Calculating EBIT optimization - Why strategic decisions on EBIT are influenced more by portfolio optimization than by individual measures

Executive Summary

EBIT (Earnings Before Interest and Taxes) is one of the key performance indicators used by modern companies. It measures the operational earning power of a company independently of its financing structure and tax effects and is therefore used worldwide by management boards, investors and supervisory bodies as a key indicator of operational performance.

In practice, however, EBIT optimization often focuses on isolated measures: Cost programs, efficiency improvements, price optimizations or individual investment decisions. These measures can have a short-term effect, but often overlook a much deeper structural cause of suboptimal results: the mathematically unoptimized combination of underlying investment and transformation projects.

As soon as companies decide on several strategic initiatives at the same time - such as new plants, product platforms, software programs, automation projects or market expansions - a combinatorial decision-making space is created. The number of possible project portfolios grows exponentially with the number of projects.

With 10 projects, there are already 2^10 possible combinations.
With 20 projects, there are 2^20 combinations.
With 50 projects, there are 2^50 combinations.

In this space, it is no longer the quality of individual projects that determines EBIT - but the mathematically optimal combination, sequence and timing of all projects under real constraints such as budget, capacity, risk or strategic targets.

Calculating the optimal project portfolio is therefore one of the most effective methods for sustainable EBIT optimization. Instead of improving individual projects, the overall structure of capital allocation is optimized - with direct effects on sales, cost structure, capital commitment and long-term profitability.

EBIT as the result of a capital allocation system

A company's EBIT does not arise by chance. It is the result of a large number of operational and strategic decisions. Every investment directly or indirectly influences future cash flows, cost structures and market positions.

Companies typically invest in different categories at the same time:

• Product development and innovation
• Production capacities and plants
• Automation and digitalization
• Software and data infrastructure
• Market expansion and distribution
• Efficiency programs and cost reduction

Each of these projects has its own characteristics:

• Investment volume
• expected sales contribution
• Changes in costs
• Risk
• Capacity requirements
• strategic importance

The problem does not arise at project level. As a rule, individual projects are economically plausible. The challenge arises at portfolio level: which combination of these projects generates the highest operating profit with limited resources?

This is precisely where the actual EBIT optimization is decided.

Why classic EBIT optimization is structurally limited

In many organizations, EBIT optimization is based on four classic approaches:

• Cost programs
• Pricing strategies
• Individual project evaluation
• Top-down budget control

These methods are important, but they do not solve a fundamental problem: the simultaneous selection from a large set of potential investment projects.

Typically, the decision-making process is carried out as follows:

• Departments propose projects
• Finance departments calculate business cases
• Committees prioritize projects
• Budget limits lead to the selection of a subset

This process makes sense from an organizational point of view, but is mathematically incomplete. The reason lies in the exponential growth of the decision space.

Even with moderate project numbers, the number of possible portfolios is so large that no human decision-making structure can examine all options simultaneously.

As a result, decisions are based on heuristic selection procedures instead of complete mathematical optimization.

The combinatorial dimension of EBIT optimization

The selection of investment projects is mathematically a combinatorial optimization problem.

If a company has N projects to choose from, there are basically 2^N possible portfolio combinations. Each combination leads to a different financial future - with different sales trends, cost structures and therefore different EBIT results.

Examples:

• 10 projects → 2^10 possible portfolios
• 20 projects → 2^20 possible portfolios
• 30 projects → 2^30 possible portfolios
• 40 projects → 2^40 possible portfolios
• 50 projects → 2^50 possible portfolios

With 30 projects, there are already over a billion possible combinations. With 50 projects, the number of possible portfolios is over one quadrillion.

No traditional planning - neither Excel nor classic business case analysis - can fully evaluate this decision space.

This means that in many companies, EBIT is not maximized, but merely improved.

The mathematical calculation of EBIT optimization

A complete EBIT optimization requires a mathematical portfolio model that takes all relevant influencing variables into account simultaneously.

A simplified model can be described as follows:

Maximize:

EBIT = sum of all project contributions to sales minus operating costs

under the constraints:

• Total budget ≤ available investment budget
• Capacity limits of the organization
• technological dependencies between projects
• Risk restrictions
• minimum strategic requirements

In mathematical terms, this results in an optimization problem in which the combination of projects is selected in such a way that future EBIT is maximized.

The decisive difference to traditional planning is that the entire portfolio is optimized simultaneously rather than evaluating individual projects.

Multi-year optimization instead of an annual budget

Another decisive factor in EBIT optimization is the time dimension.

Many companies make investment decisions on the basis of an annual budget. However, strategic projects often have an impact over several years.

Mathematical optimization therefore typically takes into account planning horizons of:

• 3 years
• 5 years
• 10 years

This creates additional degrees of freedom:

• Projects can be postponed
• Budgets can be optimized between years
• Dependencies between programs can be taken into account

This multi-year structure often leads to significantly higher EBIT results than isolated annual planning.

Example of EBIT optimization

A company has an annual investment budget of 500 million euros and is reviewing 25 strategic projects.

The projects include

• Automation programs
• new product platforms
• Digitalization initiatives
• Capacity expansions
• Cost reduction programs

Each project has an expected EBIT contribution over five years.

If the company traditionally prioritizes the projects, a project list is typically created that is implemented within the budget. This selection is usually based on ranking procedures or management decisions.

Mathematical portfolio optimization, on the other hand, analyses all possible combinations - i.e. 2^25 potential portfolios - and identifies the exact project structure that generates the maximum EBIT.

In many real scenarios, this method leads to significant improvements:

• higher total sales
• more efficient cost structure
• better capacity utilization
• higher return on capital

EBIT does not increase through individual projects - but through the optimal structure of the entire investment system.

Why small optimization improvements have an enormous impact on EBIT

The leverage of portfolio optimization is often underestimated. Even small improvements in capital allocation can have an enormous impact on EBIT.

An example:

A company invests 5 billion euros annually in strategic projects.

If mathematical optimization leads to an improvement in capital allocation of just five percent, this corresponds to an additional economic effect of

250 million euros per year.

Over a period of ten years, this results in a cumulative value impulse of approx:

2.5 billion euros.

This magnitude illustrates why capital allocation is one of the key strategic levers of modern companies.

The role of AI in EBIT optimization

The enormous size of modern decision spaces makes a complete calculation practically impossible without algorithmic support.

Modern optimization systems therefore combine various methods:

• combinatorial optimization
• Operations research
• Constraint programming
• heuristic search methods
• AI-based optimization algorithms

These technologies make it possible for the first time to systematically analyze very large decision spaces and determine the mathematically optimal project portfolio.

Instead of making decisions based solely on experience, intuition or simplified financial models, the entire decision space can be formally calculated.

Strategic effects of an EBIT-optimized organization

Companies that systematically optimize their capital allocation develop structural competitive advantages.

Typical effects are

• higher operating margins
• better utilization of existing resources
• lower opportunity costs
• faster implementation of strategic programs
• higher returns on capital

In the long term, this results in sustainable differences in profitability and company value.

Governance and decision quality

The introduction of mathematical portfolio optimization not only changes analytical processes, but also the governance of investment decisions.

For the first time, management bodies gain transparency over the entire decision-making space.

This means

• the optimal portfolio is visible
• Deviations from the optimum can be consciously made
• Opportunity costs become quantifiable

This creates a new quality of strategic decisions. Discussions shift from opinions and individual projects to clearly measurable portfolio effects.

Conclusion

In many companies, EBIT optimization is still seen as an operational efficiency issue. In reality, one of the biggest levers lies at a deeper structural level: the mathematically optimal combination of strategic investment projects.

As soon as organizations decide on several initiatives at the same time, an exponentially growing decision space with 2^N possible portfolio combinations is created. Without systematic calculation of this space, the global optimum remains invisible.

The future of strategic management therefore lies not only in better projects - but in the precise mathematical optimization of entire project portfolios.

Companies that adopt this perspective do not just optimize individual key figures. They optimize the underlying capital allocation system - and thus one of the central sources of their long-term EBIT.