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The Quantitative Approach to Decision Making: Methods, Purpose & Benefits

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  • 0:03 Quantitative Approach Benefits
  • 1:07 Quantitative Decision-Making
  • 4:10 Lesson Summary
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Lesson Transcript
Instructor: Olga Bugajenko

Olga is a registered PRINCE2 Practitioner and has a master's degree in project management.

At the core of the quantitative approach to decision-making are the estimation of probabilities of event and their consequences. Learn more about popular quantitative methods in this lesson.

Quantitative Approach Benefits

Similar to operations management, project management employs an array of quantitative techniques while performing planning, scheduling, forecasting, and monitoring tasks. The main purpose of the quantitative approach is to make an optimal decision by using mathematical and statistical models in a situation when the probability of all outcomes is uncertain.

Quantitative approach to decision-making produces the best results when the problem is clearly defined, several alternatives exist, and decision outcomes are easily measurable. However, in the case that many external factors are outside of the decision-maker's control and their probability is unknown, the quantitative methods can become unreliable.

Quantitative approach techniques, especially the ones relying on statistical software, have the advantage of suggesting the best solution to the problem without even identifying all possible alternatives. This feature is quite useful in problems where the number of possible alternatives is very large though only a few are worth considering for selection. Once the problem and conditions are defined, the decision-making process becomes quick.

Quantitative Decision-Making

A lot of quantitative tools have been developed to aid managers in effective decision-making. These can be broadly classified into several popular groups.

1. Decision Trees

Decision trees are used as a tool for selecting an optimal decision among the alternatives. A decision tree usually starts with recording the problem graphically as a starting node or root of a diagram and the alternative solutions as its branches. In the case that a solution requires further decisions to be made, more branches are added at a lower level. The probability, cost, and value of each scenario are recorded. Decision trees are also used for the robustness analysis, which counts the number of favorable scenarios at the planning horizon and compares it to the total number of possible outcomes. These decisions can be modeled using a decision tree. On screen is an example of a decision tree based on the weather conditions.

Decision tree

Imagine performing an experiment that is only valid under certain weather conditions. A diagram like this could help you quickly decide if it's a good day to run the experiment. Imagine you're planning your weekend. You have several options available: you can go to the beach, visit your family, or go to the cinema. Your decision is based on several factors including the weather and whether or not you will get paid this week. If the weather is good, you will go to the beach. If the weather is bad, your decision will depend on whether or not you get paid; if you do, you will go to the cinema, otherwise, you will visit your family.

2. Network Analysis

Network analysis is another graphical decision-making method that focuses on portraying the relationships between events and tasks. It presents all project activities as a path from the project start to the project completion, and it takes the timing of each into consideration. The most popular network analysis example is a critical path analysis. This type of analysis focuses on estimating the total project duration. On screen is an example of the critical path analysis.

Critical path analysis

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