Warehouse Scheduling: From Operator Insight to Product
The Problem
As Operations Performance & Analysis Lead at Taager, I noticed our warehouse operated on a fixed shift schedule that was misaligned with actual order volume patterns. Monday mornings: overwhelmed, overtime, missed SLAs. Thursday afternoons: idle staff, wasted capacity. The mismatch was predictable and recurring.
Overtime costs were 70,000 EGP/month — entirely avoidable. Same-day shipout rates were being dragged down by morning backlogs that were structurally predictable. The team knew it was a problem but had no data-backed solution.
Same-day shipout rate is a critical merchant satisfaction metric. Improving it directly reduces customer complaints and churn. And eliminating predictable overtime is pure margin improvement.
My Role
I identified the problem from the ground, built the initial data model myself (SQL + Excel), presented the business case to the product team, and eventually owned the productization. This was the project that transitioned me from Operations to Product.
Key Decisions
Warehouse managers were scheduling based on experience and gut. I pulled 6 months of order data to map actual volume by day-of-week and hour-of-day. The patterns were clear and consistent. Built a scheduling model on top of the data, not opinions.
Rather than pitching an idea, I came with a working model showing projected savings. This gave the PM team concrete numbers to evaluate. It also showed I could go from insight to evidence — which helped me make the case for a PM role.
Experimentation
Ran a 4-week pilot in one warehouse: implemented data-driven scheduling, measured overtime hours, same-day shipout rate, and labor cost vs. control (other warehouse on old schedule). Results were significant after week 1.
Launch
Rolled out to all warehouses. Built a simple scheduling dashboard that warehouse managers could use without data skills — visual, clear, actionable.
What I Learned
“The best PM insights come from people who've done the operational work. This project taught me that product thinking starts with direct observation — not user research reports, not analytics dashboards, but actually being in the warehouse and noticing what's broken.”