Quick-commerce dark-store location and unit economics

Kirana10 delivers groceries and daily essentials in about ten minutes in Pune. It runs 14 dark stores of 2,500 to 4,000 sq ft and fulfils about 26,000 orders a day. Average order value is ₹480. The founders have raised money for six more stores in the next two quarters, and investors want to see each one reach contribution break-even within six months of opening.

The last two stores opened on gut feel. One is doing well. The other, in a wealthy but low-density area, does 380 orders a day against a break-even of about 900, and is losing ₹9 lakh a month. The founders do not want to repeat that.

You are the strategy and operations associate. In eight weeks you will: - frame the expansion decision and the unit economics that drive it; - build a demand model on a grid of 1 km cells across Pune, using public ward population data and synthetic order history from the existing stores; - score candidate sites on ten-minute coverage, demand density, cannibalisation of existing stores, rent and rider availability; - build a per-order and per-store P&L for each shortlisted site; - test the sensitivity of break-even to AOV, basket margin, rider cost and rent; - present six recommended sites with their payback and the risks.

Done means six named sites, each with expected orders per day at month 6, contribution per order, break-even orders and payback, plus the two sites you considered and rejected and why. The founders should be able to defend the list to their board.