Proprietary AI that connects demand forecasting, inventory policy and per-store allocation to reduce stockouts and overstock without inflating working capital.
Transformation · Demand & Supply
Companies lose margin when they plan demand and inventory based on delayed signals, ignoring changes by SKU, channel, region and behavior. Without anticipation, stockouts emerge, excess inventory builds up and capital gets trapped in slow-moving products. Predictive AI transforms data into early decisions for purchasing, replenishment, allocation and assortment.
The invisible cost sits between the demand signal and the operational decision.
The demand and supply problem does not start when a product is already out of stock. It starts earlier, when signals of change already exist, but the company still cannot interpret them in time.
Every operation generates signals. Sales by SKU, turnover by store, behavior by channel, price variation, campaign activity, previous stockouts, seasonality, available inventory, lead time, margin and service level. The challenge is to turn those signals into decisions before the loss happens.
In practice, demand, purchasing, supply, trade, logistics and operations teams deal with recurring pain points:
> The most expensive combination is simple: the SKU that would sell is missing, while the SKU nobody wants to buy is sitting in stock.
Traditional forecasting does not solve volatile, granular and intermittent demand.
Many demand forecasting processes still depend on historical averages, fixed rules, spreadsheets and aggregated analyses. These methods work in stable scenarios, but fail when demand varies by SKU, store, channel, region, price, promotion and consumer behavior.
In practice, the operation needs to answer questions these models do not handle well:
> The problem is not only forecasting sales. It is turning forecasts into decisions.
Without guiding purchasing, replenishment, allocation and prioritization, the operation remains reactive. The company understands the problem more clearly, but still acts too late.
Generic AI also fails when it only delivers analysis. To create real impact, AI needs to operate across the full cycle: predict, recommend, decide, activate and continuously learn.
infinity6 applies proprietary AI engines to connect demand, inventory, assortment and allocation into a single decision cycle.
The solution runs on top of the company’s current systems, such as ERP, WMS, OMS and CRM, using existing data to generate actionable recommendations.
*Applications*: Demand and sales forecasting. Stockout and excess identification. Replenishment planning. SKU prioritization.
*Applications*: Mix and assortment recommendation. Allocation across DC, store and channel. Substitute product identification. Product prioritization.
*It answers directly:* What to order, how much, where to send it and when. Where there is stockout or excess risk. Which decision creates the highest impact.
> The difference is simple: turning forecasts into action before the loss happens.
The solution operates as an intelligence layer above current systems. The ERP continues to record purchases, orders, inventory, transfers and movements. infinity6 adds a predictive layer to guide better demand and supply decisions.
1. Business Data Integration: infinity6 connects sales, inventory, product, price, channel, store, campaign and operational data.
2. Predictive Modeling: The proprietary engines identify demand patterns, stockout risk, excess risk, turnover behavior, seasonality, volatility and allocation opportunities.
3. Operational Recommendation: AI generates practical recommendations for forecasting, purchasing, replenishment, transfer, assortment, service level and SKU prioritization.
4. Activation in the Current Process: Recommendations are delivered through API, file, dashboard or i6 Signal, without replacing current systems.
5. Continuous Learning: The models relearn from new sales, inventory, stockout, campaign, price, replenishment and operational response data.
What is predictive AI for demand and supply? It is the use of AI to analyze sales, inventory, price, product, channel and behavior data, predicting demand and guiding purchasing, replenishment, allocation and assortment decisions.
What problem does AI solve in demand forecasting? It anticipates changes before they become stockouts, excess inventory or margin loss, improving forecasts by SKU, store, channel and region.
How does AI help reduce stockouts? It identifies risk based on sales, inventory, lead time and seasonality, allowing the operation to act before products are missing.
How does AI help reduce excess inventory? It detects where inventory is above real demand, allowing the company to adjust purchasing, redistribution and assortment.
What is the difference between traditional forecasting and predictive AI? Traditional forecasting uses averages and fixed rules. AI learns granular patterns and adapts quickly to changes.
Does the solution replace the ERP or S&OP tool? No. It acts as a layer above current systems, complementing ERP and S&OP with predictive intelligence.
What data is needed to start? Sales history, products, inventory, price, channel and calendar. Additional data increases accuracy.
Does AI work for new SKUs with no history? Yes. It uses similarity with other products, attributes and behavior patterns to estimate initial demand.
Does AI work for products with intermittent sales? Yes. Models consider irregular patterns and contextual signals to improve forecasts.
Does the solution work for physical retail, e-commerce, marketplace and industry? Yes. It applies to any operation that needs to balance demand and supply with more precision.
How long does it take to see results? Usually between 30 and 90 days, depending on the data and operational rollout.
Does the solution also help with assortment? Yes. It recommends the ideal mix by store, channel or region, aligning supply with real demand.
How do demand and supply connect with behavior and conversion? Navigation, search and intent signals help anticipate demand before the sale happens.
How do demand and supply connect with predictive operations? The forecast becomes operational action: prioritization, alerts and daily decision-making.
How can demand data generate monetization? It can be used as a strategic asset to generate market intelligence and new revenue streams.