Assortment Planning & Optimization for Retail
Retail
4-6 months
5 phases
Step-by-step transformation guide for implementing Assortment Planning & Optimization in Retail organizations.
Why This Matters
What It Is
Step-by-step transformation guide for implementing Assortment Planning & Optimization in Retail organizations.
Is This Right for You?
45% match
This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.
Why this score:
- • Applicable across related industries
- • 4-6 months structured implementation timeline
- • Requires significant organizational readiness and preparation
- • High expected business impact with clear success metrics
- • 5-phase structured approach with clear milestones
You might benefit from Assortment Planning & Optimization for Retail if:
- You need: Assortment planning platform or advanced merchandising system
- You need: Historical sales data at store-SKU level (2+ years)
- You need: Store clustering and customer segmentation
- You want to achieve: Achieve a 25-40% improvement in sales per square foot
- You want to achieve: Reduce markdown rate by 10-20%
This may not be right for you if:
- Watch out for: Data silos and poor integration
- Watch out for: Resistance to AI-driven decisions
- Watch out for: Inaccurate store clustering
What to Do Next
Start Implementation
Add this playbook to your workspace
Implementation Phases
1
Foundation & Data Readiness
4-8 weeks
Activities
- Audit historical sales, inventory, and supplier data
- Standardize SKU, lot, and store data formats
- Map store clusters and customer segments
- Validate ERP, POS, and supplier integrations
Deliverables
- Data audit report
- Standardized data formats
- Store cluster and segment mapping
Success Criteria
- Data integration completed with 95% accuracy
- All historical data validated and standardized
2
Platform Deployment & AI Integration
4-8 weeks
Activities
- Deploy AI-driven assortment planning platform
- Configure AI models for demand forecasting and space optimization
- Integrate with planogram and inventory systems
- Set up real-time data pipelines
Deliverables
- Deployed assortment planning platform
- Configured AI models
- Real-time data integration setup
Success Criteria
- AI models achieve 85% accuracy in demand forecasting
- Real-time data pipelines operational with minimal latency
3
Pilot & Quick Wins
2-4 weeks
Activities
- Run pilot in top 3 categories or clusters
- Deploy assortment performance dashboard
- Use AI to rationalize slow-moving SKUs
- Test new item recommendations
Deliverables
- Pilot results report
- Assortment performance dashboard
- SKU rationalization plan
Success Criteria
- Pilot categories show a 10% increase in sales
- Reduction of slow-moving SKUs by 15%
4
Scale & Localization
4-8 weeks
Activities
- Expand AI-driven assortment planning to all categories
- Implement dynamic store clustering and localization
- Optimize space per store using AI-driven planograms
- Integrate supplier and production data for traceability
Deliverables
- Full-scale assortment planning implementation
- Dynamic store clustering model
- Optimized planograms for all stores
Success Criteria
- Sales per square foot increase by 25%
- On-shelf availability exceeds 95%
5
Continuous Refinement
Ongoing
Activities
- Monitor KPIs and adjust AI models
- Incorporate feedback from merchandisers
- Update clusters and segments quarterly
- Automate recall and compliance workflows
Deliverables
- Quarterly KPI report
- Updated AI models
- Automated compliance workflow
Success Criteria
- Continuous improvement in sales metrics
- Reduction in recall response time to under 1 hour
Prerequisites
- • Assortment planning platform or advanced merchandising system
- • Historical sales data at store-SKU level (2+ years)
- • Store clustering and customer segmentation
- • Space planning capability (planogram software)
- • New item testing framework
- • ERP integration (SAP, Oracle Retail)
- • POS system integration for real-time sales
- • Supplier data standardization (GS1, EDI)
Key Metrics
- • Sales per square foot
- • Inventory turnover
- • Markdown rate
- • On-shelf availability
Success Criteria
- Achieve a 25-40% improvement in sales per square foot
- Reduce markdown rate by 10-20%
Common Pitfalls
- • Data silos and poor integration
- • Resistance to AI-driven decisions
- • Inaccurate store clustering
- • Slow supplier data integration
ROI Benchmarks
Roi Percentage
25th percentile: 56
%
50th percentile (median): 80
%
75th percentile: 104
%
Sample size: 50