CAPEX Planning vs. Optimization: A Comparison of Project Ranking, Scenario Planning, Excel, AI, and Portfolio Optimization
CAPEX planning, project prioritization, scenario planning, PPM software, Excel, AI, and mathematical portfolio optimization address different aspects of the same management problem.
CAPEX planning structures future investments.
Project Ranking and Prioritization evaluate individual projects.
Scenario Planning examines different assumptions.
PPM software provides transparency into projects and portfolios.
Excel enables flexible analyses and custom models.
AI can analyze data, identify patterns, and support decision-making processes.
Mathematical Portfolio Optimization, on the other hand, answers a different question:
Which combination of available projects best meets our defined objectives given budget, resource, strategy, and dependency constraints?
The crucial question, therefore, is not:
“Which method is generally better?”
But rather:
“Which method answers which management question?”
This comparison highlights the differences between planning, prioritization, simulation, and optimization—and explains when companies should switch from one method to the next level of decision-making.
Table of Contents
- An Overview of Planning, Ranking, Simulation, and Optimization
- CAPEX Planning vs. CAPEX Optimization
- Project Prioritization vs. Portfolio Optimization
- Project Ranking vs. Portfolio Selection
- Project Ranking vs. Optimization
- Scenario Planning vs. Portfolio Optimization
- Scenario Planning vs. Optimization
- PPM Software vs. Portfolio Optimization
- PPM vs. Decision Intelligence
- Excel vs. Mathematical Portfolio Optimization
- Excel Solver vs. Portfolio Optimization Software
- CAPEX Optimization vs. CAPEX Prioritization
- Portfolio Optimization vs. Project Ranking
- Optimization vs. Scenario Planning
- AI vs. Operations Research for Capital Allocation
- Linear Programming vs. Combinatorial Optimization
- Monte Carlo Simulation vs. Optimization
- Alternative to Excel for CAPEX Planning
- Alternative to Project Ranking
- Alternative to Weighted Scoring Models
- Alternative to Manual CAPEX Prioritization
- The Four Levels of Modern Investment Decisions
- Example: 100 Projects and €500 Million in CAPEX
- Why Combinations Are Crucial
- When Does Mathematical Optimization Become Relevant?
- Why the Methods Can Be Combined
- From Reporting to Real-Time Boardroom Decisions
- StratePlan as a mathematical decision layer
- Frequently Asked Questions
Planning, Ranking, Simulation, and Optimization at a Glance
| Method | Key Question | Typical Result |
|---|---|---|
| CAPEX Planning | What do we want to invest in, and what can we afford to invest in? | Investment Plan |
| Project Ranking | Which project is rated higher? | Ranking |
| Weighted Scoring | How do projects perform based on multiple criteria? | Score |
| Scenario Planning | What happens under different assumptions? | Scenarios |
| PPM | What projects do we have, and how are they progressing? | Portfolio Transparency |
| Monte Carlo Simulation | How might uncertain variables be distributed? | Probability Distributions |
| AI | What patterns, forecasts, or insights can be derived from data? | Analyses, Forecasts, Recommendations |
| Mathematical Optimization | Which feasible decision maximizes or minimizes our defined objective? | Optimized Solution |
These methods are not necessarily mutually exclusive.
They answer different questions within the same decision-making process.
Planning describes the decision space. Ranking evaluates options. Simulation examines uncertainty. Optimization calculates decisions within defined rules.
CAPEX Planning vs. CAPEX Optimization
CAPEX Planning organizes future investments.
Typical elements include:
- Investment proposals
- Budgets
- Business Units
- Locations
- Project durations
- Forecasts
- Approval Status
CAPEX Planning answers questions such as:
“What investments are planned for the next three years?”
CAPEX Optimization goes one step further.
It asks:
“Which combination of these investments should we actually select within our financial and operational constraints?”
| CAPEX Planning | CAPEX Optimization |
|---|---|
| Plans investments | Optimizes the selection |
| Creates budgets | Optimizes within the budget |
| Displays planned projects | Calculates project combinations |
| Forecasting | Decision Optimization |
| What are we planning? | Which combination should we choose? |
CAPEX Planning establishes the foundation for planning. CAPEX Optimization complements the mathematical decision-making process.
Project Prioritization vs. Portfolio Optimization
Project Prioritization evaluates projects and assigns priorities to them.
For example:
High Priority
Medium Priority
Low Priority
or:
Rank 1, Rank 2, Rank 3...
Portfolio optimization, on the other hand, considers the combination of projects.
That is a fundamental difference.
A high-priority project can consume so much of the budget that several other projects with a higher combined value can no longer be implemented.
Prioritization asks: “How important is this project?”
Portfolio optimization asks: “Which combination generates the highest defined total value within our constraints?”
Project Ranking vs. Portfolio Selection
Project Ranking produces a ranking.
Portfolio selection produces a selection.
These sound similar, but they are mathematically different.
Suppose a ranking looks like this:
- Project A
- Project B
- Project C
- Project D
A simple method would be to select projects from top to bottom until the budget is exhausted.
However, this does not automatically check whether a different combination of B, C, and D would generate more value overall.
Therefore, a ranking alone does not constitute a portfolio decision.
Project Ranking vs. Optimization
| Project Ranking | Optimization |
|---|---|
| Evaluates individual projects | Evaluates combinations |
| Generates a ranking | Generates a selection |
| Relative Priority | Portfolio value |
| Budget is often determined later | Budget directly in the model |
| Dependencies are more difficult to map | Dependencies as Constraints |
| Resources are often separate | Multiple resources can be modeled simultaneously |
Ranking can remain part of the decision-making process.
However, it should not be confused with mathematical portfolio optimization.
Scenario Planning vs. Portfolio Optimization
Scenario planning examines alternative assumptions.
For example:
- Base Case
- Growth Case
- Downside Case
- Reduced CAPEX
- Higher Energy Costs
- Resource Shortage
Scenario Planning answers:
“What could happen under these conditions?”
Portfolio Optimization answers:
“Which combination of projects should we choose under these conditions?”
The two methods therefore complement each other particularly well.
A new portfolio can be optimized for each scenario.
Scenario Planning changes the world. Portfolio Optimization calculates the decision within that world.
Scenario Planning vs. Optimization
Scenario Planning and Optimization serve different functions.
| Scenario Planning | Optimization |
|---|---|
| Changes assumptions | Calculates a solution |
| Examines possible situations | Finds the best permissible decision within a situation |
| What if? | What should we do? |
| Generates scenarios | Generates optimized decisions |
A company can therefore combine both methods:
Scenario 1 → Optimization 1
Scenario 2 → Optimization 2
Scenario 3 → Optimization 3
This not only compares scenarios with one another but also identifies the best portfolio decisions within each scenario.
PPM Software vs. Portfolio Optimization
Project Portfolio Management (PPM) software helps companies manage and control projects and portfolios.
Typical PPM features include:
- Project overviews
- Status tracking
- Resource planning
- Budgets
- Roadmaps
- Workflows
- Reporting
- Portfolio Dashboards
Portfolio optimization has a different focus.
It uses mathematical analysis to determine which projects should be selected within defined constraints.
PPM provides transparency across the portfolio. Portfolio Optimization calculates the portfolio decision.
Both approaches can be combined.
PPM provides project and portfolio data.
An optimization layer can use this data for mathematical decision models.
PPM vs. Decision Intelligence
PPM focuses on the management, planning, and control of project portfolios.
Decision Intelligence focuses on the quality and structure of decisions.
This may include:
- Data
- Business Rules
- Strategic criteria
- Constraints
- Scenarios
- Mathematical Models
- Optimization
- Decision Governance
The systems therefore answer different questions.
PPM: “What is in our portfolio, and how is it performing?”
Decision Intelligence: “What decision should we make given our defined goals and conditions?”
Decision Intelligence does not, therefore, need to replace PPM.
It can be used as an additional decision layer on top of or alongside existing planning and portfolio systems.
Excel vs. Mathematical Portfolio Optimization
Excel is one of the most flexible and widely used tools for CAPEX planning.
It is ideal for:
- Data collection
- Business cases
- Calculations
- Financial models
- Reporting
- Ad-hoc Analyses
The challenge arises when the decision-making problem becomes combinatorial.
With 100 independent yes-or-no projects, there are theoretically up to:
2^100 ≈ 1.27 × 10^30 possible combinations.
A manual Excel model cannot simply compare these portfolios row by row.
Mathematical Portfolio Optimization therefore uses specialized mathematical methods to search the decision space.
| Excel | Mathematical Portfolio Optimization |
|---|---|
| Flexible Spreadsheets | Mathematical Decision Model |
| Business Cases | Portfolio Selection |
| Manual Scenarios | Algorithmic Recalculation |
| Formulas | Objective Functions and Constraints |
| Highly flexible | Specialized for complex optimization problems |
Excel is therefore not inherently incompatible with mathematical optimization. Excel can even serve as the data source while a specialized optimization engine calculates the portfolio decision.
Excel Solver vs. Portfolio Optimization Software
Excel Solver enables mathematical optimization directly within Excel.
This can be very useful for smaller or manageable optimization models.
However, as portfolio and model complexity increases, additional requirements may arise:
- Many projects
- Many Constraints
- Dependencies
- Mandatory Projects
- Multi-Year Planning
- Multiple Resources
- Recurring Scenarios
- Governance
- Reproducibility
- Management Interfaces
Specialized portfolio optimization software is designed for such recurring decision-making processes.
The relevant comparison is therefore not:
“Can Excel Solver optimize?”
Yes, it can for suitable mathematical models.
The relevant question is:
“What technical and organizational environment do we need for our specific portfolio decision-making problem?”
CAPEX Optimization vs. CAPEX Prioritization
CAPEX Prioritization evaluates which investments appear to be more important.
CAPEX Optimization calculates which combination of investments should be selected under the defined conditions.
For example, ten projects may have very high priorities.
However, the budget may only be sufficient for five or six of them.
In that case, the information “high priority” is no longer sufficient.
A decision must be made as to which combination:
- fits within the budget,
- meets resource constraints,
- takes dependencies into account,
- includes mandatory projects,
- generates the highest defined total value.
Prioritization evaluates options. Optimization mathematically selects between combinations.
Portfolio Optimization vs. Project Ranking
Project Ranking primarily considers projects individually.
Portfolio Optimization considers their combined impact.
A simple example:
| Project | Investment | Value |
|---|---|---|
| A | 100 million € | €150 million |
| B | €60 million | 100 million € |
| C | €40 million | €80 million |
Budget:
100 million €
Project A has the highest individual value.
Portfolio A:
Investment = 100 million €
Value = €150 million
Portfolio B + C:
Investment = 100 million €
Value = €180 million
Thus, with the same budget, B + C generates 30 million euros more in expected value.
The best individual option is not automatically the best portfolio combination.
Optimization vs. Scenario Planning
Optimization and scenario planning should not be viewed as alternatives.
Combining them is particularly effective.
Scenario planning can, for example, define three budgets:
€400 million
€500 million
€600 million
Optimization then calculates a portfolio configuration for each budget.
| Scenario | Budget | Optimization Question |
|---|---|---|
| Downside | €400 million | Which combination yields the highest defined value under 400 million €? |
| Base | €500 million | Which combination yields the highest defined value among those under 500 million €? |
| Growth | €600 million | Which additional investments create the highest added value? |
This transforms scenario planning into a decision-oriented comparison.
AI vs. Operations Research for Capital Allocation
Artificial Intelligence and Operations Research have different strengths.
AI can be used, for example, for:
- Forecasting
- Pattern recognition
- Natural Language Processing
- Classification
- Anomaly Detection
- Predictive Analytics
Operations research, on the other hand, focuses specifically on mathematical decision-making problems.
These include:
- Optimization
- Resource Allocation
- Scheduling
- Portfolio Selection
- Network Optimization
- Constraint Satisfaction
When it comes to capital allocation, both disciplines can complement each other.
For example, AI can generate information or forecasts.
Operations research can then use this information within a mathematical decision-making model.
AI can help predict what might happen. Operations Research can calculate which decision should be made under defined assumptions.
Hybrid approaches can combine both levels.
Linear Programming vs. Combinatorial Optimization
Linear Programming optimizes a linear objective function under linear constraints with continuous decision variables.
Combinatorial Optimization deals with discrete decisions and combinations.
In project portfolios, the decision is often:
Select project: Yes or No.
This results in a discrete decision structure.
Integer programming and mixed-integer programming are therefore important mathematical methods for such problems.
| Linear Programming | Combinatorial Optimization |
|---|---|
| Often continuous variables | Discrete combinations |
| e.g., production quantities | e.g., project selection |
| Linear models | A broader class of discrete optimization problems |
| x can be 4.7, for example | x can be, for example, 0 or 1 |
In real-world business models, both types of variables can occur together.
Monte Carlo Simulation vs. Optimization
Monte Carlo simulation and mathematical optimization address different questions.
Monte Carlo simulation uses repeated random draws to examine the effects of uncertain input variables.
For example, it can show:
- the distribution of possible NPVs
- Probability of specific outcomes
- The risk of extreme scenarios
- Sensitivity to uncertainty
Optimization, on the other hand, seeks a decision that optimizes a defined objective function under given conditions.
| Monte Carlo Simulation | Optimization |
|---|---|
| Analyzes uncertainty | Analyzes Decisions |
| What could happen? | What should we do under the model? |
| Probability Distributions | Optimized decision |
| Simulation | Optimization |
These methods can also be combined.
Simulation can quantify uncertainty.
Optimization can support decision-making under these assumptions.
Alternative to Excel for CAPEX Planning
Companies often look for an alternative to Excel for CAPEX planning as their investment models become larger and more complex.
Typical reasons include:
- Many projects
- Many business units
- Multi-year planning
- Complex dependencies
- Resource constraints
- Versioning
- Governance
- Recurring scenarios
- Portfolio Optimization
However, the alternative does not necessarily have to completely replace Excel.
One possible architecture is:
Excel → Data Input
Optimization Engine → Mathematical Decision Layer
Management Interface → Scenario & Decision Layer
This allows existing Excel processes to continue to be used, while complex portfolio decisions are calculated outside the spreadsheet logic.
Alternative to Project Ranking
An alternative to project ranking is mathematical portfolio selection.
Instead of simply sorting projects, the system directly calculates the combination that should be selected under the defined conditions.
This is particularly relevant when:
- a limited budget,
- projects of varying sizes,
- multiple resources,
- dependencies,
- mandatory projects,
- strategic constraints.
Ranking produces an order. Portfolio optimization produces a combination.
Alternative to Weighted Scoring Models
Weighted scoring models evaluate projects based on multiple criteria.
For example:
| Criterion | Weight |
|---|---|
| Financial Return | 40% |
| Strategic Fit | 30% |
| Risk Reduction | 20% |
| Innovation | 10% |
The weighted criteria result in a project score.
This can be very useful for structuring qualitative and quantitative criteria.
However, the score does not automatically solve the portfolio selection problem.
Mathematical portfolio optimization can therefore use weighted scores as input.
For example:
Maximize the total weighted strategic value of the portfolio within a budget of 500 million euros.
This links scoring and optimization.
Weighted scoring evaluates projects. Optimization determines which combination of the evaluated projects should be selected.
Alternative to Manual CAPEX Prioritization
Manual CAPEX prioritization is often based on workshops, management discussions, spreadsheets, and individual assessments.
These processes can provide important qualitative insights.
However, as the portfolio size grows, so does the combinatorial complexity.
With 20 binary project decisions, there are theoretically already:
1,048,576 combinations.
For 100 projects:
≈ 1.27 × 10^30 combinations.
A mathematical decision layer can therefore complement management discussions.
Management defines:
- Objectives
- Criteria
- Budgets
- Constraints
- Mandatory Projects
- Strategic Priorities
The optimization calculates the consequences.
Management makes the decision.
The alternative to manual prioritization is not the elimination of human decision-making. It is the calculation of the decision space.
The Four Levels of Modern Investment Decisions
Many of the methods compared can be categorized into four levels of decision-making.
1. Data & Planning
What projects, costs, returns, and resources exist?
Typical tools:
- ERP
- PPM
- Excel
- Planning Software
2. Evaluation
How attractive are individual projects?
Typical methods:
- NPV
- ROI
- Scoring
- Ranking
3. Scenario & Risk Analysis
How do results change under different assumptions?
Typical Methods:
- Scenario Planning
- Sensitivity Analysis
- Monte Carlo Simulation
- Forecasting
4. Decision Optimization
What decision should be made given these assumptions and constraints?
Typical Methods:
- Operations Research
- Mathematical Optimization
- Combinatorial Optimization
- Mixed-Integer Programming
These four levels are not mutually exclusive; they build upon one another.
Example: 100 projects and €500 million in CAPEX
A company has 100 potential investment projects.
Requested CAPEX:
€800 million
Available CAPEX:
€500 million
The existing process might look like this:
Excel collects project information.
Weighted Scoring evaluates strategic fit.
Project Ranking sorts projects.
Scenario Planning examines different budgets.
PPM visualizes the portfolio.
All of these steps provide valuable information.
However, one question remains:
Which specific combination of the 100 projects should we select within the 500 million euro budget?
Mathematical Portfolio Optimization addresses precisely this level of decision-making.
Why Combinations Matter
With 100 independent yes/no projects, there are theoretically:
2^100 ≈ 1.27 × 10^30 combinations.
Budget, resource, and governance constraints reduce the permissible solution space.
Nevertheless, the number of possible portfolios can remain very large.
This explains why manual selection methods and simple rankings can reach their limits when dealing with large portfolios.
The portfolio problem is not a sorting problem. It is a combinatorial problem.
When does mathematical optimization become relevant?
Mathematical optimization becomes particularly relevant when several of the following conditions occur simultaneously:
- More projects than the available budget
- Projects of varying sizes
- Multiple limited resources
- Project Dependencies
- Mandatory projects
- Business unit rules
- Multi-Year Budgets
- Strategic Criteria
- Many Possible Project Combinations
- Frequent “what-if” questions
The more these factors interact with one another, the less sufficient a simple ranking becomes.
Why the Methods Can Be Combined
A modern investment decision process does not necessarily have to replace existing systems.
One possible architecture is:
ERP → Financial and actual data
PPM → Project and portfolio data
Excel → Custom analyses and data preparation
AI → Forecasts, data analysis, and intelligent support
Optimization → Mathematical portfolio decision-making
Boardroom → Final management decisions
This creates a clear division of roles.
Systems of Record provide data.
Analytics provide insights.
Optimization calculates options.
Management makes the decision.
From Reporting to Live Boardroom Decisions
The difference between reporting and decision intelligence becomes particularly apparent in the boardroom.
A dashboard can show:
“Our CAPEX is 500 million euros.”
A scenario planning tool can show:
“At 450 million euros, our financial plan changes.”
Portfolio optimization can also answer:
“Which combination of projects should we select at 450 million euros?”
This creates an interactive decision-making process.
The CFO asks:
“Reduce CAPEX by €50 million.”
The portfolio is recalculated.
The CEO asks:
“Increase the strategic weight of growth.”
The portfolio is recalculated.
The Investment Committee asks:
“Make Project 27 mandatory.”
The consequences are immediately visible at the portfolio level.
Question. Calculate. Compare. Decide.
StratePlan as a Mathematical Decision Layer
StratePlan is designed as a mathematical decision layer for complex CAPEX, investment, and project portfolio decisions.
The approach is not to replace ERP, PPM, Excel, or management processes across the board.
Instead, it complements the mathematical decision layer.
Data may include, for example:
- Project ID
- Investment
- Expected Revenue or NPV
- Strategic Criteria
- Resources
- Dependencies
- Mandatory Projects
- Multi-Year Budgets
Based on this, portfolio questions can be addressed:
- Which combination of projects maximizes the defined portfolio value?
- Which projects should be selected if CAPEX is lower?
- Which additional projects become feasible with a higher budget?
- How does the selection change under new resource constraints?
- What impact do mandatory projects have?
- How do project dependencies affect the selection?
- How does a different strategic weighting change the portfolio?
- Which combination maximizes the NPV?
- How does the optimal combination change over several years?
This creates a link between existing data, mathematical optimization, and management decisions.
Excel can calculate.
PPM can manage.
Scenario planning can explore.
AI can analyze.
Optimization can calculate the best feasible portfolio for the defined objective.
Management decides.
Don’t just rank projects. Calculate the portfolio.
Frequently Asked Questions
What is the difference between CAPEX Planning and CAPEX Optimization?
CAPEX Planning organizes and plans future investments and budgets. CAPEX Optimization calculates which combination of available investments best meets a specified target given defined budget, resource, and other constraints.
What is the difference between Project Prioritization and Portfolio Optimization?
Project Prioritization evaluates the relative importance of individual projects. Portfolio Optimization examines the combination of multiple projects, taking into account shared constraints such as budget, resources, and dependencies.
What is the difference between Project Ranking and Portfolio Selection?
Project Ranking generates an order of projects. Portfolio Selection determines which projects are actually included together in the portfolio.
What is the difference between project ranking and optimization?
Ranking primarily evaluates projects individually. Optimization seeks a portfolio configuration that best fulfills a defined objective function within the modeled constraints.
What is the difference between Scenario Planning and Portfolio Optimization?
Scenario Planning examines alternative assumptions or future situations. Portfolio Optimization calculates a combination of projects within the conditions of a given scenario.
What is the difference between Scenario Planning and Optimization?
Scenario Planning primarily answers “What if?” Optimization answers, within the mathematical model, “Which decision best fulfills our objective under these conditions?”
What is the difference between PPM software and portfolio optimization?
PPM software typically supports the planning, management, tracking, and reporting of project portfolios. Portfolio optimization focuses on the mathematical selection of a combination of projects based on defined objectives and constraints.
What is the difference between PPM and Decision Intelligence?
PPM manages and controls projects and portfolios. Decision Intelligence combines data, criteria, rules, scenarios, and quantitative methods to systematically support concrete decisions.
What is the difference between Excel and Mathematical Portfolio Optimization?
Excel is a flexible tool for data, calculations, and custom models. Mathematical Portfolio Optimization uses specialized mathematical methods to calculate complex project combinations under shared constraints.
What is the difference between Excel Solver and portfolio optimization software?
Excel Solver can solve optimization models within Excel. Specialized portfolio optimization software, however, can also be tailored to recurring portfolio decisions, extensive constraints, dependencies, scenarios, governance, and management processes.
What is the difference between CAPEX Optimization and CAPEX Prioritization?
CAPEX prioritization evaluates and ranks investments. CAPEX optimization determines which combination of investments should be selected given the defined constraints.
What is the difference between portfolio optimization and project ranking?
Project ranking provides an order of individual projects. Portfolio optimization considers combinations and their collective value within budget, resource, and other constraints.
What is the difference between optimization and scenario planning?
Scenario Planning varies assumptions. Optimization calculates a solution for a defined objective function within these assumptions. Both methods can therefore be combined.
What is the difference between AI and operations research for capital allocation?
AI encompasses methods such as machine learning, pattern recognition, and predictive analytics. Operations research uses mathematical models and optimization to solve decision-making problems. In capital allocation, AI-based forecasts can serve as input for an operations research model.
What is the difference between linear programming and combinatorial optimization?
Linear programming optimizes linear models with typically continuous variables. Combinatorial optimization deals with discrete decisions and combinations. Binary project selection is a typical combinatorial problem.
What is the difference between Monte Carlo simulation and optimization?
Monte Carlo simulation analyzes uncertainty through repeated random simulations. Optimization seeks a decision that optimizes a defined objective function under given conditions.
What is an alternative to Excel for CAPEX planning?
Depending on the problem, specialized CAPEX planning, PPM, or portfolio optimization systems can be used. Excel does not necessarily have to be completely replaced and can continue to serve as a data source or analysis tool.
What is an alternative to project ranking?
Mathematical portfolio optimization is an alternative when not just a ranking but a specific combination of projects is required, subject to budget, resource, and other constraints.
What is an alternative to weighted scoring models?
Weighted scoring can be combined with mathematical portfolio optimization. Scores evaluate projects, while optimization calculates which combination of the evaluated projects should be selected under the defined constraints.
What is an alternative to manual CAPEX prioritization?
A mathematical portfolio model can complement manual prioritization processes by calculating the impact of budgets, resources, dependencies, mandatory projects, and strategic criteria on possible project combinations.
Can portfolio optimization replace Excel?
Not necessarily. Excel can still be used for data collection, business cases, and individual analyses. Portfolio Optimization adds a specialized mathematical decision layer for complex selection decisions.
Can Portfolio Optimization replace a PPM system?
The systems serve different purposes. PPM supports the management and control of project portfolios. Portfolio Optimization focuses on mathematical selection and allocation decisions. Integrating both levels can therefore be beneficial.
Can AI replace Mathematical Optimization?
AI and Mathematical Optimization solve different classes of problems and can be combined. For example, AI can provide forecasts or identify patterns, while Mathematical Optimization uses this information within an explicit decision model.
When should a company switch from ranking to portfolio optimization?
Portfolio optimization becomes particularly relevant when limited budgets, varying project sizes, multiple resources, dependencies, mandatory projects, or other constraints mean that a simple ranking no longer adequately reflects the actual portfolio decision.
What is the most important difference between prioritization and optimization?
Prioritization determines which projects are rated higher. Optimization determines which combination of projects should be selected under the defined conditions.
