Decision making skills may be defined as a set of techniques used to facilitate individual or group decision making, and the evaluation and prioritization of alternatives.
Decision making techniques aid in structuring ideas and focus attention on evaluating alternatives carefully to enable more effective decisions to be made. Evaluation criteria used in the decision making process can be qualitative, or quantitative depending on the nature of the decision to be made or the problem to be solved.
There are numerous decision making techniques which can be used. Most commonly used are decision-tree analysis and some form of forced ranking or decision matrix, sometimes known as Kepner Tregoe. Other decision making techniques include: Monte Carlo Simulation; Least Distance Regression; Factor Analysis; Game Theory; Linear and Non-Linear Programming; Goal Programming; Probability Distributions; and several more combinations of these.
Purpose of Decision Making Techniques
The purpose of decision making techniques are to facilitate group consensus-based decisions on problems, solutions, and on the evaluation of alternatives. Decision making techniques are used to distinguish the alternatives and possible consequences of a decision, or sequence of associated decisions, in advance of final recommendations.
Benefits & Weaknesses
The benefit of using decision making techniques is that it facilitates the objective evaluation of actions, recommendations, and solutions by a group to achieve an unbiased decision.
The weakness of such models, is that they were constructed for industrial-age organisations, when environments were relatively stable, drivers were certain, and there was more time to make decisions. In contrast, businesses operating in the digital age are operating in environments that are unstable – changing rapidly, with many unknown or uncertain factors at play, and very little time to make strategic decisions.
Many of these models were based on ample time to identify and consider multiple options, however in a situation where time is limited and uncertainty high we need to make good decisions, fast.
The recent introduction of advanced analytics is a valuable decision support tool. However, there is a common misconception that predictive and prescriptive analytics is totally reliable, when in reality, they are still probabilities of chance, and can have many biases hard-wired into the results – through data and programmer biases.
We need some way to identify these biases, and respond appropriately.
So, we need a combination of two things: analytics, and the underlying cognitive processes that impact decision making – including attention, working memory, and reasoning.
This means that we need new models for making decisions; taking into consideration the new elements in decision making today. This requires a combination of four capabilities: