When the Thomas Edison was asked about his success amidst failure, he said that “If I find 10,000 ways something won’t work, I haven’t failed. I am not discouraged, because every wrong attempt discarded is another step forward.”
With that kind of dedication, it’s no wonder that Edison was awarded over 1000 patents, including the light bulb, the phonograph and the motion picture camera, making him one of the most prolific inventors in history.
It also becomes clear why he regarded success as “1% inspiration and 99% perspiration.” Failing 10,000 times is a physical and mental undertaking that far exceeds most people’s endurance. Today, however, a new breed of innovators are outsourcing failure to computer simulations and it’s changing what we thought we knew about business strategy.
The Rise and Fall of The “Smartest Guys in the Room”
In corporate life, Mitt Romney was known for his acumen, strong work ethic and keen eye for talent. He carried these practices over to his political career and his campaign team was similarly bright and indefatigable They analyzed past trends, developed a theory of the case and executed their strategy efficiently. They had only one chance to get it right.
His opponent, the incumbent President Barack Obama had a different approach. He created an entire division of young, unkempt, over-caffeinated data junkies with little experience in business or politics. They had no set theory of the case, but instead ran 62,000 simulations per night and continuously updated their approach.
The result is now clear to just about everyone on the planet. The smartest guys in the room were no match for terabytes of data and smart algorithms. There is no more “theory of the case,” but thousands of them, being run constantly. The point isn’t to be right, but to be less wrong over time.
As Ria Persad, President of StatWeather – a firm that has managed to double the accuracy of weather forecasts, puts it, “There is a difference between a deterministic and a probabilistic forecast. We don’t actually predict one weather outcome. We run thousands of possibilities, present the most probable scenario and the risk associated with it.”
In effect, we’re increasingly moving towards a simulation economy, where strategic analysis gives way to reconstructing phenomena from real world data, testing hypotheses and learning.
Consumer Decision Journey or Drunkard’s Walk?
Marketers like to think of their craft as a consumer decision journey (sometimes called a path to purchase), where consumers are made aware of a brand, impelled to try it and eventually become loyal consumers who advocate the brand to their friends and family. That’s a good way to form objectives, but a horrible way to think about the real world.
In reality, our behavior looks nothing like that. I might plan on having a hamburger for lunch until my friend mentions that she’s on a diet and we opt to go for salads. Then we hear a colleague rave about a new Tex-Mex restaurant and decide to go there until a client emergency has us hunkering down in a conference room and ordering pizza.
Unfortunately, the old statistical marketing methods are ill suited for the real world because they are based on narrowing in on isolated elements, such as TV ratings and sales results, which ignore the complex interactions between the multitude of factors that go into a purchase decision. Marketing simulations offer the promise of a better way.
What’s important here is that executives will no longer have to “bet it all on a big idea” (which usually gets whittled down to a close version of what was done last quarter anyway), but can test countless “what if” scenarios before taking the plunge. The result will be not only better efficiency, but undoubtedly greater creativity as well.
There’s been a lot of talk about the new industrial revolution. Whereas before, design prototypes were usually physical objects, we now develop them in CAD software, and can produce a replica in minutes using a 3D printer or a CDC router. We can even 3D scan an existing object, alter it on the screen and then print out a new variant.
However, Icosytem, a firm based in Cambridge, Massachusetts is going beyond physical objects and helping organizations design processes as well, using a technique called agent based modeling. For example, they helped Pepsi understand how to better position its products by modeling how consumers move through a supermarket.
The same type of modeling can help buildings make decisions about energy efficiency, the military design missions in tribal Afghanistan and even reduce traffic jams. The US Navy has also contracted Icosystem to model to how it manages its personnel in order to maintain its world class fighting force.
Simulating at the Molecular Level
In Star Trek, whenever one of the Starfleet officers wanted a snack, he or she could just walk over to a replicator and order Cardassian eggs or Klingon firewine. In a few moments, the molecules would be combined into whatever they desired. That was science fiction of course, but Icosystem is helping to make it a reality.
The meals we eat, after all, are designs as well (albeit molecular ones) and can therefore be simulated. Hervé This, an expert in molecular gastronomy has developed a “mathematical grammar” for different dishes and Icosystem has been working with NASA to model the interactions in order to design the optimal process for creating food.
While the prospect of engineered food might not seem particularly appealing, if we’re ever going achieve interplanetary travel, it’s something we’re going to have to figure out how to do. Astronauts traveling to Mars would need food supplies for 3-5 years, far longer than most prepared meals can last today.
Of Gods, Men and Machines
We are generally brought up with basic rules to live by. For instance, the Bible tells us not to kill, steal or commit adultery (well most Bibles, anyway). We are also embedded with genetic programming (for example, fear of snakes is something that humans of all cultures share from birth). We use these rules to help us parse everyday information.
In computer programming, rules such as these are sometimes called “God parameters” and they serve as a useful starting point. However, with, cheap computing, the Web of Things and big data, our machines are now able to experience the real world much as we do, but on a much greater scale.
As Paolo Gaudiano, President of Icosystem notes, “ten years ago maybe one in twenty prospective clients had heard about agent-based simulation. Today it’s about one in five. Ten years from now you’ll be able to buy shrink-wrapped agent-based simulation software at Staples.”
That’s why the future of innovation is simulation. Whereas before, we might sit amongst ourselves and decide how the world might work and test our ideas in the market, now we can test them in a virtual environment built by real world data at much lower levels of cost and risk.
image credit: money.cnn.com
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Greg Satell is an internationally recognized authority on Digital Strategy and Innovation. He consults and speaks in the areas of digital innovation, innovation management, digital marketing and publishing, as well as offshore web and app development. His blog is Digital Tonto and you can follow him on Twitter.