Responsive Supply Planning for a Volatile World

An AI-powered supply planning platform generates, adjusts, and executes every supply action required to keep product flowing from raw material to store shelf — without waiting for the next planning cycle, the next S&OP meeting, or a planner to rebuild a spreadsheet. It continuously reads demand signals, measures inventory positions against targets, evaluates supplier and production constraints, and produces prescriptive orders that are ready to execute immediately.

Demand planning determines how much customers are expected to buy — by SKU, location, and time period. Supply planning takes that forecast as its input and determines how to fulfill it: which stores to replenish, which DCs to restock, which production orders to release, and which raw materials to purchase to support those production orders. A supply plan built on a stale demand forecast produces the wrong orders at the wrong time. Solvoyo runs both on the same unified data model, ensuring every supply action is always calibrated against the most current demand signal available.

BOM explosion translates a finished goods production requirement into its constituent raw material and packaging needs, using the Bill of Materials to determine what quantities are needed, when, and where. In traditional planning, this runs as a separate MRP batch — often days after the production plan is finalized, creating a lag where schedules change before procurement has acted. In Solvoyo, BOM explosion is embedded within the production planning cycle: every authorized production order is immediately decomposed into component requirements, net requirements are calculated against current inventory, and supplier purchase orders are generated automatically without a separate run.

Min/max replenishment triggers an order when inventory falls below a fixed threshold and orders up to a fixed ceiling — regardless of what demand is doing. Demand-driven replenishment sets inventory targets dynamically based on current and projected demand, safety stock requirements derived from real forecast error and lead time variability, and operational constraints like replenishment frequency and store capacity. When demand shifts due to a promotion or trend change, targets shift with it — and the change propagates automatically into DC replenishment, production scheduling, and raw material purchasing without manual intervention.

Each layer has distinct constraints and decision frequencies, but they must be consistent with each other or the plan breaks at the handoff. Store replenishment operates daily against shelf capacity constraints. DC replenishment balances transportation economics against downstream store pull. Production order planning balances line capacity against finished goods targets. Raw material purchasing, driven by BOM explosion, operates against supplier lead times that run weeks or months ahead. Solvoyo models all four layers on a single data model, so a demand change propagates through every layer in one planning cycle — not across four systems over four days.

Solvoyo’s cloud-native platform is designed for short implementation cycles, with customers consistently reporting measurable business results within 3–6 months of go-live. The modular architecture allows teams to start with one supply layer — store replenishment or DC purchasing, for example — and expand to production planning and BOM-driven raw material procurement as confidence and capability grow, without re-implementing the platform or migrating data between systems.

Traditional MRP and APS systems are built for decision support — they calculate requirements and present options for planners to review, modify, and manually release. Solvoyo is built for decision automation: executable orders are generated, validated against constraints, cross-checked against business rules, and submitted to ERP systems autonomously, with planners reviewing a focused exception set rather than the full plan. BOM explosion runs in real time rather than as a lagging batch process. And because the AI models are self-learning, the system becomes more autonomous over time — rather than requiring ongoing manual parameter tuning to maintain performance.