The modeling process Decision Critical

Decision Criticalcombines theory of constraints and forensic accounting best practice to enhance the realism of the forecasting exercise. Enter your level one assumptions on pricing, tax, payment terms, employees, processes and more, then set up your dependencies, and you are already looking at a complete enterprise model based on your own information without having written a single formula. Once you have your base case, test it under various conditions and different decision frameworks to optimize it and arrive at the best plan.

In less time than it takes to build a ratio-based spreadsheet model, achieve the robust level of analysis of a major strategic planning project. Spending less time building formulas and managing the complexity of spreadsheets means that teams can focus on better research for improved assumptions or implementation of decisions.

Modeling in Decision Critical is done in the following steps:

Step 1

Enter primary values

Any modeling effort starts with a set of assumptions, based on a company's existing knowledge, original research or a combination of the two.

Decision Critical allows teams to enter a range of primary values, including but not limited to:

Pricing of raw materials, utilities and other primary inputs

Manpower and process times

Purchase rules for goods and services

Market demand

Payroll

Equipment and tooling

Payment and collection cycles

Inventory levels

Tax

Beginning balances

Decision Critical encourages users to get through step 1 as quickly as possible, putting in best guess placeholders where good data is not readily available. Avoid using 0 as a placeholder, as this will often distort results more than a best guess. Teams should avoid getting stuck on this initial data entry step – results will show themselves to be more sensitive to a few key input values after dependencies have been mapped. Additional research efforts should be directed, first and foremost, at these assumptions.

Step 2

Map dependencies

Accurate dependencies are essential to data integrity and model manageability. Decision Critical is built around supporting the causal chains teams need to quickly and accurately convert raw data and assumptions to clear, actionable business forecasts:

Product card connects material use, labor, equipment utilization, utilities and other expenses directly to product cost

Flexible and intuitive organizational structure allows for proper bucketing and representation of cost centers

Staff and equipment can be added manually (according to a fixed schedule) or automatically (according to need)

Prioritization between different products competing for the same resources

Multiple sales and purchase channels

Step 3

Evaluate and correct course

Once primary data has been entered and causal chains established, teams can evaluate results and begin work on optimization.

Sensitivity analysis: examines the effect of changing a single input on certain key output metrics. For example, sensitivity analysis may measure the impact a change in collection terms has on cash flows and capital requirements

Scenario analysis: compares scenarios which use different sets of assumptions. For example, a "bull-bear-base" scenario set might examine the same set of management decisions subjected to three different market demand scenarios

Inflection points: Understand key thresholds, such as maximum production capacity, market saturation or break-even sales volumes

Optimize towards goals: Change inputs to achieve desired results – time outlays so as to avoid a cash crunch, explore tradeoffs between discounts / payment terms and sales volumes, simulate a rearranged production line and much more

The modeling process

Modeling in Decision Critical is done in the following steps:

Any modeling effort starts with a set of assumptions, based on a company's existing knowledge, original research or a combination of the two.

Decision Critical allows teams to enter a range of primary values, including but not limited to:

Demand-modules

Accurate dependencies are essential to data integrity and model manageability. Decision Critical is built around supporting the causal chains teams need to quickly and accurately convert raw data and assumptions to clear, actionable business forecasts:

Product card

Once primary data has been entered and causal chains established, teams can evaluate results and begin work on optimization.

Scenario comparison