If CAPEX and Finance operate in silos, the Board lacks the basis for decision-making

Executive Summary

In many companies, the information needed to make better investment decisions already exists. The problem is not necessarily a lack of data. The problem is that this data is often stored separately in different silos.

The CAPEX list shows which investments are planned. Finance has cost, profit and planning data. The operational departments know their market, their capacities, expected turnover, margins, risks and interdependencies.

However, it is only when this information is consolidated at project level that a robust basis is established for the question that is crucial for the Executive Board and the CFO:

Which combination of our planned investments, within the available budget, will generate the best overall result for the company?

Future values do not need to be perfect for this. Expected revenue, expenditure, profits or outputs may be planned figures, estimates or assumptions derived from historical averages.

After all, uncertainty is no reason to avoid making calculations. It is a reason to calculate different assumptions and scenarios.

The problem is often not a lack of data, but data silos

In large organisations, investment decisions are made across many departments.

Production is planning a new machine. Operations assesses capacity. Sales and business units are aware of expected market trends. Finance considers investment volumes, cash flow and the impact on profits. Risk assesses uncertainties. Senior management defines strategic priorities.

Each department holds a portion of the relevant information.

The problem arises when this information does not converge within a shared decision-making framework.

In such cases, whilst a CAPEX list may exist, the economic impacts of individual projects are not directly linked to it. Financial and planning data are held in other systems, spreadsheets or areas of responsibility. Dependencies between investments may, in turn, be documented elsewhere.

The data exists – but the decision-maker does not see the full picture.

The person responsible for the market often knows the best planning assumption

An important point is frequently underestimated in discussions about data quality.

The person who draws up or is responsible for financial and strategic planning usually knows their area, their market and the economic interrelationships best.

They can assess:

How might sales develop? What level of revenue is realistic? What additional costs will be incurred? What profit margins can be expected? What capacity will be required? Which investment affects which other investment?

Of course, nobody knows the future exactly.

But that is not necessary either.

When making an investment decision, expected revenue, expenditure and profits can be derived, for example, from current forecasts, business plans, empirical data or average figures from previous years.

The key thing, first and foremost, is that there is a reasonable assumption.

An estimate is not the same as having no information at all

In business planning, there is often an attempt to create a seemingly perfect data set before calculations are made or decisions taken.

However, by definition, such a perfect data set cannot exist when making decisions about the future.

Nobody knows exactly what the turnover of a new product will be in three years’ time. No one knows all the future energy prices, market changes or cost trends.

Nevertheless, the board must decide on investments today.

Therefore, the question should not be:

“Is this figure guaranteed to be correct?”

But rather:

“Which assumption do we consider plausible today – and how does our decision change if this assumption turns out to be different?”

This is precisely where planning becomes true decision intelligence.

The board should be aware of the economic consequences of its CAPEX decisions

For example, when a board decides on an investment of 20 million euros, it should not only be clear what the investment costs.

It should also be transparent what economic impact is associated with this investment.

For example:

Investment: 20 million euros
Expected additional revenue: 12 million euros per annum
Expected additional operating costs: 5 million euros per annum
Expected contribution to profit: EUR 7 million p.a.
Capacity increase: 18%
Dependency: Project B must also be
implemented Planning horizon: 5 years

These figures may be taken from detailed business cases. However, they may also be derived from forecasts or verifiable assumptions.

It is important that the economic consequences of the investment form part of the decision.

From data silos to a shared decision-making structure

This does not necessarily require the creation of an entirely new data landscape.

The first step can be surprisingly simple.

A project ID serves as the common key.

The parameters relevant to decision-making are linked to this ID:

Project ID + investment + expected revenue + expected expenditure + profit contribution + output + timeframe + constraints + dependencies + success criteria

This mathematically links CAPEX and finance data.

A unified decision-making structure emerges from several separate information silos.

And it is precisely this structure that can subsequently be optimised.

It is not the individual project that is the crucial question

Traditionally, investments are often considered in isolation:

Is Project A economically viable?
Does Project B have a positive business case?
Is Project C strategically necessary?

Yet even if all three questions are answered in the affirmative, this does not clarify whether A + B + C represent the best combination for the company.

With 100 possible CAPEX projects, there are theoretically already more than

1,267,650,600,228,229,401,496,703,205,376

possible subsets or project combinations.

This is precisely why traditional prioritisation is not always sufficient for large portfolios.

The crucial question is:

Which combination produces the best overall result given our actual constraints?

StratePlan links the data to the decision

This is where StratePlan comes in.

The existing CAPEX, finance and planning data are transferred into a common mathematical decision-making framework.

The portfolio can then be optimised combinatorially under defined targets and constraints.

The aim here is not to take the decision away from management.

The aim is to demonstrate to management, in a transparent manner, the consequences that different decisions and assumptions have for the overall portfolio.

This becomes particularly relevant when the operating environment changes.

What happens if the CAPEX budget falls by 15 per cent?
What happens if the expected turnover in a market declines by 20 per cent?
What happens if raw material or energy costs rise?
What happens if a production facility becomes available twelve months later?
What happens if higher output is to be prioritised over maximum ROI?

Forecasting becomes live simulation in the boardroom

This is precisely where the role of the board meeting is changing.

Instead of referring a question from the board back to Finance, having them recalculate it and then receiving another presentation days later, defined parameters can be adjusted directly during the meeting.

StratePlan then recalculates the portfolio.

New assumption. New calculation. New optimal combination.

This creates a much closer link between forecasting, scenario analysis, portfolio optimisation and management decision-making.

The board is not merely presented with a prepared base case, but can compare alternative future scenarios with one another.

Uncertainty thus becomes a decision-making parameter in its

own right

An estimate of 100 million euros in expected revenue need not be treated as an immutable truth.

For example, one can carry out the following calculations::

Base Case: 100 million EUR
Conservative Case: 80 million EUR
Downside Case: 65 million EUR
Upside Case: 120 million EUR

This then leads to a far more important insight::

Does the same investment combination remain optimal even under changed assumptions?

If so, this suggests a robust decision.

If not, the board can immediately see which parameter tips the balance of the decision.

This is far more valuable than attempting to map an uncertain future with a single, seemingly exact figure.

The data does not have to be perfect. It must be actionable.

Companies today possess enormous amounts of data. Nevertheless, important investment decisions are often still based on separate CAPEX lists, financial models, business cases and presentations.

The next step in development is therefore not necessarily to generate even more data.

The aim is to link the information already available at project level and make it mathematically usable for decision-making.

The business unit knows its market.

Finance knows the figures.

Management knows the strategy.

StratePlan combines this information to create a calculable decision-making framework.

Same projects. Different assumptions. Different combinations. Better decisions.

Break down the silos. Connect the figures. Calculate the optimum.