hard📋 Customer-Facing Case Study

A logistics company wants to reduce detention fees by 20%. How do you approach this?

**Background:** Detention fees are charges incurred when a truck waits at a facility beyond the agreed free time. Your client (a large 3PL) is paying $4M/year in detention fees and wants to cut that by 20%. They have data from their TMS (Transportation Management System) including: load details, driver check-in/out times, facility dwell times, appointment times. **Your job as the FDE:** 1. How do you scope and prioritize this problem? 2. What data analysis would you do first? 3. What solution would you propose, and how would you validate it?

💡 Hints (3)

1.Start with data, not solutions. What does the distribution of detention events look like? Which facilities? Which carriers?
2.The 80/20 rule almost certainly applies here — a few facilities likely drive most fees.
3.Consider both predictive (alert before detention happens) and operational (fix the process) solutions.

✅ View Solution

**Discovery:** - Pull detention events by facility, carrier, time-of-day, day-of-week - Hypothesis: 20% of facilities → 80% of fees (confirm with data) - Identify root causes per cluster: missed appointments? Slow unloading? Staffing gaps? **Analysis:** - Average dwell time vs scheduled dwell time per facility - Correlation: which appointment slots have highest detention rate? - Carrier-level analysis: is detention concentrated with specific drivers/carriers? **Proposed solution:** - Real-time dwell time alerting: when a truck hits 80% of free time, auto-alert the facility manager - Appointment slot optimization: reschedule high-risk slots to low-contention windows - Carrier scorecard: surface detention rate per carrier to procurement team **Validation:** - Pilot at top 3 detention facilities - A/B test: treated vs control appointment slots - Target: 20% reduction in avg dwell time at pilot sites within 60 days