RetailSense — AI Decision Intelligence for Retail & CPG

RetailSense is an autonomous AI decision-intelligence system built for multi-channel retail and Consumer Packaged Goods (CPG) networks. Rather than stopping at passive sales forecasts, RetailSense synthesizes 9 disparate enterprise signals across the connected decision chain to prescribe, explain, and execute measurable store-level business actions.

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🏛️ System Architecture

The platform follows a closed-loop Observe → Predict → Reason → Decide → Act → Measure architecture designed for enterprise scalability:

┌─────────────────┐       ┌────────────────────┐       ┌────────────────────────┐
│  Data Sources   │ ────► │  Data Processing   │ ────► │     FastAPI Backend    │
│  • POS Sales    │       │  • Unobserved      │       │  • Orchestration       │
│  • Live Stock   │       │    Demand Recon    │       │  • Business Rules      │
│  • Promos/Lead  │       │  • Anomaly Clamp   │       │  • JWT Auth & Security │
└─────────────────┘       └────────────────────┘       └───────────┬────────────┘
                                                                   │
                  ┌────────────────────────────────────────────────┼────────────────────────────────────────┐
                  ▼                                                ▼                                        ▼
      ┌───────────────────────┐                        ┌───────────────────────┐                ┌───────────────────────┐
      │  Forecasting Module   │                        │ Inventory Optimization│                │Decision Recommendation│
      │  • ARIMA Baseline     │                        │ • Stockout Detection  │                │ • 7 Canonical Actions │
      │  • LightGBM / XGBoost │                        │ • Dynamic Safety Stock│                │ • Capacity Guardrails │
      │  • P10-P90 Fan Charts │                        │ • EOQ & Reorder Point │                │ • Trade-off Rationale │
      └───────────┬───────────┘                        └───────────┬───────────┘                └───────────┬───────────┘
                  │                                                │                                        │
                  └────────────────────────────────────────────────┼────────────────────────────────────────┘
                                                                   ▼
                                                       ┌───────────────────────┐
                                                       │ Explainability & SHAP │
                                                       │ • Feature Importance  │
                                                       │ • Expected Impact     │
                                                       └───────────┬───────────┘
                                                                   ▼
                                                       ┌───────────────────────┐
                                                       │  Multi-Channel Action │
                                                       │  • Web Console        │
                                                       │  • WhatsApp Alert Bot │
                                                       │  • Automated PO Export│
                                                       └───────────────────────┘

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🔗 The Connected Decision Chain

Retail decisions never operate in silos. A single merchandising discount ripples across the entire enterprise:

Demand  ──►  Inventory  ──►  Replenishment  ──►  Logistics  ──►  Margin  ──►  Customer Experience

• Demand Lift: Festival promotions or heatwaves increase consumer velocity (+35% to +160%).
• Inventory Strain: Days of cover collapse below supplier lead time.
• Replenishment Trigger: The system evaluates order batch sizes, supplier MOQs, and lead times.
• Logistics Coordination: Intra-district store transfers or regional DC cross-docking avoid expedited freight surcharges.
• Margin Protection: Dynamic price markdown clears aging lots before write-off, recovering tied working capital.
• Customer Experience: Shelves remain 96.8%+ stocked, eliminating walked sales and brand churn.

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⚡ The 7 Canonical Business Actions

RetailSense strictly prescribes one of 7 canonical business decisions for every store and SKU:

#	Action	Purpose & Trigger Condition	Typical Impact
1	Replenish	Days of cover < supplier lead time; demand lift exceeds safe inventory buffer.	Prevents critical stockout; protects ₹38,000+ lost margin per incident.
2	Transfer Inventory	District store A has surplus stock (>45 days) while adjacent store B faces stockout (<2 days).	Balances network inventory with $0 new manufacturer PO spend.
3	Increase Allocation	Weather anomaly or local stadium event creates extreme localized demand spike.	Captures incremental margin via central DC priority slotting.
4	Markdown	Inventory shelf-life expires in <30 days with run-rate leaving unsold deadstock.	Recovers 70% cost basis before 100% total perishable spoilage write-off.
5	Adjust Promotion	Upstream supplier delays risk premature stockout under active promo discounts.	Dampens discount from 20% to 10%, stretching run-rate by 6 days.
6	Substitute Product	Primary brand SKU suffers national supplier stockout.	Cross-merchandises equivalent substitute on front endcaps (70% conversion recovery).
7	Take No Action	Inventory run-rate and supplier lead times operate within optimal safety bands.	Eliminates unnecessary labor churn and logistics overhead.

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📡 9 Enterprise Signals Synthesized

1. SKU Sales Velocity: Historical sales run-rate, unconstrained baseline velocity, and hourly POS trends.
2. Inventory On-Hand: Current shelf stock, backroom storage, safety stock, and days of cover.
3. Promotions & Marketing Elasticity: Active flyer features, digital coupons, and historical price elasticity.
4. Unit Pricing & Margin Structure: Cost of goods sold (COGS), list price, current gross margin, and salvage value.
5. Supplier Lead Times & Reliability: Vendor fulfillment lead times (days), minimum order quantities (MOQ), and variance history.
6. Store Location & Channel Format: Express convenience, downtown hub, suburban hypermarket, or dark store / e-commerce DC.
7. Local Events: Stadium sports matches, cultural festivals, public holidays, and marathon footfall.
8. Weather Anomalies: Heatwaves (+14°F above norm), unseasonal rains, cold snaps, and storm warnings.
9. Competitor Benchmarks: Competing supermarket price points and weekly promotional flyers.

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📈 Proven Success Measures (AI vs Baseline Proof)

Replayed over a 6-week historical holdout backtest across 120 retail SKUs:

Metric	Traditional Forecasting Baseline	RetailSense AI Decision Engine	Delta / Improvement	Business Value
Stockout Rate	14.8%	4.2%	-71.6%	10.6% fewer out-of-stock incidents
Service Level (In-Stock SLA)	85.2%	96.8%	+11.6%	Exceeds 95% enterprise customer availability SLA
Excess Deadstock Working Capital	₹24,500	₹7,800	-68.2%	₹16,700 unlocked cash per store cluster
Gross Margin Rate	18.2%	23.4%	+5.2%	+₹16,429 net profit expansion
Annualized Working Capital Savings	—	₹4,12,000 / store	4.8x ROI	Substantial inventory holding cost reduction

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🛡️ Data Quality & Audit Ledger

Raw enterprise POS feeds are frequently corrupt or incomplete. RetailSense embeds an automated data cleaning and reconstruction audit ledger:

• Unconstrained Lost Demand Reconstruction: When a SKU records 0 sales because on-hand stock was 0, traditional models assume zero demand. RetailSense automatically reconstructs true latent demand (~42 units on Basmati Rice) to prevent perpetual under-ordering.
• Negative Stock Anomaly Clamping: POS sync glitches recording negative on-hand quantities (e.g. -3) are clamped to 0 and recorded in the audit ledger.
• Lead Time Variance Buffering: Supplier delays dynamically adjust safety stock buffers rather than triggering premature expediting penalties.

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📱 Multi-Channel Action Execution & Live Decision Studio

1. Live Decision Intelligence Studio (9 Signals → 6 Stages):

   • Interactive workbench allowing operators to tune all 9 real-world enterprise signals live.
   • Real-time execution of the 6-stage lifecycle: Observe → Predict → Reason → Decide → Act → Measure.
   • Outputs one of the 7 canonical actions with action rationale, connected decision chain ripple narrative, and side-by-side comparison with the traditional forecasting baseline.
   • 1-click Commit & Dispatch Live Decision workflow pushing authorized orders into the PO queue.

2. Groq LLM Connected Decision Chain Copilot (qwen/qwen3.8-27b):

   • Explain with AI: Natural language synthesis of multi-signal enterprise tradeoffs across Demand → Inventory → Replenishment → Logistics → Margin → Customer Experience.
   • AI What-If Copilot: Simulates supply chain shocks (e.g. monsoon delays + marathon footfall) with rupee-quantified business outcomes.

3. 4 Role-Adaptive Enterprise Workspaces:

   • Store Manager (Priya Sharma): Store floor prescriptions, shelf stockout triage, backtest proof, interactive WhatsApp simulator, and floor staff execution checklist.
   • Supply Chain Lead (Marcus Vance): District lateral store-to-store transfers (4-hour transit), Central DC capacity slotting, and Supplier OTIF reliability scorecards.
   • Category Lead (Elena Rostova): Dynamic markdown elasticity optimizer slider, promo dampener, brand substitution matrix, and competitor price matching.
   • Executive Cockpit (David Sterling): Omnichannel performance cockpit, district network heatmaps, and Connected Decision Chain ripple simulator.

4. WhatsApp Bot Simulator:

   • Interactive conversational bot simulation (/api/v1/whatsapp/chat).
   • Push stockout alerts directly to store managers with quick-tap ordering commands (Approve / Reject).

5. Purchase Order Exporter:

   • 1-click CSV export of approved purchase orders ready for ERP ingestion (/api/v1/export/purchase-orders).

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👥 Demo Enterprise Personas & Credentials

Role	Persona	Email	Password	Primary Workflow
Store Manager	Priya Sharma	manager@retailchain.com	Password123!	Shelf triage, stockouts, staff checklist
Supply Chain Lead	Marcus Vance	supplychain@retailchain.com	Password123!	Lateral transfers, DC slotting, OTIF
Category Lead	Elena Rostova	category@retailchain.com	Password123!	Markdowns, promo dampening, pricing
Executive VP	David Sterling	executive@retailchain.com	Password123!	Omnichannel cockpit, network heatmaps

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🛠️ Tech Stack & Ports

• Frontend:
  • React 19, Vite, Tailwind CSS
  • Lucide React icons, Plotly.js (plotly.js-dist-min)
  • Google OAuth (@react-oauth/google)
  • Dev Port: http://localhost:5174/ (or 5173)
• Backend:
  • FastAPI (Python 3.12+), Pydantic v2
  • PostgreSQL (voiceofcustomer_db on localhost:5432)
  • SQLAlchemy 2.0 ORM, PyJWT (Dual Access & Refresh Tokens), bcrypt
  • Uvicorn on Port 8001: http://localhost:8001/

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🚀 Quickstart Guide

1. Prerequisites

• Node.js 18+ and npm
• Python 3.12+
• PostgreSQL running locally on port 5432

2. Backend Setup

cd server

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Start FastAPI server on Port 8001
uvicorn app.main:app --host 0.0.0.0 --port 8001 --reload

Interactive API docs available at: http://localhost:8001/docs

3. Frontend Setup

cd client

# Install packages
npm install

# Run frontend development server
npm run dev

Open your browser at: http://localhost:5174/

4. Running Verification Checks

# Frontend ESLint verification (0 errors, 0 warnings enforced)
cd client && npm run lint

# Production bundle build test
cd client && npm run build

# Backend API health test
curl -s http://localhost:8001/api/v1/dashboard/kpis | jq
curl -s http://localhost:8001/api/v1/recommendations | jq

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🔒 Security & Git Hygiene Standards

• Dual-Token Authentication:
  • Access Token: 60-minute expiry (type: access).
  • Refresh Token: 7-day expiry (type: refresh).
  • Refresh tokens are rejected on protected decision endpoints.
• Zero Secrets in Git: Local .env files are ignored by both root and client .gitignore.
• Single-Viewport Auth Experience: Login and signup pages are constrained to fit within standard desktop viewports (100vh) with zero vertical scrollbars.

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📄 License

RetailSense is developed for the AI for Business (Retail / CPG) Decision Intelligence Challenge. Released under the MIT License.