Scenario Planning Software for FP&A Teams — deFacto Global

How scenario planning works in deFacto

A finance analyst opens a live report in the Excel add-in that connects to the deFacto model. They run Goal Seek — adjusting one variable to hit a target outcome — or Excel Solver for a small constrained what-if with multiple variables. When the result is ready, they use Send Data to write it back into the connected deFacto model as a named scenario. That scenario is now part of the plan: visible to the team, comparable against actuals and budget, and available in every report and dashboard the model serves.

The stickiness of this approach is the write-back, not the solver brand. A what-if that stays in a local spreadsheet disappears after the meeting. A what-if that becomes a deFacto scenario is permanent, auditable, and shareable. Finance teams that present scenario comparisons to a CFO can pull the scenario directly from the same model that stores the budget — no reconciliation step.

For teams that need more optimization power than Goal Seek and Solver provide, deFacto supports a solver ladder: open-source engines (PuLP with CBC, HiGHS, OR-Tools) for larger constrained problems at no license cost, and commercial partnerships (Gurobi, IBM CPLEX, D-Wave Ocean, HKQuantum) for enterprise-scale requirements. The ladder is designed so teams pick the cheapest engine that answers inside the decision window — not the most powerful engine available. Constrained product-mix optimization under capacity constraints lives on the prescriptive optimization page as a separate capability.

Class A what-if: Goal Seek and Solver write back into the plan

Class A what-if in deFacto is Goal Seek or Excel Solver run on a live report, with the result submitted via Send Data into a named deFacto scenario. The solver runs against the live deFacto model data — not a static export — so the what-if reflects the current state of the plan when it is run. Goal Seek handles single-variable target problems; Solver handles small constrained problems with multiple decision variables. Both write back into the same model used for budget and actuals.

The solver ladder: cheapest engine that fits the window

deFacto supports a glidepath of optimization engines, from free to enterprise: Excel Goal Seek and Solver for standard planning what-if scenarios; open-source PuLP with CBC, HiGHS, and OR-Tools for medium-scale constrained problems; and commercial partnerships with Gurobi, IBM CPLEX, D-Wave Ocean SDK, and HKQuantum for enterprise-scale or specialty requirements. The guiding principle is to pick the cheapest engine that returns an answer inside the decision window — not to over-engineer the solver for problems Goal Seek can handle.

From manual what-if to automated scenario generation

Finance teams spend significant time building what-if analyses by hand — adjusting one variable, running a solver, saving a version, then starting again with a different assumption. That process works, but it scales poorly when the decision space is large. Which production mix maximizes contribution margin given current capacity constraints? What sales targets are achievable given a fixed headcount? How does the answer change if raw material costs shift by 10%?

This is where deFacto’s optimization tooling shifts the work. Instead of a planner manually building each scenario, the solver generates a large number of feasible solutions across the defined constraint set — and surfaces the ones that deliver a specific result. An optimized production plan for maximum revenue. An optimized territory allocation for fixed staff size. A set of budget scenarios that satisfy margin thresholds across multiple product lines. Each result writes back into the connected deFacto model as a named scenario, indistinguishable from a manually submitted one.

The difference is speed and coverage. A planner building scenarios by hand might test three or four variations in a session. The same problem handed to a solver with defined objectives and constraints returns dozens of feasible outcomes, ranked. Finance picks the one that fits the business context, reviews it against actuals and budget in the same model, and submits it. The scenario becomes the plan.

This capability extends across the solver ladder — from open-source PuLP with CBC and HiGHS for standard planning problems, to commercial engines like Gurobi and IBM CPLEX for larger constraint sets, to D-Wave Ocean SDK for hybrid quantum approaches at scale. The planning model is the constant; the solver is chosen to match the problem.

Excel-native: planners work where they already work

deFacto is Excel-native — planners use the Excel add-in for scenario planning, data entry, and forecast review, with the connected model serving as the system of record. Scenario planning does not require a separate application, a new interface, or a web browser. The planner opens a report in Excel, runs the what-if, and submits — all in the familiar Excel environment. (Source: BPM Partners Vendor Landscape Matrix, July 2025.)

Strong budgeting scores support the scenario foundation

Scenario planning is built on top of a strong budgeting and financial planning foundation. deFacto scores 4.80 on budgeting and financial planning in the mid-market peer table — above Vena (4.30) and Planful (4.45). (Source: BPM Partners Vendor Landscape Matrix, July 2025.) A scenario is only as reliable as the actuals and budget it is compared against; the strong budgeting score reflects the quality of the underlying data the scenario runs against.

4.93 overall satisfaction, 16 years, cloud or on-prem

deFacto has been delivering planning software for 16 years. The platform deploys cloud or on-prem from the same codebase — no separate on-prem build. Overall satisfaction: 4.93 vs industry average 4.40, in the Outstanding satisfaction band (4.65+), Key Competitors placement in the Vendor Landscape Matrix. Price band: $1,500–1,999 per user per year. (Source: BPM Partners Vendor Landscape Matrix, July 2025.)

Where scenario planning fits in the deFacto model

Scenario planning is the foundation that makes deFacto’s AI layer useful. dF Foresight writes TimeGPT-2.1 forecasts into a chosen deFacto scenario. dF Anomaly writes drift flags into companion scenarios. Both require a working scenario structure to write into. For more advanced constrained optimization, prescriptive optimization covers the product-mix proof of concept. For optimization comparisons across vendors, see compare optimization platforms.

See also: AI planning software hub · Compare Excel planning options · Compare to Anaplan · Compare to Pigment