What are the most effective performance indicators for multi-criteria decision analysis?

1 Criteria and attributes

The first step in MCDA is to define the criteria and attributes that reflect the objectives and preferences of the decision-makers and stakeholders. Criteria are the broad categories that describe what is important in the decision problem, such as cost, quality, safety, or sustainability. Attributes are the specific measures that quantify the criteria, such as dollars, ratings, percentages, or units. The criteria and attributes should be relevant, clear, comprehensive, and measurable. They should also be independent, meaning that they do not overlap or influence each other.

2 Performance matrix

The second step in MCDA is to construct a performance matrix that shows the scores of each alternative on each attribute. The scores can be obtained from data sources, expert judgments, surveys, or models. The performance matrix should be consistent, accurate, and complete. It should also be normalized, meaning that the scores are converted to a common scale, such as 0 to 1 or 1 to 10, to allow for comparison. Normalization can be done using different methods, such as linear scaling, rank order, or goal achievement.

3 Aggregation and ranking

The third step in MCDA is to aggregate and rank the alternatives based on their scores and weights. Weights are the relative importance of the criteria and attributes, which can be determined by the decision-makers or stakeholders using various techniques, such as pairwise comparison, rating, or swing weighting. Aggregation is the process of combining the scores and weights into a single value for each alternative, which can be done using different methods, such as weighted sum, weighted product, or outranking. Ranking is the process of ordering the alternatives from best to worst based on their aggregated values.

4 Sensitivity analysis

The fourth step in MCDA is to perform sensitivity analysis to test the robustness and reliability of the results. Sensitivity analysis is the process of changing the inputs or assumptions of the MCDA model, such as the scores, weights, or aggregation methods, and observing the effects on the outputs or rankings. Sensitivity analysis can help data analysts identify the key drivers of the decision, assess the uncertainty and variability of the data, and explore different scenarios and trade-offs.

5 Validation and communication

The fifth step in MCDA is to validate and communicate the results to the decision-makers and stakeholders. Validation is the process of checking the validity and quality of the MCDA model, such as the logic, consistency, and transparency of the criteria, attributes, scores, weights, and aggregation methods. Validation can be done using various tools, such as consistency ratio, concordance index, or discordance index. Communication is the process of presenting and explaining the results in a clear, concise, and convincing way, such as using tables, charts, graphs, or dashboards.

6 Evaluation and improvement

The sixth step in MCDA is to evaluate and improve the MCDA model and the decision process. Evaluation is the process of measuring and monitoring the performance and outcomes of the decision, such as the efficiency, effectiveness, satisfaction, or impact of the chosen alternative. Evaluation can be done using various indicators, such as cost-benefit analysis, return on investment, key performance indicators, or balanced scorecard. Improvement is the process of learning and adapting from the feedback and experience of the decision, such as identifying the strengths, weaknesses, opportunities, or threats of the MCDA model and the decision process. Improvement can be done using various methods, such as benchmarking, best practices, or continuous improvement.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?