hardCustomer-Facing Case Study
Reduce detention fees for a logistics customer
Turn operational data into a narrow intervention with measurable financial impact.
How to practice
- 1. FrameName the user, workflow, pain, and success metric.
- 2. DecidePropose the smallest useful version and explain trade-offs.
- 3. ProveDefine validation, rollout, ownership, and failure handling.
Customer scenario
**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?
Interviewer nudges3 prompts · Open
- 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.