README

AHPd is a fully objective, multi-criteria decision-making system built on a modern, data-driven evolution of the classic Analytic Hierarchy Process (AHP) method.

Unlike traditional AHP, AHPd eliminates subjective judgment and uses real, measurable data to generate consistent, auditable, and mathematically justifiable business decisions.

🔗 PHP Native Extension 🔗 REST API 🔗 CLI (Coming Soon)

🚀 The AHPd Advantage

The AHPd (AHP Data-Driven) system provides a quantitative framework for comparing alternatives—be it products, projects, suppliers, or strategies—based on their quantitative attributes (price, performance, quality, resource consumption, etc.).

AHPd automatically calculates the weight and importance of each criterion by analyzing the statistical dispersion of the input data, eliminating the need for human input on criterion priority.

This means your decisions are:

100% Objective: Criteria weights are mathematically derived from the data itself.
Highly Consistent: No manual subjectivity or inconsistent pairwise comparisons.
Reproducible: The same data always yields the same, justifiable result.

🎯 Key Strategic Benefits

Benefit	Description	Value for Decision Makers
Objective Decisions	Decisions are based purely on real-world data, removing human bias and politics from complex choices.	Mitigate Risk and ensure regulatory compliance.
Full Explainability	Every result is detailed with the percentage contribution of each criterion and attribute.	Justify Investment and easily audit results.
High Performance	Implemented with a highly optimized C/Rust core, handling large-scale data quickly.	Accelerate Time-to-Decision in real-time systems.
Seamless Integration	Available as a PHP extension, REST API, and CLI tool.	Embed Intelligence directly into existing BI, ERP, or recommendation systems.

🧠 How AHPd Works

AHPd transforms raw data into strategic insight through a simple process:

Define Preferences: Specify the criteria and indicate whether you want to maximize or minimize each one (e.g., "Max Quality," "Min Price").
Provide Raw Data: Input the quantitative data for all options being compared. No data normalization or pre-processing is needed.
AHPd Calculates:
- Analyzes the statistical spread of values across all options.
- Automatically assigns the weight of importance to each criterion.
- Determines the relative performance (priority) of each alternative.
Audit & Rank: The output is a clear, auditable percentage ranking, showing which alternative is best and why.

📊 Intuitive Example

Alternative	Price (Minimize)	Performance (Maximize)	Autonomy (Maximize)
Product A	200	70	10
Product B	250	90	12
Product C	180	60	8

➡️ AHPd Ranking:

Product B — 36.8% (Best performance and autonomy)
Product A — 33.1% (Best price)
Product C — 30.1% (Intermediate)

💡 AHPd Explanation: Product B won because the Performance criterion showed the greatest difference between candidates, thus gaining the highest automatic weight, overshadowing the contribution of Product C's slightly better price.

Example visual (with Chart.js)

This simple example demonstrates the decision to purchase a device, comparing the features that the user considers relevant.

Option	price US$ (min)	storage GB (max)	memory GB (max)	camera Mpx (max)	battery mAh (max)
Phone A	9494	128	6	48	4323
Phone B	4139	256	8	50	4500
Phone C	4429	256	8	50	4300
Phone D	1885	128	6	64	5065

Note that the data was passed without any transformation, without normalization, exactly as it is in the real world.

The user only needed to indicate whether a lower "price" is better or a larger "battery" is better.

The screenshot below intuitively shows the results, allowing you to see precisely how much each feature (criterion) contributed to the final ranking score. This visually validates the weights calculated by AHPd.

🧾 Practical Use Cases

Area	Application	Strategic Outcome
Finance	Comparing investments based on return, risk, and liquidity.	Optimized portfolio construction and risk alignment.
IT & Engineering	Selecting vendors, software architectures, or technologies.	Reduced deployment costs and increased system efficiency.
Operations	Choosing optimal equipment, routes, or maintenance strategies.	Streamlined efficiency and reduced operational overhead.
Product & Marketing	Prioritizing features, analyzing competitor products, or setting prices.	Data-driven product roadmaps and competitive advantage.
HR & Procurement	Evaluating candidate suitability or selecting raw material suppliers.	Consistent, measurable selection criteria.

🧩 Integration Options

AHPd is designed to be platform-agnostic, supporting several integration paths:

Type	Description	Link
PHP Native Extension	Native C/Rust implementation for maximum performance within PHP systems.	View PHP Documentation
REST API	JSON-compatible web service for integration with any programming language or BI tool.	Online Service
CLI Application	Command-line tool for direct use in automated pipelines and scripts.	(Under Development)
GUI Application	Desktop application for end-user analysis and reporting.	(Planned)

🔍 Transparency and Auditability

Every decision made by AHPd is transparent. The system provides detailed percentage contributions, showing exactly how much each criterion impacted the final result.

This allows for decisions that are not only efficient but also fully reliable, accountable, and justifiable to stakeholders.

Example of Interpretive Output:

Product B — 36.8% (Winner)
  ↳ Autonomy (36.9% contribution)
  ↳ Performance (37.8% contribution)
  ↳ Price (25.3% contribution)

📚 Core Concepts: AHPd vs. Traditional AHP

The table below summarizes the key differences that make AHPd the ideal choice for automated, data-driven decision systems, in contrast to the manual approach of classical AHP.

Feature	AHPd (Data-Driven Evolution)	Traditional AHP (Classic Method)
Input Source	Real, Quantitative Data (e.g., price, speed, capacity)	Subjective Judgments (Expert opinions, verbal comparisons)
Criterion Weighting	Automatic. Weights are mathematically derived from the data's statistical dispersion.	Manual. Weights are derived from subjective pairwise comparisons of criteria importance.
Decision Speed	High-Speed (Designed for automated systems and high-volume analysis).	Slow/Time-Consuming (Requires collecting, validating, and inputting expert opinions).
Objectivity	Fully Objective. The model results are consistent and reproducible given the same data.	Subjective/Semi-Objective. Results depend on the initial consistency and bias of human judges.
Primary Goal	Multi-criteria Optimization and Auditable Ranking based on performance.	Multi-criteria Prioritization based on perceived importance.
Output	Clear Percentage Ranking + Detailed Contribution Reports per criterion.	Priority Vector (Raw Weights) + Consistency Ratio (CR).

🧬 Licensing & Availability

The AHPd system is free to use for both personal and commercial applications. Its high-performance computational core remains proprietary to Wead Technology®, ensuring consistency, integrity, and continuous evolution.

While usage is unrestricted, enterprise services — including technical support, custom integration, and cloud-based AHPd API access — are available for organizations seeking enhanced performance, reliability, and dedicated assistance.

For partnerships, OEM integration, or large-scale deployment support, enterprise collaboration opportunities are available upon request.

wead / ahpd

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