95%+
+40%
An AI-powered supply planning platform generates, adjusts and executes the supply actions that keep product moving from raw material to store shelf. It runs continuously. It reads demand signals, compares inventory positions with targets and checks supplier and production constraints, then produces prescriptive orders that are ready to execute. Nothing waits for the next S&OP meeting or for a planner to rebuild a spreadsheet.
Demand planning forecasts how much customers will buy, by SKU, location and time period. Supply planning takes that forecast and decides how to meet it: which stores to replenish, which DCs to restock, which production orders to release and which raw materials to purchase for those production orders. A supply plan built on a stale forecast puts the wrong orders in at the wrong time. Solvoyo runs both on one data model, so each supply action is set against the latest demand signal.
BOM explosion turns a finished goods production requirement into the raw material and packaging needs behind it, using the Bill of Materials to work out what quantities are needed, when and where. In traditional planning it runs as a separate MRP batch, often days after the production plan is finalized, and schedules can change before procurement has acted. In Solvoyo it sits inside the production planning cycle. Each authorized production order is broken into component requirements immediately, net requirements are calculated against current inventory, and supplier purchase orders are generated automatically without a separate run.
Demand-driven replenishment sets inventory targets from current and projected demand. Min/max replenishment uses fixed numbers: it triggers an order when inventory falls below a threshold and orders up to a fixed ceiling, whatever demand is doing. The demand-driven targets also draw on safety stock derived from real forecast error and lead time variability, and on operational constraints like replenishment frequency and store capacity. When a promotion or trend change shifts demand, the targets shift with it, and the change flows on to DC replenishment, production scheduling and raw material purchasing automatically.
Solvoyo models all four layers on a single data model, so a demand change reaches every layer in one planning cycle. Each layer has its own constraints and decision frequency, and the layers must agree with each other or the plan breaks at the handoff. Stores are replenished daily against shelf capacity constraints. DC replenishment weighs transportation economics against downstream store pull. Production order planning balances line capacity against finished goods targets. Raw material purchasing, driven by BOM explosion, works against supplier lead times that run weeks or months ahead. Without a shared model, the same change would cross four systems over four days.
Customers consistently report measurable business results within 3 to 6 months of go-live. Solvoyo’s cloud-native platform is designed for short implementation cycles. It is modular, so teams can start with one supply layer, such as store replenishment or DC purchasing, and expand to production planning and BOM-driven raw material procurement as confidence and capability grow. That needs no re-implementation of the platform and no data migration between systems.
Solvoyo is built for decision automation, and traditional MRP and APS systems are built for decision support. Those systems calculate requirements and present options for planners to review, modify and manually release. Solvoyo generates executable orders, validates them against constraints, cross-checks them against business rules and submits them to ERP systems autonomously. Planners review a focused exception set, and BOM explosion runs in real time. The AI models are self-learning, so the system becomes more autonomous over time and needs no ongoing manual parameter tuning to maintain performance.
No. Fragmented data is common. In the Digitize step, Solvoyo’s automated data diagnostics assess your supply data (lead times, capacities, BOMs, supplier parameters) and flag what’s missing or unreliable. AI-driven enrichment then fills the gaps and improves input data quality. You start supply planning on data you can trust from day one, with a faster path to autonomous, no-touch planning.