Scenario Planning
Model the full range of outcomes before you commit to a plan
Single-point forecasts express false precision. Scenario Planning lets teams create named scenarios with tracked assumptions, compare their inventory impact side-by-side, and commit to a plan only after the range is visible.
Book a DemoNamed
Scenarios with configurable demand assumptions tracked and compared side-by-side
Side-by-side
Inventory impact comparison before any plan is committed
Full audit
Trail of which scenarios were considered before a plan was locked
What does scenario planning do in demand AI software?
Scenario planning in demand AI software lets planning teams create named scenarios with configurable assumptions, see the inventory, service-level, and working-capital impact of each side-by-side, and commit to a plan only after the full range of outcomes is visible — with every scenario considered preserved in the audit trail. No spreadsheets. No single-point forecasts.
The Problem
Why single-point forecasts leave teams exposed
The Capability
How Scenario Planning structures the decision before the commitment
The S&OP Shift
The S&OP meeting should debate the decision, not the data
When all participants in an S&OP review can see the same named scenarios with the same inventory and financial implications, the meeting changes character. It stops being a session to establish a shared view of the numbers and starts being a session to choose between documented options. Scenario Planning makes that shift possible by giving every participant the same view before the conversation starts.
In Practice
A fashion retailer avoids a markdown crisis with pre-season scenario planning
The Situation
A fashion retailer managing 4,000 SKUs across 80 stores was running a single optimistic demand plan into the season buy. When early-season sell-through came in 15% below plan, the team had no pre-built response scenario and no visibility into what inventory actions the slower start required. Markdowns were reactive and deeper than necessary.
What Demand OS Did
Before the following season, the planning team created three named scenarios in Demand OS: fast start (sell-through 20% above baseline), base case (on-trend), and slow start (15% below baseline). Inventory impact for each scenario was calculated against the current buy plan. The slow-start scenario pre-defined the markdown trigger points and reorder hold thresholds before the season opened.
The Outcome
When the following season opened 12% below the base case, the team activated the pre-built slow-start response within 48 hours. Markdown depth was 9 percentage points lower than the prior season's unplanned response. The S&OP review that week focused on executing the pre-committed plan rather than building one under pressure.
Key Metrics
What planning teams gain from structured scenario visibility
Explore more Demand OS capabilities
Demand OS for CPG
Trade promotion modeling, cannibalization, and SKU-retailer-week forecasting.
Learn moreDemand OS for Manufacturing
BOM-level disaggregation and intermittent spare-parts demand handling.
Learn moreDemand OS vs SAP IBP
Native SAP S/4HANA and ECC integration without the consultant-dependent IBP rollout.
Learn moreFrequently Asked Questions
Scenario planning questions answered
How many scenarios can be active simultaneously in Demand OS?+
Can scenarios be shared with stakeholders outside the planning team?+
Can Demand OS model the inventory impact of a specific promotional event as a scenario?+
How does Scenario Planning connect to the consensus process?+
Is there an audit trail of which scenarios were considered when a plan was committed?+
Get Started
Ready to plan with confidence?
Talk to a demand planning specialist and see scenario planning live on your own data. You leave with accuracy benchmarks, a scoped pilot plan, and published pricing — all in one conversation.