o9 anchors forecasting improvement in FVA analysis that quantifies the impact of every step in the process. Teams can clearly distinguish positive FVA (where human input improved the forecast), negative FVA (where adjustments reduced quality), and touchless areas where no intervention is required. This transparency drives behavior change, reduces unproductive manual work, and creates sustained improvement rather than temporary gains.
Beyond accuracy, o9 incorporates stability and reasonability metrics to ensure forecasts remain consistent across planning cycles and make sense in year-over-year and cumulative comparisons. These guardrails prevent the “whiplash” effect that comes from overfitting or reacting to noisy signals.
Instead of a single algorithm approach, o9 applies multi-model AI at scale — combining statistical models, gradient-boosted trees, and deep learning techniques — to deliver stronger accuracy, better stability, and clearer explainability through ensembles.
Multi-Echelon Inventory Optimization: Optimizes inventory across the full supply chain network rather than at isolated locations. This helps balance service levels and costs across echelons and improves where inventory is positioned.
Demand Variability Modeling: Uses forecast error, demand standard deviation, NPI forecast error, demand pattern identification, bias correction, and outlier correction. This is critical because safety stock depends heavily on uncertainty in demand.
Lead Time and Network Variability Modeling: Accounts for procurement, manufacturing, distribution, lead time demand, customer order lead time, replenishment frequency, and network structure. This ensures inventory targets reflect actual supply chain behavior.
Service Level Policy and Segmentation: Supports service level configuration, ABC segmentation, and differentiated service levels. This lets businesses align inventory investment with commercial priorities instead of applying one service target everywhere.
Group Service Level / Budget Optimization (GSLO/BO): Optimizes service levels for user-defined groups within an inventory budget, or calculates inventory cost and optimized service levels for a given group target service level. This supports executive trade-off decisions between service and working capital.
Inventory Constraints and Policy Controls: Supports storage constraints, shelf-life constraints, min-max ranges, and inventory overrides. This prevents recommendations that are mathematically optimal but operationally infeasible.
Inventory Target Recommendations: Produces time-phased safety stock, reorder point, ROQ/EOQ/cycle stock, max stock, expected service level, target service level, and inventory holding cost. These outputs convert optimization into executable inventory policies.
Scenario Analysis, Diagnostics, and Workflows: Enables scenario comparison, what-if analysis, input exception review, root cause analysis, output analytics, hierarchical approval, overrides, and publishing to OP/MP/IBP. This makes MEIO practical for governed planning decisions.
"We really wanted to invest in a platform that gave us end-to-end capabilities and was able to kind of link the full planning journey together. We met with a lot of partners, ultimately we selected o9 for the ability that we thought to have one platform to cover the end-to-end. "
Global VP, Supply Chain planning, AB InBev
Multi-Echelon Inventory Optimization determines optimal inventory levels across multiple stages of the supply chain, considering demand variability, lead times, and service targets at the network level.
