Background
Calvin Klein, a global apparel brand, faced challenges in managing inventory and optimizing sales across its vast retail network. The company aimed to improve in-stock rates, streamline inventory allocation, and drive up both sales and margins.

Challenges

1. Excessive Promotions: Frequent discounts were diluting brand value and impacting margins. Calvin Klein needed a solution to optimize full-price sell-through and decrease reliance on markdowns.
2. Manual Rebalancing: Planners manually analyzed daily sales data and rebalanced inventory across stores, taking approximately 4 hours with a turnaround time of about a day.
3. Scenario and Seasonal Planning: The brand needed to adjust strategies dynamically based on real-time sales and inventory data. Demand forecasting systems provided only basic insights, requiring extensive manual work to create detailed scenarios for decision-making.
4. Limited Implementation Bandwidth: Calvin Klein needed a quick solution that could be integrated smoothly without disrupting business processes.

Solution

Intelo AI collaborated with Calvin Klein to deploy a suite of AI agents tailored to address these specific challenges:

1. Full-Price Sell-Through Optimization Agent
   Intelo AI’s full-price optimization agent focused on maximizing revenue by boosting full-price sell-through. This agent generated actionable insights, enabling Calvin Klein to transition from static spreadsheets to adaptive strategies that drive results.
2. Store Inventory Rebalancing Agent
   The inventory rebalancing agent automated the process of daily sales analysis and inventory rebalancing across stores. By reducing the rebalancing process from a full day to just minutes, planners could execute inventory strategies more rapidly, improving inventory availability.
3. Replanning Scenarios Agent
   Intelo AI’s replanning scenarios agent generated multiple actionable scenarios based on current sales and inventory data. This allowed the planning team to evaluate and implement optimal strategies aligned with quarterly goals, transforming planners from reactive responders to proactive strategists.
4. Seasonal Planning Agent
   This seasonal planning agent used Calvin Klein’s demand forecasting data to run real-time scenarios, enabling quick scenario generation and strategic evaluations. Leveraging the company’s existing Microsoft investments minimized disruptions, while helping the planning team make data-driven decisions with less manual effort.
5. Modular AI Agents
   Intelo AI provided modular agents that could be deployed independently, integrating smoothly with Calvin Klein's existing systems. These agents addressed key pain points without disrupting Calvin Klein's operations, allowing the company to target specific areas of improvement effectively.

Results

The partnership between Intelo AI and Calvin Klein yielded substantial benefits:

• In-Stock Rate: Improved by 9.6%, ensuring product availability when and where customers needed it.
• Sales Growth: Increased by 22%, driven by enhanced inventory management and availability.
• Margin Improvement: Margins rose by 24% as a result of better inventory control and a decrease in markdowns.

These improvements were enabled by Intelo AI’s agents, which empowered Calvin Klein to swiftly adapt and implement efficient inventory strategies. The emphasis on actionable insights, rather than mere data visualization, ensured timely, data-informed decisions.

Conclusion

By leveraging Intelo AI’s suite of AI agents, Calvin Klein overcame its inventory and strategic planning challenges, achieving significant gains in in-stock rates, sales, and margins. The modular structure of these AI agents enabled rapid deployment with minimal disruption to existing processes, making Calvin Klein a leader in intelligent retail operations.

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