Ken Gabriel, Draper Lab CEO will be speaking at the IRI meeting in Cambridge, MA on May 12th.  Draper was a Google executive and a senior DARPA official.  Other attendees will include folks from: Draper Lab, Entegris, FM Global, Gen3, HP Hood, Monsanto, Sappi Fine Paper, Saudi Aramco, Schlumberger, Teknor Apex.

When:  Tuesday, May 12; 4:00pm – 7:00pm (drinks and hors d’oeuvres will be served)

Where:  Draper Lab; Cambridge, MA (Hill Building; One Hampshire Street, Cambridge, MA )

Registration:  online (or download a registration form)

Fee:  $35

What does this mean for TRIZ-based innovation methods? If TRIZ is a universal approach for solving problems, it should work on all systems including information-based ones, right?   To date, however, TRIZ has not immersed into the world of information-related problems.  It continues to be applied in the world of mechanical, chemical and physical innovations. Here, TRIZ has statistical success, depth and breadth of application, and a richness of examples [2].

I have no doubt that TRIZ can be adapted to serve information-based needs, as well. The question is: what specifically should be done to spearhead this adaptation? The following seems like a reasonable course of action:

1)      Define what an information system is. Is it information itself, an information carrier, software, an algorithm or anything IT-related? A clear understanding of what is meant by an information system, what are its defining characteristics and what distinguishes it from the “physical” system is a necessary first step.

2)      Triage existing TRIZ-based tools into “information-friendly” and “non-information friendly” categories. For example, some tools, like Flow Analysis, fluently incorporate information as an object of its analysis. Other tools, like Function Analysis, deal with substances and fields but don’t deal with information. Yet, other tools, like Inventive Principles, don’t offer specific advice on information-related problems but can provide general guidance. Such tools have to be made more instrumental for information-based problems.

3)      Adapt existing TRIZ tools to deal with information specifically. What does the Trend of Increasing Dynamization mean for Facebook? Are some parameters (e.g., frequency) more typical for information-based problems?

4)      Develop new tools specialized in the analysis and problem solving of information systems. TRIZ is not what it was 50 years ago – new tools have been developed to enable the methodology to deal with new types of problems, objectives and types of systems. For example, Analysis of Main Parameters of Value (MPVs) was introduced to connect technical solutions to market needs, and Function-Oriented Search was introduced to deal with “non-inventive innovation”—an adaptation of available technologies that became available over the years. Similarly, it is very likely that new tools will be needed to solve problems related to the information systems.

5)      Develop examples. We need to create a quality database of information-based problems solved using TRIZ tools. While there is a growing number of TRIZ books that demonstrate how TRIZ can be applied to IT problems, these books often reverse-engineer solutions and/or show only basic TRIZ tools. We need examples of problems that were actually solved using TRIZ and with tools beyond Altshuller’s Matrix.

If TRIZ based innovation methods can be successfully intergrated into the information world, then TRIZ can be taken to Google, Facebook, TripAdvisor and Amazon – who wouldn’t want to go to those places. Any takers?

References:

1. Rich Karlgaard, Forbes Magazine (2013) Atoms Atoms Versus Bits: Where To Find Innovation (http://www.forbes.com/sites/richkarlgaard/2013/01/23/atoms-versus-bits-where-to-find-innovation/)
2. Haydn Shaughnessy, Forbes Magazine (2013) Samsung Gets Ahead Through Its Russian Connection (http://www.forbes.com/sites/haydnshaughnessy/2013/03/13/samsung-gets-ahead-by-using-cheap-russian-science/)

Could solely relying on your firm’s technical expertise be a barrier to achieving breakthrough innovation?  The answer is a resounding yes. A growing body of evidence shows that 1: cross-disciplinary knowledge is a driver of breakthrough innovation and that 2: an exclusive dependence on familiar expertise diminishes innovation impact. In a recent article in SUCCESS Magazine, popular innovation speaker Stephen Shapiro put it straightforwardly: “(…) the best breakthroughs are often found by connecting with entirely different areas of expertise.”

Look Beyond what’s Familiar…

Researchers Enkel and Gassmann of the Swiss Institute of Technology Management agree. From their investigation into the optimal cognitive distance between innovation partners, they concluded “in industry or field of experience, distance is not counterproductive but can be a source of both disruptive and incremental innovation.” There is empirical research to support this. In just one example, L. Rosenkopf and A. Nerkar studied the effects of what they term “organizational boundary spanning” (searching for knowledge outside the firm) and “technological boundary spanning” (searching for knowledge outside the technological domain). It was found that seeking knowledge beyond the firm’s domain expertise leads to “the possibility of new platforms that provide longer-term gains.”

…To Outperform the Competition

A study published in the Journal of Strategic Innovation and Sustainability showed that: “(…) organizations which strive to create radical innovations (…) will greatly outperform their competitors which look to produce more incremental innovations.”

If outperforming competitors depends on breakthrough innovation, and breakthrough innovation requires the ability to tap into unfamiliar external knowledge, why are so many firms staying within their industry comfort zones? A recent study based on 8,180 observations showed that the most frequent sources of knowledge — outside internal R&D departments — were from the stakeholders in the supply chain (suppliers, end users, etc.), not different industries or subject matter domains. Companies aren’t looking beyond what’s familiar because of two big challenges:

Challenge No.1: The Worldwide Knowledge Explosion

There is too much valuable knowledge creation to keep track of and too much irrelevant information obfuscating it.

Since the mid 1990s, investment in knowledge has grown more rapidly than investment in machinery and equipment and it has even surpassed the latter in the United States. Worldwide, the annual spending on R&D has grown to a staggering $1.4 trillion. As a result, industrial and academic knowledge has become a proverbial ocean that no one wants to boil.

What’s more, the exponentially larger and more general “Information Explosion” that surrounds R&D knowledge like anti-matter between galaxies, is continuing unabatedly. Eric Schmidt, the former CEO of Google, said: “There was 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days, and the pace is increasing.”

Challenge No. 2: The “Tower of Babel”

Even if relevant knowledge were easily identifiable, it would likely come from a discipline we know nothing about, using a scientific language that’s foreign to us.

Within the knowledge “Big Bang” there are many scientific disciplines that use many different “languages.” In academia, “interdisciplinary translation and synthesis” is increasingly talked about as critical for today’s complex scientific research.

Basarab Nicolescu, the President and Founder of the International Center for Transdisciplinary Research and Studies, recently asked: “Is a modern tower of Babel inevitable? (…) How can a theoretical particle physicist truly dialogue with a neurophysiologist? Disciplinary language is an apparently insurmountable barrier for a neophyte, and each of us is a neophyte in some area.”

Now more than ever, R&D teams need an effective method to break down cross-disciplinary barriers.

Bridging the Gap: A Functional Language

The most effective way to bridge the gap between different domains is with a language that reformulates problem statements in a way that can be understood by the entire technological and scientific universe. Functional Language achieves this:

• What is a function? A function is an action performed by one material object to change or maintain a parameter of another material object – to push, to stop, to move, to hold, etc. Often, we talk about the function that one component in a product/ process performs on another. For example, “HOLD” between the peel and the flesh of a banana.
• How are functions identified? Through Function Analysis performed “manually” or with software. Function Analysis allows an R&D team to list all the components in a system and then determine what functions they perform on each other. The result is a Functional Diagram.
• How do functions help with cross-disciplinary innovation? Because functions are so bare and technology neutral, they exist in many knowledge domains, making cross-disciplinary migration possible. To demonstrate, consider again the function “HOLD”. The banana peel HOLDS the flesh; the fuselage in a plane HOLDS the pressured air; the Champagne bottle HOLDS the Champagne. As shown, functions are consistent across the scientific landscape and allow R&D to identify enabling technologies in distant industries that could be critical for unlocking breakthrough innovation.
• But how does this work? A tool called Function Oriented Search (developed by Simon Litvin) provides a pathway for identifying other cognitively remote technologies and areas of science in which the function underlying your innovation target has greater importance or is performed under more demanding conditions. In other words, R&D teams can target their efforts without boiling the ocean.

Google’s new technology – Google Glass – made a statement on the runway in New York during Fashion Week. But these futuristic looking glasses are much more than a trendy wardrobe ensemble. Google Glass is one of many products that deploys augmented reality¹, a technology that displays information where and when it’s needed. And while the wonders of augmented reality are wooing both techies and everyday consumers alike, the real excitement lies in the possibilities of…what’s next? This question does not need to be left to the imagination, however. Future technology trends are predictable; to demonstrate this, let’s consider augmented reality from the perspective of technology evolution.

All technological systems evolve over time. The “now” is caused by the “before” and also influences what the “after” will look like – these evolutionary stages are intimately connected. To go from one stage to another, a disruptive jump is required.  But what are these evolutionary progressions and how can you predict the disruptive jump that will move a technology from one stage to the next?

In systematic innovation, these progressions are referred to as Trends of Engineering System Evolution (TESE). TESE postulates that all technological systems, from soap to aircraft engines, develop according to the same objective trends. In other words, the evolutionary paths for all different kinds of technologies are actually similar.

In particular, there is one documented trend that applies to augmented reality called The Trend of Increasing Coordination. This trend states that as the system evolves, it becomes more and more coordinated with its surroundings in a number of ways, including shape, rhythm, information, etc.  To demonstrate the progression of technology that has led up to augmented reality, it’s applicable to refer to the recently developed sub-trend of increasing coordination of information. ²

The Trend of Increasing Coordination of Information

Example: Traveling from Boston to New York City

Step 0, the system does not exist: Think back 200 years – you’d need to navigate through the woods and learn the path to New York through trial and error.

Step 1, the system exists, but does not provide information specific to the object of interest:  Think back 20 years – before traveling you would buy a generic map of the East Coast, find your house in Boston, find your destination in NYC, and then trace the route on your own.

Step 2, the system provides information specific to the object of interest: Think back 10 years – you would obtain point by point written directions (using Google Maps, for example) before  traveling.

Step 3, the system provides information specific to the object of interest when it’s needed:  Used by most today, a GPS system reliably guides you to your destination in real time, but you’re required to look back and forth between the road and your GPS screen.

Step 4, the system provides information specific to the object of interest when and where it’s needed – hail, augmented reality: The route is displayed on your windshield, or instantaneously, on the road itself.

 Information Technology for Traveling: Yesterday, Today and Tomorrow

As demonstrated above, step 4 is the most advanced stage of this trend. But the real question still remains, what will the “after” look like? According to the Trend of Increasing Coordination of Information, here’s to the future:

Step 5, there is no need for the system to provide information: Gone are the days of maps, GPS’s and augmented reality implementations; there is no longer a need for any system to provide you with information on how to get from Boston to New York. Why? Because after entering your destination address, a smart car will drive you all on its own, leaving you with nothing to do besides kick back and enjoy the ride.

It should be noted that TESE does not predict specific implementations; rather, it operates at the level of general recommendations. Specific implementations depend on factors including market needs, the availability and maturity of a particular technology, the specifics of a given industry, etc. Augmented reality can make its way to consumers through more than just its implementation in Google Glass. For example, apps on smart phones – such as Wikitude World Browser – can make daily tasks more efficient by generating real time information, and some online stores even allow shoppers to try clothing on, virtually.

In conclusion, TESE not only provides a historical perspective on how technologies have developed over time, but it’s also a powerful tool for long-term technology forecasting – a reliable approach for companies to surface breakthrough ideas that can secure future competitive advantage.

Sources:

• http://en.wikipedia.org/wiki/Project_Glass

• Sigalovsky, I.S. (2009) Trend of Information Coordination: new subtrend and its mechanism. Proceedings of TRIZfest 2009, St.Petersburg, Russia

• http://tomorrowawards.com/showcase_archive.php?showcase_id=523

• http://www.howstuffworks.com/augmented-reality.htm