A weighted decision matrix tool for structured decision-making. Define criteria with custom weights, score options using numeric scales or letter grades, and see color-coded final results. All processing happens locally in your browser.
Click Add Criterion to create rows. Name each criterion (e.g., Price, Location, Quality) and set a weight to indicate its importance. Higher weight means the criterion counts more toward the final score.
For each criterion, select either a numeric scale (0-10, 0-50, or any custom maximum) or letter grades (A+ through F). Different criteria can use different scales in the same matrix.
Click Add Option to create columns. Name each option (e.g., house addresses, product names, job offers). Use the notes icon in the column header to add details about each option.
Enter a score for every criterion-option combination. Cells are automatically color-coded from red (low) through yellow to green (high) so you can spot strengths and weaknesses at a glance.
Click the small note icon in any cell to add context or reasoning for that specific score. Notes are saved alongside your matrix.
The bottom row shows the weighted final score for each option. Toggle between numeric (0-100) and letter grade (A+ to F) output. Export the entire matrix as CSV or copy the results to your clipboard.
The decision matrix implements Multi-Criteria Decision Analysis (MCDA), a structured approach from operations research. Each criterion is assigned a weight representing its importance. Scores are normalized to a 0-1 scale regardless of the original scoring system (numeric or letter grade), then multiplied by the criterion weight. The final score is the weighted average across all criteria with valid scores. This method is equivalent to the Simple Additive Weighting (SAW) model, the most widely used MCDA technique in practice.
Numeric scores are normalized by dividing by the scale maximum (e.g., 7/10 becomes 0.7). Letter grades are mapped to fixed values on the same 0-1 scale: A+ = 1.0, A = 0.95, A- = 0.9, B+ = 0.85, B = 0.8, B- = 0.75, C+ = 0.7, C = 0.65, C- = 0.6, D+ = 0.55, D = 0.5, D- = 0.45, F = 0.0. This mapping follows standard academic grade point scales and allows mixing numeric and letter-graded criteria in the same matrix.
Cell background colors are computed using HSL color space interpolation. A normalized score of 0 maps to hue 0 (red), 0.5 maps to hue 60 (yellow), and 1.0 maps to hue 120 (green). The saturation and lightness are fixed at values that remain readable in both light and dark themes. This provides an intuitive traffic-light visual indicator without requiring users to read every number.
The entire matrix state — criteria, options, scores, notes, and settings — is automatically saved to your browser's localStorage after every change. No data is sent to any server. You can export the matrix as CSV for external use or sharing. Presets let you save and restore different matrix configurations.
A decision matrix (also called a Pugh matrix, criteria matrix, or weighted scoring model) is a structured tool for comparing multiple options against defined criteria. You list your options as columns, your evaluation criteria as rows, assign importance weights to each criterion, and score how well each option meets each criterion. The tool calculates a weighted final score so you can objectively identify the best option. Decision matrices are widely used in engineering, product management, real estate, hiring, and personal decisions like choosing a car or apartment.
