Is it possible to make good decisions based solely on quantitative analysis of available hard data?
Is it possible to make good decisions based solely on intuition?
The key question behind this article is:
Is it valuable, and possible, to combine intuition together with quantitative data in a structured decision-making process in order to make better decisions?
For the sake of this article, I use the following definition of intuition:
The ability to understand something immediately without the need for conscious reasoning
Intuition is basically subjective information about reality. Intuitive decision makers take their decisions based on their intuitive information, including predictions about the reaction of people to specific actions and happenings. Intuition is led by a variety of assumptions to form a comprehensive picture of the relevant reality. For instance, the VP of Marketing might claim, based on intuition, that a significant part of the market is ready for a certain new technology. While intuition is a source of information, its accuracy is highly questionable due to a lack of data and rational reasoning.
Decisions are based on emotions, which dictate the desired objective, but should also include logical analysis of potential consequences. Intuition replaces logic when there is not enough data, or time, to support good-enough prediction of the outcomes of an action. We frequently make decisions that use intuition as the only supporting information, together with emotions determining what we want to achieve.
From the perspective of managing organizations, with a clear goal, using intuition contradicts the desire for optimal results, because intuition is imprecise, exposed to personal biases and very slow to adjust to changes. But, in the vast majority of the cases, the decision-makers do not have enough “objective data” to make an optimal decision. So, there is a real need for using intuition to complement the missing information.
Any decision is about impacting the future, so it cannot be deterministic as it is impacted by inherent uncertainty. The actual probabilities of the various uncertainties are usually unknown. Thus, using intuition to assess the boundaries of the relevant uncertainty is a must.
So, while intuition is not based on rational analysis it provides the opportunity to use logical quantitative analysis of the probable ramifications when both available hard data and complementing intuitive information are used.
Assessing the uncertainty by using statistical models, which look for past data for similar situations, is usually more objective and preferable than intuition. However, in too many times past data is either not available, or can be grossly misleading as it could belong to basically different situations.
People make intuitive decisions all the time. Intuition is heavily based on life experience where the individual recognizes patterns of behaviors that look as if following certain rules. These rules are based on cause-and-effect, but without going through full logical awareness. Intuition is also affected by emotions and values, which sometimes distort the more rational intuition.
Taken the imprecise nature of intuition and its personal biases raises the question of what good can it bring to the realm of managing organizations?
The push for “optimal solutions” forces managers to go through logically based quantitative analysis. However, when some relevant information is missing then the decisions become arbitrary. This drive for optimal solutions actually pushes managers to simply ignore a lot of the uncertainty when no clear probabilities can be used.
A side comment: The common use of cost-per-unit is also backed up by the drive for optimal solutions because the cost-per-unit allows quantitative analysis. Mathematically the use of cost-per-unit ignores the fact that cost does not behave linearly. The unavoidable result is that managers make decisions against their best intuition and judgment and follow a flawed analysis, which seems like being based on hard data, but present a distorted picture of reality.
The reality of any organization is represented by the term VUCA: volatility, uncertainty, complexity, and ambiguity. From the perspective of the decision-maker within an organization, all the four elements can be described together as ‘uncertainty’ as it describes the situation where too much information is missing at the time when the decision has to be made. In the vast majority of the VUCA situations the overall uncertainty is pretty common and known, so most outcomes are not surprising. In other words, the VUCA in most organizations is made of common and expected uncertainty, causing any manager to rely on his/her intuition to fill the information required for making the final decision. Eventually, the decision itself would also be based on intuition, but having the best picture of what is somewhat known, and what clearly is not known, is the best that can be sought for in such reality.
What is it that the human decision-maker considers as “reasonably known”?
On top of facts that are given high confidence in being true, there are assessments, most of them intuitive, which consider a reasonable range that represents the level of uncertainty. The range represents an assessment of the boundaries of what we know, or believe we know, and what we definitely don’t know.
An example: A company considers the promotion of several products at a 20% price reduction during one weekend. The VP of Sales claims that the sales of those products would be five times the average units sold on a weekend.
Does the factor of five times the average sales represent the full intuition of the VP of Sales?
As the intuition is imprecise in nature it probably means the VP has a certain range of the impact of the reduced price in her mind, but she is expected to quote just one number. It could well be that the true reasonable range, in the mind of the VP of Sales, is anything between 150% and 1,000% increase, which actually means a very high level of uncertainty or a much narrower range of just 400% to 500% of the average sales.
The point is that if the actual intuitive range, instead of an almost arbitrary number, be shown to the management it’d lead to a different decision. With a reasonable possible outcome of 150% of the average sales, and assuming the cost of material is 50% of the price, then the total throughput would go down!
Throughput calculations: The current state in sales = 100 and throughput = 100 – 50 = 50. During the sales we get sales = 100*0.8*1.5 = 120, throughput = 100*0.8*1.5 – 50*1.5 = 45.
So, if the wide range is put on the table of management, and the low side would produce a loss, then management might decide to avoid the promotion. The other range supports going for the promotion even when the lower side is considered as a valid potential outcome.
Comment: In order to make a truly good decision for the above example, more information/intuition might be required. I only wanted to demonstrate the advantage of the range relative to one number.
What is the meaning of being “reasonable” when evaluating a range?
Intuition is ambiguous by nature. Measuring the total impact of uncertainty (the whole VUCA) has to consider the practicality of the case and its reality. Should we consider very rare cases? It is a matter of judgment as the practical consequences of including rare cases could be intolerable. When the potential damage of a not too-rare case might be disastrous then we might “reasonably” take into account a wider range. But, when the potential damage can be tolerated, then a somewhat narrower range is more practical. Being ‘reasonable’ is a judgment that managers have to make.
Using intuition to assess what is practically known to a certain degree is a major practical step. The next step is recognizing that most decisions have a holistic impact on the organization, and thus the final quantitative analysis, combining hard data and intuitive information, might include several ‘local intuitions’. This wider view lends itself to develop conservative and optimistic scenarios, which consider several ranges of different variables that impact the outcomes. Such a decision-making process is described in the book ‘Throughput Economics’ (Schragenheim, Camp, and Surace).
Another critical question is: If we recognize the importance of intuition, can we systematically improve the intuition of the key people in the organization?
When the current intuition of a person is not exposed to meaningful feedback from reality, then signals, which point to significant deviations, are not received. When the statement of the intuition of a person is expressed as one number then the feedback is almost useless. If the VP of Sales assessed the factor on sales as 5 and it was eventually 4.2, 3.6, or 7, how should she treat the results? When a range is offered then the first feedback is: was the result within the range? When many past assessments are analyzed then the personal bias of the person can be evaluated and important learning from experience can lead to considerably improved intuition in the future.
Once we recognize the importance of intuition then we can appreciate how to enhance it effectively.