Description: Analytic Hierarchy is a structured method that allows for the decomposition of complex decisions into multiple levels, facilitating the prioritization and analysis of alternatives. This approach is based on creating a hierarchy that organizes the relevant criteria and sub-criteria for decision-making, enabling analysts to systematically evaluate and compare different options. Analytic Hierarchy is commonly used in various fields, including data science, statistics, management, economics, and engineering, where rigorous evaluation of multiple variables is required. This method not only helps clarify the relationships between different decision elements but also provides a framework for weighting each criterion, allowing for a more objective and substantiated evaluation. The clarity offered by Analytic Hierarchy is especially valuable in contexts where decisions must be justified and supported by data, making this approach an essential tool in informed and strategic decision-making.
History: Analytic Hierarchy was developed by mathematician Thomas L. Saaty in the 1970s. Saaty introduced this approach in his book ‘Decision Making for Leaders’ published in 1982, where he presented a framework for complex decision-making. Since then, the methodology has evolved and adapted to various disciplines, including management, economics, and engineering.
Uses: Analytic Hierarchy is used in various fields, including project management, strategic planning, risk assessment, and supplier selection. It is especially useful in situations where multiple criteria and alternatives must be considered, allowing decision-makers to more effectively evaluate available options.
Examples: A practical example of Analytic Hierarchy is its application in supplier selection, where criteria such as cost, quality, and delivery time can be established. By breaking down these criteria into sub-criteria and assigning weights to them, companies can make more informed decisions about which supplier to choose.
