Optimizing Local Optima with AI: Why Better Decisions Can Still Be Wrong

Executive Summary: Why Successful Optimization Doesn't Automatically Lead to the Best Results

What happens when every department optimizes its projects, every budget is used efficiently, and yet the company as a whole squanders its economic potential?

In companies and public organizations, optimization is part of day-to-day business. Production processes are made more efficient, investment projects are prioritized based on economic metrics, budgets are adjusted, and resources are deployed as productively as possible.

The results often appear convincing: costs fall, individual returns rise, and operational metrics improve.

Yet this is precisely where a fundamental problem in strategic corporate management lies.

A locally optimal decision is not necessarily part of the globally optimal overall solution.

A project may be economically excellent within a department while simultaneously preventing the company from achieving a higher overall contribution with the same resources.

The reason lies in the interactions between projects, budgets, capacities, and strategic goals.

While traditional management processes often optimize individual measures, mathematical portfolio optimization considers their combination.

StratePlan combines mathematical methods from operations research with AI-driven approaches to systematically explore large combinatorial decision spaces.

The goal is to identify project combinations that achieve the highest possible overall contribution within the defined economic and operational constraints.

The crucial management question is therefore not: How can we further improve our individual projects?

Rather: Which combination of our projects achieves the best overall result with the available budget?

1. What are local optima and global optima?

The terms “local optimum” and “global optimum” originate from mathematical optimization.

A local optimum refers to a solution that, within a defined neighborhood, cannot be improved by a small permissible change.

A global optimum, on the other hand, is a solution that achieves the best value of the defined objective function within the entire permissible decision space.

The difference is crucial.

A solution may be optimal within its immediate vicinity, even though a significantly better solution exists elsewhere in the decision space.

In business, this principle can be applied to various situations:

• A production department optimizes its own investments without considering the impact on other locations.
• A business unit maximizes its individual ROI, even though a different allocation of capital would enable a higher contribution to group value.
• A project portfolio is optimized within a fixed budget, even though a cross-departmental reallocation would be more economically attractive.
• A municipality prioritizes individual measures based on urgency without examining the best possible combination of all available investment alternatives.

These decisions may be understandable and economically sound within their respective scopes of consideration.

However, they may not produce the best outcome for the organization as a whole.

Local optimization improves a solution within certain constraints. Global optimization seeks the best permissible solution within the entire defined decision space.

2. Why Local Optima Arise Particularly Frequently in Companies

Organizations are structured into departments, business units, programs, and lines of responsibility.

This division is necessary to manage complex organizations.

However, it also influences the way decisions are made.

Each organizational unit often has its own goals, budgets, and key performance indicators.

A department optimizes its costs. A business unit maximizes its return on investment. A project manager focuses on meeting deadlines and staying within budget.

From the perspective of the respective unit, these decisions may be correct.

But the overall result depends on how all these decisions interact.

Typical causes of local optimization include:

• Separate budgets: Capital is optimized within individual business units rather than being considered across units.
• Different performance metrics: Departments track their own KPIs, which do not fully align with the company’s overall goal.
• Limited transparency: Decision-makers are often only aware of the projects within their area of responsibility.
• Project dependencies: Individual initiatives affect the profitability or feasibility of other projects.
• Resource conflicts: Multiple projects compete for the same employees, machinery, or technical capacity.

This can lead to decisions that make sense locally but are economically suboptimal when viewed as a whole.

The company optimizes its individual components without automatically optimizing the entire system.

3. The Mathematical Problem: 2^N Possible Project Combinations

As soon as multiple projects are available for selection at the same time, a combinatorial decision space arises.

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

With N independently selectable projects, there are theoretically 2^N possible project combinations.

The number grows exponentially:

• 7 projects: 2^7 = 128 combinations
• 10 projects: 2^10 = 1,024 combinations
• 20 projects: 2^20 = 1,048,576 combinations
• 30 projects: 2^30 = 1,073,741,824 combinations
• 40 projects: 2^40 = 1,099,511,627,776 combinations
• 50 projects: 2^50 = 1,125,899,906,842,624 combinations
• 100 projects: 2^100 = approximately 1.27 × 10^30 combinations

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

Not all of these combinations are permissible due to budget, resource, or project dependencies.

Nevertheless, the sheer scale illustrates why traditional management processes can only consider a small subset of all possible portfolios.

A board of directors can evaluate 50 projects individually.

However, it cannot fully compare more than one quadrillion possible project combinations with one another.

The challenge, therefore, is not to evaluate individual projects more effectively, but to find the economically optimal permissible combination within an exponentially large decision space.

4. Why Improving Individual Projects Can Worsen the Overall Portfolio

A common management approach holds that if each project is economically optimized, the overall result automatically improves.

However, this assumption does not hold true without qualification.

Individual projects may compete for the same limited resources.

If one project is prioritized, the funds allocated to it are no longer available for other initiatives.

As a result, an economically attractive individual decision can prevent an even more attractive combination of projects.

For example, a production facility may generate a high individual return, but at the same time tie up so much investment capital that several smaller automation measures can no longer be financed.

If these smaller measures together would generate a higher net value contribution, the original selection at the portfolio level would be suboptimal.

A similar effect can occur with regard to human resources.

A highly profitable project may require precisely those specialists who would be needed for several other projects.

The economic quality of a measure therefore depends not only on its own outcome but also on which other opportunities are precluded by its implementation.

These interactions cannot be fully captured by an isolated project evaluation.

5. Calculation Example: Why Two Smaller Projects Can Be More Economically Viable Than One Large One

A company has an investment budget of 10 million euros.

Three projects are under consideration.

Project A has the highest single net value contribution.

If this project is selected, 3 million euros will remain in the budget.

This is not enough to implement any of the other projects as well.

The selected portfolio thus achieves a net value contribution of 4 million euros.

However, a mathematical analysis of the possible project combinations reveals a better alternative.

Projects B and C together require 10 million euros and generate a combined net value contribution of 6 million euros.

Assuming the projects are independent and with no other restrictions, B + C is the optimal combination for maximizing the net value contribution.

The optimized portfolio generates a net value contribution that is 50% higher than the original selection.

The company does not need any additional investment capital to achieve this.

It simply needs to select a different combination of its existing projects.

The original selection is not necessarily a local optimum in the strict mathematical sense. Whether it is locally optimal depends on which changes are defined as a permissible neighborhood.

However, it illustrates the central management problem: A compelling decision regarding a single project may be economically inferior to an alternative portfolio combination.

6. The Difference Between Local Search and Global Optimization

In mathematical optimization, there are various methods for finding efficient solutions.

Local search methods often start with an existing solution and attempt to improve it through modifications.

For example, a project is removed from a portfolio and replaced with another.

If the objective function improves, the new combination is adopted.

This process can be repeated until no further improvement is found within the considered neighborhood.

The result may be a local optimum.

The problem: A significantly better solution may lie outside this neighborhood.

To reach it, it might be necessary to swap out several projects at the same time.

Individual intermediate steps could initially lead to worse results.

A simple local search algorithm might not implement such changes under certain circumstances.

Global optimization methods, on the other hand, aim to identify the best feasible solution within the entire defined decision space.

Various mathematical approaches can be used for this purpose, such as:

• Integer and mixed-integer optimization
• Branch-and-bound methods
• Dynamic programming
• Metaheuristics
• Multi-start methods and combined search strategies
• AI-based evaluation and search methods

Under suitable conditions, some methods can mathematically prove the existence of a global optimum.

Others provide approximately optimal solutions without providing a complete proof of optimality.

The key is not to fully compute each of the theoretically possible 2^N combinations individually, but rather to systematically explore the decision space using appropriate mathematical methods.

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

7. Why Excel, Scoring Models, and Traditional Prioritization Can Favor Local Solutions

Excel, scoring models, and management workshops are established tools for investment planning.

They enable a structured evaluation of individual projects and the creation of various scenarios.

The challenge arises when a ranking of individual projects is equated with a mathematically optimized portfolio selection.

Excel: Individual evaluations do not constitute global optimization

Excel is ideal for calculating business cases, returns on investment, and investment scenarios.

With the appropriate Solver functions, mathematical optimization problems can also be addressed.

However, a simple Excel list in which projects are sorted by ROI or net present value does not automatically account for all possible project combinations.

For large combinatorial decision spaces, specialized optimization methods may be necessary or more efficient.

Scoring Models: The Best Ranking Is Not Necessarily the Best Portfolio

Scoring models evaluate projects based on defined criteria.

The projects with the highest scores are prioritized for implementation.

This approach provides transparency regarding individual priorities.

However, it does not guarantee that the selected combination will achieve the highest overall economic or strategic contribution.

Management Workshops: Limited Scenario Analysis

In workshops, several prepared scenarios are often discussed.

For example, a base-case scenario, a conservative budget model, and a growth-oriented investment plan.

These variants may have been carefully developed.

However, they often represent only a small subset of the entire mathematical decision space.

The problem is not the quality of the discussion, but the limited number of combinations actually examined.

8. How AI-Driven Optimization Helps Overcome Local Optima

AI-driven decision optimization takes a different approach than simply improving individual processes.

It does not examine only the current solution but takes into account the larger combinatorial decision space.

Projects are modeled collectively.

Budget limits, resource constraints, dependencies, and economic objectives are described mathematically.

Suitable optimization algorithms then examine possible project combinations.

This allows for the identification of alternatives that might otherwise be overlooked when focusing on local improvements to existing project planning.

The strategic shift in perspective is:

• From individual projects to portfolios: The focus is not on the isolated attractiveness of a project, but on its contribution to the overall solution.
• From rankings to combinations: Selection is based on the combined impact of multiple measures.
• From fixed budgets to scenarios: Different capital limits can be analyzed mathematically.
• From implicit assumptions to constraints: Strategic guidelines are incorporated into the decision-making model.
• From local improvements to systematic search: Even significantly altered combinations of projects are considered as possible alternatives.

The benefit of AI-supported optimization lies in revealing economically more efficient solutions beyond the project combinations originally considered.

9. StratePlan: Mathematical Portfolio Optimization Instead of Isolated Project Improvement

StratePlan was developed to mathematically optimize complex investment and project portfolios.

The technology combines 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 per project are initially required:

1. Project ID: Unique identification of the project
2. Expenses: Planned expenditures or investments
3. Income: Expected revenue or economic returns

In addition, the available investment budget is specified.

The project data can, for example, be imported from existing Excel investment plans.

StratePlan analyzes possible project combinations and calculates a portfolio based on the selected objective function.

With “Maximize Net Contribution,” the focus is on maximizing the total expected net value contribution.

The portfolio ROI can also be reported as an economic metric.

Advanced models can account for additional constraints and evaluation parameters:

• Project dependencies
• Strategic weightings
• Resource and capacity constraints
• Multi-year investment budgets
• Time constraints
• Risk and sustainability criteria

The result is not merely a ranking of individual projects, but a mathematically calculated combination of projects within the defined parameters.

StratePlan thus enables a shift from the isolated improvement of individual projects to the systematic optimization of the entire investment portfolio.

10. Live at the Board Meeting: What does it cost to stick with a local solution?

Mathematical portfolio optimization becomes particularly relevant when different strategic objectives can be directly compared with one another.

Let’s imagine an industrial conglomerate with 100 planned CAPEX projects.

The total of all investment requests exceeds the available budget.

Several business units have already prioritized and evaluated their own projects from a financial perspective.

The executive board now wants to know which combination of all projects yields the highest expected net value contribution.

Scenario A: Cross-Divisional Optimization

The CFO asks:

“What changes if we stop optimizing each business unit individually and instead consider all investment projects collectively?”

The projects are modeled in a single portfolio.

StratePlan calculates an optimized combination while taking the defined budget limit into account.

A comparison with the previous division-based planning can show whether a different allocation of capital would enable a higher overall economic contribution.

Scenario B: Strategically Prioritized Projects

The CEO asks:

“What economic contribution might we be forgoing if certain projects must be implemented regardless of their individual economic viability?”

The projects in question are treated as binding constraints.

The portfolio is recalculated.

The comparison shows the impact of this constraint on the expected net value contribution.

Scenario C: Budget Reduction

The CFO wants to know:

“Which combination of projects should we choose if we have to reduce our CAPEX budget by 20%?”

The budget constraint is adjusted.

StratePlan calculates a new combination of projects.

Management can see which measures are being eliminated and how the expected net value contribution changes.

Scenario D: Additional Investment Capital

Another member of the executive board asks:

“How much additional economic value could we generate if we invested another 10 million euros?”

The budget is increased, and a new optimization is performed.

The results allow for a comparison between the additional capital investment and the expected value contribution.

Scenario E: Changed Strategic Priorities

The executive board asks:

“How does the portfolio change if we place greater emphasis on energy efficiency, digitalization, or specific growth areas?”

The corresponding parameters are adjusted.

The portfolio is recalculated and compared with the previous scenarios.

Change strategic parameters. Recalculate the portfolio. Compare economic impacts. Make more informed decisions.

This allows even decisions that deviate from purely economic optimization to be made in a conscious and transparent manner.

11. Opportunity Costs: The Price of a Locally Optimized Decision

A project decision that is locally attractive may result in other, economically more efficient combinations not being implemented.

This results in opportunity costs.

They describe the lost benefits of the best alternative that was not chosen.

In strategic capital allocation, opportunity costs can arise, for example, when:

• a single project consumes a large portion of the available budget,
• a business unit ties up capital that could generate a higher return in another unit,
• several interdependent projects are evaluated separately,
• resources are reserved for less effective measures,
• an existing combination of projects is not compared with alternative portfolios.

Mathematical portfolio optimization makes it possible to compare the originally planned portfolio with an optimized combination of projects.

The difference between the expected net value contributions reveals the modeled potential for improvement.

If a global optimum has been mathematically proven, the distance between a selected solution and this optimum can be precisely determined within the model.

For approximately optimal solutions, the difference from the best solution found is first determined.

The key added value lies in identifying potential economic drawbacks of an existing project plan before capital is committed.

12. Why the global optimum does not always have to be the final management decision

A mathematically optimal solution is always tied to a defined objective function and specific constraints.

Management may need to take additional strategic, regulatory, or societal requirements into account.

For example, a company may decide to implement a specific project for long-term strategic reasons, even though another combination would yield a higher net value contribution in the short term.

A municipality may have to finance a legally mandated infrastructure project, even if other projects appear more attractive within a purely economic model.

Such decisions are not automatically wrong.

What matters is that their implications are made transparent.

Mathematical portfolio optimization can calculate different scenarios:

• A portfolio without additional strategic guidelines
• A portfolio with projects that must be implemented
• A portfolio with changed budget constraints
• A portfolio with additional weightings or impact goals

Comparing these scenarios helps reveal economic trade-offs.

The goal is not to replace every management decision with an algorithm, but to make the economic consequences of different courses of action transparent.

13. Sensitivity Analysis: How stable is the optimal project combination?

Investment decisions are based on assumptions about future economic developments.

Project costs may rise. Revenue projections may fall. Resources may become scarcer. Strategic priorities may change.

Therefore, it makes sense not to rely exclusively on a single portfolio optimization.

Sensitivity analyses allow for the examination of different economic scenarios.

Typical questions include:

• Which projects will remain part of the portfolio even with a reduced budget?
• How does the project mix change as investment costs rise?
• Which measures are particularly sensitive to lower revenue expectations?
• How does the portfolio change under additional resource constraints?
• Which projects remain economically attractive under various scenarios?

These analyses can show how robust a calculated project mix remains under changing assumptions.

A strategically sound decision takes into account not only the best calculated solution under a single scenario, but also the effects of potential changes.

14. 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 reveal.

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

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

The key finding: A better combination of existing investment alternatives can enable a significantly higher overall economic contribution—without an additional investment budget.

15. Where local optima particularly often limit economic potential

The risk of isolated optimization of individual measures exists in numerous economic and public sectors.

Industry and CAPEX Planning

Production facilities, automation projects, capacity expansions, and site investments compete for shared investment budgets.

The isolated optimization of individual plants or business units can prevent a more economically efficient allocation of capital across all areas.

Digitalization and AI Transformation

Companies often identify numerous potential AI and digitalization projects.

Simply prioritizing individual use cases does not automatically reveal which combination yields the highest overall economic contribution.

Real Estate

Project developments, renovations, modernizations, and location investments can be considered within a shared capital budget.

The selection of individual projects influences the financing options for the entire real estate portfolio.

Energy and Infrastructure

Energy projects, grid investments, efficiency measures, and infrastructure modernizations often require long-term capital commitments.

Mathematical portfolio optimization can help compare different investment combinations under economic and technical constraints.

Municipalities and Public Budgets

Cities and municipalities must finance numerous investment projects within limited budgetary constraints.

Schools, roads, administrative buildings, infrastructure, and social services compete for the same public resources.

Prioritizing individual departments in isolation does not automatically lead to the combination of measures with the highest defined overall social benefit.

Mathematical optimization can examine different combinations of projects while taking into account budget constraints, mandatory responsibilities, and policy objectives.

The common thread among all use cases is the question of whether optimizing individual measures actually leads to the best possible overall solution.

16. Budget Simulation and Simultaneous Portfolio Optimization: Overcoming Local Solutions in the Public Sector

The problem of local optimization becomes particularly evident in the planning of public investment budgets.

Municipal departments often have their own project lists and priorities.

The school district prioritizes investments in education. The building department evaluates infrastructure projects. Other departments plan digitization, energy, or real estate projects.

Each of these prioritizations may make sense within the respective area of responsibility.

The crucial question, however, is:

Which combination of all municipal projects achieves the highest defined overall economic or social benefit within the available budget?

A simplified example:

A city has an investment budget of 81 million euros.

At the same time, there are 50 projects to choose from with a total investment requirement of 220 million euros.

With 50 binary project decisions, there are theoretically 2^50 = 1,125,899,906,842,624 possible combinations.

Mathematical portfolio optimization can examine various permissible project combinations within the defined budget and impact constraints.

A simultaneous budget simulation allows for different financial conditions to be taken into account.

For example:

• Which project combination yields the highest modeled total benefit with a budget of 81 million euros?
• How does the portfolio change if the budget is reduced?
• What additional measures could be implemented if revenues were higher?
• How do mandatory policy projects affect the outcome?
• What are the effects of different weightings for education, infrastructure, or climate protection?

The quality of the results depends on the data used and the definition of the economic and social targets.

Combining budget simulation with mathematical portfolio optimization can help translate department-specific priorities into a comprehensive, transparent basis for decision-making.

17. From Local Process Optimization to Strategic Decision Intelligence

Many companies already have powerful systems for analyzing and managing their business processes.

ERP systems track resources and business transactions.

Business intelligence solutions visualize key metrics and trends.

Controlling systems analyze budget variances and financial results.

These systems answer important questions:

What happened? How are our KPIs trending? Which projects are within budget?

Decision Intelligence expands this perspective by providing mathematical support for future decisions.

Which combination of projects should we choose? What constraints limit our potential value contribution? Which alternatives yield a better overall result?

StratePlan supplements existing planning and controlling processes with a layer of mathematical optimization.

The required project data can initially be provided via simple Excel spreadsheets.

The strategic advancement lies not merely in managing individual projects more efficiently, but in mathematically improving the selection and combination of the projects themselves.

18. Conclusion: Those who focus solely on improving local optima may overlook the better overall solution

Local optimization is an important component of professional business management.

It improves processes, reduces costs, and increases the efficiency of individual measures.

However, a locally optimal solution is not necessarily globally optimal.

When numerous projects compete for limited budgets and resources, a combinatorial decision space emerges.

Even with just 50 projects, there are more than one quadrillion theoretically possible combinations.

Therefore, improving individual projects in isolation does not automatically answer the question of what the economically best overall solution is.

Mathematical portfolio optimization makes it possible to systematically examine different project combinations and compare their economic impacts.

To this end, StratePlan combines methods from operations research with AI-driven approaches.

This enables companies, executive boards, and public decision-makers to calculate alternative portfolios, examine strategic scenarios, and identify untapped economic or societal potential.

The crucial question is not: How can we further optimize our existing projects?

Rather: Have we even selected the right combination of projects?

This is precisely where the transition from local optimization to mathematical portfolio optimization begins.

StratePlan – Not just improving projects. Calculating the right combination.

FAQ – Overcoming Local Optima with AI

What is a local optimum?

A local optimum is a solution that, within a defined neighborhood, cannot be improved by a small permissible change.

However, better solutions may exist outside this neighborhood.

What is the difference between a local optimum and a global optimum?

A local optimum is optimal within a limited range.

A global optimum, on the other hand, achieves the best value of the defined objective function within the entire feasible decision space.

Why can local optima be problematic for companies?

Because a decision that is optimal within a department or business unit does not automatically lead to the company’s highest overall economic contribution.

Projects compete for shared budgets, resources, and capacities.

As a result, economically superior cross-departmental combinations may be overlooked.

How can AI help overcome local optima?

AI-supported mathematical optimization can systematically explore large combinatorial decision spaces.

This makes it possible to identify project combinations that lie outside the alternatives under immediate consideration and enable a higher overall economic contribution.

Can AI always calculate the global optimum?

With suitable mathematical models and optimization methods, a global optimum can be calculated and verified.

For particularly complex problems, approximately optimal solutions can also be used.

The assessment of solution quality depends on the method used and, where applicable, on proof of optimality.

What does 2^N mean in portfolio optimization?

2^N describes the number of theoretically possible combinations for N independent binary project decisions.

Each project can either be selected or not selected.

With 20 projects, there are 2^20 = 1,048,576 possible combinations.

With 50 projects, there are 2^50 = 1,125,899,906,842,624 combinations.

Do all 2^N combinations have to be calculated individually?

No.

Modern mathematical optimization methods can efficiently explore large decision spaces without having to fully evaluate every theoretically possible combination individually.

Which methods are suitable depends on the mathematical structure of the problem and the defined constraints.

Why isn’t a traditional ROI ranking sufficient?

An ROI ranking evaluates individual projects based on their individual return on investment.

However, it does not automatically take into account which combination of projects within the available budget achieves the highest absolute net value contribution.

Therefore, the best ranking is not necessarily the best investment portfolio.

Can Excel solve global optimization problems?

Yes. Excel can handle mathematical optimization tasks using appropriate Solver functions.

However, for large combinatorial decision spaces and complex constraints, specialized optimization methods may be necessary or more efficient.

What data does StratePlan need for an initial optimization?

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

• Project ID
• Expenses – planned expenditures
• Income – expected revenue or economic returns

In addition, the available investment budget must be specified.

More complex models can take additional constraints and evaluation parameters into account.

What does “Maximize Net Contribution” mean?

“Maximize Net Contribution” refers to maximizing the total expected net value contribution of a project portfolio within defined constraints.

The portfolio ROI can also be reported as an economic metric.

Maximizing the absolute net value contribution and maximizing the portfolio ROI are different mathematical objectives.

Can StratePlan highlight the economic drawbacks of an existing project plan?

By comparing an existing combination of projects with an optimized alternative, the difference in expected net value contributions can be calculated.

This difference illustrates the modeled economic improvement potential based on the underlying assumptions.

Is the global optimum always the right management decision?

Not necessarily.

A mathematically optimal solution always refers to the defined objective function and the constraints taken into account.

Strategic, regulatory, or societal requirements may necessitate additional constraints.

Mathematical optimization helps make the effects of such requirements transparent.

In which areas is overcoming local optima particularly relevant?

It is particularly relevant for investment portfolios with numerous competing projects and limited resources.

Typical areas of application include industry, CAPEX planning, digitalization, AI use cases, real estate, energy, infrastructure, and municipal budget planning.

What is the most important takeaway for CEOs and CFOs?

Improving individual projects does not automatically lead to the best overall economic solution.

Mathematical portfolio optimization makes it possible to examine alternative project combinations and improve capital allocation at the enterprise-wide level.

The crucial question is: Are we optimizing our projects—or are we optimizing the selection of our projects?

Don’t just optimize individual projects. Calculate the best combination.

Find out which combination of your planned investment projects can achieve the highest expected net value contribution within your available budget.

Compare your current project planning with mathematically optimized investment scenarios and examine whether a different combination of existing projects could yield a higher overall economic contribution.

You can optimize up to 15 projects for free.

Optimize up to 15 projects for free now →