The primary goal of the advanced analytics and quantitative approach is to find an optimal solution by using econometric models in situations where the outcomes are uncertain. The method provides the best outputs when the problem is clearly defined, alternatives exist, and decision outcomes are easily measurable. The tools for analysis can be divided into nine popular groups:
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Decision trees. This is used to choose the optimal decision among different options. The tree is usually presented graphically, with the problem at the top and the alternatives branching out from the decision node.
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Network analysis. This method graphically constructs a network structure that focuses on uncovering hidden relationships between variables. The tool is effective and often provides more reliable results than other techniques.
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Linear programming. This tool helps to find the maximum (or minimum) value of an objective under constraints such as time, money, or resources.
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Game theory. This approach simulates rivalries or conflicts between individuals or companies as a game. The goal for managers is to discover ways of gaining at the expense of competitors.
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Queuing theory. Every company faces waiting times or queues related to communications, services, tools, or resources. The purpose of this theory is to reduce waiting times and minimize investments in these areas.
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Simulations. By considering different scenarios, managers attempt to find the decision that best aligns with their requirements and assumptions.
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Regression analysis. This technique examines the relationship between two or more independent variables and a dependent one.
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Cluster analysis. This technique identifies similar observations and assigns them to a group. For example, out of 300 observations, three clusters may be created, each containing similar data points.
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Factor analysis. This technique uncovers hidden relationships between all observed variables, without requiring one to be a dependent variable.
Quantitative methods for decision-making are widely used in nearly all departments of a company and across various industries. The last two techniques are innovative and not yet widely known or used. Nowadays, many researchers are exploring new ways of dealing with data and visually presenting results. Additionally, large corporations are discovering new applications for quantitative methods, such as computer vision in acquiring, processing, and analyzing digital images.
