Decision Engineering

Decision engineering is a framework that unifies a number of best practices for organizational decision making. It is based on the recognition that, in many organizations, decision making could be improved if a more structured approach were used. Decision engineering seeks to overcome a decision making "complexity ceiling", which is characterized by a mismatch between the sophistication of organizational decision making practices and the complexity of situations in which those decisions must be made. As such, it seeks to solve some of the issues identified around complexity theory and organizations. In this sense, decision engineering represents a practical application of the field of complex systems, which helps organizations to navigate the complex systems in which they find themselves. Decision engineering can also be thought of as a framework that brings advanced analytics techniques to the desktop of the non-expert decision maker, as well as incorporating, and then extending, inductive reasoning and machine learning techniques to overcome the problems articulated in Black swan theory.
Decision engineering proponentsbelieve that many organizations continue to make poor decisions.In response, decision engineering seeks to unify a number of decision making best practices, described in more detail below.
Decision engineering builds on the insight that it is possible to design the decision itself, using principles previously used for designing more tangible objects like bridges and buildings.
The use of a visual design language representing decisions is an important element of decision engineering, since it provides an intuitive common language readily understood by all decision participants. A visual metaphor^[5] improves the ability to reason about complex systems as well as to enhance collaboration.
In addition to visual decision design, there are other two aspects of engineering disciplines that aid mass adoption. These are: 1) the creation of a shared language of design elements and 2) the use of a common methodology or process, as illustrated in the diagram above.

Notes

• Enterprise Decision Management (EDM) is a closely related discipline that focuses on automating decisions across an enterprise. Decision engineering is from this point of view a superset of EDM, since it encompasses both manual and automated decision making processes, unifying them into a common methodology that, when effective, breaks down barriers between quantitative analysis / analytics tools and departments and those with a more qualitative / strategic / management focus.
• The term "decision engineering" is used in several industries with more specific meaning than the framework described here. For instance, the Australian Software Research Centre has an IT evaluation approach called Decision Engineering; Idea focuses on emergency management under the heading of "Decision Engineering Analysis"; and the National University of Singapore includes an organization called the Biomedical Decision Engineering Group. Each of these has a meaning that is distinct from what is discussed in the present article.
• In behavioral economics, "decision engineering" can mean the deliberate manipulation of consumer choices, as in this Journal of Consumer Research study: People choose healthy meals, if given more choice: Study. In this use of the term, decision engineering is roughly analogous to soft paternalism - a quite different meaning than is covered in the present article, referring as it does to the engineering of decisions made by consumers, rather than the use of engineering principles to aid in complex decision making. Although distinctly different, this practice draws on much of the same decision-making research as does decision engineering (such as, for the example, the work of Richard Thaler as described in this article about Barack Obama's University of Chicago connections to this school of thought).
• Cost engineering measures the costs of engineering projects. Cost engineering is sometimes grouped into product engineering and design optimization as "decision engineering". This can be distinguished from the broader framework of this article, which goes beyond the arena of engineering decisions to all decisions faced by organizations.
• Operations research is a largely quantitative approach to decision making that attempts to identify optimal or near-optimal solutions to decision making problems.