Merchandise planning is both art and science for outdoor retail brands and suppliers. Planning the right assortment that aligns with consumer demand requires data-driven insight and creative vision. As the outdoor industry shifts towards omnichannel, DTC, and e-commerce, new complexities arise in brand merchandising strategy. THis post will examine the top 10 merchandise planning challenges currently facing outdoor retailers and suppliers and how AI-powered retail software like OmniThink.AI can provide solutions.
Challenge #1 – Granular Demand Forecasting
Outdoor consumer purchasing is highly seasonal, event-driven, and dependent on regional weather patterns. Granular geospatial and temporal demand modeling is critical but difficult to achieve manually.
AI retail merchandising software can ingest billions of external market data signals – like weather, events, social media – at a hyperlocal level. Machine learning identifies highly relevant demand drivers for enhanced bottom-up forecasts vs. just relying on historical data.
Challenge #2 – Optimizing Product Assortment
Determining the right product mix across categories, brands, styles, colors, and sizes is exponentially complex. Outdoor retailers can stock 30,000+ SKUs across apparel, gear, and hardgoods.
AI-based assortment optimization tools can run millions of simulations to determine the Pareto-optimal product portfolio tailored to each location’s customer demographics, purchasing behavior, and local dynamics.
Challenge #3 – Inventory Planning and Allocation
Outdoor inventory is difficult to plan due to long supplier lead times, seasonal peaks, and unpredictability. Misallocation leads to stockouts, markdowns or excess inventory.
AI inventory planning algorithms account for lead times, seasonality, events, and demand signals to optimize time-phased reorder plans. Automated allocations improve localization and rebalance omnichannel inventory dynamically.
Challenge #4 – Cannibalization and Halo Effects
Product adjacencies impact incremental sales, market basket metrics and attach rates. Minimizing product cannibalization and maximizing halo effects is an endless balancing act.
AI-based planogram tools can quickly identify complementary and SUBSTITUTE products to place together or apart by analyzing historical baskets, customer data, and sales correlations. This improves arrangement decisions.
Challenge #5 – Price and Promotion Optimization
Complex omni-channel pricing and limitless promotion options make identifying the most profitable tactics across thousands of SKUs extremely challenging.
AI retail merchandise planning software with price optimization engines leverage elasticity modeling, competitive intelligence, inventory data, and demand transfer analysis to recommend profit-maximizing omni-channel pricing. AI also optimizes promotion calendars, frequency, depth, etc.
Challenge #6 – Geospatial and Channel Planning
As omnichannel marketplaces and DTC selling expands, understanding performance by geospatial zone and by channel becomes imperative but difficult.
AI sales attribution models quantify performance by hyper-local zones, channels and touchpoints. This allows merchants to optimize tactics at extremely granular levels.
Challenge #7 – Sourcing, Placement and Lead Times
Navigating global sourcing options, production slots, and long supplier lead times make appropriate order placement and delivery projections complex. Stockouts are hard to avoid.
AI supply chain planning tools factor in historical lead times, geographic risk, cargo/shipping lane velocities, partner reliability and real-time events to enhance order placement decisions and delivery estimates.
Challenge #8 – Omnichannel Visibility and Execution
Limited inventory visibility across channels and lack of centralized control makes rebalancing stock levels challenging. Inventory gets stranded or unproductive.
AI inventory management centralizes visibility and orchestrates movement of inventory across channels based on real demand signals. This keeps inventory utilized profitably.
Challenge #9 – Agile Decision Making
Merchandising has traditionally relied on slow, batch planning processes versus continuous, rapid decision making needed in digital retail.
AI demand planning software enables always-on daily reforecasting, hyper-personalization at scale, real-time dynamic pricing, instant inventory movements and other agile merchandising tactics.
Challenge #10 – Institutional Knowledge Capture
Merchant intuition and expertise is hard to capture and scale. Loss of critical human insights affects merchandising strategy over time.
AI use NLP interfaces to capture merchant expertise. That knowledge is integrated into AI models to retain institutional learnings.
The Next Generation of Merchandise Planning
AI represents a new paradigm for merchandise planning – one that combines the strengths of human creativity and judgment with the speed, granularity and optimization of machine intelligence. Leading outdoor retailers are already using AI to enhance critical inventory, pricing, assortment, allocation and geospatial decisions. As AI capabilities continue to rapidly advance, outdoor brands can leverage the technology to strategically tackle their unique merchandise planning complexities and exceed consumer expectations. The future of successful outdoor retail will increasingly rely on AI.
