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pcha1125/
retail-ultraplan

Name: retail-ultraplan
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🏢 Almond House — Retail Intelligence Platform (UltraPlan v3)

What This Is

Complete analysis of Almond House POS data — 11 retail stores, 728 items, 38K+ receipts across 10 days (March 2026).

Built from the actual data in AI_Forecasting_Main.xlsx. Every number below comes from your real transactions.

Key Findings

Metric	Value
Total Revenue	₹3.77 Cr (10 days)
Daily Average	₹37.7 L/day
Top Store	KPHB (₹60.8L, 6,854 receipts)
Bottom Store	BMT1 (₹16.2L, 1,950 receipts)
Top Item	Kaju Burfi (₹23.5L alone!)
Top Category	SWEET (51.6% of all revenue)
Avg Basket	₹980, 2.5 items
Peak Hour	6:00 PM (9.9% of daily revenue)
Best Day	Saturday (₹47.5L)
Cross-sell Rules	2,321 item pairs discovered

Critical Data Findings (from audit)

Net Amount is NEGATIVE (ERP convention) → Revenue = -Net Amount
Price == Net Price 100% → NO discounts in data → promo analysis not possible
Gross Amount is RECEIPT-level, not line-level
9 warehouse stores separated from 11 retail stores
Feb data is sparse (83 rows) → only March used
47% revenue from KGS items (sold by weight: 0.25kg, 0.5kg)

Output Files (`output/` folder)

File	What	Rows
`00_cleaned_sales.csv`	Cleaned transaction data	94,413
`00_returns.csv`	Return transactions separated	303
`00_warehouse_transfers.csv`	Warehouse/production data	5,876
`01_store_performance.csv`	Store ranking & KPIs	11 stores
`02_daily_sales.csv`	Daily revenue trend	10 days
`02_hourly_pattern.csv`	Hourly sales pattern	17 hours
`03_item_performance.csv`	All items ranked by revenue	671 items
`04_category_performance.csv`	Category breakdown	19 categories
`04_brand_performance.csv`	Brand breakdown	8 brands
`04_product_line_performance.csv`	Product line breakdown	39 lines
`05_baskets_detail.csv`	Every receipt with items	38,478 baskets
`05_crosssell_rules.csv`	"Bought together" pairs	2,321 rules
`05_category_copurchase.csv`	Category pairs	78 pairs
`06_inventory_abc_xyz.csv`	Item classification + safety stock	725 items
`07_store_item_matrix.csv`	Store × Item sales	5,370 combos
`07_item_store_coverage.csv`	Which items at which stores	725 items
`07_store_category_mix.csv`	Category mix per store	11 × 19
`08_executive_kpis.csv`	Summary KPIs	22 metrics

How to Use

bash

# Open any CSV in Excel/Google Sheets/Power BI
# Or re-run analysis:
pip install pandas numpy scikit-learn scipy openpyxl
python ultraplan_v3.py --data AI_Forecasting_Main.xlsx --outdir my_results

pcha1125/retail-ultraplan