Retail Operations Transformation & Profitability Optimisation

Independent consulting simulation. All operating data are synthetic. Scenario results are modelled estimates, not verified business outcomes.

Contents: original 25-page report, six unchanged CSV source tables, and original SQLite analysis queries. CSV periods: April 2024 to March 2026; FY25 = Apr 2024-Mar 2025; FY26 = Apr 2025-Mar 2026. Currency: INR; 1 crore = INR 10,000,000.

Dataset grains: 38,400 store-SKU-month records; 480 store-month records; 20 stores; 80 SKUs; 24 months; 2 fiscal-year HQ costs.

The original report includes an executive presentation and a Power BI dashboard specification. No Power BI .pbix or Excel workbook was supplied. The original Python scripts are omitted from this reading package. The website independently recomputes the central financial and operational results without executing attached code.

Reconciliation notes: there are five regions in dim_store.csv (including Central), despite four stated in the report. Use detailed Tables 6.3/6.4 for run-rate margins: 18.8% base, 18.1% conservative, 17.5% stress; the executive slide uses a fixed revenue denominator. Estimated lost gross margin recalculates to INR 1.28 crore using the supplied SQL method, whereas a report headline says 1.20 crore. The original PDF is preserved unchanged.

Load the six CSVs into tables of the same filename stem before running the SQLite views and analysis queries. Average inventory is the mean of 12 monthly closing balances. Contribution excludes store rent, labour, utilities, marketing, shrinkage, other costs and allocated HQ. Operating profit includes those costs. Estimated lost sales extrapolate sales per in-stock day into stock-out days; validate this assumption before applying it to a real retailer.
