Demand Solutions (by Logility)
AI-native supply chain planning and demand sensing for the autonomous enterprise.
Unified retail planning and supply chain optimization powered by high-speed AI and Living V3 architecture.
RELEX Fashion AI represents the vanguard of unified retail planning for 2026, utilizing its proprietary Living V3 in-memory database to process massive datasets in real-time. Unlike legacy siloed systems, RELEX integrates demand forecasting, replenishment, and markdown optimization into a singular AI-driven workflow. This technical architecture allows fashion retailers to manage the extreme volatility of short-lifecycle products and seasonal shifts with granular accuracy. The platform specializes in attribute-based forecasting, which is critical for New Product Introductions (NPI) where historical data is absent. By leveraging machine learning for size-level optimization and hyper-local demand patterns, RELEX reduces out-of-stocks by up to 30% and minimizes waste through precision markdowns. As a market leader in the 2026 landscape, its focus has shifted toward 'Autonomous Planning,' where the AI handles routine inventory balancing, allowing human planners to focus solely on strategic exceptions. The system's scalability supports global enterprises managing millions of SKU-store combinations, providing a significant competitive edge in the fast-paced omnichannel fashion environment.
A proprietary high-speed data processing engine that allows for recalculations of massive supply chains in seconds.
AI-native supply chain planning and demand sensing for the autonomous enterprise.
The AI-driven Operating System for modern supply chain orchestration and factory sourcing.
The world's first all-in-one fashion operating system powered by generative AI.
AI-Driven Apparel Manufacturing & Supply Chain Orchestration for Global Brands.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses machine learning to forecast demand for new items by analyzing the performance of similar attributes (material, collar type, fit).
Simulates price elasticity to determine the optimal timing and depth of discounts.
Integrates planogram data with inventory levels to ensure stores never receive more stock than they can display.
AI calculates store-specific size profiles based on local demographics rather than generic curves.
Calculates the carbon footprint of replenishment decisions and optimizes for lower waste.
Incorporates weather, local events, and social trends into short-term forecast adjustments.
Ensuring the right mix of a new collection reaches stores without historical sales data for those specific items.
Registry Updated:2/7/2026
Excess inventory sitting in stores taking up space for new arrivals.
Stock-outs of 'Medium' in some stores while 'Large' remains unsold in others.