Fight Back Against Black Swan Fatigue

In today’s leanjust-in-time, and over optimized world, it’s not uncommon for executives roll their eyes when the term “Black Swan” is brought up in risk management discussions.  That’s because even though preparing for extreme events makes logical sense, there’s also a cost associated with redundancy and robust disaster planning.  In addition, no one is ever judged a hero for saving the company from what never (or is never supposed to) happen.  Business executives must fight back against Black Swan fatigue, because in today’s interconnected and highly correlated world, the next extreme event could be the one that shoves your company off a cliff.

Image Courtesy of Flickr

When it comes to preparing for low probability but high impact events (i.e. Black Swans), the sad truth is most business executives will do nothing.  Why? Nassim Taleb, author of the Black Swan, explains; “It is difficult to motivate people in the prevention of Black Swans. Prevention is not easily perceived, measured, or rewarded; it is generally a silent and thankless activity. History books do not account forheroic preventive measures.”

Taleb is right. No one will be labeled a hero for keeping extra inventory on hand. No one will be characterized a hero for divvying orders among various suppliers just in case the favored and most cost effective supplier goes belly up. And spending money on strategy and risk management consultants to disaster and scenario plan for worst case developments? Forget about it. These are all just costs, and cannot be afforded in today’s bottom line economy, right?

Someone wise once said that risk management is much like insurance. You hate to spend money on it, but you’re darn glad you have it when all hell breaks loose.

But wait you say, don’t most business executives plan for disaster? Perhaps, but there’s a difference between hiring a consultant to produce a disaster planning report which promptly collects dust, and actually preparing for and assuming extreme events will occur as part of your overall business plan.  And even when managers believe they’re prepared for worst case events, sometimes it’s not enough—with potentially horrific consequences.

As detailed in the March 27, 2011 issue of the Financial Times, executives at Tepco’s Fukushima Daiichi plant were prepared for earthquake. In fact, they were also prepared for tsunami—having built a seawall 20 feet tall. What they did not expect is that the March 11, magnitude 9.0 earthquake would cause a tsunami wave 40 feet tall! The tsunami promptly washed away the sea wall and also the diesel powered generators cooling the spent nuclear fuel rods housed at Fukushima.

Executives at Fukushima had planned for disaster. They had built a 20 foot seawall. They had redundancy with backup generators in case the cooling system failed. And the nuclear plant powered down once the 9.0 magnitude earthquake hit. Everything worked as planned. But they were not prepared for the “unthinkable” extreme event.

Predictive modeling based on historical data will only take you so far. Even extrapolating with Bayes isn’t going to be of much use for “unknown, unknowns”.  As business managers we must fight Black Swan lethargy, especially when all oars in the boat are rolling towards lands of “optimization” and “cost effectiveness”. As managers, we must continue to sound the alarm, even though probability of the extreme event is of the smallest percentages.

Play up the risk of future Black Swans and then prepare for the extreme event. Here’s to hoping you’ll never be proved right.

Black Swan Alert: Low Tech Links Devastate High Tech Supply Chains


Find your supply chain best practices checklist. Just-in-time processes? Check.  Optimum sourcing strategies? Check. Lean logistics? Check. Best-in-class technologies? Check.  Black swan avoidance? Uh-oh.

Bangkok, Thailand is under water.  In perhaps the world’s worst flooding to devastate such a large city in modern times, Bangkok is home to ten million citizens and has an additional ten million people outside city limits.  The Chao Praya river drives down the middle of Bangkok and is supported by dikes and drainage canals for rainy seasons. But this month, the river is eight feet above normal, and while dikes have not failed, flooding has caused most residents to leave the city.

The human tragedy of such a flood aside, what’s occurring in Thailand is on just about every global CEO’s radar screen. And that’s because Bangkok is a crucial link in many high tech supply chains.

David Pilling of the Financial Times notes that companies such as Mazda, Toyota and Toshiba all have suppliers and factories in Thailand. And Honda’s Thai assembly plant, which traditionally manufactures 250,000 cars a year, has been shut down for three weeks.   With such interconnected and complex supply chains, sometimes spanning many countries, Pilling says these links are “prone to strain, particularly when paired with the just-in-time practices pioneered by Japan.”

Many supply chain gurus push companies to optimize inventory practices, adopt just-in-time strategies, and reduce redundancy wherever possible. And in “normal times” this approach makes sense. However, when disaster strikes, manufacturers are discovering their thin margins for error are in fact, leaner than they should be.

Nassim Taleb, author of The Black Swan, and professor of Risk Engineering at New York University is no stranger to interlocking fragility. He says most business professionals assume we live in a world of mild randomness, where events don’t stray far from the mean. Taleb argues the opposite, that in fact, because of tightly coupled financial and consumer markets (much less recurring natural disasters) we live in periods of wild randomness where small probability events carry large impacts (i.e. Black Swans).  These extreme events—Thai flooding or Fukushima disaster, for example, are shutting down entire supply chains for weeks and even months, causing hundreds of millions in lost profit opportunities.

A solution proposed by Dr. Taleb and others is to build a robust system (in this case supply chain) with redundancies and disaster recovery processes to properly manage extreme event risk. And while some experts argue redundancy adds costs to already paper thin margins, there are surely costs to not supplying marketswith needed product because an unforeseen event has silenced your supply chain.

Bob Lutz, former vice chairman of General Motors says it best; “Running your procurement purely on a short term, point in time, cost minimization model is like shopping for rock bottom home insurance. It looks real smart until your house burns down.”  He goes on to say; “What happens when ‘just in time’ is ‘just plain late’”?

Good question and one which many global CEOs are still struggling to answer.

Zero Latency: Faster Isn’t Always Better

Vendors often promise some derivative of the term “faster” in marketing and sales literature (i.e. faster decisions, quicker time to value, rapid implementations etc…). And to be sure, in plenty of cases, speed wins especially in terms of gaining insights into markets and customers before competitors get a clue. However, when it comes to decision making, too much speed without attention to improvements in logic and business processes can be disastrous.

It’s easy to confuse “fastest” with “best”. That’s what Jennifer Hughes writes in a Financial Times article on the arena of high frequency trading (HFT). The term HFT refers to buying and selling financial instruments in microseconds with the help of supercomputers, sophisticated algorithms, and in most instances co-location of equipment near stock exchange servers. In HFT, the goal is to make profitable trades faster than competitors, and this means that massive amounts of data must be examined in real time and buy/sell decisions executed in microseconds.

While an extreme case, high frequency traders are truncating the decision making window between “event” and “action” to near zero. In the previously mentioned Financial Times article, Kevin Rogers of Deutsche Bank says; “With some parts of the market we are getting to the point where the speed of light (is the only constraint).” And certainly, if one company can spot deals and trade faster than another, microseconds can be a significant advantage in profitability.

However, while in many cases speed wins, there are concerns, especially in terms of cost. After all, throwing millions of dollars in compute power to shave off a couple of microseconds might not be worth the investment. “We’re looking at a tipping point,” says Harpal Sandu, founder of electronic trading network Integral Development. “Trading isn’t going to get much faster than a few dozen microseconds—physical machines don’t run much faster than that.”

In addition, making decisions faster than competitors is useless if careful attention is lacking in data input, decision logic (possibly manifesting in algorithm development) and continual process improvement.  Moreover, the best decision today, or even ten minutes ago, might not be the best decision tomorrow, especially because external conditions make for a moving target with governmental policy changes, mergers and acquisitions, new technology development and more.

A final consideration is fragility. In high frequency trading for example, as trading decisions move closer to zero latency, there is less opportunity to remedy a potential mistake whether it consists of a “fat finger order”, or simply a poor trading decision that a company would like to correct. Adding insult to injury, in a complex environment such as stock markets, a poor decision made quickly can cause cascading effects to other players creating a massive market disruption.

In the countdown to zero latency, the focus is currently on speed. However, the returns on faster decision making are diminishing and equal opportunity should also be given to risk management considerations, business process improvement, and monitoring of business conditions to continually upgrade and refine decision making logic.


  • Can speed drastically increase without introducing fragility?
  • Does a focus on speed provide an opportunity for companies to “get better” in how they deliver products and services?

Eight Things You Should Know About Statistics

Statistics have been called “an engine of knowledge” by one risk management expert. And while it’s true that some business managers don’t have a fundamental grasp of statistical concepts, we also know there is opportunity for misuse of mathematics. Is statistics the “new grammar” or are efforts to attach certainty to life’s events doing more harm than good?

In May 2010’s issue of Wired Magazine, author Clive Thompson laments the poor mathematical literacy of his fellow citizens. For example, he cites people laughing at the concept of global warming as they face some of the harsher winters on record, or the extra-vocal debate on vaccines and possible links to autism. Mr. Thompson would tell us that it’s the trend lines that matter, and we too often look at the trees and miss the forest.

The problem, he says, is that “statistics is hard” and an overall understanding of this important discipline is severely lacking. He says, “If you don’t understand statistics, you don’t know what’s going on, and you can’t tell when you’re being lied to.”

Thompson is correct that statistics are difficult for most of us, and that thinking by the numbers takes training and much effort. It’s also true that one must understand statistical concepts, especially when percentages, populations, and probabilities are bandied about in business and technical press. However, broader acceptance of the power of statistics should be tempered with limitations of this mathematical science.

Before accepting any statistic, study or experiment as gospel, the following should be considered (there may be more…):

1. Assumptions: What are the assumptions underpinning the research? As seen from recent debate on CBO numbers for the U.S. health reform package, assumptions matter tremendously.
2. History: How much historical data was used in the study? What was the time scale? As seen from the 2008 financial crisis, the models used by Wall Street mavens often only took into account 10 years of data in judging the volatility and probability of failure of complex financial instruments.
3. Samples: Are the samples selected randomly? From what populations? Is there enough data for statistical significance?
4. Data Quality: The output is only going to be as good as the quality of data feeding the analysis. Garbage in, garbage out.
5. Survivorship Bias: Author Nassim Taleb points out that “losers are often not in the sample.” Does the analysis include a population of survivors and those who also failed?
6. Falsification and Omission: Yes, in an era of IPCC’s Climate Gate, one needs to ascertain if data are hidden, missing or outliers ignored.
7. Association equals causation fallacy: Correlation does not equate to causation (a common mistake made by marketing and finance executives alike).
8. Proper Application of Statistics: The effective use of statistics by insurance actuaries, scientists, and even casino managers is well-documented. However, real danger results when mathematical concepts are used to denote certainty indecision-making and divining behavior of markets.

Now, please don’t get me wrong. Statistical analysis is very important for many industries (e.g., health care, transportation, and manufacturing). Statistics, however, can give us an illusion of control in a world that’s much more complex than our models suggest. Nassim Taleb, author of the Black Swan likes to remind us that “(real) life isn’t a casino.”

Statistical analysis is definitely a powerful gadget in the business manager’s decision-making toolkit. But one needs to understand the limitations of this science.

After all, Taleb points out that many of today’s statistical models work as though we have “full knowledge of the probability of future outcomes.” And this just isn’t so, especially when it comes to fat tails, or the “ten sigma” event. Indeed, sometimes those rare events have extremely large impacts. Were he alive today, the former captain of the Titanic, E.J. Smith would wholeheartedly agree.

• Clive Thompson calls statistics “the language of data.” How important is it for marketers to understand and apply statistical concepts?
• “Lies, damned lies, and statistics” is a phrase popularized by Mark Twain in the context of using statistics to unduly persuade, obfuscate or even swindle. Can statistics get its reputation back? If so, how?