A Filipino food delivery startup has seen its average delivery time increase by 20% over the past quarter, leading to customer complaints. As a consultant, how would you help them diagnose and fix this issue?

Situation

In my interview, I was given a scenario about a food delivery startup facing longer delivery times. I needed to structure a diagnosis and propose solutions, similar to a real Bain operations project.

Task

The goal was to identify root causes of the delay and recommend actionable improvements, showing an end-to-end process understanding.

Action

I began by mapping the delivery process into stages: order placement, restaurant preparation, rider assignment, pick-up, transit, and drop-off. I hypothesized that delays could stem from any stage. To narrow down, I asked the interviewer for data: average times per stage, volume trends, rider availability, restaurant peak hours. Without data, I proposed a structured analysis: first, segment by geography (Metro Manila vs. provincial), time of day, and restaurant type. I assumed that Metro Manila's traffic might be a major factor, but I needed to check if delays were uniform or clustered. If food preparation time increased, maybe the startup aggressively onboarded restaurants without kitchen capacity. If rider assignment lagged, the dispatch algorithm might be suboptimal. I suggested quick wins like dynamic fleet rebalancing during peak hours, better ETA communication to customers, and partnering with restaurants for expedited meal prep. I also considered long-term ideas like dark kitchens or micro-fulfillment centers. I structured my answer as an issue tree: total delivery time = restaurant prep time + rider waiting + transit. For each branch, I listed potential causes, data needed, and solutions.

Result

The interviewer seemed pleased that I broke down the problem into clear components and linked symptoms to possible operational levers. I demonstrated Bain's real-client-work mindset by not jumping to solutions without diagnosis.