How to Use Decision Analytics

How to Use Decision Analytics

Decision analysis can help you make better decisions. This business decision-making methodology enables companies to model the real-world consequences of a decision, calculate the best possible outcome, and then take appropriate action.


What Exactly Is Decision Analysis?

Decision analysis formalizes the decision-making process by dissecting each component of a business decision and evaluating all of its potential outcomes. Risk analysis, operations research, new product launches, and optimization strategies all rely on decision analysis. It ensures that a company considers all pertinent information before making a critical decision. Businesses, for example, may use decision analysis when weighing a large investment decision.

Several decision-making tools facilitate the decision-making process by visualizing every aspect of the decision in question. Decision trees, influence diagrams, and expected value equations assist businesses in making strategic decisions by comparing all potential outcomes and their associated risks, opportunities, trade-offs, and utility functions. Decision-making tools can be simple or involve extremely complex algorithms.


How Decision Analysis Works

A quantitative and systematic approach to making optimal decisions is known as decision analysis. The specifics of the decision analysis process vary depending on the problem being solved, but every decision analysis process includes the following steps.

Determine the issue. When faced with a difficult decision, the first step is to identify the factors that make the decision difficult so that you can seek appropriate decision support. For example, a company changing its pricing strategy must consider all potential consequences and whether the change will help them achieve a desired outcome.

Perform data analysis. Businesses can benefit from descriptive and predictive analytics to make more informed decisions. These business intelligence metrics provide historical context and forecast future trends or opportunities. Analysts collect and model this data using a decision analysis tool, which allows their company to plan its next steps.

Make use of decision modeling. Throughout the decision-making process, influence diagrams and decision trees can assist you in visualizing the problem and identifying potential solutions. Influence diagrams can help you get a high-level view of a decision problem. They entail labeling each decision variable with a box or circle. Each decision node is linked by an arrow to a possible outcome, uncertainty, or alternative. Decision trees are trees that represent a decision problem. Each "branch" represents a possible decision outcome. Because of their simplicity, these models are the most common. They enable decision analysts to model and evaluate every variable in a decision problem in order to find the best possible solution.

Determine the expected value. Calculating the expected value (EV) of each business decision aids in determining the average outcome and avoiding uncertain outcomes. To determine the expected value of a decision, you must assess the likelihood of each outcome and assign a numerical or monetary value to it. If you're familiar with Bayes' theorem, you could use it to calculate the probability. Alternatively, once you've gathered and modeled all relevant data, plug it into the following equation to calculate the expected value: EV = (Probability A multiplied by Expected Profit A) + (Probability B x Expected Profit A).


Decision Analysis Example

Consider the following real-world decision analysis example: A restaurant is thinking about expanding to a second location in Chicago or New York. Opening in either city will have different costs and success rates. The project management team hires a risk management firm to collect data and input it into a decision tree for risk assessment purposes. The decision tree depicts the monetary values that will be used to calculate the expected value.

In terms of Chicago, the data indicates a 20% chance of success and an 80% chance of failure. With regard to New York, there is a 40% chance of success and a 60% chance of failure. The firm estimates that opening in Chicago will cost $1 million and opening in New York will cost $3 million. Furthermore, they anticipate a $15 million profit in Chicago and a possible $2 million loss. In New York, they anticipate a $30 million profit and a possible $9 million loss.


The firm then runs these figures through the expected value equation:

EV (Chicago) = (0.2 x $15,000,000) + (0.8 x -$2,000,000) = $1,400,000

EV (New York) = (0.4 x $30,000,000) + (0.6 x -$9,000,000) = $6,600,000

The firm must subtract the expected value from the upfront costs to calculate the net gain and net loss for each location:

Chicago: $1,400,000 - $1,000,000 = $400,000

New York: $6,600,000 - $3,000,000 = $3,600,000


These calculations show that New York will produce the best results, implying that the restaurant's stakeholders should open their second location in New York rather than Chicago.


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