Intelligent Inventory Planning: Autonomous, Network-Wide, Precise

Inventory planning determines the right inventory investment across every node in your network — setting the optimal stock targets, safety stock levels, and reorder policies that balance service level performance against working capital cost. Replenishment is the operational execution of those targets: generating and submitting the orders that keep inventory positions in line with the targets inventory planning has set. The two must be connected: replenishment orders built against the wrong targets create either stock-outs that lose revenue or excess stock that erodes margins, regardless of how efficiently the ordering logic runs. Solvoyo runs both on the same platform and the same data model, ensuring replenishment always serves the inventory strategy it is meant to execute.

Safety stock protects against forecast error and supply variability — it is the buffer inventory held above cycle stock to absorb the gap between what demand was expected to do and what it actually did, and between when supply was expected to arrive and when it actually did. Solvoyo calculates safety stock dynamically for every SKU and location by measuring actual forecast error, actual supplier lead time variability, and actual OTIF performance — rather than applying a fixed days-of-supply rule uniformly across the portfolio. When demand variability increases or supplier performance degrades, safety stock targets adjust automatically. When variability decreases, safety stock is released — freeing working capital without a manual parameter update.

Multi-echelon inventory optimization adjusts stock targets across different echelons of the same network — for example, across stores, regional DCs, and a central warehouse simultaneously — protecting service levels dynamically rather than optimizing each node in isolation. It matters whenever inventory at one echelon directly affects the service risk at another: a DC that is too lean creates store stock-outs; a DC that is too full ties up working capital that could be deployed more productively elsewhere in the network. Single-echelon optimization misses these interdependencies entirely, producing locally optimal targets that are globally suboptimal. Solvoyo’s multi-echelon model solves across all network levels simultaneously, setting targets that minimize total network inventory investment while protecting end-customer service levels.

Solvoyo’s inventory loss tree differentiates all physical inventory by purpose — safety stock, cycle stock, allocated inventory, in-transit inventory, excess stock consuming working capital, dead inventory that should be cleared, and investment inventory for seasonal builds or opportunity buys. Excess and dead inventory are surfaced automatically through continuous diagnostics that identify root causes — 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. Prescriptive actions are attached to each excess position: markdown recommendations, inter-location transfer opportunities, or supplier return options — so planners act on a prioritized resolution queue rather than a passive inventory report.

Solvoyo automatically adjusts stock targets in different echelons of the network, protecting service levels dynamically and in certain cases totally autonomously. Forecasts, safety stocks, and replenishment recommendations are automatically adjusted in light of changing demand and operational constraints — so planning teams can focus on strategic decisions rather than manual parameter maintenance. Automated diagnostics identify the root causes of issues such as stock-outs or service level declines by analyzing patterns and trends related to demand variability, forecast bias, and supplier performance — enabling planners to focus on solutions rather than diagnosis. The result is an inventory plan that stays current with the business automatically, without a quarterly parameter review cycle or manual recalibration between planning periods.