The Leading Source Of Original Research And Insights On Business Model Strategy, Digital & Tech Business Models
What Is Decision Analysis? Decision Analysis In A Nutshell
Stanford University Professor Ronald A. Howard first defined decision analysis as a profession in 1964. Over the ensuing decades, Howard has supervised many doctoral theses on the subject across topics including nuclear waste disposal, investment planning, hurricane seeding, and research strategy. Decision analysis (DA) is a systematic, visual, and quantitative decision-making approach where all aspects of a decision are evaluated before making an optimal choice.
Fundamentally, decision analysis enables organizations to evaluate or model the potential outcomes of various decisions so they can choose the one with the most favorable outcome. The tool assesses all relevant information and incorporates aspects of training, economics, psychology, and various management techniques.
Another part of decision analysis requires the business to examine uncertainty around a decision. Uncertainty is measured by probability. In other words, what are the chances the outcome will occur? From this point, the organization can make a decision based on the value and likelihood of success of a decision. Alternatively, it can base the decision on the likelihood of failure and its corresponding impact.
Decision analysis is extremely valuable in the project planning stage and during periodic reviews of project progress by senior management. Since most projects are characterized by decisions made with high uncertainty, decision analysis has multiple applications. For one, the analysis helps project teams obtain accurate activity duration estimates. Decision analysis also assists in risk analysis, “what-if” analysis, and subproject terminating in a research and development context.
The decision analysis process can be explained in the following steps.
1 – Identify the problem
What is the problem to be solved or the decision to be made?
Once this has been determined, a list of possible options should be devised. For instance, a non-profit that receives a large endowment may have several ways they can put the money to good use.
2 – Research options
Each choice or option must then be researched, with any relevant data set aside to develop a decision model later in the process. Data may be quantitative or qualitative, depending on the context.
It is important to consider each outcome in terms of its costs, risks, benefits, and probability of success or failure.
3 – Create a framework
To allow the business to properly assess its options, an evaluation framework must be created.
One way to achieve this is by using key performance indicators (KPIs) to measure and indicate progress. For example, a business looking to expand may stipulate that each potential new market causes a minimum increase in monthly sales volume.
Like the research from the previous step, KPI data may be qualitative or quantitative.
The expected value (EV) is the weighted average of all potential decision outcomes. To calculate the expected value, multiply the probability of each outcome occurring by the resulting value – sometimes referred to as the expected payoff. Then, sum the expected values for each decision.
For example, consider a large architectural firm that designs stadiums. During a public tender process, the firm submits two designs which the city council must evaluate for viability. For the sake of this article, we will call them Design A and Design B.
The city council determines that Design A, once completed, has a 55% chance of a $350 million valuation and a 25% chance of a $275 million valuation. The expected value of Design A is (0.55 x 350,000,000) + (0.25 x $275,000,000) = $261.25 million
On the other hand, Design B has a 20% chance of being valued at $400 million and a 60% chance of being valued at $290 million. The expected value of Design B is (0.20 x 400,000,000) + (0.60 x 290,000,000) = $254 million.
In this instance, the council should choose Design A.
Decision analysis is a systematic, visual, and quantitative decision-making approach where all aspects of a decision are evaluated before making an optimal choice.
Decision analysis is used in the project planning stage and during periodic reviews of project progress by senior management. The approach is especially suited to project management where there is often uncertainty around decision outcomes.
Decision analysis occurs via five steps: identify the problem, research options, create a framework, develop a decision model, and calculate the expected value. At the heart of this process are the decision tree framework and the calculation of expected value.
Strictly Necessary Cookies
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.