutting rework by ~65% and improving order close-out time from T+2–3 to same-day or T+1. Established a returns triage and resale pipeline processing ~200–350 recoverable units per month, generating ~$10K–$25K in recovered revenue and reducing returned-inventory aging by ~40–55%. Partnered directly with founders to support operational prioritization and product selection decisions based on sell-through velocity, margin potential, and returns risk, improving cash conversion and reducing dead stock exposure.

Built Power Automate workflows integrated with internal web systems to automate data-driven content publishing, reducing manual administrative effort and improving process consistency. Prepared Excel-based analytics and reporting on web traffic, engagement, and campaign performance, supporting internal business reviews and stakeholder decision-making. Worked with business users to clarify reporting requirements and translate business questions into repeatable analytical outputs.

§ Analyzed data related to R&D, different products’ sales, and personnel costs. § Built visualized charts to analyze those main reasons for profit growth in previous periods. § Developed a regression linear model to predict the changes in the sales of various products at different volumes. Projecting the future costs to align with revenue in different scenarios. § Created a visualized dashboard of product sales and expenses by regions and genre of products in excel as a monthly report for finance department.

§ Used Python to reformat the data, which improved the accuracy of the company's internal experimental vehicle data, such as the monthly admission fees and the count of experimental vehicles of different brands. § Categorized the experimental cars by department and engineers to improve the data table quality. § Utilized Excel’s data analysis tools to calculate insurance and fuel costs for experimental vehicles.