The basic idea is that decisions quantitative techniques for decision making pdf based on our understanding of how actions lead to outcomes. Decision intelligence is a discipline for analyzing this chain of cause-and-effect, and decision modeling is a visual language for representing these chains.
DI 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. In addition to visual decision design, there are other two aspects of engineering disciplines that aid mass adoption. The need for a unified methodology of decision making is driven by a number of factors that organizations face as they make difficult decisions in a complex internal and external environment. The car is becoming an expression of identity, values, and personal control in ways that move far beyond traditional segmentation and branding. What percent of the parts are recyclable?
What is the vehicle’s total carbon footprint? Are there child labor inputs? Toxic paints, glues, or plastics? How transparent is the supply chain? Is the seller accountable for recycling? Are fair labor practices employed?
The GM Solution: Life Boats, Not Life Support. We live in a dynamic world in which the pace, scope, and complexity of change are increasing. The continued march of globalization, the growing number of independent actors, and advancing technology have increased global connectivity, interdependence and complexity, creating greater uncertainties, systemic risk and a less predictable future. These changes have led to reduced warning times and compressed decision cycles.
Decision engineering has the potential to improve the quality of decisions made, the ability to make them more quickly, the ability to align organizational resources more effectively around a change in decisions, and lowers the risks associated with decisions. Furthermore, a designed decision can be reused and modified as new information is obtained. Decision engineering seeks to create a visual language that serves to facilitate communication between them and quantitative experts, allowing broader utilization of these and other numerical and technical approaches. As an example, one link might represent the connection between “mean time to repair a problem with telephone service” and “customer satisfaction”, where a short repair time would presumably raise customer satisfaction. The functional form of these dependencies can be determined by a number of approaches.
In this way, a decision model represents a mechanism for combining multiple relationships, as well as symbolic and subsymbolic reasoning, into a complete solution to determining the outcome of a practical decision. Decision engineering seeks to bridge this gap, creating a critical mass of users of a common methodology and language for the core entities included in a decision, such as assumptions, external values, facts, data, and conclusions. If a pattern from previous industries holds, such a methodology will also facilitate technology adoption, by clarifying common maturity models and road maps that can be shared from one organization to another. Decision engineering is both a very new and also a very old discipline. Yet the realization that these elements can form a coherent whole that provides significant benefits to organizations by focusing on a common methodology is relatively new.
In 2013, Lorien Pratt and Mark Zangari, founders of Decision Engineering vendor Quantellia, chose to re-brand their offering under the name “Decision intelligence” for marketing reasons. Their rationale was that the word “Engineering” connoted a discipline too technical for management professionals. DI” was a natural extension to this group. Each of these has a meaning that is distinct from what is discussed in the present article. 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.