Optimize operating budgets and capital programs to meet debt service covenants — month by month, simultaneously — using your existing Excel spreadsheets.
In structured finance, project finance, and asset-backed lending, the cashflow model is the spreadsheet. Analysts build layered Excel workbooks with price forecast decks feeding into revenue projections, which cascade through operating expenses, capital programs, debt service waterfalls, and coverage ratio calculations. These models are the institutional source of truth — validated, audited, and version-controlled. Nobody wants to rewrite them.
The problem is simple to state and brutally hard to solve: find the set of budget decisions that satisfies every financial covenant in every month of the forecast horizon.
Some budget levers affect future revenue, not just current-period costs. Deferring tenant improvements reduces occupancy next quarter. Skipping equipment overhauls degrades throughput over time. Reducing marketing spend today lowers pipeline conversion rates for months. The spreadsheet captures these dynamics through cumulative formulas that expand across the forecast horizon — creating a non-linear, path-dependent optimization surface that gradient-based solvers cannot navigate.
Financial covenants like Debt Service Coverage Ratios (DSCR) must be satisfied in every single period, not on average. A solution that meets the annual target but breaches in one quarter is a covenant violation. This means the optimizer must satisfy 12, 24, or more binding constraints simultaneously — each one a function of the same interconnected budget decisions.
Not all budget decisions are equal. Some levers are purely subtractive — cutting a line item saves that exact amount with no side effects. But others create trade-offs: reducing capital reinvestment saves money now, but asset performance degrades over time, reducing future revenue and potentially worsening the very ratios you're trying to improve. The optimizer must understand which levers help and which ones bite back.
Revenue depends on commodity prices, interest rates, or market indices that are exogenous — taken from a forward curve or price deck that the analyst does not control. Seasonal patterns (e.g., winter energy premiums, summer demand peaks) create month-to-month revenue volatility that the budget must absorb. The optimizer must find solutions that work across the full seasonal cycle.
Financial analysts already know this problem is intractable with existing tools. Here's why:
Single-objective optimizers that can target one cell subject to constraints. But coverage ratios across 12 or 24 months are dozens of simultaneous objectives — not one objective with a few constraints. Solver treats them as hard constraints and fails to find feasible regions in the non-convex, path-dependent search space. And it requires the analyst to manually specify gradients or hope the finite-difference approximation works — it usually doesn't.
The status quo: an analyst adjusts one lever, recalculates, checks all the coverage ratios, adjusts another lever, recalculates again. With dozens of budget levers across a 12-month horizon, there are effectively infinite combinations. Analysts spend days or weeks on what should be a minutes-long computation. And they can never be sure they've found the best answer — just the first one that works.
globalMOO wraps your existing Excel spreadsheet — no model rewrite, no data export, no simplification. The spreadsheet is the simulator. globalMOO learns the inverse mapping from desired outputs (covenant targets) to required inputs (budget decisions), then converges on solutions that satisfy all constraints simultaneously.
The globalMOO Excel Add-in connects directly to your financial model through a simple interface. No external software, no cloud dependency, no data leaves your machine.
Define the inputs the optimizer can adjust: operating expense overrides, maintenance capital allocations, discretionary spending — month by month across a sliding forecast window. Set minimum and maximum bounds that reflect real-world constraints (you can't spend negative dollars; you can't exceed approved budgets).
The spreadsheet already calculates everything you care about: net cashflows, coverage ratios, residual cash, total spending. globalMOO reads these outputs after each recalculation. No formulas to rewrite — just point to the cells.
Specify which outcomes matter and what their targets should be. DSCR above minimum thresholds in every period. Or minimize total spending subject to coverage floors. Or maximize net present value while maintaining all covenants. Mark objectives as active or informational — the optimizer focuses on what you tell it matters.
Financial forecasts are living documents. As actuals replace forecasts each month, the optimization window slides forward. globalMOO re-optimizes from the new starting position in seconds, giving the analyst an updated budget recommendation every reporting period. No need to rebuild the model or retrain from scratch.
Financial spreadsheets share a common architecture regardless of asset class. There are inputs the analyst controls, external assumptions they don't, a computation engine (the formulas), and periodic targets that must be met. globalMOO maps cleanly onto this structure:
Staffing levels, vendor contracts, reinvestment budgets, pricing decisions, allocation percentages, hedging positions. These become the variables — the levers globalMOO is allowed to adjust within bounds you define.
Market rates, commodity prices, customer behavior, regulatory schedules, macro forecasts. These are exogenous assumptions baked into your spreadsheet. globalMOO optimizes around them, not over them.
The formulas connecting inputs to outputs — P&L rollups, balance sheet projections, ratio calculations, scenario waterfalls. This is the “simulation” that globalMOO evaluates. It can be as complex as the analyst needs; globalMOO treats it as a black box.
Periodic financial targets and compliance thresholds — minimum coverage ratios, maximum leverage limits, liquidity floors, profitability benchmarks. These become the objectives that must hold in every reporting period, not just on average.
The key insight: this structure already exists in the analyst's spreadsheet. globalMOO doesn't replace the model — it inverts it.
The inverse model maps objectives to outcomes by position. If your spreadsheet computes dozens of metrics but you only care about a subset, you still need every metric represented in the objective list — with the non-binding ones marked as informational. The active objectives must be in the correct positional slots. This is the most common setup error and the easiest to prevent.
Not every computed metric needs to be optimized. Net cashflows are useful to monitor but the binding constraint is the DSCR derived from them. Mark non-binding metrics as inactive so convergence norms reflect only the constraints that matter. This dramatically improves convergence speed and solution quality.
In our deployments, we found that different budget categories behave in fundamentally different ways. Some cuts are pure savings with no knock-on effects. Others trigger delayed revenue declines — deferred maintenance leads to lower throughput, reduced tenant improvements lead to higher vacancy, skipped marketing leads to pipeline erosion. globalMOO discovers these asymmetries automatically during training — the inverse model learns which levers have non-linear, delayed side effects and which are approximately linear.
Financial modelers have already encoded decades of domain expertise into their spreadsheets — amortization schedules, seasonal adjustments, waterfall priorities, tax treatments, reserve mechanics. globalMOO treats all of this as a black box. It doesn't need to understand the formulas; it just needs to evaluate them. This means deployment takes hours, not months.
Any scenario where a financial spreadsheet computes periodic coverage metrics from adjustable budget inputs:
| Excel Solver | Manual Iteration | globalMOO | |
|---|---|---|---|
| Simultaneous constraints | 1 objective + constraints | Check one at a time | All simultaneously |
| Path dependency | Cannot handle | Trial and error | Learned automatically |
| Non-convex surfaces | Gets stuck in local optima | Gets stuck in local optima | Global inverse solution |
| Model changes required | Must reformulate | None | None — wraps existing sheet |
| Time to solution | Minutes (often fails) | Days to weeks | Seconds |