Run your data through Geneva in parallel and compare outcomes by segment. You keep your current process while you validate accuracy, bias, and stability with real history.
Pick a time that works. The rest happens on the call.
A Parallel Forecast Test lets you evaluate a new forecasting engine using your real data while keeping your current process intact. We run your historical demand and planning hierarchies through Geneva in parallel, then compare forecast accuracy, bias, and stability by segment. You get a clear, decision ready view of where performance improves, where exceptions appear, and what it would take to operationalize the workflow for planners. This is ideal for teams who want modern forecasting without the rollout pain, system overhead, and slow change cycles that can come with legacy planning environments.
A Parallel Forecast Test runs your data through a new forecasting engine alongside your current forecast so you can compare accuracy, bias, and stability without changing your live process.
It starts with a short scoping call, then we align on the data extract and segmentation. Most teams can review initial results quickly once data is available.
Historical actuals, your forecast history if available, key hierarchies, calendars, and any events or promotions that materially impact demand.
A results summary with forecast accuracy and bias by segment, plus practical recommendations for how to operationalize the workflow.
No. The parallel test is designed to reduce risk and validate performance before any technology decisions are made.
Teams forecasting demand, sales, or inventory that want modern forecasting performance with a planner friendly workflow and clear governance.