Inventory is one of the biggest investments a business makes, yet many organizations still rely on relatively static approaches to managing it. Traditional inventory classifications, such as ABC analysis, provide a useful starting point for prioritizing products, but they don't always reflect the changing needs of the business, shifting market conditions, or where inventory investment can deliver the greatest value.
As supply chain leaders balance customer service, working capital, and profitability, the opportunity is to move beyond fixed inventory rules and make more informed, data-driven decisions. Combining AI with established inventory planning methodologies opens up new possibilities, helping businesses understand not just how much inventory they need, but where to focus their attention and why.
Move Beyond Static Inventory Segmentation
Traditional inventory segmentation relies on predefined rules. ABC analysis, for example, classifies products according to a selected attribute, such as sales volume, revenue, or inventory value. Some planning systems allow businesses to create multiple classifications using different criteria, but determining which approach is most appropriate often remains a manual exercise.
The challenge is that product importance isn't necessarily fixed. A product that contributes little to revenue today might become strategically important next quarter because of changing demand, supply constraints, customer priorities, or business objectives. Similarly, the inventory strategy that makes sense for one region or product category might not be appropriate elsewhere.
AI changes the game. Rather than relying on a single attribute or predefined classification, AI can analyze multiple data points and business parameters to identify the most meaningful way to segment inventory at a given time. By evaluating different combinations of criteria, it can help supply chain teams determine which segmentation approach best supports current business priorities.
And what’s very important is that this approach can evolve. The most effective inventory segmentation today might look very different three or six months from now. As demand patterns, supply conditions, and business objectives change, AI can help identify when existing classifications need to be reconsidered.
For planning teams, this means moving from simply assigning products to fixed categories toward dynamically understanding which products matter most, where they matter, and what level of service and inventory investment they require.
From Data Analysis to Actionable Inventory Decisions
The real value of AI goes beyond processing more data, as it helps companies turn that data into better decisions.
Consider a business managing tens of thousands of SKUs across multiple regions. Rather than manually testing different segmentation rules or waiting for overnight batch processes to produce results, AI can explore alternative approaches and understand their potential impact all on its own and then present the findings to planners along with the expected outcome and impacts across departments, suppliers, customers, and more.
What if inventory were segmented according to forecast accuracy rather than product value? What if customer service requirements carried greater weight? What if certain products in a particular region were prioritized because of current supply risks or changing demand?
AI helps evaluate these alternatives, compare outcomes, and surface opportunities that might otherwise go unnoticed, creating a more interactive approach to inventory planning. Teams can use natural-language interactions to explore different scenarios and ask questions about the data, where AI can help identify patterns and opportunities, and provide guidance.
By combining traditional analytical techniques with AI-driven insights, organizations can make inventory decisions that are more responsive to changing business requirements.
Taking Inventory Optimization Further with MEIO
While dynamic segmentation helps businesses understand where to focus their inventory efforts, Multi-Echelon Inventory Optimization (MEIO) takes the process a step further by considering inventory across the entire supply chain network.
Traditional inventory optimization often focuses on determining appropriate stock levels at individual locations or stages of the value chain. But MEIO considers the interconnected relationships between inventory positions across multiple echelons, from suppliers and manufacturing facilities to distribution centers and customers.
Using advanced mathematical optimization, MEIO evaluates the many variables influencing inventory requirements, including demand patterns, lead times, replenishment policies, transfer rules, service-level targets, and supply constraints. The result is a more holistic inventory strategy that balances product availability with working capital and operational costs.
When combined with AI and machine learning, these capabilities can become even more valuable. Organizations can leverage richer data and more sophisticated analysis to understand changing inventory requirements, evaluate trade-offs, and identify opportunities to improve performance across the network.
Make Better Inventory Decisions with Atlas: Combine AI, Segmentation, and MEIO to Find Greater Value
Dynamic segmentation and MEIO address different but complementary aspects of inventory optimization. MEIO provides the advanced mathematical capabilities to solve complex, multi-echelon inventory problems. AI-powered segmentation helps you determine where those optimization efforts should be focused and which products, regions, or service requirements deserve greater attention at a particular point in time.
For large organizations with extensive product portfolios and complex distribution networks, this distinction is particularly important. Running a comprehensive optimization across an entire network can be valuable, but teams also need the flexibility to investigate specific opportunities, test alternative priorities, and understand where improvements will have the greatest impact.
The Atlas Planning Platform from John Galt Solutions brings together advanced inventory optimization, MEIO, AI, and machine learning into a single, intuitive platform to help organizations like yours get more value from inventory. With an end-to-end view of the supply chain, Atlas enables your team to evaluate inventory requirements across locations, understand the broader implications of individual decisions, and explore multiple scenarios before committing to a strategy.
Galt AI adds another layer of intelligence to help you analyze data, investigate alternative segmentation approaches, identify opportunities, and make more confident decisions as priorities evolve.
Let us show you how combining AI-driven insights with inventory optimization empowers supply chain leaders to build a more dynamic approach to inventory planning, to improve customer service while making better use of working capital.
