Svetlana ILINA

Svetlana ILINA

project manager & HR professional

Fast decision making

“Under pressure you do not rise to the occasion – you fall to the level of your training.” Micael Jordan

A prompt commercial launch of a fresh product or a featured upgrade of an existing one, an introduction of novel technology, or a creation of a new market niche, ahead of competitors, are prevailing targets for plenty of companies. Being able to operate at a faster tempo could be a powerful lever in different business areas. There are a lot of tools and methods that enable professionals to navigate initiatives with proven efficiency in quick decision-making. Many of them came from the aviation and military fields, where the ability to make rapid and correct decisions is a vital necessity. In addition to modern navigation systems and prescribed procedures that empower humans to take suitable actions, pilots are constantly trained on how to react to a wide variety of incidents, so they can respond automatically. That provides a consistent basis for ensuring proper, in-time decisions and maintaining productivity, quality, and safety at high levels.

Enterprises permanently need to make critical choices under pressure, thus favoring the adoption of structured decision-making approaches from other areas. Usually, the bulk of issues faced by a company require some programmed steps or application of well-known business techniques (Root Cause Analysis, Gap analysis, 7S, MOST, SWOT, PESTLE, CATWOE, BPM, ADKAR, etc.) to decide on and pick the right course of action. A set of borrowed frameworks (initially developed for pilots) that break down the decision-making process into phases to facilitate systematic work through challenges and consider relevant factors before taking action is depicted below:

Tempo forges a persuasive advantage for a company, and experienced people are able to generate prompt and plausible decisions without fixating on the steps of a process. However, speed through the decision loop is not always the determining factor. If an issue turns out to be a complex one, there is a need for slower acting, but still faster thinking, to win a competition. For example, before launching a new product, it is worth having a deep dive into customers’ preferences, estimating a range of potential sales revenue of this product in short-, mid-, and long-term perspectives, how quickly and what volume can be sold, and so on. It might happen that the initial decision has to be iteratively revised. So, it requires systems thinking and in-depth analysis to elaborate, not a rapid, but the most beneficial shot. A solid problem-solving layer interwoven into the decision-making frameworks can be illustrated by the OODA model designed by US Air Force Colonel John R. Boyd in the early 1970s:

The OODA is a continuous cycle in which every action generates new observations. It comprises four phases:

  • Observe: systematic data collection (market data, competitor activities, customer feedback, internal metrics, etc.),
  • Orient: interpretation of information through mental models, cultural background, experience, and analytical strategies,
  • Decide: selection of a course of action based on the orientation,
  • Act: rapid execution of the made decision.

The orientation phase is the core element of this model, while the same observation leads to different choices depending on its interpretation, and it is a common cause of misjudgment. Implementing the OODA loop allows individuals and groups to make quick decisions despite imperfect information and when things keep changing in daily life (driving in heavy traffic) and business (reacting faster than competitors to a market shift, introducing a new product, blocking an active cyber attack in real-time, and so on). Nowadays, it belongs to the standard toolbox of agile decision-making to effectively navigate unknown cases.

The DODAR (often expanded as T-DODAR) is another well-defined process used by aviation crews (British Airways), paramedical, and rescue teams to assist in decision-making during abnormal situations or emergencies. It embeds:

  • (Time) – figure out available time before a decision or fix is required;
  • Diagnose – identify the problem and root out the cause;
  • Options – generate a range of possible solutions;
  • Decide – evaluate options and select the most appropriate course of action;
  • Assign – communicate clearly among team members and assign them tasks, precisely implement the chosen decision;
  • Review – continuously assess the outcome and make adjustments if conditions change or new data appear.

This approach finds its acknowledgment in business areas where sudden failures might happen that demand a promptly harmonized teamwork. For example, PR-events, complex projects, incidents in mines, or any other hazardous production facilities. It serves to reduce chaos and panic by giving each team member explicit instructions on what to do and reducing guesswork by relying on facts and analytical methods.

The FORDEC method was developed by Lufthansa and the German Aerospace Center (DLR) to speed up a decision-making process in convoluted situations. It aims to structure disparate information into a holistic picture and guides the analytical efforts of a team to find an optimum. The acronym stands for:

  • Facts – gather relevant data, identify and prioritize problems;
  • Options – list possible ways to resolve the situation;
  • Risk & Benefits – consider down- and upsides of each option, identify an optimal one;
  • Decision – decide what level of risk to take and select a course of action, elaborate on the plan;
  • Execution – put the plan into action;
  • Check – monitor results and adjust the plan when necessary.

The model has found applications in various areas where a balance of high tempo and prevention of impulsive choices, logical fallacies, or perceptual biases is to be maintained, like emergency medicine, project management, software development, and many others.

Besides the described approaches, it is also worth mentioning some other well-known models with an embedded analytical component in the process of decision-making:

  • DECIDE (Detect, Estimate, Choose, Do, Evaluate),
  • NMATE (Navigate, Manage, Alternatives, Take action, Evaluate),
  • PIOSEE (Problem, Information, Options, Selection, Execution, Evaluation),
  • SAFE (State the problem, Analyze the problem, Fix the problem, Evaluate the result),
  • GRADE (Gather information, Review information, Analyze, Decide, Evaluate),
  • RAISE (Review the problem, Analyze the alternatives, Take action, Evaluate),
  • CLEAR (Clarify the problem, Look for data and share information, Evaluate options, Act on decision, Review performance),
  • 3Ps (Perceive, Process, Perform).

This list is not exhaustive. New models tailored to a specific area or general ones that offer an ordered approach to decision-making appear regularly. As a rule, they possess common ideas and drivers, and the application of any structured tool provides a more valuable outcome compared to chaotic actions.

Concurrently, for repetitive tasks or challenges to which some similarities from previous cases can be identified, a recognitional strategy appears to be highly efficient. In a familiar environment, decision makers often use their experience to generate a reasonably good option for “that” type of situation as the first to consider. The focus lies on situational assessment rather than elaboration and comparison of alternatives. This approach to rapid decision-making, or how people bring their experience to bear on a decision, was described by the American research psychologist Gary A. Klein as the Recognition-Primed Decision (RPD) model. During experiments, Klein found that under time pressure, even for complicated cases, the preference was given to a recognition strategy by most professionals. Under similar circumstances, analytical strategies were more frequently used by decision-makers with less experience.

In contrast to classical models, the RPD relies on finding the first satisfactory option, not necessarily the best one. Often experienced decision makers evaluate an option by conducting mental simulations of a course of action to see if it will work. Experience capacitates a person to understand a situation in terms of reasonable goals, relevant cues, expectancies, and typical actions. This strategy enables a decision maker to be continually prepared to initiate moves by committing to the workable option, apart from waiting until all analyses are completed and the highest-rated alternative is found. It corresponds with the core idea of the automatic functioning of System 1 proposed by the Israeli-American psychologist Daniel Kahneman in his theory of the Two Systems of Thinking. System 1 is a human thinking process that operates fast and automatically, without conscious effort. Patterns of ideas, including beliefs and biases, that drive many of our choices vegetate here. It can learn and acquire some new associations, knowledge, and skills through prolonged practice. When something does not happen naturally and requires mindful activity, the deliberate System 2 comes into play. It is a slow, effortful, and logical mode that “can construct thoughts in an orderly series of steps” to support System 1 in overcoming a difficulty.

Recognitional and analytical approaches have their functions and may be applied in the same decision task. Facing a challenge to speed up the process and prevent a slip into “analysis paralysis” mode, it makes sense to combine multiple strategies with the distribution of roles among a decision-making team: “firefighters” – RPD-driven members, and inquirers – analysis-driven comrades. The recognitional approach is highly adaptive and allows experienced people to respond effectively without delay. The probability of getting a decision closer to the optimal one increases with the acquisition by a professional of more relevant experience, training his/her abilities to find pitfalls by mentally playing varied application scenarios and to make changes when necessary. It is appropriate under time pressure and ambiguity, and the experience of a decision maker is the crucial success factor. In very complicated cases with abstract data, a huge combination of options, or a demand to justify decisions, an analytical strategy is more sound. Worth noting that the utilization of systematic approaches requires, besides rationality in the selection of their relevance, also permanent exercises. Theoretical knowledge of a variety of analytical methods with no practice to apply, most probably won’t be able to ensure the quality of decisions. Therefore, experience and standing training turn out to be influential aspects of skilled problem-solving and fast decision-making.

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