Retail Enterprise
Omnichannel transformation and AI-driven inventory optimization
The Business Challenge
A traditional retail chain with 500+ stores faced declining foot traffic and fierce e-commerce competition. Their siloed online and offline channels created inconsistent customer experiences, while outdated inventory systems resulted in stockouts and excess inventory simultaneously.
Key Issues
- 1Disconnected online and in-store experiences
- 2Inventory visibility limited to store level (no real-time enterprise view)
- 37-day replenishment cycles causing frequent stockouts
- 4Excess inventory tying up $120M in working capital
- 5Limited data-driven decision making
- 6No buy-online-pickup-in-store (BOPIS) capabilities
Strategic Architecture Approach
Designed an omnichannel commerce platform with unified inventory visibility, implemented AI-driven demand forecasting and replenishment, and created seamless customer experiences across all touchpoints.
Phase 1: Unified Commerce Foundation
Single view of inventory and customer
- Enterprise inventory management system
- Customer data platform (unified profile)
- Order management system (OMS)
- Real-time inventory visibility across channels
Phase 2: AI-Driven Operations
Demand forecasting and intelligent replenishment
- AI/ML demand forecasting engine
- Automated replenishment system
- Dynamic pricing and markdown optimization
- Supply chain control tower
Phase 3: Omnichannel Experiences
Seamless customer journeys
- BOPIS and curbside pickup
- Endless aisle (ship from store)
- Mobile app with in-store navigation
- Personalized recommendations engine
Transformation Roadmap
Q1-Q2: Foundation
- Inventory system consolidation
- Cloud data platform deployment
- OMS implementation
- Customer 360 platform
Q3: Quick Wins
- BOPIS launch (pilot stores)
- Real-time inventory visibility
- Mobile app launch
- Demand forecasting pilot
Q4-Q5: Scale AI
- AI forecasting rollout (full network)
- Automated replenishment
- Dynamic pricing engine
- Store fulfillment optimization
Q6: Optimize
- Personalization engine launch
- Endless aisle implementation
- Supply chain control tower
- Legacy system retirement
Technology Landscape
Platforms
- • Google Cloud Platform (BigQuery, GKE, Cloud Run)
- • Shopify Plus for e-commerce
- • Manhattan Associates for OMS and WMS
- • Segment for customer data platform
- • Blue Yonder for supply chain planning
Frameworks
- • Microservices architecture (Go, Python)
- • React for web, React Native for mobile
- • TensorFlow and Vertex AI for ML models
- • Apache Airflow for workflow orchestration
- • Looker for analytics and BI
Practices
- • Event-driven architecture for real-time inventory
- • MLOps for model deployment and monitoring
- • A/B testing for experience optimization
- • Agile delivery with 2-week sprints
- • FinOps for cloud cost management
Business Outcomes
32% reduction in excess inventory through AI forecasting
Improved availability through intelligent replenishment
Omnichannel capabilities drove digital revenue
Buy-online-pickup-in-store became major channel
Ship-from-store enabled faster delivery
Unified experience improved NPS significantly
Key Lessons
**Quick wins build momentum**: BOPIS pilot success secured leadership support for broader transformation
**Data quality is foundational**: Inventory accuracy improvement was prerequisite for AI success
**Store teams are key**: Change management and training investment ensured adoption
**Start with high-velocity SKUs**: AI forecasting pilot focused on top sellers proved value quickly
**Real-time matters**: Event-driven architecture enabled true omnichannel experiences
**Measure incrementally**: Clear KPIs and regular reporting maintained stakeholder confidence