Key points
- Shared definitions and units
- A source and owner for each assumption
- Scenarios remain comparable
- Every model retains an explicit scope
Identify differences in definitions
The same expression—addressable market, active customer, margin or launch—can cover different definitions. Before comparing models, the file states units, periods and inclusions.
This prevents an apparent difference from being caused only by different vocabulary or timing.
Build an assumption table
Each material assumption states its value, range, source, business owner and review date. Disagreements remain visible instead of being silently resolved in a spreadsheet.
| Assumption | Strategy view | Marketing view | Finance view |
|---|---|---|---|
| Market | Scope and priority | Accessible audience | Revenue base |
| Price | Positioning | Acceptance and discount | Margin and collection |
| Volume | Target share | Conversion and channel capacity | Ramp-up |
| Cost | Required capacity | Acquisition and activation | P&L and cash |
Compare scenarios over the same horizon
Scenarios should use a coherent timetable: launch date, ramp-up, recruitment, marketing spending and cash collection. An option should not look better because it moves costs outside the period.
Specialist workspaces produce complementary views brought together around shared definitions, assumptions and horizons.
Turn differences into trade-offs
The deliverable separates accepted assumptions, assumptions still under discussion and decisions required. It shows how each difference affects the selected option, budget or trajectory.
- Shared dictionary
- Reference scenario
- Contested assumptions
- Sensitivities and consequences
- Decisions required
Workspaces relevant to this question
Each link states the role of the workspace. Connections rely only on supported shared objects and demonstrated exchanges for the decision file.
Frequently asked questions
How should models from different functions be connected?
Expertise remains distinct; alignment focuses on the definitions and assumptions needed for the decision.
How should two incompatible forecasts be handled?
Compare their sources, scopes and assumptions, then test the effect of the gap on the decision instead of arbitrarily averaging them.
How does data move between modules?
Connections rely on objects explicitly attached to the decision file and on demonstrated exchanges.