Inventory Planning

What is inventory planning in retail?

Inventory planning is the process of forecasting demand, setting stock levels, and distributing inventory across stores and channels so the right products are available at the right locations at the right time — minimizing stockouts and excess stock simultaneously.

What is the difference between inventory allocation and replenishment?

Allocation is the initial distribution of stock to stores or channels at the start of a season, based on demand forecasts and store rankings. Replenishment is the ongoing restocking triggered automatically when sell-through thresholds are met — ensuring top sellers never go out of stock mid-season.

How does AI improve inventory distribution for retail brands?

AI-powered inventory distribution uses real-time sell-through data, localized demand signals, and predictive forecasting to automate allocation, replenishment triggers, inter-store transfer recommendations, and markdown timing — replacing manual, spreadsheet-driven processes with dynamic, data- led decisions.

What are inter-store transfers and when should retailers use them?

Inter-store transfers are in-season inventory movements from low-demand locations to stores with higher demand for the same product. Retailers should use them when sell-through rates diverge significantly across locations, when broken sizes can be consolidated elsewhere to complete full-size runs, or when stockouts are forecast at high-performing stores.

How do you reduce end-of-season markdowns with inventory planning?

Reducing markdowns requires early demand sensing, accurate size-level sell- through tracking, and timely inter-store rebalancing throughout the season. Markdown optimization tools further reduce losses by recommending the right discount depth and timing per SKU and location — clearing inventory without sacrificing margin unnecessarily.

What is True Rate of Sale (ROS) in inventory management?

True Rate of Sale (ROS) measures how fast a product actually sells when it is in stock — calculated only from healthy, in-stock sales days. Unlike raw sales velocity, True ROS excludes stockout periods so it accurately reflects real demand, making it a more reliable input for allocation and replenishment decisions.

How do fashion brands handle inventory management for styles with many size and color variants?

Fashion brands manage inventory for styles with many size and color variants by planning and tracking stock at the SKU level — independently for each size- color combination — rather than at the style level. This means each variant is allocated to stores based on that store's historical demand for that specific size and color, preventing stockouts on fast-moving options while avoiding excess on slow ones. Mid-season, inter-store transfers consolidate broken size runs into locations where they complete full sets, recovering full-price sell-through that would otherwise be lost to markdowns. AI-powered tools like Increff automate these decisions at scale, using True Rate of Sale data for every store- style-SKU to keep size availability healthy across the entire network.

What tools help fashion brands optimize end-of-season sale pricing?

The tools that help fashion brands optimise end-of-season sale pricing are markdown optimisation platforms that recommend the right discount depth and timing at the SKU and store level — rather than applying blanket, chain-wide discounts. These tools work by analysing remaining inventory levels, time left in the season, local demand signals, and price elasticity for each product and location. The best ones sequence markdowns progressively: starting with shallow discounts to protect margin, and deepening only where sell-through velocity demands it. This approach maximises revenue recovery on end-of- season stock while avoiding over-discounting on products that could still sell at a higher price. Increff Markdown Optimization applies this logic automatically, generating location-specific discount recommendations so fashion brands clear end-of-season inventory efficiently without sacrificing avoidable margin.