Integrative Case 5

Panda Express — Where Should the Next Store Go?

Five candidate sites, weighted factors, real data. Run a factor-rating site-selection analysis end-to-end in Excel, then validate it against the Python notebook. The deliverable is a recommendation Panda's real estate committee would actually use.

Site Selection Factor Rating Capacity Capstone

The business problem

Panda Express is opening one new location next quarter. Five candidate sites have made the short list. Each has a different cost basis, traffic profile, competitive density, and labor pool. Your job is to weight the factors that matter, score each site, and recommend one — with the kind of analysis a real-estate committee can defend to the board.

Why two files

Excel is the format real-estate teams use. The Python notebook shows the same analysis at scale and adds a quick map. Doing both deepens your understanding; either alone is sufficient.

Case kit

Everything you need
Deliverable

A one-slide recommendation to the real-estate committee:

  1. The pick. One site, with the weighted score that wins it.
  2. The risk you accepted. The factor that scored lowest at your winner, and why you accepted it.
  3. The capacity plan. Initial store size and the break-even date.
Topics you'll be applying

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