Retail
Retail Inventory & Merchandising Analytics
Retail inventory analytics breaks when POS sell-through, warehouse receipts, and planogram files live in different rhythms. Inventory turnover retail looks fine at chain level while slow categories trap cash in specific regions. Stock cover analysis fails if safety stock rules ignore promotion calendars or supplier lead-time drift. Dead stock retail accumulates quietly when no one reconciles zero-scan SKUs to on-hand and on-order. Planogram compliance scores as a monthly audit miss daily shelf gaps that explain lost sales on high-margin facings.
FireAI unifies SKU-store-day sales, stock positions, inbound and transfers, and space maps so retail inventory analytics answers which categories under-turn versus peers, where stock cover analysis flags early stockout or overhang, which SKUs need markdown or exit plays, and how category space productivity compares to plan intent.
The domain covers inventory turnover by category and SKU, stock cover days and reorder trigger discipline, dead stock and markdown requirement analysis, and sales per shelf foot with planogram compliance context, through chat, dashboards, and causal chains operations and merchandising can act on the same week. See how it works: get a demo.
Inventory turnover by category and SKU
Inventory turnover retail by category and SKU hides in blended reports when fast urban stores offset slow tier-two formats. Buyers need comparable turns with seasonality and promotion overlays, not a single chain average.
FireAI joins POS velocity, average inventory value, and calendar events so inventory turnover retail ranks SKUs and categories with consistent cost and quantity rules. Drill-down ties weak turns to allocation, pricing, or assortment depth without exporting five systems.
How FireAI solves the problem: It normalizes inventory turnover retail across stores with optional peer clusters (format, city tier, footprint) and surfaces outliers where working capital sits too long for the sales you actually capture.
What FireAI tracks:
- Annualized turns and days on hand by category, subcategory, and SKU-store where data allows
- Turn variance versus trailing period and versus format peer median
- Promotion and seasonality tags on turn moves
- Cash value in bottom-decile turn SKUs
Merchandising and finance use retail inventory analytics to rebalance buys, transfers, and exit lists before quarter close.
Ask FireAI about turnover
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Turnover and DOH
Causal chain: promo to slow turns
Stock cover days and reorder trigger
Stock cover analysis fails when reorder points ignore lead time variance, seasonal uplift, or store-level demand noise. Stores stock out on heroes while DC holds excess on long-tail.
FireAI computes forward cover using recent velocity, service targets, and supplier calendars you maintain. Stock cover analysis highlights stores breaching max cover before cash traps, and min cover before lost sales.
How FireAI solves the problem: It simulates reorder triggers with exception queues for phantom inventory, pending transfers, and promotional lifts so buyers trust the number of days cover, not just the ERP flag.
What FireAI tracks:
- Days of cover by SKU-store and DC with confidence bands
- Reorder trigger hits versus actual order placement lag
- Stockout risk score next 14 days
- Over-cover value above policy band
Replenishment and store ops use stock cover analysis to align allocation with real sell-through.
Ask FireAI about cover
See how your team can ask questions in plain language and get instant analytics answers.
Cover and triggers
Causal chain: count drift to stockout
Dead stock and markdown requirement analysis
Dead stock retail accumulates when no one owns the intersection of zero or near-zero scans, on-hand value, and margin after clearance. Markdown requirement analysis needs rupee impact and recovery paths, not a static aging report.
FireAI classifies dead and at-risk SKUs using velocity bands, inbound risk, and seasonality. Markdown requirement analysis ranks exit options: transfer, mark down, bundle, or return-to-vendor where contracts allow.
How FireAI solves the problem: It ties dead stock retail to owning buyer and store list with suggested depth and window, and tracks execution versus plan.
What FireAI tracks:
- Dead stock retail value and units by category and region
- Markdown requirement analysis scenarios with gross margin after discount
- Inbound that would worsen dead positions
- Recovery rate versus historical clearance
Finance and merchandising align on dead stock retail before quarter-end write-off surprises.
Ask FireAI about dead stock
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Dead stock and markdown
Causal chain: allocation to dead stock
Category space productivity (sales per shelf foot)
Planogram compliance on paper does not equal sales per shelf foot when facings shrink, secondary displays block plan, or stores substitute SKUs without master-data updates. Category space productivity needs photo or audit signals where available, and sales density where not.
FireAI blends planogram targets, actual space measures or audits, and POS by facing group. Planogram compliance scores tie to revenue per linear foot so merchants see whether space matches productivity.
How FireAI solves the problem: It highlights stores where planogram compliance diverges from chain template and where sales per shelf foot lags peer stores with the same plan class.
What FireAI tracks:
- Sales per shelf foot by category block and store cluster
- Planogram compliance rate versus sales lift where pilots ran
- Void and substitution rate impacting category space productivity
- Top and bottom quintile stores for remerchandising visits
Space planning and field teams use planogram compliance and category space productivity to prioritize store walks and resets.
Ask FireAI about shelf productivity
See how your team can ask questions in plain language and get instant analytics answers.