Optimal Decision™
Your strategically for ensuring relevant decisions.
Extending the clarity of judgment when human capabilities have reached their limits.
MANAGED RISK OF INCOHERENCE.
Pairwise comparison is more aligned with the way the brain naturally functions.
It is the simple yet precise evaluation method used by OptimalDecision™.
The level of inconsistency is significantly reduced, even when dealing with numerous, ambitious, and conflicting criteria.
OptimalDecision™ provides a coherence index and alerts the user when the coherence level is insufficient.
RESULTS AVAILABLE IMMEDIATELY.
The evaluation process of OptimalDecision™ is easy to understand, and assessments are immediate.
Multiple scenarios can be easily managed by adjusting certain values or adding new criteria.
Stored and secured data can be updated at any time to adapt to new conditions or anticipate potential changes.
Data can also be exported to other applications for more flexible result utilization.
COMMON LANGUAGE FOR ASSESSMENT
With OptimalDecision™, the decision-maker is guided step by step using commonly understood words that are more evocative and therefore more effective for precise evaluations.
This approach captures, with greater accuracy, what the right brain has synthesized.
For each word, OptimalDecision™ assigns a numerical value, which the algorithm uses to ensure unbiased judgment by the decision-maker.
BOOSTS YOUR CONFIDENCE CAPITAL
Decision-making errors undermine a manager’s self-confidence, creating feelings of incompetence or doubt.
This can lead to more hesitant decisions, increased stress, and a loss of confidence in taking initiative. Over time, these effects weaken performance, diminish perceived authority, and harm the manager’s credibility.
OptimalDecision is an essential tool that boosts your self-esteem and that of your colleagues, positioning you as an insightful decision-maker capable of making the best choices, even in the most complex situations.
CROSS-PLATFORM AND SECURE DATA.
OptimalDecision™ operates seamlessly on desktops, tablets, and smartphones.
Its simplicity and versatility earn it the title of the
“Swiss Army Knife of Decision-Making.”
Once you try it, you won’t want to part with it.
Data can be stored on either internal or external servers, carefully selected to provide the highest levels of reliability and data protection.
EASE AND SHARED DECISION-MAKING.
The decision-maker always retains control over the choice, but OptimalDecision™ ensures impartiality, avoiding conflicts and tensions that often lead to poor decision-making.
With OptimalDecision™, decisions are based on a mathematical framework that accommodates, in a weighted manner, a certain degree of subjective judgment while ensuring overall coherence across evaluations.
This approach prevents the isolation of individuals with differing viewpoints, fostering constructive discussions, bridging perspectives, and enriching the debate.
DECISIONS WITHOUT EMOTIONS OR PRESSURE.
The pairwise evaluation method limits the influence of emotions, which often become more pronounced when addressing an issue holistically.
This ensures that each evaluation remains neutral and objective, avoiding stalemates caused by entrenched positions.
OptimalDecision™ also enables collective evaluations, whether in person or through audio/video conferencing. In such cases, evaluations are conducted simultaneously and confidentially to eliminate any undue influence that could compromise the quality of the final decision.
This approach offers significant savings in both time and logistical costs.
CAPABLE OF MAKING DECISIONS EVEN WITH GAPS.
We don’t always have the necessary data for an informed decision, nor the time to search for missing information.
OptimalDecision™ relies on both quantitative data and qualitative, synthesized information. It stimulates the cognitive capabilities of the right brain, transforming qualitative assessments into numerical values that can be processed mathematically.
While OptimalDecision™ translates words into numbers, the decision-maker remains in control, as the algorithm is guided by their input.
OptimalDecision™ does not rely on artificial intelligence but leverages collective intelligence through a purely mathematical static algorithm.
Cases for which
Optimal Decision™ Truly Excels
BUSINESS STRATEGY
Problem: Prioritizing Strategic Objectives
Not all objectives hold the same weight within a strategy.
Failing to assign appropriate weights or incorrectly prioritizing them can lead to flawed decisions about which activities to pursue.
OptimalDecision™ helps you prioritize them, ensuring that your choices align more closely with the overall strategy.
Problem: Prioritizing Projects According to Strategic Objectives
Ideas often outnumber available resources. OptimalDecision™ works with you to assess each project’s contribution to weighted strategic objectives, establishing a coherent, objective, comprehensible, and widely accepted ranking.
SALES STRATEGY
Problem: Selecting the Most Effective Commercial Plan Among Multiple Scenarios
Balancing expenses, revenues, risks, and opportunities, OptimalDecision™ identifies the most relevant scenario to penetrate a new market or weaken a competitor.
This minimizes dispersion and avoids the resulting loss of operational efficiency.
Problem: Guiding Clients in Their Choices
Helping your clients decide among multiple options enhances your leadership and professionalism.
With just a tablet and within the meeting’s allotted time, OptimalDecision™ will impress your client with the precision of the analysis you provide, saving them time and boosting their confidence in the project.
They might even ask you for a copy of OptimalDecision™!
PRODUCT STRATEGY
Problem: Prioritizing Customer Needs
Product definition “wish-lists” are often lengthy and likely excessive. Fearing dissatisfaction, organizations attempt to address every need.
OptimalDecision™ enables impartial trade-offs, ensuring no significant needs are overlooked while minimizing the risk of customer dissatisfaction.
Problem: Prioritizing Features and Performance Levels
Features must address customer needs while also considering other factors, such as sales arguments, the number of variants, and specification classifications (“exciting” vs. “expected”).
OptimalDecision™ structures these priorities, offering a clear and organized vision so that each team focuses on aligned objectives with proportional engagement based on respective weights.
PRODUCT DEVELOPMENT
Problem: Selecting a Solution Among Multiple Technical or Technological Options
Complex situations often involve multiple criteria and require interdisciplinary expertise to identify the optimal choice.
OptimalDecision™ simplifies decision-making, whether individual or collective, while ensuring confidentiality.
In cases of disagreements, it helps find a compromise that maintains overall reasoning coherence, integrating extreme viewpoints without dismissing them.
Problem: Choosing the Best Supplier Sourcing Solution
Sourcing at the best overall cost—not merely the lowest price—is a complex challenge.
Often, critical data like component management costs are difficult to obtain. OptimalDecision™ is designed to work with qualitative data and can incorporate criteria such as potential price improvements, quality enhancements, lead times, and co-design.
Thanks to its ability to process both data (digital) and descriptive inputs (analog), nothing is beyond OptimalDecision™.
OPERATING STRATEGY
Problem: Defining the Best Configuration for a Production Line
Space, investment, operating costs, capacity… These are complex criteria that OptimalDecision™ evaluates with structured coherence.
Even in highly elaborate scenarios, OptimalDecision™ simplifies the complexity to deliver clearer and more insightful decisions.
Problem: Identifying the Best Production Site Across Multiple Countries
The criteria include currency exchange risks, geopolitical factors, labor costs, and workforce quality.
Using its “pairwise comparison” method, OptimalDecision™ enables efficient navigation of these decisions, even when some critical data is missing.
CUSTOMER CARE
Problem: Defining and Organizing a Call Center for After-Sales Service
Key clients, urgent situations, and service contracts are just a few of the many criteria to consider.
OptimalDecision™ helps you evaluate your options effectively to maintain alignment with your strategic positioning.
Problem: Evaluating Customer Experience
Based on feedback and surveys, OptimalDecision™ can process the information to identify areas for improvement or help you discover new services to offer.
TESTIMONIALS
Fulvio F.
We used it for the first time to develop our strategic plan. At first, it was a bit confusing to understand the rule of pairwise comparisons, but we quickly got the hang of it. The very high inconsistency rate during the initial evaluations eventually stabilized over time.
Didier P.
Very useful for product definitions. The sales team wanted numerous features, marketing disagreed, and operations had yet another perspective. This tool allowed us to make well-informed and thoroughly discussed decisions that were ultimately recognized as useful and accepted by everyone. I can’t imagine working without it now.
Ivano R.
During the production flow restructuring phase, having a readily accessible tool was invaluable for providing relevant answers to the various scenarios considered. Our products require heavy machinery for component manufacturing and assembly. We couldn’t afford any mistakes, as reversing decisions would have jeopardized the project’s objectives. OptimalDecision™ gave us greater confidence in our judgment capabilities.
Enrico V.
We successfully used OptimalDecision™ for a product portfolio cleanup. We didn’t all agree on what to keep or discard, but with unanimous agreement on the weighting of each criterion, decisions were made collectively. The algorithm proved to be incredibly powerful.
Michele D.
With four divisions that are vastly different from one another, developing a strategy and strategic action plans was greatly facilitated by OptimalDecision™. Its strength lies in its algorithm—easy to understand and unanimously recognized for the reliability of its results.
Shu N.
We used OptimalDecision™ during the product definition phase for a very specific pump, where not all the necessary information was available. This did not prevent us from progressing and ultimately arriving at product specifications that were highly focused on meeting the essential needs.
DISCOVER WHO WE ARE
Our Mission
“Providing simple and efficient tools, based on proven concepts, to facilitate quick and relevant decision-making, even in the most complex situations.”
OptimalDecision Team
Our journey towards Optimal Decision™
Consultants, not software developpers
“We are first and foremost consultants who, for over 25 years, have been supporting SMEs and large companies in Europe and around the world to improve their operational and strategic performance.
For demanding decision-makers
Decision-making is at the heart of managerial activity, as well as our consulting practice. When faced with multi-criteria choices that are often conflicting and demanding, ensuring the best decision is made becomes a significant source of stress, especially when the decision is strategic for the company.
Certainly, there are simplistic solutions whose evaluation scale proves to be insufficiently scientific or whose assessment conditions can be influenced by colleague votes.
A purposefully designed tool
It is from this somewhat unsatisfactory observation that OptimalDecision™ was born, the result of more than 5 years of development and continuous improvements in direct collaboration with our most demanding clients on this subject.
And this is just the beginning: many challenges and opportunities remain to be explored.
OptimalDecision™ will continue to evolve, with new features to come. That’s a promise!”
Our Beliefs Behind the Creation of Optimal Decision™
A high level of assurance
OptimalDecision™ is powered by the approach and algorithm developed by Dr. Thomas L. Saaty, a renowned mathematician widely recognized as a pioneer in the field of multicriteria decision-making.
Dr. Saaty worked on defense-related issues for the U.S. government and his work continues to be used in software tools and academic research, highlighting the enduring impact of his ideas.
His core principle can be summarized as: “Without a structure, complex decisions cannot be properly made.”
The Saaty algorithm, also known as the Analytic Hierarchy Process (AHP), was developed in the 1970s. It is designed to structure and solve complex decision-making problems, particularly those involving multiple and sometimes conflicting criteria.
The AHP algorithm consists of four key phases:
1. Pairwise Comparison
•The heart of AHP lies in pairwise comparative evaluations of different criteria or options.
•Each element is compared to another in terms of relative importance using an intensity scale (from 1: equal importance, to 9: extremely higher importance of one criterion over another).
2. Judgment Matrix
•The pairwise comparisons are organized into a square matrix, where each element represents the relative importance between two criteria or options.
3. Priority Calculation
•From the matrix, the algorithm calculates relative weights or priorities for each criterion, determining their overall importance in the decision-making process.
4. Consistency of Judgments
•AHP measures the consistency of evaluations provided by the decision-maker using a Consistency Index (CI). If judgments are too inconsistent, it prompts the decision-maker to revise the comparisons.
Key Innovation of AHP
AHP’s main innovation lies in its ability to translate qualitative judgments (often subjective) into quantitative results. This helps decision-makers prioritize effectively and select the best alternative. By bridging the world of words with mathematics, AHP enables a structured and logical decision-making process.
Optimal Decision™ : Our solution
Optimal Decision™: Unique Features Tailored for Diverse Situations
OptimalDecision™ stands out with its unique features, organized into modules designed to address a wide range of decision-making scenarios.
Its primary focus lies in strategic decision-making across all major business processes, where the impact of a well-informed decision is most critical.
For this reason, we prioritized adapting the algorithm to align with the HOSHIN KANRI process (strategic management) and, in particular, the “catch-ball” sessions that characterize it:
Integration of Dr. Akao’s Principles, the founder of the QFD method, to:
- Develop a comprehensive strategy and strategic action plan,
- Effectively manage project portfolios for new products,
- Define product specifications to maximize customer value.
Enhancements to the AHP Algorithm:
- OptimalDecision™ offers alternatives in cases of inconsistent evaluations,
- Facilitates collective decision-making while ensuring confidentiality of individual evaluations,
- Enables collaborative assessments via videoconferencing.
A True “Swiss Army Knife” for Managers:
- Accessible on any device,
- Adaptable to the complexity of any situation,
- Essential for making strategic decisions.
SOME REFERENCES FROM OUR CLIENTS
RESOURCES
Advantages of the AHP Algorithm Compared to Other Decision-Making Methods
Abstract: AHP stands out for its simplicity, flexibility, and consistency checks, making it an ideal tool for complex problems with multiple criteria. Although it may not suit every context (e.g., interdependent problems like those managed by ANP), it remains one of the most versatile and accessible methods for robust multi-criteria decision-making.
AHP (Analytic Hierarchy Process): Key Benefits Compared to Other Decision-Making Methods
1.Clear Hierarchical Structuring
•Advantage: AHP breaks down complex problems into a hierarchy of levels (overall objective, criteria, sub-criteria, alternatives), making it easier to understand relationships between elements.
•Comparison: Other methods like TOPSIS or PROMETHEE do not offer such explicit hierarchical structuring, which can complicate modeling for multidimensional problems.
2.Integration of Subjective Judgments
•Advantage: AHP uses pairwise comparisons, an intuitive approach for expressing subjective judgments. Decision-makers don’t need to directly assign weights, reducing evaluation errors.
•Comparison: Methods like SMART or SAW require precise weighting from the outset, which can be challenging for complex criteria.
3.Priority Calculation and Preference Quantification
•Advantage: AHP converts qualitative judgments into numerical priorities (relative weights), enabling rigorous and transparent evaluations. Priorities directly reflect the relative importance of criteria or alternatives.
•Comparison: Methods like ELECTRE or PROMETHEE rely more on relative rankings without producing precise weights for each criterion.
4.Consistency Verification
•Advantage: AHP uniquely measures judgment consistency through a Consistency Ratio (CR), ensuring that comparisons made by decision-makers are not contradictory.
•Comparison: Most other methods (e.g., TOPSIS, SMART) lack such a consistency check, potentially reducing the reliability of results.
5.Ease of Application for Hierarchical Problems
•Advantage: AHP is well-suited for problems involving hierarchical dependencies among criteria or alternatives, making it ideal for scenarios like project selection, resource allocation, or strategic decisions.
•Comparison: Methods like ANP or MAUT can also handle such dependencies but are often more complex to configure and explain.
6.Versatility Across Applications
•Advantage: AHP applies to diverse fields such as project management, investment selection, supplier evaluation, and risk assessment.
•Comparison: Methods like DEA or Monte Carlo Simulation are more specialized and may not suit all decision types.
7.Ease of Use with Software Tools
•Advantage: AHP benefits from various software solutions (e.g., Expert Choice, SuperDecisions, Excel) that simplify its application, even for non-expert users.
•Comparison: While tools exist for other methods (e.g., PROMETHEE, ELECTRE), AHP’s software ecosystem is generally richer and more user-friendly.
Summary of Benefits:
Purpose | AHP | Others Methods |
Hierarchical Structuring | Yes | Less Explicit (TOPSIS, SMART) |
Integration of Subjective Judgments | Intuitive Comparisons | Direct Weighting (SMART, SAW) |
Quantitative Priorities | Yes | Ordering only (PROMETHEE) |
Consistency checking | Yes (Consistency Ratio) | Rarely Available |
Adaptability | For hierarchical problems | Less suited for hierarchy |
Wide applicability | Versatile | Often specialized |
Software support | Abundant and user-friendly | Variable |
Human Memory Capacity (Miller’s Law)
Abstract:
Miller’s Law highlights a crucial limitation of human working memory: our brains are designed to process only a limited number of elements at a time. Whether in information design, decision-making, or learning, this simple rule has significant practical implications. By structuring information and respecting these cognitive constraints, we can reduce mental overload, improve retention, and foster better decision-making—a valuable lesson in a world of ever-growing information.
Miller’s Law: Why Our Working Memory Has Its Limits
In 1956, American psychologist George A. Miller published a groundbreaking article titled “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information.”
This article shed light on a fundamental characteristic of human working memory: its limited capacity to simultaneously manage a small number of information elements.
Known as Miller’s Law, this observation remains a cornerstone of cognitive psychology and has practical applications across numerous fields.
Working Memory by the Numbers
Working memory is the brain’s temporary capacity to store and manipulate information to perform cognitive tasks like problem-solving or decision-making.
According to Miller, this capacity is limited to approximately 7 ± 2 units of information, or between 5 and 9 distinct items for most individuals.
These “units” can take various forms—numbers, letters, words, concepts, or even visual objects. For example, when trying to remember a phone number (like 123-456-7890), working memory processes each group of digits as a distinct unit.
The Role of “Chunking”
One of the key aspects of Miller’s Law is the concept of “chunking,” or grouping. This mechanism allows the brain to combine multiple pieces of information into a single meaningful unit, increasing the amount of information that can be processed.
For instance:
•Retaining “1, 2, 3, 4, 5, 6, 7” as a list of 7 digits can be challenging.
•But grouping these digits into “123,” “456,” and “7” reduces the cognitive load to 3 chunks, making memorization easier.
Chunking is particularly useful in contexts where information must be quickly assimilated, such as memorizing lists, reading, or learning new skills.
Practical Implications of Miller’s Law
Understanding this cognitive limitation has practical applications in various domains:
1. Information Design
•Conciseness: When presenting choices or instructions, limiting options or categories to 7 or fewer prevents user overload.
•Structuring: Organizing information into logical groups or hierarchies aids comprehension. For example, a navigation menu with clear subcategories enhances the user experience.
2. Decision-Making
.In environments where decisions rely on numerous criteria or options, information overload can lead to errors or inconsistencies.
.Reducing criteria to 5-7 maximum ensures greater consistency in decision-making processes.
3. Learning and Teaching
.Teachers and trainers can optimize retention by structuring content into small, digestible chunks and ensuring learners are not overwhelmed with too much information at once.
Limitations and Criticisms of Miller’s Law
While widely accepted, Miller’s Law is not without its critiques. Subsequent research has shown that working memory capacity can be lower than 7 elements in some cases:
•Information Complexity: When elements are complex or abstract, working memory capacity decreases.
•Individual Context: Experience, familiarity with the content, and memorization strategies greatly influence assimilation capacity.
Furthermore, the number “7” should not be interpreted rigidly but rather as a general estimate of human cognitive limits.
Recognize Your Limits to Make Better Decisions
Abstract:
This article demonstrates that our cognitive limitations quickly become obstacles when faced with complex problems. As the number of criteria, options, or evaluation levels increases, we risk significant inconsistencies. In such cases, it becomes essential to rely on methodological or computational tools to structure and verify our decisions, ensuring their relevance and reliability.
Making a Good Decision: Accepting Cognitive Limits
Making sound decisions begins with acknowledging that our capacity to evaluate options is constrained by our cognitive abilities. Although this limit varies from person to person, it exists for everyone. Exceeding it risks compromising the quality of our choices.
A Concrete Example: Buying a Car
When purchasing a car, you compare several models based on specific criteria, such as:
1.Price
2.Energy consumption
3.Cargo capacity
To evaluate these criteria pairwise (comparing each against the others), you need to perform 3 evaluations. This remains manageable.
Adding New Criteria
Suppose you add two more criteria:
4.Engine type
5.Comfort
The number of required evaluations then increases to 10. If you further expand to 10 criteria, this number jumps to 45 evaluations.
Human Limits in Evaluation
Research shows that humans can maintain good consistency in their judgments when the number of criteria remains between 4 and 7. This corresponds to 6 to 21 comparisons.
However, when the number exceeds 7 criteria (21 comparisons), inconsistencies often arise, making the decision less reliable.
What Happens When We Add Evaluation Levels?
Now, consider the same 5 criteria but introduce 3 evaluation levels (e.g., moderate, high, very high).
•The total number of combinations becomes 243.
If we increase to 5 levels (e.g., moderate, high, very high, extremely high, excessively high), this number skyrockets to 3,125 combinations.
Comparing Multiple Vehicles
Now imagine you want to evaluate 3 different vehicles considering these criteria and evaluation levels:
•The total number of combinations to analyze rises to nearly 9,400.
This requires a fine-grained analysis and perfect consistency throughout, which far exceeds the cognitive abilities of even the most capable individuals.
The AHP Algorithm in Detail
The AHP Algorithm (Analytic Hierarchy Process): How Does It Work?
The Analytic Hierarchy Process (AHP), developed by Thomas Saaty, is a multicriteria decision-making method.
It enables the structuring of complex problems into a clear hierarchy and facilitates the systematic comparison of different options.
Key Steps of AHP
1. Define the Problem and Establish a Hierarchy
The problem is broken down into multiple hierarchical levels:
•Overall Goal: What you want to achieve.
•Criteria: The dimensions to be evaluated to achieve the goal.
•Sub-Criteria and Alternatives: The options or solutions to compare.
Example:
To choose a car, the hierarchy might look like this:
•Goal: Select the best car.
•Criteria: Price, energy consumption, comfort, cargo capacity, engine type.
•Alternatives: Model A, Model B, Model C.
2. Pairwise Comparisons
Each criterion (and sometimes sub-criteria) is compared in pairs to determine their relative importance in relation to the overall goal.
•Decisions are made using a scale from 1 to 9 (or their reciprocals):
•1: Equal importance.
•3: Slight importance.
•5: Strong importance.
•7: Very strong importance.
•9: Absolute importance.
Example of a Comparison:
If price is considered more important than comfort, a score of 5 could be assigned to the Price vs. Comfort comparison.
This process creates a pairwise comparison matrix, like the following for three criteria:
3. Calculating Relative Weights (Priorities)
Once the matrix is completed:
•Column Normalization: Each element in a column is divided by the sum of the elements in that column.
•Row Averages: The average of the normalized values in each row is calculated. These averages represent the relative weights or priorities of the criteria.
Example:
If the calculated relative weights for the criteria are:
•Price: 50%
•Comfort: 30%
•Energy Consumption: 20%
Price is 1.67 times more important than comfort. In other words, for every unit of importance given to comfort, 1.67 units are given to price.
4. Comparing Alternatives
The alternatives (e.g., different car models) are also compared in pairs for each criterion to determine their relative contribution to that criterion.
Example for the Price Criterion:
•Model A vs. Model B: A is moderately better → Score 3.
•Model A vs. Model C: C is significantly cheaper → Score 1/5.
This process is repeated for each criterion.
5. Aggregating Results
The priorities of the alternatives for each criterion are multiplied by the relative weights of the criteria and then summed. This gives an overall score for each alternative.
Example:
If Price accounts for 50% of the weight and Model A scores 0.8 on Price, the contribution to the final score is:
0.8 × 50% = 0.4
6. Consistency Check
AHP measures the consistency of judgments using the Consistency Ratio (CR):
1.Consistency Index (CI): This is calculated based on the initial matrix and the relative weights.
2.Consistency Ratio (CR): The CI is compared to a reference value (the Random Index, RI) for the same number of criteria.
•If CR < 0.1, the judgments are considered acceptable.
•If CR > 0.1, the comparisons should be reviewed to reduce inconsistencies.
Summary of the Process
1.Break down the problem into a hierarchy of criteria and alternatives.
2.Pairwise compare the elements to determine their relative importance.
3.Calculate priorities and overall scores for each alternative.
4.Verify the consistency of judgments.
AHP provides a transparent and reproducible method for structuring decision-making, while effectively addressing the inherent subjectivity of human evaluations.
Benefits of the AHP Algorithm
Abstract:
The primary benefits of the Analytic Hierarchy Process (AHP) lie in its ability to structure, clarify, and quantify complex decisions while offering unparalleled transparency and robustness. Whether for strategic or operational decisions, AHP helps individuals and organizations make informed and consistent choices.
Benefits of the AHP Method
The Analytic Hierarchy Process (AHP) is a powerful tool for structuring and streamlining decision-making in complex situations. Here are the key advantages it offers:
1. Clear Structuring of Complex Problems
•Hierarchical organization: AHP breaks down a complex problem into a structured set of goals, criteria, sub-criteria, and alternatives.
•Comprehensive view: The method provides an overview that helps better understand the priorities and interactions among the various elements.
2. Incorporation of Subjective Judgments
•Flexible evaluation: AHP integrates the subjective judgments of decision-makers by comparing criteria and alternatives in pairs.
•Intuitive scale: The 1-to-9 scale used to assess preferences is easy to understand and apply.
•Multiple perspectives: For collective decisions, AHP aggregates the opinions of participants, leading to a consensus-based outcome.
3. Calculation of Objective Priorities
•Quantification of preferences: The method converts qualitative judgments (e.g., “important” or “very important”) into quantitative weights, enabling objective comparisons of options.
•Reliable results: The calculated priorities accurately reflect expressed preferences, ensuring transparency and traceability in the final decision.
4. Effective Multicriteria Decision-Making
•Adaptability to complexity: AHP excels in scenarios with competing criteria, each carrying varying levels of importance.
•Balancing priorities: The weights assigned to criteria ensure decisions are harmonized according to true priorities.
5. Identification and Correction of Inconsistencies
•Consistency measurement: AHP calculates a Consistency Ratio (CR) to ensure decision-makers’ judgments are coherent.
•Bias correction: In cases of inconsistency (CR > 0.1), the method helps identify and adjust problematic judgments, enhancing result reliability.
6. Transparent Comparison of Alternatives
•Ranked results: AHP provides global scores for each alternative, making comparison and selection of the best option straightforward.
•Clarity in decision-making: The method offers a clear rationale for decisions, which is especially valuable in professional or collaborative environments.
7. Broad Applicability
•Versatility: AHP is used across various fields, including project management, resource allocation, urban planning, healthcare, supplier selection, and more.
•Adaptability: The method is suitable for simple or complex problems, accommodating a variable number of criteria and alternatives.
8. Integration with Technological Tools
•Available software: Many tools (e.g., Excel, Expert Choice, SuperDecisions) automate calculations and simplify the application of AHP.
•Support for large-scale decisions: These tools enable the handling of numerous criteria and alternatives without compromising consistency.
The Triple Penalty of a Poor Decision
Abstract:
A poor decision doesn’t end with a simple unsatisfactory outcome. It can lead to a triple penalty: immediate impacts, future biases, and a loss of trust. The key to limiting these consequences lies in a methodical and proactive approach to decision-making, combined with the ability to quickly identify and correct mistakes. This way, failure can be transformed into learning, paving the way for better-informed and more robust decisions in the future.
The Triple Penalty of a Poor Decision: Three Major Consequences Not to Be Underestimated
Making a bad decision goes beyond a one-time failure.
Its repercussions can be profound and multifaceted, affecting not only immediate outcomes but also future processes and organizational or personal dynamics.
1. First Penalty: Immediate Consequences
A poor decision often results in immediate repercussions on the intended objectives. These effects manifest in various ways depending on the context:
•Financial loss: Misallocation of resources, poor investment choices, or inappropriate business strategies can lead to significant losses.
•Operational impact: In an organizational setting, a poor decision can slow down processes, reduce efficiency, or disrupt production.
•Dissatisfaction: When decisions affect customers or stakeholders, discontent can erode trust or lead to lost opportunities.
Example: A company opting for a cheaper but unreliable supplier risks supply chain delays, potentially disrupting overall operations.
2. Second Penalty: Compromised Future Decisions
A poor decision, especially if not quickly identified or corrected, can undermine future choices because an initial error can lead to:
•Systemic bias: Future decisions may be based on faulty assumptions stemming from the initial poor choice. For example, investing further to “save” a failing project instead of abandoning it illustrates the “sunk cost” effect.
•Loss of direction: Decision-makers may doubt their own judgment or adopt overly cautious strategies to avoid repeating the mistake.
•Domino effect: An initial flawed decision can trigger a series of other biased choices, amplifying negative consequences.
Example: In a poorly defined project, all subsequent adjustments risk being ineffective because they are based on an already shaky foundation.
3. Third Penalty: Erosion of Trust
Trust—whether personal, organizational, or collective—is a cornerstone of effective decision-making.
A poor decision can lead to a loss of trust:
•In oneself: Decision-makers may feel paralyzed by failure, affecting their confidence in making future decisions.
•Within the team or organization: Team members may lose faith in their leaders, impacting engagement, collaboration, and motivation.
•From external stakeholders: A perceived major error can tarnish the organization’s reputation among customers, investors, or partners, sometimes irreversibly.
Example: A failed business strategy can not only cause financial losses but also make shareholders doubt the leadership’s ability to manage the company effectively.
How to Avoid the Triple Penalty?
To minimize these consequences, it is essential to implement mechanisms for more informed decision-making and effective error management:
•Structure the decision-making process: Use tools like AHP or multicriteria approaches to better evaluate options.
•Quickly identify errors: Establish monitoring and feedback mechanisms to detect problematic decisions before they lead to cascading effects.
•Learn from mistakes: Foster a culture where failures are constructively analyzed to extract valuable lessons.
•Leverage collective intelligence: Incorporate perspectives and skills from multiple stakeholders to reduce individual biases.
•Manage communication: When an error is identified, adopt clear and transparent communication to maintain trust among affected parties.
Overview of Different Decision-Making Methods
Abstract:
This is a quick overview of the main algorithms and decision-support methods used across various contexts, each with its specific features and areas of application.
Overview of Different Decision-Making Methods
1. AHP (Analytic Hierarchy Process)
•Use: Hierarchical multicriteria comparison.
•Principle: Breaks a complex problem into a hierarchy (goal, criteria, sub-criteria, alternatives).
•Advantage: Aggregates subjective and quantitative judgments into a coherent decision.
•Limitation: Can become inconsistent with a large number of criteria.
2. ANP (Analytic Network Process)
•Use: Extension of AHP for problems with interdependencies between criteria.
•Principle: Relationships are not only hierarchical but can also be reciprocal or circular.
•Advantage: Handles complex interrelationships between criteria.
•Limitation: More difficult to implement than AHP.
3. TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
•Use: Evaluating alternatives based on their proximity to an ideal solution.
•Principle: Ranks options based on closeness to an ideal solution and distance from a negative solution.
•Advantage: Easy to understand and apply.
•Limitation: Sensitive to data normalization.
4. PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation)
•Use: Ranking alternatives based on partial preferences.
•Principle: Compares alternatives pairwise to establish a ranking.
•Advantage: Easily incorporates decision-makers’ preferences.
•Limitation: Requires well-defined data for each criterion.
5. ELECTRE (Elimination and Choice Expressing Reality)
•Use: Assists in selection or ranking among alternatives.
•Principle: Uses outranking relationships to eliminate less relevant options.
•Advantage: Useful in contexts with conflicting criteria.
•Limitation: Can be complex to configure.
6. SMART (Simple Multi-Attribute Rating Technique)
•Use: Simple evaluation of weighted criteria.
•Principle: Assigns scores to alternatives and calculates a weighted overall value.
•Advantage: Easy to use and understand.
•Limitation: Oversimplified for complex problems.
7. VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje)
•Use: Identifying a compromise solution in multicriteria problems.
•Principle: Evaluates alternatives based on their proximity to an ideal solution.
•Advantage: Well-suited for decisions requiring compromise.
•Limitation: Less intuitive for novice users.
8. MAUT (Multi-Attribute Utility Theory)
•Use: Modeling preferences in multicriteria contexts.
•Principle: Calculates an overall utility based on decision-makers’ preferences.
•Advantage: Theoretically rigorous.
•Limitation: Requires precise definition of utility functions.
9. SAW (Simple Additive Weighting)
•Use: Weighted evaluation of alternatives.
•Principle: Adds normalized values of weighted criteria.
•Advantage: Simple and fast.
•Limitation: Does not handle interdependencies between criteria.
10. DEA (Data Envelopment Analysis)
•Use: Evaluates the relative efficiency of entities (production units, projects, etc.).
•Principle: Compares inputs (resources) and outputs (results).
•Advantage: Useful for efficiency or performance evaluations.
•Limitation: Designed for specific efficiency-related problems.
11. Monte Carlo Simulation
•Use: Decision-making under uncertainty.
•Principle: Simulates scenarios using probability distributions.
•Advantage: Highly effective for uncertainty-sensitive analyses.
•Limitation: Requires precise probability data.
12. Weighted Sum Model (WSM)
•Use: Simple evaluation with fixed weights.
•Principle: Each criterion is multiplied by a weight, and the results are summed.
•Advantage: Easy to implement.
•Limitation: Sensitive to weighting.
Choosing the Method
The choice of algorithm depends on the problem to be solved:
•For hierarchical problems, prioritize AHP/ANP.
•For decisions requiring compromise, consider PROMETHEE, ELECTRE, or VIKOR.
•For simple decisions, use SMART, SAW, or TOPSIS.
•In cases of uncertainty, Monte Carlo Simulation may be the best option.
Each method has its strengths and limitations, but all share the same objective: to structure and rationalize decision-making.
What is the Hoshin Kanri Method?
Abstract:
Hoshin Kanri is a powerful method for transforming strategic vision into concrete actions, aligning the entire organization around common objectives. By practicing this approach with rigor and collaboration, companies can achieve their strategic goals while enhancing their ability to adapt to change.
What is Hoshin Kanri?
Hoshin Kanri (literally “management by compass” or “strategic deployment”) is a Japanese strategic management method that aligns an organization’s long-term objectives with its operational actions.
Developed in Japan in the 1960s, it is widely used by companies like Toyota.
The primary goal of Hoshin Kanri is to ensure that all organizational levels work in the same direction, combining strategic planning with effective execution.
Key Principles of Hoshin Kanri
1.Long-term vision: Define clear strategic objectives, often over 3 to 5 years.
2.Alignment of objectives: Translate the strategy into annual goals and cascade them across all organizational levels.
3.Rigorous tracking: Regularly measure progress and adjust as needed.
4.Collective engagement: Involve all teams in planning and execution to ensure buy-in and effectiveness.
How to Practice Hoshin Kanri?
1. Define the Strategic Vision
•Goal: Identify long-term strategic objectives (3-5 years) based on the organization’s mission and vision.
•Tools: SWOT analysis, market analysis, and stakeholder consultations.
Example: “Become a leader in the electric vehicle sector within 5 years.”
2. Set Annual Objectives (Breakthrough Objectives)
•Goal: Break down long-term objectives into clear, measurable annual priorities.
Example: “Reduce battery production costs by 10% this year.”
3. Cascade Objectives
•Goal: Translate annual objectives into specific actions for each organizational level. This step is called Catchball, a collaborative exchange process across hierarchical levels.
•Approach:
•Objectives are discussed and adjusted between leaders and operational teams to ensure feasibility.
•Each level defines its own contributions to the overall goal.
Example:
•Leadership: Reduce battery costs by 10%.
•R&D Team: Optimize battery cell design.
•Production Team: Minimize material losses during manufacturing.
4. Implement Action Plans
•Goal: Turn objectives into concrete actions, with assigned responsibilities and deadlines.
•Tools: Roadmaps, Gantt charts, PDCA (Plan-Do-Check-Act).
Example: Plan a project to reduce production losses at each stage.
5. Monitor and Adjust (PDCA)
•Goal: Regularly track progress, detect deviations, and adjust actions as needed.
•Key Practices:
•Monthly or quarterly meetings: To review progress.
•Key Performance Indicators (KPIs): Track results against objectives.
•Corrective actions: Quickly identify causes of deviations and adjust plans.
6. Annual Review (Hansei)
•Goal: Analyze annual results, identify lessons learned, and integrate them into future planning.
•Practices:
•Reflect collectively on successes and failures.
•Adjust objectives for the following year.
Key Tools in Hoshin Kanri
1.X-Matrix:
•A visual tool to connect strategic goals, annual priorities, indicators, and actions at all levels.
2.Catchball:
•A process for exchanging ideas across hierarchical levels to align objectives.
3.KPIs and Dashboards:
•Monitor progress and make informed decisions.
Benefits of Hoshin Kanri
1.Strategic Alignment: All levels work together to achieve common objectives.
2.Effective Execution: Detailed planning minimizes gaps between strategy and implementation.
3.Collective Engagement: Participation from all levels strengthens motivation and buy-in.
4.Continuous Improvement: Rigorous tracking and the PDCA cycle ensure constant optimization.
What is the QFD Approach?
Abstract:
MODERN QFD and BLITZ QFD are evolutions of the QFD methodology tailored to meet the needs of modern organizations. MODERN QFD enhances modularity and efficiency for structured projects, while BLITZ QFD focuses on rapid decision-making by prioritizing essential elements. These methods ensure greater agility without compromising customer satisfaction.
MODERN QFD and BLITZ QFD: What Are They and How to Practice Them?
Quality Function Deployment (QFD) is a structured methodology designed to translate customer needs and expectations (the “voice of the customer”) into clear technical specifications for product or service development.
MODERN QFD and BLITZ QFD are variations developed to make QFD faster and more suitable for modern, dynamic environments.
MODERN QFD
MODERN QFD (MODular and Efficient Response to Needs QFD) is an evolution of traditional QFD, designed to address the limitations of the classic model, often perceived as cumbersome and complex. MODERN QFD emphasizes simplification and efficiency while maintaining a high level of rigor.
Key Principles of MODERN QFD:
1.Modularity:
The process is divided into specific modules, allowing focus only on relevant parts for a given project.
2.Prioritization:
Customer needs and technical solutions are prioritized upfront to avoid wasting time on non-essential elements.
3.Effective Visualization:
Traditional matrices (House of Quality) are simplified for greater clarity and faster decision-making.
4.Digital Integration:
Use of digital tools to automate calculations and visualize results.
How to Practice MODERN QFD?
1.Collect Customer Needs:
Identify customer expectations through surveys, interviews, or market analyses.
2.Modularization:
Break down needs into specific modules (e.g., design, performance, cost).
3.Build the Main Matrix:
Create a simplified matrix linking customer needs to technical specifications.
4.Analyze and Prioritize:
Assess the importance of each need based on its contribution to overall customer satisfaction.
5.Monitor and Adjust:
Implement rapid iterations to refine technical specifications.
BLITZ QFD
BLITZ QFD is a simplified, fast version of the QFD methodology. It was developed for projects requiring short cycles (e.g., in an agile environment). BLITZ QFD focuses on the most critical aspects of a product or service, reducing traditional QFD matrices to their essential elements.
Key Principles of BLITZ QFD:
1.Simplicity:
Retain only essential information for quick decision-making.
2.Iterative Approach:
Progress in small steps with regular reviews.
3.Focus on Priorities:
Limit to customer needs and specifications with the greatest impact.
4.Rapid Execution:
Significantly reduce the time required to complete the process.
How to Practice BLITZ QFD?
1.Quickly Identify Critical Needs:
Identify only the top 5–10 key customer needs.
2.Build a Mini-Matrix:
Link these critical needs to a reduced number of technical specifications.
3.Analyze and Decide:
Prioritize actions on the most important needs based on feasibility and impact.
4.Execute Quickly:
Immediately translate results into concrete actions, with regular reviews.
Comparison Between MODERN QFD, BLITZ QFD, and Traditional QFD
Aspect | Traditional QFD | MODERN QFD | BLITZ QFD |
Complexity | Highly detailed, sometimes cumbersome | Simplified, modular | Very lightweight, focused |
Time Requirement | Long | Medium | Short |
Approach | Comprehensive | Flexible | Minimalist |
Target Audience | Complex projects | Modular projects | Fast/agile projects |
Visualization | Large matrices | Simplified matrices | Critical mini-matrices |
Practical Applications: When to Use Which Method?
•Traditional QFD:
Suitable for complex projects requiring a comprehensive analysis of all aspects of a product (e.g., design of new industrial or aerospace systems).
•MODERN QFD:
Ideal for projects needing in-depth analysis with modularity or efficiency constraints (e.g., development of modular products or complex software systems).
•BLITZ QFD:
Best for projects requiring rapid decisions or operating in agile environments (e.g., startups or short-cycle development projects).
International Standards Related to QFD
1. ISO 16355 – Application of QFD
•Full Title: Application of statistical and related methods to new technology and product development process.
•Publication: This series of standards was published by ISO (International Organization for Standardization).
•Objective: It provides detailed guidelines for applying the QFD method in product and technology development processes.
•Key Points Covered:
•Translating customer needs (Voice of Customer) into technical specifications.
•Deploying and prioritizing requirements.
•Integration with other tools, such as Pareto diagrams, correlation matrices, and multicriteria analyses.
2. ISO 9001 – Quality Management Systems
.Although QFD is not explicitly mentioned, this standard encourages the use of tools to understand stakeholder needs and translate them into operational requirements, which is a core process of QFD.
3. ISO/TS 16949 – Quality Management in the Automotive Industry
.This industry-specific standard recommends using tools like QFD to ensure that products meet customer expectations.
Which cognitive biases can influence our decision-making.
Abstract:
Cognitive biases are systematic distortions that influence our perceptions, judgments, and decisions. They can arise from overconfidence, emotions, available information, or social influence. Common biases include confirmation bias, anchoring effect, and loss aversion bias. These biases can lead to judgment errors, but by identifying them and adopting a critical approach, their impact can be minimized, enabling more informed decision-making.
Which cognitive biases can influence our decision-making.
The algorithm used by OptimalDecision helps minimize the impact of cognitive biases and, in all cases, flags inconsistencies through the non-coherence index, indicating that biases have affected the evaluations.
Key Types of Cognitive Biases
Cognitive biases are systematic distortions in how individuals perceive, analyze, and make decisions. Below are the main categories and examples of common biases:
1. Biases Related to Available Information
These biases occur when the access to or exposure to certain information influences decisions.
•Availability bias: Giving more importance to easily accessible or memorable information.
•Representativeness bias: Categorizing situations based on stereotypes or similar examples.
•Anchoring bias: Relying excessively on the first piece of information received (the anchor) when making decisions.
2. Memory-Related Biases
These involve distortions in recalling or interpreting memories.
•Recency effect: Giving more weight to the most recent information.
•Primacy effect: Remembering the first information received more strongly.
•False memories: Creating inaccurate or distorted memories influenced by external factors.
3. Emotion and Affect-Related Biases
These biases are driven by emotions or personal attachment.
•Confirmation bias: Favoring information that supports existing beliefs while ignoring contradictory evidence.
•Optimism bias: Overestimating chances of success or underestimating risks.
•Status quo bias: Preferring the current situation due to fear of change.
4. Social Biases
These relate to how we interact with or perceive others.
•Halo effect: Judging a person or situation based on a single positive trait.
•Horn effect: Judging negatively based on a single negative trait.
•Authority bias: Giving more weight to opinions from authority figures, sometimes over personal reasoning.
5. Decision-Making Biases
These directly affect judgments and choices.
•Loss aversion bias: Placing greater emphasis on potential losses than equivalent gains.
•Overconfidence bias: Overestimating one’s abilities or knowledge.
•Framing effect: Decisions vary depending on how a situation is presented (as a gain or loss).
6. Biases Related to Adaptation and Group Dynamics
These biases are shaped by the environment or the behavior of others.
•Conformity bias: Adopting group opinions or behaviors to fit in.
•False consensus bias: Believing one’s opinions or behaviors are more widespread than they actually are.
•Alignment bias: Focusing on what aligns with collective expectations or goals while ignoring other factors.
7. Biases in Processing Uncertainty
These biases emerge when decisions are made in ambiguous contexts.
•Availability heuristic bias: Relying on recent or striking examples to evaluate probability.
•Ambiguity bias: Avoiding options with uncertain information, even if they are better.
•Polarization effect: Discussions or deep reflection reinforcing initial positions, regardless of justification.
The Role of OptimalDecision’s Algorithm
The algorithm employed by OptimalDecision minimizes the impact of cognitive biases. It also uses a non-coherence index to flag when biases have rendered evaluations inconsistent, ensuring more reliable decision-making.
Conclusion
Cognitive biases are numerous and influence decisions across various contexts. By being aware of their existence and impact, individuals can reduce their influence, improving the quality of judgments and decisions. The OptimalDecision algorithm offers an effective tool for mitigating cognitive biases and ensuring greater consistency in evaluations.
Which behavioral biases can influence our decision-making.
Abstract:
Behavioral biases shape our actions and decisions, often without us realizing it. By identifying them and adopting strategies to mitigate their effects, we can improve our quality of life and interactions with others.
Behavioral Biases: Understanding Our Decisions and Actions
Behavioral biases are systematic gaps between what we know or want and what we actually do. These biases influence our decisions and actions, often unconsciously, and are shaped by cognitive biases, emotions, or even social and environmental pressures. But what are these biases, and how can we recognize them to better overcome them?
What is a Behavioral Bias?
A behavioral bias reflects a discrepancy between intention and behavior. It can result from mental mechanisms (such as cognitive shortcuts) or external influences (such as social norms). These biases affect how we act, choose, or even delay certain decisions.
Key Behavioral Biases to Know
1. Status Quo Bias
We tend to stick to the current state of things, even when change would be beneficial. For example, not switching energy providers despite better deals being available.
2. Loss Aversion
We fear losses more than we value gains. For example, refusing a risky investment even if it offers high potential profit.
3. Procrastination Bias
We delay important tasks, even when fully aware of the negative consequences, such as postponing a critical medical appointment.
4. Preference for Immediate Rewards
This bias pushes us to favor short-term gratification over long-term benefits. Example: impulsively buying a gadget instead of saving for a more meaningful project.
5. Herd Effect
We mimic the behaviors of others, regardless of logic, like investing in a stock simply because it is popular.
6. Overconfidence Bias
We overestimate our abilities or knowledge, such as thinking we can succeed in a poorly understood field.
7. Framing Effect
The way information is presented influences our decisions. For instance, preferring a product labeled “90% fat-free” over “10% fat,” even though they are identical.
8. Sunk Cost Fallacy
We continue to invest time or money in a failing project simply because we’ve already put effort into it.
9. Endowment Effect
We value what we own more than its actual worth. For example, refusing to sell an item at a fair price because of its sentimental value.
10. Availability Bias
We assess the likelihood of an event based on how easily it comes to mind, like fearing a plane crash more after seeing a news report about one.
Why Is It Important to Understand These Biases?
Behavioral biases influence critical areas of our lives, such as finances, health, or relationships. Recognizing them can:
•Improve Decision-Making: Better evaluate situations and avoid common mistakes.
•Facilitate Change: Reduce harmful behaviors like procrastination.
•Enhance Relationships: Anticipate others’ biases to communicate and collaborate more effectively.
How to Overcome Behavioral Biases
1.Raise Awareness: Simply knowing about biases can reduce their impact.
2.Structure Your Environment: For example, set reminders or automate tasks.
3.Use Tools Like Nudges: These small pushes encourage beneficial behaviors without restricting choices.
4.Adopt a Rational Approach: Question your intuitions and seek objective data.
What behavioral biases have you noticed in your daily life?
Between Cognitive Biases and Behavioral Biases: Who Influences Whom?
Abstract:
Cognitive and behavioral biases are closely linked: the former influences our decisions, while the latter translates these distortions into actions. Understanding this interaction is essential to improving our choices and behaviors, both personally and professionally.
The Relationship Between Cognitive Biases and Behavioral Biases: Who Influences Whom?
Cognitive biases and behavioral biases play a central role in how we make decisions and act in our daily lives. While closely linked, they operate at different levels: cognitive biases influence thinking, while behavioral biases manifest in actions. But who influences whom? And how do these biases interact with each other?
1. What Are Cognitive and Behavioral Biases?
Cognitive Biases: Distortions in Thinking
Cognitive biases are mental shortcuts that lead to errors in how we process information. They affect perceptions, judgments, and decisions even before an action is taken.
•Examples:
•Confirmation Bias: Seeking only information that supports our beliefs.
•Anchoring Bias: Giving too much importance to the first piece of information received.
Behavioral Biases: The Gap Between Intention and Action
Behavioral biases, on the other hand, appear in our actions. They represent a mismatch between what we know or want and what we actually do.
•Examples:
•Procrastination: Delaying an important task indefinitely.
•Herd Effect: Mimicking others’ behavior without rational analysis.
2. Who Influences Whom?
Cognitive Biases: The Root of Behavioral Biases
Cognitive biases are often the source of behavioral biases, as they influence our decisions upstream. By distorting how we evaluate a situation, they lead to specific behaviors.
•Example:
•The status quo bias (a preference for the current state) fuels the inertia bias, where we avoid any change, even when it is beneficial.
Behavioral Biases Reinforce Cognitive Biases
Conversely, repetitive behaviors influenced by behavioral biases can strengthen certain cognitive biases. For example, consistently following group opinions (herd effect) may intensify confirmation bias as we seek evidence to justify our choice.
3. Key Difference: Thought vs. Action
•Cognitive Biases Act on Thinking:
They alter our perception of reality and influence decisions before we act.
•Behavioral Biases Translate Into Actions:
They show the gap between intention (thinking) and what we actually do.
4. Concrete Examples of Their Interaction
Case 1: Delaying an Important Task
1.Cognitive Bias: The availability bias leads us to exaggerate the immediate difficulty of a task.
2.Behavioral Bias: This results in procrastination, where the task is continuously postponed.
Case 2: Investing in a Risky Project
1.Cognitive Bias: The optimism bias causes us to underestimate risks.
2.Behavioral Bias: This can lead to impulsive behavior, such as investing without properly assessing the consequences.
5. A Circular Relationship
The relationship between cognitive and behavioral biases is often circular:
•A cognitive bias (thinking) triggers a behavioral bias (action).
•A behavioral bias can reinforce a cognitive bias (repeated thought patterns).
For example, a person who procrastinates (behavioral) may convince themselves that the task is insurmountable (cognitive), further reinforcing the procrastination.
6. Why Is It Important to Understand This Relationship?
Recognizing the interaction between these two types of biases is crucial to improving our decisions and behaviors:
•Identify Cognitive Biases: This helps us understand why we make irrational decisions.
•Act on Behavioral Biases: Changing behaviors can weaken distorted cognitive patterns.
7. How to Reduce Their Impact?
1.Increase Awareness: Be mindful of our thinking and behavior patterns.
2.Create Structured Environments: Use tools like reminders or priority lists to reduce the impact of behavioral biases.
3.Take a Step Back: Question your intuitions and seek objective data.
What biases have you noticed in your decisions or actions?
PARTNERSHIP
Sharing viewpoints on common challenges stimulates creativity and often leads to simple and effective solutions. Faced with difficulties related to survival and evolution, companies, often under pressure due to time constraints, tend to choose standardized solutions driven by habit rather than a thorough analysis tailored to their needs.
We believe that partners play a crucial role in reversing this trend. Their contribution goes beyond simply promoting our solutions: they enrich our approach by sharing their needs and suggestions.
This collaboration is designed to create mutual value, benefiting OptimalDecision, our partners, and our clients. To this end, we have developed various programs tailored to different levels of technical expertise and partner involvement. These programs range from connecting with qualified potential clients to full implementation management.
We also offer a certification training program for partners who wish to strengthen their skills and maximize their impact.
Profiles of partnership
Referral
- Carry out the promotion.
- Establish contact with the prospect.
- Does not participate in the sales process.
Reseller
- Carry out the promotion and the sale with our support.
- They are provided with technical support upon request
Integrator
- Carry out the promotion, sales, and delivery independently.
- Only provides standard services.
Business Developper
- Delivers services and provides support to identified clients.
- Participates in the development of new solutions.
FAQ
Data Security
What is the security level of the selected cloud platform?
OD: We have chosen Google Cloud Platform (GCP) for its robust security in both cybersecurity and data protection. GCP ensures the highest European standards of security:
•ISO27001 for data protection and security
•ISO27017 for cloud security
•ISO27018 for cloud data privacy
Algorithm performance
How to Ensure the Algorithm’s Ability to Lead to the Right Decision
OD: With OptimalDecision, we are not operating in the realm of artificial intelligence.
The algorithm is fixed, based on mathematical formulas developed by a globally renowned mathematician, known for their expertise in decision-support tools.
OptimalDecision™ will never make the decision for you. Instead, it organizes and structures data to clarify the complexity of the decision.
And that’s already a lot!
Why Choose AHP When So Many Other Algorithms Exist?
OD: Indeed, there are many other algorithms. We have listed the main ones in the resources chapter, where you’ll find more detailed explanations.
In summary, no algorithm is perfect. AHP is renowned for its simplicity in both understanding and implementation.
We wanted to offer a tool that requires minimal prior knowledge to use.
We’ve successfully tested it in numerous applications across very different fields. It is essential to us to provide a tried-and-tested product.
Moreover, we’ve enriched it with unique features, making it stand out in ways no other method currently can.
What Are the Sources of the AHP Algorithm?
OD: 1. Origins of the AHP Method
•Creator: Thomas L. Saaty, who formalized AHP to provide a logical and mathematical framework for complex decision-making.
•First Publication:
•The concept was introduced in the book “The Analytic Hierarchy Process,” first published in 1980.
•Reference Work: Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill.
2. Theoretical Foundations
•Matrix Theory and Eigenvalues:
•AHP relies on linear algebra to derive the relative weights of criteria and alternatives.
•Priority calculations are based on eigenvalues and eigenvectors of a pairwise comparison matrix.
•Multicriteria Utility Theory:
•AHP is connected to utility theory, aiming to maximize a global utility function based on multiple criteria.
•Cognitive Psychology and Human Preferences:
•The fundamental scale used for pairwise comparisons (1 to 9) is rooted in psychological observations about human perception of differences in importance.
3. Applications and Validation
•Initial Applications:
•Initially used in military planning and complex strategic decision-making.
•Later expanded to fields such as management, economics, healthcare, and engineering.
•Critiques and Validation:
•AHP has been extensively studied and validated in academic research but has also faced critiques, particularly regarding the subjectivity of comparisons and sensitivity to inconsistency.
4. Key Academic Sources
•Books by Thomas Saaty:
•Saaty, T. L. (1990). Multicriteria Decision Making: The Analytic Hierarchy Process. RWS Publications.
•Saaty, T. L. (2001). Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World. RWS Publications.
•Important Academic Articles:
•Saaty, T. L. (1977). A Scaling Method for Priorities in Hierarchical Structures. Journal of Mathematical Psychology, 15(3), 234–281.
•Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary? European Journal of Operational Research, 145(1), 85–91.
Conclusion
AHP continues to be a central tool in multicriteria decision-making due to its clear structure and adaptability. OptimalDecision further enhances its practicality and reliability by integrating unique features that minimize the impact of cognitive biases and ensure consistent evaluations.
Source Code Security
What happens if the owning company and/or the software house goes bankrupt?
OD: The source code is secured on GitHub and is accessible to all registered clients in case of external structural failure.
GitHub is a collaborative development platform based on Git, a distributed version control system. It enables developers and teams to manage, collaborate on, and share source code for software projects. GitHub is one of the most popular platforms for open-source and commercial projects, offering powerful tools for version control, project management, and collaboration.
Technical Support
How is technical support organized?
OD: Technical support includes all quick-resolution requests, system bugs, and errors not attributable to the client.
For clients seeking premium assistance, we offer technical support via phone for an additional fee.
The subscription guarantees a response within 24 hours of ticket submission.
How much does app updating cost?
OD: Updates are included in the license price, so there is no additional cost.
App Usage
How Many People Can Interact on the Platform Simultaneously?
OD: In collective decision-making, coordination among various decision-makers is essential. A facilitator must be designated to assign voting links to the members of the decision committee.
Is There a Limit to the Number of Licenses or Projects?
OD: OptimalDecision™ is designed to be hosted on a server, with its capacity adjustable to meet the company’s needs.
What Specific Skills Are Required to Use OptimalDecision?
OD: This depends on how it is used. A training session lasting from half a day to three days may be required. No prerequisites are necessary to participate in the training.
Customization
Is It Possible to Have Full Customization of the Application with Company Branding and Style Guidelines?
OD: As OptimalDecision™ is designed, complete customization is very easily achievable.
Can the Application’s Structure Be Adapted to Specific Project Management Needs We Have Defined?
OD: This is not a standard feature, but specific development can be carried out upon request.
