Technical Decision Intelligence

PRECISION ANALYSIS
FOR COMPLEX SYSTEMS

Elevate your decision-making with mathematically rigorous MCDM frameworks and real-time computational analytics.

Empirical Foundations
of Decision Intelligence

In a world of information overload, subjective choices lead to suboptimal outcomes. We built this application to provide a mathematically rigorous framework for objective evaluation, ensuring every decision is backed by solid data and proven algorithms. Our approach combines classical MCDM theories with modern computational optimization to ensure results are both statistically significant and practically actionable.

Objective Evaluation

Eliminate bias using normalized decision matrices.

Real-time Computation

Complex calculations processed in milliseconds.

Scientific Rigor

Based on peer-reviewed MCDM methodologies.

Fig 01. Correlation Matrix
Input DataDecision Matrix
CriteriaWeightProfit ▲Quality ▲Cost ▼
Alt A8.57.5250015
Alt B7.28.1210018
Alt C9.16.8280012
Alt D6.57.9190020
Raw performance values for each alternative against criteria.
92.4%Accuracy Rate
26+Methods
Operation Protocol

How It Works

Simple integration, powerful results. Follow our streamlined process to extract actionable insights from your data.

I

Input Data

Upload Excel or manually enter your alternatives and criteria scores.

II

Apply Weights

Select between Equal, Entropy, CRITIC, or AHP weighting methods.

III

Run Algorithm

Choose from 18+ MCDM methods like SWEI, SWI, or TOPSIS.

IV

Analyze Graphs

Instantly view ranking comparisons and sensitivity analysis.

Requirement Guide

Operational constraints and system capabilities

Data Structure

  • Define clear Alternatives (e.g., Suppliers, Projects, Locations).
  • Identify Criteria with specific weights and polarities (Max/Min).
  • Provide Numerical Scores for each alternative-criteria intersection.

System Capabilities

  • Supports up to 100 alternatives and 100 criteria.
  • Excel import compatibility (.xlsx, .csv).
  • Interactive sensitivity analysis for weight testing.

Application Merits

Dynamic Visualizations

Switch between Bar, Line, Radar, and Heatmap charts instantly.

Mathematical Accuracy

Implements high-precision floating point arithmetic for all matrices.

Method Comparison

Compare results from different MCDM methods side-by-side.

Sensitivity Matrices

Analyze how weight shifts impact your final ranking stack.

Excel Export

Download full calculation transcripts and rankings to Excel.

Formula Transparency

Every method includes a detailed MathJax formula breakdown.

Experimental Data

Methodology & Analytics

Deep-dive into the technical foundations of our engine.

Mathematical Formulation
Rigorous algorithmic implementation
Equation Analysis

SWEI Core Formulation

\[SWEI''_i = \sum_{i=1}^{m} Score_{i,j} \]
\[SWEI''_i = \sum_{j=1}^{n} \left( \log_{2} \left\{ \frac{1}{\overline{IDM}_{i,j}} \right\} \right)^{w_j} \tag{5}\]

Ranking Criteria

$$ Rank(A_i) \uparrow \text{ as } SWEI''_i \downarrow \text{ (Ascending)} $$
Ref: Dwivedi et al. (2025)
DOI: 10.1016/j.rser.2025.115791
Consolidated Performance
Metric efficiency analysis
Ranking Comparison Graph
Cross-methodological delta Analysis
Sensitivity Analysis Matrix
Rank variation across weight shifts (C1)
Data Appendix

Data Synthesis

Comprehensive calculation transcripts produced by the engine.

Table: Ranking Comparison

AlternativeSWEI RankSWI RankAvg. Score
Alt-1120.902
Alt-2440.795
Alt-3210.914
Alt-4330.841

Table: Comparison Matrix

MetricsSWEITOPSISWASPAS
Consistency Ratio0.0820.0710.075
Computation Time12ms18ms15ms
Correlation Coefficient0.980.950.97
Sensitivity LevelHighMedHigh
Collaboration

Get in Touch

Have questions about our MCDM framework or want to collaborate? Send me a message and I'll get back to you as soon as possible. We welcome inquiries regarding institutional access, research partnerships, and methodological customisations.

Fast Response

Typically responds within 24 hours.

Direct Access

Straight to Dr. Pankaj Prasad Dwivedi.