Why Traditional CapEx Planning Is Systematically Suboptimal—and How AI Calculates the Global Optimum

Executive Summary: The best investment is not automatically the best investment decision

What if companies with the same investment projects and the same CAPEX budget could achieve a significantly higher overall economic contribution—simply by mathematically optimizing the combination of their projects?

This is precisely where one of the greatest—and so far underutilized—opportunities in strategic capital allocation lies.

CAPEX decisions do not necessarily fail due to a lack of data, inadequate business cases, or insufficient management experience.

The real challenge often lies in the mathematical complexity of the decision space.

As soon as numerous investment projects are up for consideration at the same time, the number of possible project combinations grows exponentially.

Traditional investment planning often focuses on evaluating and prioritizing individual projects. Yet even economically attractive individual projects do not automatically result in the best possible investment portfolio when combined.

The crucial question, therefore, is not which projects are attractive individually, but which combination of all planned investments achieves the highest overall economic contribution within the available budget and resource constraints.

StratePlan addresses this question through mathematical portfolio optimization and AI-supported methods.

The goal: to systematically calculate investment decisions before they are implemented, compare alternative scenarios, and identify untapped value creation potential.

1. CAPEX is not a single-project decision—but a combinatorial portfolio problem

In traditional investment processes, CAPEX projects are often evaluated individually.

Typical decision criteria include:

• Return on Investment (ROI)
• Internal Rate of Return (IRR)
• Net Present Value (NPV)
• Payback Period
• Strategic Importance
• Risk and Resource Requirements

These metrics are important and provide valuable information about the economic attractiveness of individual investments.

However, they do not automatically answer the crucial portfolio question:

Which combination of available projects maximizes the total economic value contribution within a limited investment budget?

A project may have a high ROI and still not be part of the economically optimal portfolio.

The reason is simple: investment projects compete for the same financial and operational resources.

If a large portion of the CAPEX budget is allocated to a single project, other economically attractive projects may no longer be feasible.

Optimal capital allocation therefore depends not only on the attractiveness of individual projects, but also on how they interact with one another.

CAPEX optimization is thus a combinatorial decision-making problem from the field of operations research.

Another key aspect is the uncertainty surrounding future investment outcomes.

Read more in the article “Investment Decisions Under Uncertainty—Why Traditional Methods Are Insufficient.”

2. The Underestimated Decision Space: Why Complexity Grows Exponentially

The number of possible investment portfolios does not grow linearly with the number of projects.

In a simplified model, each project can either be implemented or not implemented.

Thus, with N independently selectable projects, there are^{theoretically 2N} possible combinations.

• 7 projects: 128 possible combinations
• 10 projects: 1,024 possible combinations
• 20 projects: 1,048,576 possible combinations
• 30 projects: 1,073,741,824 possible combinations
• 40 projects: 1,099,511,627,776 possible combinations
• 50 projects: 1,125,899,906,842,624 possible combinations
• 100 projects: approximately 1.27 ×^10³⁰ possible combinations

A company with 50 planned investment projects thus faces a theoretical decision space of approximately 1.126 quadrillion project combinations.

Not every combination is permissible due to budget constraints, project dependencies, or resource limitations.

Nevertheless, the sheer scale illustrates why traditional prioritization methods and manual scenario analyses quickly reach their practical limits.

An investment committee cannot fully cover this decision space through discussions, experience, or a simple ranking.

The real challenge lies in identifying, within this vast decision space, those combinations that best meet the defined economic objectives.

3. Why Excel, scoring models, and investment committees alone are not enough

Excel, scoring models, and investment committees are established components of professional investment planning.

They fulfill important roles in data collection, economic feasibility assessment, and decision-making.

Their limitations become particularly apparent when numerous projects must be optimized simultaneously under multiple constraints.

Excel: Powerful, but not automatically a solution for portfolio optimization

Excel is excellent for creating business cases, financial models, and investment overviews.

Mathematical optimizations are also generally possible using the appropriate Solver functions.

However, large combinatorial decision spaces, numerous binary variables, and complex constraints can place a significant strain on the performance of simple Excel Solver models.

The actual limitations depend on the Solver used, the mathematical model, and the problem structure.

An Excel spreadsheet containing 100 investment projects does not constitute a mathematically optimized selection of those 100 projects.

Scoring Models: Prioritization Is Not Optimization

Scoring models evaluate projects based on defined criteria and generate a ranking from them.

The problem: A ranking does not automatically take into account the economic interdependencies and budget trade-offs between the projects.

A project with a lower individual score may be part of an economically superior overall combination.

The order of the projects is therefore not synonymous with the optimal composition of the portfolio.

Investment Committees: A Few Scenarios Instead of Systematic Optimization

Investment committees often discuss several prepared CAPEX scenarios.

For example:

• The originally proposed investment portfolio
• A reduced-budget scenario
• A growth-oriented investment scenario
• A conservative, lower-risk scenario

These variants may be carefully worked out.

However, they typically represent only a very small subset of the mathematically possible project combinations.

As a result, economically more attractive alternatives may remain undiscovered.

Further background is explained in the article “Understanding Mathematics and Optimization—Why Better Decisions Require a Different Computational Logic.”

4. A Simple Example: Why the Best Individual Project Doesn’t Necessarily Make for the Best Portfolio

A company has a CAPEX budget of 10 million euros.

There are three investment projects to choose from.

Project A has the highest single net value contribution.

If Project A is implemented, 3 million euros of the CAPEX budget will remain. This is not enough for any of the other projects.

The total net value contribution is therefore 4 million euros.

If, on the other hand, Projects B and C are implemented together, the entire budget will be used.

The combined net value contribution is 6 million euros.

The portfolio consisting of B and C thus achieves a 50% higher net value contribution with the same investment budget.

This simplified example illustrates the fundamental difference between individual project evaluation and mathematical portfolio optimization.

With 50, 100, or 300 investment projects, the search for the best combination becomes correspondingly more complex.

5. Ex-ante Optimization: Calculating the Best Project Combination Before the Investment Decision

Traditional investment management often focuses on the retrospective evaluation of decisions that have already been made.

KPIs such as ROI, EBIT, return on investment, or budget variances show what results were achieved.

Mathematical ex-ante optimization begins earlier.

Even before capital is committed, it examines which combination of planned projects achieves the highest expected overall economic contribution under the defined framework conditions.

To do this, relevant parameters are incorporated into a unified optimization model:

• Available CAPEX budgets
• Expected revenues and expenses
• Project dependencies
• Resource and capacity constraints
• Time constraints
• Risk and strategy parameters
• Binding Investment Projects

This information forms the basis for a mathematical decision-making problem.

The goal is to determine the best possible permissible combination of projects for the defined objective function.

Unlike a simple prioritization, the projects are not considered in isolation but are analyzed as a cohesive portfolio.

6. How StratePlan Enables Mathematical CAPEX Optimization

StratePlan combines mathematical optimization methods from operations research with AI-driven approaches to efficiently analyze large combinatorial decision spaces.

For a basic economic optimization model, only three pieces of information are initially required per project:

1. Project ID: Unique identifier for the investment project
2. Expenses: Planned investment expenditures
3. Income: Expected revenue or economic returns

The data can be imported, for example, from an existing Excel investment plan.

In addition, the available CAPEX budget is defined.

StratePlan then examines possible project combinations and calculates a portfolio based on the specified objective function.

When optimizing for “Maximize Net Contribution,” the focus is on the total expected net value contribution of the selected portfolio.

Advanced models can account for additional constraints and valuation parameters.

The result is not merely a ranking of individual investment projects, but a mathematically calculated combination of projects within the available budget.

Depending on the problem structure and the method used, a provably optimal or an approximately optimal solution can be determined. A mathematical proof of the global optimum requires corresponding optimality criteria.

7. Live in the Board Meeting: Modifying and Recalculating CAPEX Scenarios

Mathematical portfolio optimization becomes particularly interesting when it is directly integrated into strategic management decisions.

Let’s imagine a company with 100 planned investment projects.

The total of all investment requests exceeds the available CAPEX budget.

At the board meeting, an optimized combination of projects is first presented based on existing assumptions.

Now the strategic discussion begins.

Scenario A: Maximize total net value contribution

The CFO asks:

“Which combination of our planned investments achieves the highest expected net value contribution within the existing CAPEX budget?”

StratePlan calculates a corresponding project portfolio.

Management receives an overview of the selected projects, the required capital investment, and the expected total economic contribution.

Scenario B: Reduce CAPEX by 15%

The CEO asks:

“What happens if we have to reduce our investment budget by 15%?”

The budget constraint is adjusted.

StratePlan recalculates the portfolio.

The board can see which projects will be eliminated and how the expected total economic contribution will change.

Scenario C: Give Greater Weight to Strategic Investments

Another board member asks:

“How does our portfolio change if we place greater emphasis on digitalization, energy efficiency, or certain strategic projects?”

The relevant parameters are adjusted, and the portfolio is optimized again.

The results can be compared with those of the previous scenarios.

Scenario D: Allocating Additional Investment Capital

The CFO wants to know:

“What additional net value contribution could we achieve if we increase our CAPEX budget by 20 million euros?”

After adjusting the budget, another calculation is performed.

This allows the modeled additional value contribution to be compared with the required capital investment.

Change the budget. Adjust parameters. Recalculate the portfolio. Compare scenarios. Make more informed decisions.

This transforms static investment planning into a dynamic basis for management decision-making.

8. Sensitivity Analysis: How robust is the optimal investment combination?

A single portfolio optimization initially yields a result based on the defined assumptions.

In practice, however, investment costs, market conditions, resources, and strategic priorities change.

That is why examining alternative scenarios is particularly valuable.

By making targeted changes to the input parameters, the following questions—among others—can be examined:

• Which projects remain part of the portfolio even with a reduced CAPEX budget?
• Which investments are particularly sensitive to rising costs?
• How does the net value contribution change when revenue expectations are lower?
• What combination of projects results from changed strategic weightings?
• Which projects compete particularly strongly for the same resources?

These sensitivity analyses make it possible to examine the stability of an investment strategy under different assumptions.

Management thus not only receives a calculated project mix but can also understand how alternative economic and strategic conditions affect the overall portfolio.

9. Opportunity Costs: The Economic Value of Unselected Alternatives

Every investment decision ties up capital.

This capital is subsequently no longer available for other investment opportunities.

The resulting opportunity costs are often only partially apparent in traditional investment processes.

A company may select economically attractive individual projects and yet leave significant value-creation potential untapped.

Mathematical portfolio optimization allows for a comparison between the originally planned portfolio and an optimized combination of projects.

In particular, the following metrics can be compared:

• Total CAPEX expenditure
• Expected net value contribution
• Portfolio ROI
• Selected and non-selected projects
• Remaining budget flexibility
• Differences between alternative investment scenarios

The difference between the modeled results shows the potential for economic improvement under the underlying assumptions.

The key added value does not come from additional investments, but rather from a better allocation of the capital already available.

10. Case Study: Klybeck Site in Basel – Approximately 63% Higher Return on Investment

The StratePlan case study of the Klybeck site in Basel illustrates the economic potential that mathematical portfolio optimization can unlock.

As part of the portfolio optimization for Central Real Estate AG and Swiss Life AG, existing investment alternatives were analyzed using mathematical optimization methods.

The modeled result:

• Original return on investment: 7.0%
• Optimized return on investment: 11.4%
• Relative return improvement: approximately 63%
• Additional modeled economic value contribution: approximately 30 million CHF

The improvement was modeled by changing the combination of existing investment alternatives while maintaining the same budget.

These results are project-specific and do not constitute a general guarantee of return.

The key insight: A higher overall economic contribution can be achieved simply by better combining existing investment projects—without an additional CAPEX budget.

11. Why the global optimum is a mathematical statement, not a political one

Within a mathematically defined model, a global optimum refers to a valid solution with the best possible objective function value.

However, this does not mean that every mathematically calculated solution automatically represents a proven global optimum.

For large combinatorial decision spaces, various optimization methods can be employed.

Some methods allow for a mathematical proof of optimality under suitable conditions.

Other methods produce highly efficient approximate solutions within a limited computation time.

It is therefore crucial to distinguish between:

• Proven globally optimal solutions
• Approximate optimal solutions with documented solution quality
• Manually selected or merely prioritized project portfolios

The quality of a mathematical optimization also depends on the quality of the underlying data, objectives, and constraints.

Mathematical optimization does not replace managerial responsibility. It provides an additional, systematic foundation for better investment decisions.

12. From Traditional CAPEX Planning to Decision Intelligence

Traditional CAPEX planning focuses on evaluating, prioritizing, and budgeting individual investment projects.

Decision Intelligence expands this approach to include the mathematical analysis of possible decision combinations.

The difference lies in the central question.

Traditional CAPEX planning:

“Which of our projects are economically attractive and should be prioritized?”

Mathematical portfolio optimization:

“Which combination of our projects maximizes the total economic value contribution given the available budget and resource constraints?”

StratePlan supplements existing ERP, controlling, and business intelligence systems with this mathematical decision-making layer.

Companies can continue to use their existing investment data and get started with optimization using simple Excel spreadsheets.

This makes mathematical portfolio optimization accessible not only to large corporations but also to medium-sized companies with more complex investment portfolios.

13. Conclusion: Don’t Just Finance More Projects—Calculate the Right Combination

Traditional CAPEX planning isn’t fundamentally wrong.

Business cases, ROI calculations, scoring models, and investment committees remain important components of professional corporate management.

However, evaluating individual projects in isolation does not automatically answer the question of the economically optimal combination of all planned investments.

As the number of projects increases, the theoretical decision space grows exponentially.

With 50 projects, there are already more than a quadrillion possible combinations.

This complexity cannot be managed solely through additional meetings, more extensive spreadsheets, or ever-changing prioritization lists.

It requires appropriate mathematical optimization methods.

StratePlan enables the systematic analysis of complex investment portfolios, taking into account economic objectives, available budgets, and defined constraints.

This enables companies to calculate alternative project combinations, compare different CAPEX scenarios, and identify untapped economic potential.

The crucial question is no longer: Which investment projects are the most attractive individually?

Rather: Which combination of our investments achieves the highest overall economic contribution with the available capital?

This is precisely where the transition from traditional investment prioritization to mathematical CAPEX optimization begins.

StratePlan – Same projects. Same budget. Optimal combination. Greater results.

How much more economic value can your CAPEX budget generate?

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