M101-6: Major Model Elements

From Parts to Purpose

Every financial model—whether built for a city budget office, a regional water authority, or a university business office—relies on the same foundational elements. These components turn scattered data and assumptions into a coherent decision-support system.

In the public sector, models often grow organically: one spreadsheet becomes ten, new tabs are copied from old versions, and key formulas evolve with each budget cycle. The result is a tool that works, but only until it doesn’t. By understanding the core elements of modern modeling, leaders can transform these piecemeal efforts into transparent, durable frameworks that strengthen foresight and accountability.

Business Objects: The Building Blocks of a Model

Every model begins with business objects—the core financial elements that represent how an institution or utility actually operates. Rather than modeling entire departments or divisions, business objects mirror the financial components that drive activity, cost, and revenue.

These objects serve as the model’s “moving parts,” capturing distinct financial behaviors that, when combined, recreate the full economic picture of the organization.

Examples of business objects include:

• Student types (e.g., first-year, continuing, graduate)
• Ratepayer categories (e.g., residential, commercial, industrial)
• Employee groups (e.g., faculty, staff, operators)
• Investments
• Debt instruments
• Capital projects

Each business object is a financial element at the right level of granularity—specific enough to behave accurately, but general enough to support efficient modeling and scenario analysis.

• In spreadsheets, business objects often appear as separate tabs or reports, copied from past versions and prone to drifting out of sync.
  
  
• In structured models, they are defined once, then reused wherever relevant—ensuring every department or initiative follows consistent logic.

A municipal utility’s model might include business objects for Rate Revenue, Capital Projects, Debt Service, and Operating Expenses. Each interacts to show how policy or cost changes flow through the broader financial picture.  

Parameters: How Business Objects Behave

Each business object is governed by parameters—the data, assumptions, and formulas that describe its behavior. Parameters answer questions like: What drives this cost? What causes this revenue to grow?

Typical parameter types include:

Parameters can interact:

Common Parameter Types

Here is where the concrete math belongs — describing exactly how finance teams think about these elements in spreadsheets and structured models.

This interplay is what makes the model an engine, not just a ledger.

Relationships and System Behavior

Models gain depth when parameters influence one another through clear, intentional relationships. These interactions allow the model to simulate real-world behavior rather than simply calculate isolated values. For instance, when a city or district invests in new infrastructure, the model must reflect not only the upfront cost but the downstream effects—additional depreciation, future debt service, and ongoing maintenance obligations.

These relationships help modelers trace cause and effect across the entire financial system. In public-sector forecasting, even small changes—such as a 1% rate adjustment or a slight shift in demand—can echo across multiple funds, programs, or departments. Understanding these interdependencies is essential: it ensures that model behavior reflects institutional reality and gives decision-makers a clear view of how individual choices shape long-term outcomes.

Time-Series Forecasting: Turning Logic into Trajectory

The engine’s value emerges when its components operate over time.

A time series forecast links the past to the future:

• Historical Data records what actually happened.
• Assumptions adjust parameters to reflect expected change.
• Formulas project results year by year.
• Outputs appear as evolving line items—revenues, expenses, balances—that reveal financial trajectory.

A state agency evaluating a new funding formula can model how annual appropriation shifts flow through multi-year commitments, revealing timing pressures that aren’t visible in a single-year view.

Defining the Modeling Engine

When business objects, parameters, relationships, and time-series behavior work in concert, they form the modeling engine—a self-contained system capable of simulating how strategic decisions unfold over time. The strength of this engine lies in its structure. 

A well-designed model is modular, allowing components to evolve as policies, programs, or market conditions change. It is transparent, enabling auditors, analysts, and stakeholders to trace outcomes back to the assumptions and logic that produced them. And it is consistent, maintaining its integrity across fiscal cycles and leadership transitions without requiring constant reinvention.

Ultimately, the engine transforms isolated data points into a coherent forecasting framework. It ensures that every assumption leads logically to an outcome, and every outcome remains traceable to its source. In the public sector—where long-term obligations, community expectations, and regulatory pressures intersect—this coherence turns a model from a static spreadsheet into a living decision framework that supports strategic clarity and institutional confidence.

Adjusting Inputs

Recasting often begins with adjusting the inputs that drive the model’s behavior. Analysts may revise inputs such as inflation expectations, rate structures, grant renewal timelines, staffing levels, or capital project schedules to reflect emerging conditions or leadership priorities. Even small adjustments in these inputs can meaningfully shift long-term outputs, revealing how sensitive an institution’s financial trajectory is to change. 

Recasting inputs shows how individual changes ripple through the model without disrupting the engine.

Cases: Alternative Sets of Parameters

Sometimes analysis requires comparing bundles of assumptions rather than adjusting inputs one at a time. Cases serve this purpose by grouping related parameters into distinct, overwriting core assumptions, making comparisons cleaner and more transparent. 

Cases bring structure to what-if analysis, enabling teams to evaluate the trade-offs between multiple strategic paths and understand how different configurations of assumptions shape long-term outcomes.

Modelers often create several alternative sets of assumptions—such as a base case, best case, or worst case—to explore different plausible futures. 

By keeping each set separate from the baseline, analysts can switch between perspectives without overwriting their core assumptions, making comparisons clearer and more intentional. 

Initiatives: Switchable Add-Ons

While cases represent full sets of alternative conditions, initiatives represent discrete decisions that leadership may choose to implement or defer. Initiatives function like switches—turning a policy, program, or operational change on or off within the model. This allows analysts to test potential actions, understand timing implications, and isolate the financial effect of individual decisions while preserving the broader structure of the model. 

Initiatives help connect strategic conversations to specific, quantifiable impacts.

Scenarios: Full Combination of Assumptions, Cases, and Initiatives

A scenario is a complete portfolio that brings together assumptions, cases, and initiatives to represent a fully formed view of a possible future. In practice, financial decisions rarely occur in isolation. Changes in enrollment, rate structures, staffing levels, capital plans, or funding strategies all tend to happen at the same time. Scenarios allow modelers to bring these distinct elements together into a single institutional point of view.

By combining these elements, scenarios allow analysts to compare strategic pathways, mix and match assumption sets, and evaluate how multiple decisions interact over time. They make it possible to see the long-term implications of alternative choices, test the resilience of plans under different conditions, and continually refine the analysis as new information emerges. 

Scenarios are the backbone of public-sector decision-making because they clarify trade-offs under uncertainty and give leaders a structured way to weigh competing priorities.

Dimensionality: Viewing the Organization Through Different Lenses

Dimensionality allows analysts to view the model through different lenses, isolating subcomponents of the organization institution without altering the underlying structure. Attributes label business objects so they can be grouped, filtered, and examined based on relevant characteristics such as department, program, asset type, or fund. Parts then aggregate those labeled objects into meaningful subsets, allowing analysts to isolate and understand how individual components contribute to broader financial outcomes. Together, attributes and parts make it possible to examine slices of the model without changing how the engine itself operates.

By enabling selective focus, dimensionality gives decision-makers the ability to examine the institution from multiple perspectives—whether looking at the model by service area, cost center, project type, or funding source. This helps stakeholders explore the model in ways that align with their responsibilities, whether they oversee a program portfolio, a capital plan, a fund, or a department.

Organize Your Financial Story

A strong financial story begins by clarifying the core questions the model is meant to answer. Whether assessing reserve strength across a capital program, evaluating long-term sustainability under shifting demand, or identifying rate paths that balance affordability and infrastructure needs, these questions anchor the analysis and guide interpretation.

The narrative must also match the audience. Finance teams require detailed drivers and variance explanations; executives need visibility into strategic trade-offs; boards and councils must clearly see risks, outcomes, and community impact. Aligning the narrative to each audience ensures comprehension rather than confusion.

Finally, effective communication weaves assumptions, drivers, and outcomes into a coherent storyline. A well-constructed narrative shows where the institution is headed, what forces are shaping that trajectory, what alternatives were tested, and which choices carry the greatest leverage. This narrative clarity builds trust and strengthens decision-making.

Craft Dashboards That Drive Analysis

Dashboards and interfaces transform a model from a technical asset into an accessible decision-support tool. By elevating the assumptions that matter most—growth rates, cost drivers, policy levers, and capital schedules—interfaces give stakeholders visibility into what shapes outcomes.

Interfaces also create structure for exploring alternatives. Users can switch between predefined cases, combine assumptions and initiatives into full scenarios, and test the implications of different strategic choices. Initiatives allow decision-makers to toggle actions on and off and immediately see financial consequences, making the model interactive and accessible.

Visualization is equally important.   Clear charts, tables, and reports help stakeholders absorb complex information quickly and trace outcomes back to the factors that produced them. While finance teams may be comfortable navigating dense spreadsheets, many board members, council members, and senior leaders are not. Effective visuals translate the model’s narrative into an accessible format—helping decision-makers understand the story behind the numbers.

When outputs are presented clearly and consistently, discussions about trade-offs become more focused, transparent, and grounded in shared understanding