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.

Understanding decision analysis

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. 

How does decision analysis work?

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.

4 – Develop a decision model

Now it is time to combine the framework with a decision model. One of the most popular decision analysis models is the decision tree, where each choice has branches representing different outcomes. 

Influence diagrams can also be used when there is a high amount of uncertainty around a decision or goal.

5 – Calculate the expected value

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.

Key takeaways:

  • 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.

Connected Product Development Frameworks

New Product Development

Product development, known as the new product development process comprises a set of steps that go from idea generation to post-launch review, which help companies analyze the various aspects of launching new products and bringing them to market. It comprises idea generation, screening, testing; business case analysis, product development, test marketing, commercialization, and post-launch review.

BCG Matrix

In the 1970s, Bruce D. Henderson, founder of the Boston Consulting Group, came up with The Product Portfolio (aka BCG Matrix, or Growth-share Matrix), which would look at a successful business product portfolio based on potential growth and market shares. It divided products into four main categories: cash cows, pets (dogs), question marks, and stars.

Ansoff Matrix

You can use the Ansoff Matrix as a strategic framework to understand what growth strategy is more suited based on the market context. Developed by mathematician and business manager Igor Ansoff, it assumes a growth strategy can be derived by whether the market is new or existing, and the product is new or existing.

User Experience Design

The term “user experience” was coined by researcher Dr. Donald Norman who said that “no product is an island. A product is more than the product. It is a cohesive, integrated set of experiences. Think through all of the stages of a product or service – from initial intentions through final reflections, from first usage to help, service, and maintenance. Make them all work together seamlessly.” User experience design is a process that design teams use to create products that are useful and relevant to consumers.

Cost-Benefit Analysis

A cost-benefit analysis is a process a business can use to analyze decisions according to the costs associated with making that decision. For a cost analysis to be effective it’s important to articulate the project in the simplest terms possible, identify the costs, determine the benefits of project implementation, assess the alternatives.

Empathy Mapping

Empathy mapping is a visual representation of knowledge regarding user behavior and attitudes. An empathy map can be built by defining the scope, purpose to gain user insights, and for each action, add a sticky note, summarize the findings. Expand the plan and revise.

Perceptual Mapping

Perceptual mapping is the visual representation of consumer perceptions of brands, products, services, and organizations as a whole. Indeed, perceptual mapping asks consumers to place competing products relative to one another on a graph to assess how they perform with respect to each other in terms of perception.

Value Stream Mapping

Value stream mapping uses flowcharts to analyze and then improve on the delivery of products and services. Value stream mapping (VSM) is based on the concept of value streams – which are a series of sequential steps that explain how a product or service is delivered to consumers.

Read the remaining product development frameworks here.

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