Inventory Optimization Solutions for Your Network

Inventory planning decides how much stock to hold at every node in your network. It sets the stock targets, safety stock levels and reorder policies that balance service level performance against working capital cost. Replenishment carries those targets out by generating and submitting the orders that keep inventory positions on target. The two must connect: orders built against the wrong targets cause either stockouts that lose revenue or excess stock that erodes margins, regardless of how efficiently the ordering logic runs. Solvoyo runs supply planning, which generates the replenishment orders, and inventory planning on the same platform and the same data model. Replenishment always serves the inventory strategy it executes.

Safety stock is the buffer inventory held above cycle stock. It absorbs the gap between expected and actual demand, and between expected and actual supply arrival. Solvoyo calculates it for every SKU and location from measured forecast error, supplier lead time variability and OTIF performance, with no fixed days-of-supply rule applied across the portfolio. When demand variability increases or supplier performance degrades, safety stock targets adjust automatically. When variability decreases, safety stock is released, which frees working capital without a manual parameter update.

Multi-echelon inventory optimization (MEIO) sets stock targets across all echelons of one network at the same time, for example stores, regional DCs and a central warehouse. It matters whenever inventory at one echelon changes the service risk at another. A DC that is too lean causes store stockouts. A DC that is too full ties up working capital that could be used elsewhere in the network. Optimizing each node on its own misses these links and produces targets that are optimal for the node and suboptimal for the network. Solvoyo’s multi-echelon model solves across all network levels at once and sets targets that minimize total network inventory investment while protecting end-customer service levels.

Solvoyo flags excess and dead inventory automatically and attaches a prescribed action to each position. Its inventory loss tree sorts all physical inventory by purpose: safety stock, cycle stock, allocated inventory, in-transit inventory, excess stock that consumes working capital, dead inventory that should be cleared, and investment inventory for seasonal builds or opportunity buys. Continuous root-cause diagnostics trace each excess to causes such as demand forecast bias, a promotional shortfall, a supplier over-delivery or a replenishment parameter that was never adjusted after a product’s demand pattern changed. The prescribed action is a markdown recommendation, an inter-location transfer opportunity or a supplier return option. Planners work from a prioritized resolution queue.

Solvoyo adjusts stock targets across the echelons of the network automatically, and in some cases fully autonomously, to protect service levels. Forecasts, safety stocks and replenishment recommendations update as demand and operational constraints change, so planning teams spend their time on strategic decisions. Automated diagnostics find the root causes of issues such as stockouts or service level declines by analyzing patterns and trends in demand variability, forecast bias and supplier performance, so planners start from the cause and work on the fix. The inventory plan stays current with the business, with no quarterly parameter review cycle and no manual recalibration between planning periods.

Yes. Solvoyo does not wait for perfect data: the Digitize step comes first in our process. Automated diagnostics check your stock, demand variability and lead time data for completeness and accuracy. AI fills in missing parameters that spreadsheet-based processes often lack, such as service level targets or lead time distributions. You get better inventory decisions immediately, and a faster route to fully autonomous replenishment.

Solvoyo plans at the batch level and tracks lot age, expiry dates and remaining shelf life. AI models trained on your historical data estimate waste and spoilage rates by category and product type. Together they give an accurate estimate of true sellable inventory, so replenishment decisions reflect what will actually be available to sell. Stockouts and write-offs both fall.