Diagnose and reduce Flipkart's problems delivering large, heavy items
Company: Flipkart
Role: Product Manager
Category: Product / Decision Making
Difficulty: medium
Interview Round: Other
Flipkart is running into problems getting bulky (large, heavy) items delivered to its customers. As the product manager responsible, work out what is going wrong and propose how to reduce those problems.
```hint Locate the failure first
Break a bulky order into its stages, from checkout to the item standing ready to use in the customer's home, and decide which stage the problems come from before proposing any fix.
```
### Clarifying Questions
- What do the problems look like: late deliveries, failed delivery attempts, items damaged on the way, cancellations and returns, or delivery cost per order?
- Which items count as bulky: large appliances, furniture, TVs, or anything over a size or weight threshold?
- Is this a recent change or a long-standing issue, and is it concentrated in certain cities, categories or sellers?
- Do installation after delivery and orders that sellers ship themselves fall within scope?
### What a Strong Answer Covers
- A specific definition of the problem being reduced and how it is measured
- A stage-by-stage breakdown of a bulky order that locates where failures start
- Root causes separated into those Flipkart controls and those it can only influence
- Fixes ranked with explicit criteria, including the trade-offs each one creates
- A primary metric with guardrails, and a plan to pilot the fixes
### Follow-up Questions
- How would your plan differ for a small town far from the nearest large-item warehouse compared with a big metro?
- How would cash-on-delivery orders change your analysis and your fixes?
- If the fixes cut failed deliveries but raised the cost of each delivery, how would you decide whether to keep them?
Overview: A product manager problem-solving case about Flipkart's trouble delivering bulky, heavy items to customers. It tests defining which delivery problem matters, breaking a large-item order into stages to find root causes, prioritizing operational and product fixes, and choosing metrics with guardrails for a pilot.