The plan is in place, but no one can explain why a specific value appears in a particular cell—let alone what action to take next? This is where modern approaches to simulation and forecasting come into play: they link numbers to their underlying drivers and provide a sound basis for decision-making rather than simply extrapolating figures.
The problem with budget-based planning processes
In many companies, planning is a top-down process: goals are set, budgets are allocated, and departments fill out planning templates. The end result is a set of numbers that may be organizationally consistent but rarely reveals the underlying logic. What’s missing is a causal model—an understanding of the factors that influence each metric, what drives it up, and what constrains it. Without this understanding, every number remains a mere assertion, and every decision based on it a risk.
Bissantz offers a solution: AI-powered forecasts and scenario simulations provide immediate insight into plausible future developments and the drivers behind them.
Driver-based planning: Cause and effect instead of gut feeling
Instead of assigning target values top-down, driver-based planning reflects how individual variables are actually interrelated: for example, in sales, the number of field sales representatives determines how many customer visits are possible each month. Based on the conversion rate, customer visits lead to orders, which contribute to order intake and, consequently, revenue. Meanwhile, production capacity acts as a constraint.
This means that driver-based planning is not a single calculation step, but rather a comprehensive cause-and-effect model that considers different versions of the future from the outset and highlights their impact on key business metrics.
and you have data.
Simulate what's coming,
and you have control."
Simulation as the basis for informed decisions
This is where simulation shows its strength: instead of relying on a single projected value, the driver-based model allows you to adjust any variable and immediately see its impact on the overall result.
How does a price increase affect the contribution margin?
What happens if labor costs rise?
Which sales strategy delivers the best results in terms of revenue?
For example, if you increase the number of field sales representatives, the model automatically calculates the entire chain of effects through to revenue. Constraints become immediately apparent: if order intake eventually exceeds production capacity, the overall result suffers. The model reveals this before it turns into a costly real-world mistake.
This scenario simulation turns planning into a tool for exploring different courses of action before committing resources or setting planning targets. Instead of endless discussions about the supposedly right approach, the simulation immediately shows which actions work under which conditions—and which do not.
Planning, simulation, and decision intelligence go hand in hand
Bissantz incorporates this logic directly into DeltaMaster. Based on a digital model of the company, various scenarios can be simulated interactively: planners can change an assumption, such as the number of field sales representatives or the conversion rate, and immediately see the impact on all relevant metrics.
Both positive and negative effects become clearly visible, and drilldowns to higher levels of detail are possible at any time. This turns simulation from a mathematical exercise for specialists into a practical, user-friendly tool for corporate planning.
Combined with decision intelligence, driver-based planning offers clear benefits: the question “What happens if…?” becomes “Which decision is the right one under these conditions?” Decision intelligence makes different courses of action visible and helps evaluate their impact. The approach not only helps to understand possible developments, but also to turn these insights into specific recommendations and make well-informed decisions.
Conclusion: Turning numbers into a basis for decision-making
The real purpose of planning is not the number itself, but the decision it enables. Driver-based planning provides the underlying logic, while simulation makes that logic actionable: multiple possible futures can be simulated, evaluated, and weighed against one another before a single decision is made.
Anyone who wants to take the first step toward driver-based planning doesn’t need to launch a large-scale project. The starting point is where data is already available—but it is still unclear what action to take based on it.
