We predict future demand for every product and channel with market-leading forecast accuracy. These predictions can then be used to optimize your order and replenishment decisions.
The resulting reduction in out-of-stock and overstock rates will increase your long-term profitability and enable sustainable and faster growth.
Our AI-driven forecasts enable better order decisions more fitted to the future demand and avoid overstock at the end of the season.
Example: For a product with a projected 18-month sales volume of 500 units, 7Learnings automatically modifies your purchase recommendation to 500 units.
We help you ensure you order the right amount to avoid out-of-stock situations.
Example: 7Learnings increases the order volume of a product where we predict a higher demand.
Our solution enables you to increase order levels with confidence. This enables higher growth rates in the next season.
Example: If forecasts reveal untapped demand, confidently boost order volumes to capture it rather than under-ordering out of caution.
Price changes directly affect demand. 7Learnings factors price elasticity and planned promotions into replenishment forecasts so inventory always matches dynamic demand.
Example: If a 20% price drop is set to increase demand by 75%, the system automatically scales your reorder suggestion upward.
The reduction of out-of-stock and overstock will ultimately increase your long-term profitability.
Example: Improving sell-through from 80% to 85% minimizes end-of-season clearance losses and increases profit.
We automatically pull in accurate and up-to-date data like minimum order quantity and next order purchase price
You define the order period and product assortment based on your business goals in our software
You can add specific rules you want to consider on a product level, such as a minimum profit margin
You can set specific goals such as maximizing profitability, hitting a certain revenue growth or a certain profit margin
Review detailed forecasts for sales, revenue, and profit for different optimization scenarios
Automatically sync optimized order quantities with your purchasing system for a streamline execution
We turn your raw data—including sales history, pricing trends, and inventory levels—into highly accurate predictions of demand for your products. Our advanced algorithms analyze patterns across multiple dimensions and generate a long-term forecast of demand. We help you to stay ahead of seasonal shifts, promotional campaigns, or pricing adjustments.
Take the guesswork out of reordering decisions. We enable dynamic optimization that considers critical factors such as price fluctuations, historical stockouts, market trends, and sales velocity. Our system ensures that the quantities of your orders align with your business goals, reducing inefficiencies and improving agility.
We ensure you maintain full control over your ordering strategy. Set parameters that fit your business needs, such as markets, time frames, and sales channels. Define specific goals like maximizing profit, minimizing costs, or hitting sell-through targets, and our system delivers specified recommendations that achieve your desired results.
Our forecasts break down sales, revenue, and profit projections at the individual product level. We give you a holistic overview of how each SKU contributes to your business goals, so you can make informed decisions with a clear view of your inventory's performance across all channels and regions.
We use advanced AI algorithms to analyze raw data from your sales, pricing, and inventory systems. Our solution then generates a long-term forecast of demand which is used to optimize reorder quantities, incorporating constraints like delivery times, minimum order quantities, and future purchasing costs. As a result, your purchasing team can make precise decisions that align with your business goals.
7Learnings utilizes machine learning algorithms trained on granular transaction history, inventory levels, price elasticities, and location-specific data. By isolating demand drivers across channels (DTC, marketplaces, physical stores), the platform generates automated forecasts at the SKU–location level. This ensures purchase orders match true localized velocity rather than blended averages.
7Learnings connects inventory ordering directly with its AI-driven markdown and clearance optimization module. By predicting end-of-season sell-through before orders are placed, you can evaluate markdown risk upfront. If excess inventory occurs, the platform determines the minimum necessary discount depth to clear remaining stock by a target date while maximizing gross margin.
The platform incorporates operational constraints directly into its optimization algorithms. When calculating reorder volumes, 7Learnings balances unit pricing, lead times, and projected demand elasticity against supplier MOQs and price-break tiers. This ensures suggested purchase orders satisfy vendor minimums while minimizing tied-up capital and holding risks.
7Learnings dynamic forecasting continuously measures demand volatility, forecast variance, and supplier lead-time uncertainties. Rather than applying static rule-of-thumb buffers, the system dynamically calculates the minimum required safety stock per SKU. This ensures high service levels and target fill rates while avoiding unnecessary inventory build-up.
Because 7Learnings natively combines predictive pricing with demand forecasting, future price points are central to the order quantity calculation. The engine simulates price elasticity and demand curves for your planned future prices, marketing pushes, or promotional calendars, converting targeted price strategies into accurate purchase volumes.
Overstock typically happens when purchasing is disconnected from real-time sales velocity and future price elasticity. 7Learnings aligns order quantities with granular sell-through predictions across the entire product lifecycle. By continuously refining reorder recommendations based on live stock levels and demand trends, the system prevents excess capital from being trapped in slow-moving inventory.
Stockouts occur when lead times shift or demand surges outpace static reorder triggers. 7Learnings continuously forecasts baseline demand, seasonal trends, and promotional lift. By automatically factoring in lead times and safety stock buffers, the platform generates proactive purchase orders before inventory reaches critical thresholds.
7Learnings uses machine learning to isolate baseline sales from temporary lifts driven by marketing campaigns, discounts, and seasonal peaks. By decoupling these historical drivers, the forecasting engine models exact promotional lift and seasonal curves, preventing artificial demand spikes from causing inflated purchase orders.
The forecasting engine evaluates performance at the individual variant level (size, color, style) across different lifecycle phases, from initial launch to maturity and end-of-life. By modeling size-curve distributions and variant-specific velocity, 7Learnings optimizes the exact SKU mix per order, preventing common issues like running out of core sizes while sitting on fringe inventory.
Book a demo with one of our experts and learn how our software can grow your profit and revenue.
