In the world of fuzzy logic, two statements can be both true– even though they contradict each other.
Humans have a remarkable capability to reason and make decisions in an environment of uncertainty, imprecision, incompleteness… of information. Fuzzy logic is a form of multi-valued logic or probabilistic logic; it deals with reasoning that is approximate, rather than fixed and exact.
Fuzzy logic was developed by Lotfi A. Zadeh, in 1965, and it’s been applied to many fields, e.g.; control theory, artificial intelligence, decision-making… and, also to business management decision-making, which involves tough decisions with limited information that’s usually– vague, uncertain…
There are many misconceptions about fuzzy logic: To begin; fuzzy logic is not fuzzy. In large measure, fuzzy logic is precise, and it’s much closer to the way the human brain actually works. To better understand fuzzy logic ideas let’s relate its principles to human reasoning, for example; the statement– ‘today is sunny’– in reality might be 100% true, if there are no clouds; 80% true, if there are a few clouds; 50% true, if it’s hazy; 0% true, if it’s raining…
in the example, the fuzzy logic would actually represent these multiple conditions, whereas in traditional solutions, it’s either; true or false (i.e., its sunny or it’s not). It’s also used in some spell checkers for suggesting a list of probable words to replace a misspelled one, whereas in traditional solutions, it’s either; right or wrong (i.e., its spelled right or it’s not). Often knowledge used in discussing and making strategic decisions is expressed in terms of; rules of thumb, management principles, business rules …
Many experts suggest these types of rules are actually fuzzy rules that can be combined, using fuzzy logic, into a knowledge system that recognizes more than just simple; ‘true and false’ values. In the examples; fuzzy logic represented the multiple ‘degrees of a condition’, which in reality represents most management decision-making, and exactly situations when a fuzzy logic based decision process can be most effective…
In the article To Get Better Decisions, Get a Little Fuzzy by Bob Frisch writes: It’s easy to equate– crisp, clear, black-and-white decisions with good decisions. I’ve seen this classic model of decision-making dominate companies in practically all industries and corporate cultures. But, as often as not, this drive toward clarity and closure — and the need for precision that accompanies it — leads senior management teams to waste time and make meaningless decisions.
Often, it’s better to be fuzzy, to deliberately introduce imprecision into your team’s decision-making process. For example, an executive team charged with creating a high-level timeline for launching various strategic initiatives or determining whether a specific resource should be deployed would spend innumerable hours debating which ‘ones’ will be launched, immediately, and which ‘ones’ later this quarter, or which ‘ones’ two quarters from now versus next year, or even which in 60 versus 90 versus 120 days. The fact is; it doesn’t really much matter. Once reality of implementation intrudes on even the best-laid plans; decisions made by the senior management team often become more like suggestions anyway.
A few years ago, one of the senior management groups I work with tried something different: Fuzzy logic. It’s a branch of mathematics concerned with deliberately introducing imprecision into decision science. For example; washing machines, were being programmed to heat water to ‘warm’ without a specific temperature being assigned to that concept — ‘warm’ was simply the state of being hotter than cold and colder than hot. So, we decided to try a similar approach using fuzzy logic in management decisions.
Rather than just putting initiatives into many specific time buckets, I asked the senior management team to make one decision, namely: Was their initiative something that would start ‘now’ (i.e., immediately — tomorrow morning 9 a.m.) or was it something that would start ‘later’ (simply defined as ‘not now’)? This made executives uncomfortable, so we introduced a third bucket, namely– ‘soon’ — defined as; ‘later than now but sooner than later’.
Suddenly, the task of assigning initiatives to timeframes became dramatically simpler. The challenge of determining whether something was 30, 60, 90, or 120 days out was transformed into a much more manageable and meaningful discussion: Was their initiative something we are going to do ‘now or not’? If it’s not going to happen ‘now’, should it happen ‘soon’? That’s the discussion the CEO wanted to have, and the energy of the conversation was refocused into a much more productive channel.
We soon extended this notion of fuzzy decisions not just to time, but to importance. Rather than rank each initiative in an ordered single list, as we had been doing for years, we began assigning the initiatives to priority buckets, e.g., ‘must do’, ‘should do’, and ‘nice to do’.
The approach changed the task from judging the relative merits of each initiative against each other one to clustering projects with similar importance together. Since it eliminates both; problems of drawing artificially fine distinctions, on the one hand, and trying to compare apples to oranges, on the other.
By using this simplified approach it enabled the team to have a truly meaningful conversation about the relative strategic urgency, rather than the relative merits, of the various initiatives. Of course when we began, few sponsors were willing to categorize their initiatives, as either; ‘later’ or ‘nice to do’. So, most projects were initially put in the ‘must do’ and ‘now’ buckets.
But over the course of the conversation, a ‘high must-do’ cluster emerged that was clearly more important than the ‘low must-do’s’. As the ‘low must-do’ category devolved, predictably, into ‘high should-do’, the borders of the buckets were soon aligned into the appropriate clusters of initiatives.
Assigning relative importance (e.g., ‘must do’, ‘should do’, ‘nice to do’) and relative time (e.g., ‘now’, ‘soon’, ‘later’) to a set of initiatives, and then stepping back to examine the clusters of activities– isn’t precise, isn’t black-and-white, and isn’t crisp, however, because it more closely matches the way companies are actually run, it’s a far more effective tool for aligning the various priorities of the management team.
In the article Dealing with the Early Phase of the Innovation Process Characterized by High Degree of Ambiguity by Vadim Kotelnikov writes: The early stage of an innovation process is ripe with opportunity, but it is also devoid of many definitive facts. Due to its high degree of ambiguity, the development phase has become known as ‘the fuzzy front end’.
While the situations that fuzzy logic addresses are ambiguous; fuzzy logic itself is a very well-defined methodology. Business leaders use the managerial equivalent of fuzzy logic to address the ambiguity of ‘the fuzzy front end’. The core elements of the approach include:
- No plan survives: Whatever plan you create will not be one you will ultimately implement.
- Having a plan is better than not: Having a collective understanding of the business and intentions enables the team to select and communicate alternate directions knowledgeably and quickly.
- Make decisions at an early phase: You’ll never have all the data. Waiting for more data can go indefinitely, and much of what is called data, such as market research, is more opinion than fact.
- Be flexible: Ongoing thinking and action brings strategy to life. Scan constantly for new developments and be ready to retarget briskly when needed. Locking into any plan when the competitive context continues to change is a foolish approach.
- Think of product families: One-off thinking yields one-off products.
- Find a partner: Don’t do it alone. Focus internally on making a world-class contribution and then leverage others who can bring their complementary skills, resources and capabilities to the party.
- Balance customer feedback with technology potential: Listen to your current customers, but don’t always believe them. Although customers can be overly conservative, technology by itself rarely wins.
- Focus on opportunity, not financial returns: Money isn’t everything. Measuring return, risk, and investment solely in terms of dollars is a mistake.
Fuzzy logic is designed to deal with imperfect information, which in one or more respects is imprecise, uncertain, incomplete, unreliable, vague or partially true. In the real world, such information is the norm rather than exception.
Fuzzy logic is usually defined as an approach for decision-making based on ‘degrees of truth’ rather than the usual ‘true or false’. With today’s information overload, it has become increasingly difficult to analyze the huge amounts of data and to make appropriate management decisions.
It’s important to extend traditional decision-making processes by adding intuitive reasoning, human subjectivity and imprecision. Most publications in management and marketing do not address the problems which arise when using just traditional, non-fuzzy, or crisp methods.
According to Tomasz Korol; globalization has led to the emergence of a complex network of relationships in the business environment, which means increased complexity and uncertainty of factors affecting all businesses. For example; many phenomena in finance and economics are fuzzy, but are treated as if they were crisp… the vague and ambiguous concepts of fuzzy logic can define terms, such as; ‘high risk’ or ‘low risk’ and make them very much relevant…
According to João Paulo Carvalho and José A. B. Tomé; management of uncertainty is an intrinsically important issue in the design of decision systems, because much of the information in the knowledge base is imprecise, incomplete… In the existing systems, uncertainty is dealt through a combination of; predicate logic and probability-based methods.
A serious shortcoming of these methods is that they are not capable of coming to grips with the pervasive fuzziness of information and, as a result, are mostly ad hoc in nature. A feature of fuzzy logic, which is of particular importance to ‘management of uncertainty’ is that it provides a systematic framework for dealing with fuzzy quantifiers, for example, terms as; most, many, few, not very many, almost all, infrequently, about…
Fuzzy logic makes it possible to deal with different types of uncertainty within a single conceptual framework… Fuzzy logic is all about the relative importance of precision: How important is it to be exactly right when a rough answer will do?
As complexity rises, precise statements lose meaning and meaningful statements lose precision. ~Lotfi Zadeh