Estimate the operational capacity needed to support basket completion for quick commerce replenishment under scale, incentive, and regulatory constraints

Problem Statement Description

Zepto wants to understand the operational capacity required to help busy households complete replenishment baskets reliably during quick-commerce ordering. The focus is not just total demand, but the capacity needed across dark-store inventory, picking, substitution handling, packing, and last-mile dispatch so that customers can place a full basket without key household items being unavailable or delayed.

In this guesstimate, you should size the capacity needed under realistic scale conditions, including peak-hour demand, repeat household usage, incentive-driven demand spikes, and operational constraints such as stockouts, picker accuracy, delivery partner availability, freshness requirements, and local labor or delivery regulations. You may choose a city, serviceable region, or national-scale scope, but you should state it clearly and keep the unit of capacity explicit.

The estimate should be framed as an operational planning problem for quick commerce: how many orders, baskets, items, picker-hours, inventory units, dark-store throughput, or delivery slots are needed to support a target level of basket completion. Your assumptions should distinguish between normal replenishment behavior and promotional or subscription-driven usage.

The experience should consider:

- Scope definition: geography, time horizon, household segment, serviceable population, and whether the estimate is daily, hourly, or peak-hour capacity.

- Demand assumptions: number of busy households, ordering frequency, average basket size, replenishment categories, and repeat usage patterns.

- Basket completion unit: what counts as “completed” — full availability, acceptable substitutions, partial fulfillment threshold, or on-time delivery of essential items.

- Operational conversion: translating orders and item demand into dark-store inventory, picker workload, packing time, delivery capacity, and buffer stock.

- Adoption and incentives: impact of discounts, subscriptions, free delivery thresholds, and payday or weekend spikes on order volume and basket size.

- Regulatory and practical constraints: working-hour limits, delivery curfews, traffic rules, rider availability, food safety, freshness, and local compliance requirements.

- Sensitivity checks: which assumptions most affect capacity, such as peak-to-average ratio, stockout rate, picker productivity, delivery radius, or substitution acceptance.

- Sanity checks: comparing implied orders per store, items picked per hour, delivery trips per rider, and inventory turnover against realistic quick-commerce operations.

The goal is to produce a structured, defensible estimate that shows how you break down an ambiguous operational capacity question, choose useful units, make transparent assumptions, identify the biggest drivers, and test whether the final capacity requirement is plausible for Zepto’s quick-commerce replenishment model.