Assortment planning is where analytics meets the shop floor. Whether you’re a category manager, merchandiser or a small-chain owner, choosing the right AI tools for assortment planning changes how you forecast demand, optimize inventory and present products (planograms included). In my experience, the mix of machine learning, demand forecasting and inventory optimization can cut waste and boost sales—if you pick the right platform. Below I walk through top vendors, show real-world fits, and give a practical comparison so you can move faster and smarter.
Why AI matters for assortment planning
Simple truth: shoppers want relevance and stores want efficiency. AI helps with both by using historical sales data, promotions, store profiles and external signals (weather, events) to improve assortment decisions.
Key gains: better demand forecasting, smarter category management and faster planogram optimization.
Top AI tools for assortment planning (shortlist)
What I’ve noticed—vendors often specialize. Some are masters at demand forecasting, others at inventory optimization or planogram-level assortment.
1. RELEX Solutions
RELEX combines forecasting, replenishment and assortment optimization in one platform. It’s built for retailers who want tight inventory control and automated replenishment. Many grocers use RELEX for perishable categories.
Official site: RELEX Solutions.
2. Blue Yonder
Blue Yonder (formerly JDA) offers strong AI-driven forecasting and category planning tools. Their strength is scaling to very large assortments across many stores.
Official site: Blue Yonder.
3. Oracle Retail
Oracle’s retail suite includes assortment planning modules that integrate with wider retail systems—useful if you need deep ERP ties and enterprise reporting.
4. SAS (SAS Forecasting & Retail Solutions)
SAS provides advanced analytics and machine learning models for demand forecasting and inventory optimization. It’s a fit when you need heavy analytics and explainable models.
5. Symphony RetailAI
Focused on category management and shopper-centric insights, Symphony helps convert customer behavior into actionable assortment changes.
6. Microsoft Azure AI + Dynamics 365
For teams that want cloud-native AI and custom modeling, Azure AI with Dynamics 365 gives flexibility to build tailored demand forecasting and assortment rules.
7. Manhattan Associates
Manhattan has robust inventory and supply chain optimization modules—good for omnichannel retailers balancing online and in-store assortments.
Comparing the top tools
Short table to help you scan strengths quickly.
| Tool | Best for | Strengths | Consider |
|---|---|---|---|
| RELEX | Grocery & perishables | Forecast accuracy, replenishment automation | Enterprise pricing |
| Blue Yonder | Large multichannel retailers | Scalability, category planning | Implementation complexity |
| Oracle Retail | Enterprises with ERP needs | Integration, reporting | Costs and customization |
| SAS | Analytics-heavy teams | Advanced ML, explainability | Requires analytics resources |
How to pick the right AI assortment planning tool
Think about people first. Tools don’t solve problems—teams do. Ask these quick questions:
- Do you need out-of-the-box models or custom ML?
- How many SKUs and stores are you managing?
- Is integration with ERP/POS required?
- Do you need planogram-level optimization or category-level only?
Practical selection checklist
- Data readiness: clean sales, master data, promotions, store attributes.
- Forecasting needs: intermittent demand vs continuous SKUs.
- Optimization: do you need inventory optimization and multiechelon modeling?
- Usability: will merchandisers use it daily without analytics help?
Real-world examples
Example 1: A regional grocery chain moved to RELEX for perishables. They tightened order cadence and reduced waste—margins improved in 6 months.
Example 2: A fashion retailer used Blue Yonder to harmonize online and in-store assortments. They reduced overstocks in slow-season SKUs and increased sell-through during promotions.
Important features to evaluate (detailed)
Demand forecasting
Look for ML models that handle seasonality, promotions and intermittent demand. Forecasting is the backbone of assortment planning.
Inventory optimization
Multiechelon optimization and safety stock calculations minimize stockouts while reducing carrying costs.
Category management & planogram optimization
Tools that understand shopper behavior and localize assortments tend to outperform one-size-fits-all category plans. See background on category management: Category management (Wikipedia).
Integration & scalability
Pick platforms that integrate with POS, ERP and ecommerce platforms. If you’re growing fast, scalability matters more than flashy features.
Costs, ROI and implementation tips
Expect implementation to take weeks to months. ROI often arrives within 6–12 months if you prioritize high-turn categories first.
- Start with a pilot on top-selling categories.
- Measure improvements in forecast accuracy, stockouts and markdowns.
- Iterate—treat models as living assets that need retraining and governance.
Future trends to watch
AI models are getting better at using external data (social trends, weather, footfall). Planogram optimization using computer vision and shopper analytics is becoming real. Retailers who combine customer insights with assortment algorithms will win.
Further reading and trusted sources
For vendor details and product specs, check vendor sites like RELEX Solutions and Blue Yonder. For frameworks on category strategy, the Wikipedia entry on category management is a good primer.
Next steps you can take today
- Audit your data: clean SKUs, sales history, store attributes.
- Run a two-month pilot with clear KPIs (forecast accuracy, stockouts).
- Choose a vendor that matches your scale and integration needs.
Wrap-up
Choosing the best AI tool for assortment planning comes down to fit: the right mix of forecasting, inventory optimization and category management that your team can operate. From what I’ve seen, starting small and scaling fast beats over-engineering a solution that never gets used.
Frequently Asked Questions
There’s no one-size-fits-all. RELEX and Blue Yonder excel for large retailers and perishables respectively, while Oracle and SAS fit enterprise integrations and heavy analytics. Choose based on scale, data readiness and integration needs.
AI uses historical sales, promotions and external signals to model seasonality and trends. This improves forecast accuracy, reduces stockouts and cuts markdowns by aligning supply with expected demand.
Many retailers see measurable ROI in 6–12 months after deployment, especially if they start with high-turn or high-margin categories and maintain strong data governance.
Not always. Small retailers often benefit from cloud-native, modular solutions or managed services that provide forecasting and inventory optimization without heavy IT lift.
Clean historical sales, SKU attributes, store profiles, promotion history and supply lead times are essential. External signals like weather and events help improve model accuracy.