Inventory is one of the most capital-intensive parts of the supply chain. Effective inventory management reduces holding costs, improves service levels, and enhances profitability. But not all inventory items are created equal—nor should they be treated that way.

To manage stock with precision, supply chain leaders use segmentation frameworks like ABC classification, and increasingly, advanced forecasting tools powered by AI and digital twins.

What Is ABC Classification?

ABC classification segments inventory based on consumption value and prioritization.

This method ensures that high-impact items (A) are monitored closely, while lower-priority items (C) are managed with minimal effort.

For background, see:

👉 NetSuite: ABC Inventory Analysis

Beyond ABC: Other Segmentation Strategies

In more complex supply chains, companies often combine ABC with other classification models, including:

FSN (Fast, Slow, Non-moving): Based on frequency of usage
VED (Vital, Essential, Desirable): Based on operational criticality
SDE (Scarce, Difficult, Easy): Based on ease of procurement

These multi-dimensional approaches provide deeper insights into reorder policies, stocking levels, and supplier engagement.

Demand-Driven Replenishment

Strategic inventory planning combines segmentation with replenishment logic:

Reorder Point (ROP): Trigger replenishment when stock hits a threshold
Min-Max Systems: Maintain inventory between set floor and ceiling levels
Forecast-Based Models: Use past and predicted demand to time orders

With the rise of digital twins, businesses now simulate inventory flows across different demand scenarios, locations, and lead times—prior to making real-world decisions.

Explore the role of digital twins in inventory optimization:

👉 McKinsey: Digital Twins & Supply Chain Optimization

AI in Inventory Planning

AI brings automation and predictive intelligence to inventory management:

Predicts future demand spikes or slowdowns
Dynamically recalculates reorder points
Identifies excess, obsolete, or safety stock risks
Integrates external data (e.g., weather, POS data) to fine-tune forecasts

Hylios uses digital twins paired with AI to model inventory performance under various scenarios—helping companies optimize service levels and minimize waste.

Learn more about AI in this context:

👉 Forbes: AI in Inventory Management

Key KPIs to Track

Inventory Turnover
Days of Inventory on Hand (DOH)
Stockout Rate
Carrying Cost of Inventory
Obsolete Inventory %

Monitoring these KPIs regularly ensures alignment between inventory strategy and business goals.

Conclusion: Make Inventory Strategy a Competitive Weapon

From classic ABC to AI-powered forecasting, inventory strategy continues to evolve. Companies that take a proactive, data-driven approach—powered by segmentation, simulation, and automation—are better equipped to reduce costs, increase service levels, and respond to market volatility.

Next Up

Learn how to protect against demand variability in

4.2: Safety Stock Calculations: How to Prevent Stockouts.