Wholesale & Distribution

Demand forecasts are generated across thousands of active SKUs — including slow-movers, intermittent sellers, and long-tail items that traditional statistical models handle poorly — using AI/ML methods that select the best forecasting approach per SKU based on demand pattern, velocity, and history length. Seasonal profiles, customer-specific order patterns, and promotional events are incorporated automatically, giving distributors a reliable demand signal across the full portfolio rather than just the top-selling items that get manual attention.

Inventory targets are optimized simultaneously across every node in the distribution network — central warehouses, regional DCs, and forward stocking locations — using MEIO with dynamic safety stock calculation that continuously recalibrates buffer levels based on service level targets, demand variability, and supply uncertainty at each echelon.

Rather than setting static safety stock rules that quickly become outdated as demand patterns shift, the platform recalculates optimal buffers in response to changing forecast error, lead time variability, and customer-specific service commitments — ensuring high-priority accounts and contractual SLAs are protected without over-investing in inventory across nodes where demand is more predictable.

Purchase orders are generated automatically using demand-driven replenishment models that evaluate multiple suppliers simultaneously — comparing lead times, unit costs, minimum order quantities, and volume discount thresholds to select the cost-minimizing sourcing option while meeting OTIF targets for each customer or customer segment.

When a preferred supplier cannot meet the required delivery window, the platform automatically identifies the next-best alternative that preserves service commitments without defaulting to the most expensive option by default. Vendor collaboration workflows automate PO sharing, confirmation tracking, and delivery status monitoring, giving procurement teams real-time visibility into inbound supply across the entire supplier base — without chasing individual vendors for updates or managing exceptions manually.

Purchase orders are generated automatically by consolidating replenishment needs across multiple distribution nodes — aggregating demand signals from every warehouse and DC into unified supplier orders that meet minimum order quantities, pallet configurations, and full truckload thresholds without manual coordination between sites.

Rather than each node placing independent orders that individually fall short of MOQs or leave trucks running half-empty, the platform identifies consolidation opportunities across the network in real time — reducing freight cost per unit, unlocking volume discount thresholds, and improving supplier relationship economics without sacrificing node-level service targets.

Procurement teams gain a clean, automated workflow from demand signal to executable PO, with full visibility into order consolidation logic, supplier lead times, and budget constraints at every step.

Inbound and outbound transportation decisions are optimized concurrently with inventory and replenishment planning — so freight costs, carrier constraints, and delivery windows are factored into purchasing and fulfillment decisions at the moment they are made, not reconciled after the fact by a separate logistics team. Inbound planning consolidates supplier shipments to maximize load utilization and minimize cost-per-unit landed, while outbound routing optimizes multi-stop, multi-modal delivery schedules against customer service windows, fleet constraints, and hours-of-service regulations.

The result is a total cost-to-serve view that lets distributors make explicit trade-offs between inventory positioning, transportation spend, and service level — rather than optimizing each in isolation and absorbing the cost of misalignment.

Slow-moving, excess, and at-risk inventory is identified continuously using AI-driven diagnostics that flag items approaching obsolescence before write-offs become unavoidable — triggering recommended actions including price reductions, reallocation across the network, or supplier return coordination. Working capital tied up in dead stock is quantified at the SKU and category level, giving supply chain and finance teams a shared, actionable view of inventory health rather than discovering obsolescence at period-end.

Solvi, Solvoyo’s AI planning agent, continuously monitors demand signals, inventory positions, supplier OTIF performance, and stock cover across every node in the distribution network — alerting procurement and supply chain teams the moment a service level risk, excess inventory build-up, or supplier reliability issue is detected before it breaches a customer commitment or inflates safety stock unnecessarily. Rather than discovering a coverage gap or a slow-moving inventory position at period-end, Solvi diagnoses the root cause, quantifies the working capital and service level impact of inaction, and generates prioritized recommendations — from replenishment consolidation and supplier escalations to network rebalancing and safety stock recalibration — that distribution planners can execute directly within the platform.