Commentary by Michael S. Slocum

More and more the economic pressures will increase the need for proficiency unilaterally. One way for us to respond to this challenge would be to optimize those things that we currently do. This means of course that we need to get better- and we may already be very good. So although this sounds logical that doesn't mean it is easy to do. In fact, it can be very difficult. Especially as financial resources cause us to reduce headcounts and streamline other expenses. That compounds the issue adding the complexity of having to do more with less. And the more has to be more efficient than it was before. This forces the less into a higher state of expectation. So it is quite a conundrum. Clearly we cannot proceed with a business as usual mindset. Things have to change.

You can't expect things to change just because you need them to. The type of change needs to be intentioned. Also, capability must be provided to those expected to deliver the needed change. This makes these difficult times an ideal opportunity to provide training. As more is expected with less, it seems natural that the innovative ability of all needs to be increased. Therefore, intentional and structured innovative techniques are found to be more important than ever before.

The impetus for change is here. Take advantage of that and get the capability of your staff increased. Let innovation guide us through the storm. It has before and it can again.

Regardless of how extensively you deploy TRIZ, or some other systematic innovation engine, one of the first steps you take is to define your ideal state. In TRIZ, this is your Ideal Final Result (IFR), a philosophical construct that provides a measurable framework within which you can gauge progress on an innovation project, as well as an overall innovation roadmap. The IFR can also be used to create the perfect solution to strive for in problem solving.

Leonardo da Vinci has suggested that it's good practice to think of the end before the beginning, suggesting the definition of a target before taking aim. The TRIZ methodology proposes that you develop this target, so that you don't find yourself randomly shooting, and then feel surprised when you don't hit anything. From this perspective, it's not important whether the IFR is practicably attainable; what does matter is that you release the creative process from the hold of psychological inertia, and that you accept the possibility for a perfect innovation event to occur.

The IFR is a tremendous improvement over current approaches that promote the search for mediocrity, which, of course, people refer to as "compromise." If you don't envision the IFR, you never really know how weak your resolutions are, and you never know how to gauge innovation progress. Therefore, four IFR criteria apply to the configuration of any IFR for any innovation project:

One, the IFR does not introduce new harm into the system at hand.

Two, the new solution preserves all advantages of the existing system.

Three, the new solution eliminates the disadvantages of the existing system.

Four, there is minimal or no increase in complexity.

Pragmatically, the IFR of any innovation problem is conceptualized into a metric called Ideality, which is the sum of the useful functions in a system divided by the harmful functions in a system. Although the IFR is philosophical in nature, Ideality is mathematical in nature. Ideality is a useful metric, because IFR attainment is usually not possible, but multi-generational progress toward the IFR is possible and expected.

In other words, concepts develop during TRIZ problem solving are not equal, and the litmus test for all innovation ideas is the metric of Ideality, which, simply stated, is the inverse of the distance between the current state of a system and the ideal state of the system. Therefore, the closer the current state is to the ideal state, the higher Ideality is.

In all, the notions of the IFR and the Ideality equation are critical in the battle against mediocrity and are, therefore, absolutely necessary ingredients of systematic innovation. If you can increase the useful functions in my system and decrease the harmful functions, with no additional cost per unit of benefit, you've achieved the objective of innovative adaptation.

It is the intention of the TRIZ practitioner to maximize Ideality by maximizing the numerator and minimizing the denominator. However, the actual calculation of Ideality may never be strictly necessary, or possible, as it's difficult to capture every element in a system, then perfectly distribute each element's impact on the numerator and the denominator — let alone normalize all the units of measure involved.

Many times a system is improved by increasing the performance of a particular function in a system. For example, if the function of fuel efficiency in an automobile is important, a system improvement might involve a system change that increases fuel efficiency by 5%. This is an improvement to the system-although an incremental one. The performance of fuel efficiency had been improved to another point on the curve that is asymptotically approaching the theoretical maximum for that system. Incremental functional improvement is necessary; however, it is not the only way to improve a system.

Sometimes it is necessary, for strategic reasons, to create a system improvement that introduces a new functional performance level that is unobtainable by the current system. In our example, this might be replacing a combustion engine in our automobile with some other non-combustion system. This replacement might give us an initial fuel efficiency vastly superior to the theoretical maximum achievable with the existing combustion engine system. A new functional performance curve has now been created and it is discontinuous versus the previous.

Functional Discontinuity is an important evolutionary tactic. We need to take advantage of this technique as much as possible. It allows us to evolve on the curve as well as off the curve. This gives us current generation improvement as well as the opportunity to create the next generation of functional performance.

Open innovation is practiced when the search space is intentionally increased and redirected based on certain specific techniques (not exhaustive):

(1) Leverage non-subject matter expertise

a. Add non-SMEs to the problems solving team and utilize techniques designed to solicit their input in an effective manner

b. Add multiple functional areas to the problem solving team

c. Add customers to the problem solving team

(2) Search for intellectual property in areas similar to the problem composition but that are also outside the industry/technology in question

a. The Theory of Inventive Problem Solving (TRIZ) has many techniques that may be used to achieve this

b. Use patent search techniques as well as Patterns of Evolution

(3) Adapt solutions from analogous problems to suit your current purpose

These three items will constitute an effective open innovation approach to problem solving and allow the introduction of discontinuous solutions into the solution space. This increases the effectiveness of problem solving and allows for adaptation.

Brownian motion is the random movement of particles suspended in a liquid or gas. This phenomenon was first observed by botanist Robert Brown in 1827 ("A brief account of microscopical observations made in the months of June, July and August, 1827, on the particles contained in the pollen of plants; and on the general existence of active molecules in organic and inorganic bodies.") and later introduced to the realm of physics by Albert Einstein in 1905 ("Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen.").

To illustrate the concept, consider a beach ball 30 feet in diameter. Imagine this beach ball in a sold out soccer stadium for a match between Manchester United and Arsenal. The ball is large enough to lie on top of many members of the crowd. The fans hit the ball at different times and in different directions with the motions being completely random. The ball is pushed in random directions, so it should not move on average. At other times, we might have 20 fans pushing right, and 21 other fans pushing left, where each fan is exerting equivalent amounts of force. In this case, the forces exerted from the left side and the right side are imbalanced in favor of the left side; the ball will move slightly to the left. This type of imbalance exists at all times, and it causes random motion. If we look at this situation from a helicopter above the stadium, so that we cannot see the fans, we see the large ball as a small object animated by erratic movement. Consider this animation to help illustrate the concept:

Click here to view animation.

Now let's use this concept to describe an organizational dynamic all too common. If you consider the intentions of the members of an organization being positive (excluding then any malicious misdirection) we can assume that all employees are acting in a manner they believe to be beneficial to the organization that they serve. However, with no concise set of daily operational actions that was generated to support organizational objectives, the employee moves from crisis to crisis creating as many solutions as possible given the time they have to spend on them. This is far from ideal. This is ignoring the future to focus on the present. Sometimes it seems like this is necessary, but in the long run this behavior will make sure there isn't a long run.

Let's consider our animation of Brownian motion. The large blue disk is the organizations location in the domain of organizational performance. With a known starting location (the organization's current state) and no organized employee support for a specific future state (goal), the actions of the employees (the red disks) will move the current state (blue disk) slightly depending on any local bias without organized or intentional movement of the organization towards any goal let alone the ideal goal. This is Organizational Brownian Motion. Many organizations experience this dynamic on a daily basis and it is counter-productive. Trial and error derived solutions with no characterized ideal resolution as a target allows problem solving in an organization to produce the same effect in the evolutionary map of a system. There is no intentional maturation of a system in relation to the strategic adoption of functionality but rather a haphazard amalgam of problems with sub-optimal resolutions. Every time we allow this to happen we create competitive opportunity. So ultimately, lack of organizational focus, at the macro-scale and the micro-scale, is all our competitors need to take market share from us. Studies have shown how difficult it is to create loyal customers and any erosion of their confidence yields more losses in that area. We need an organized strategic planning apparatus that decomposes organizational goals into actions for every person in an organization. We also need a systematic approach to problem solving that yields solutions that not only support the aforementioned strategies but also optimizes system evolution and minimizes the creation of any competitive opportunity.

Dissipative dynamical systems contain some sort of friction. The chief feature of a dissipative system is loss of energy. A pendulum swinging in air will have dissipation. Energy is lost continuously through the various kinds of friction experienced by the pendulum. We call a system where energy is maintained, a conservative dynamical system. This would describe a system with no friction. Heavenly bodies sustain so little friction we describe their motion as being conservative; no energy is lost. Mathematics tells us that the long-term behavior of dissipative systems may be described by a simple pattern of motion whose final state is a point or a limit circle. The final state of a dissipative system that is highly complex and demonstrates chaos is described as a strange attractor. Let us describe a strange attractor that we may utilize to describe the phenomenon:

The Feigenbaum Scenario (TFS)

Let us focus on The Feigenbaum Scenario to illustrate a few innovation analogies. Notice how the TFS is described by a series of bifurcations. The bifurcations increase geometrically until the model describes the domain of deterministic chaos. However the first several bifurcations determine the final region that comprises the Deterministic Chaos. It is therefore critical that the domain of Conscious Choice be exploited. This is where the concept of Ideality comes in. Ideality is the ultimate outcome of the problem solving process (at least from a tactical innovation scenario where you are solving a problem in an existing system). Therefore, Ideality is a philosophical construct that dictates the direction problem solving should take to yield the perfect resolution. Ideality can also be described as a series of decision points that must be satisfied in order to describe the target resolution. These decision points are analogous to the bifurcations in the Conscious Choice Domain of the TFS. The innovation process can be controlled to the extent allowable as the problem statement and ideal goal are specified. At a certain point the bifurcations are too numerous and determinism is no longer possible. Therefore, systematic innovation is possible to a certain extent but determinism breaks down at a certain point. The fuzzy front end of problem solving can be brought into focus but the mental operations of the human brain still allow for chaotic thought generation.

A look into string theory from the field of physics will give us an interesting analogy to leverage. String theory is a method of reconciling the fact that general relativity as a theory only works if we ignore the implications of quantum mechanics and perceive the world in a purely classical sense. String theory is an attempt to resolve this discrepancy. String theories come in two flavors (not to be confused with the flavors of quarks). Strings are either closed or open and include or do not include fermions (particle that makes up matter). Supersymmetry is the string theory version that allows fermions to enter the theory as long as each fermion is coupled with a boson (particle that transmits a force). So supersymmetry relates the particles that transmit forces to the particles that make up matter. So for every particle of matter there is a force partner. The same is true in the field of innovation.

I have written previously about four significant aspects of a deployment (Race Cars and Corporations):

• Culture
• Infrastructure
• Method and
• Proficiency

If we now consider that these four aspects are material components of a successful innovation deployment we can add the forces responsible for each, thereby comprising a sort of supersymmetry. So let us discuss the force partners for the material components.

Culture: Culture is the ability to shape innovative behavior and practices on a widespread scale. The force partner for the proper formation of a culture conducive to a successful innovation deployment is the CEO of the organization. The tactical and strategic implications of systematic innovation create a need that can only be shaped by the senior strategist in the organization and this is the CEO. You could argue that Preservationary activities could be led by the COO but it is clearly the prevue of the CEO to shape the future (Evolutionary activities). So the innovation supersymmetry pair here is:

CEO/COO »» Culture

Infrastructure: Infrastructure is the technology and management supports that are necessary to grow and reinforce innovation. The force partner for the proper establishment of the infrastructure necessary to support an innovation deployment is a larger team than that listed for the cultural formation. Infrastructure has many components. Some of the key components are:

1. Information Central for the Deployment (website etc)
2. Job descriptions for deployment roles
3. Integration with Strategy
4. Internal Training and Consulting team
5. Incentive Program
6. Training Schedules
7. Proficiency Requirements (and many others)

The formation of these items clearly necessitates the formation of a larger team. A senior executive reporting to the CEO would organize the effort and you could then probably assign this responsibility to a deployment manager (maybe a VP). Therefore, the establishment if infrastructure would be led by the VP of Innovation. So the innovation supersymmetry pair here is:

VP of Innovation »» Infrastructure

Method: Methodology is the standard roadmap for implementing innovation projects with a high probability of payoff. It is also the selection of tools and techniques that will be used to create a systematic approach to problem solving and concept generation. The VP of innovation will need to work with a team of internal and/or external experts to identify the composition of the method portfolio. The need to problem solve must be analyzed along with the need to create novel concepts. The methods selected must also integrate with existing methods like Six Sigma and/or Lean. So the innovation supersymmetry pair here is:

VP of Innovation/SMEs (internal and/or external) »» Method

Proficiency: Proficiency is the ability to ramp up world-class innovation capability in the shortest possible amount of time. Again, the leader of the deployment and the SMEs need to establish the criteria that will identify when the learning objectives have been met. A key criterion is being able to apply the methods to effect change and produce economic benefits. So the innovation supersymmetry pair here is:

VP of Innovation/SMEs (internal and/or external) »» Proficiency

Supersymmetry has more than one analog here. The application of systematic innovation as a deployment needs to focus on improving existing systems as well as producing new concepts for future revenue generation. This means that each aspect discussed above has two parts. This makes the activity more complex and amplifies the amount of planning needed prior to execution. It also reiterates the necessity of all the force transmitters identified above to work together in support of a common strategy that is set by the CEO.

Before a problem should be solved it is important that it be decomposed into sub-systems. That way each sub-system can be analyzed and the problem can be solved one part at a time. This is an important aspect of actual problem solving. Many problem solvers have been thwarted by attacking a complex system and then being overwhelmed by the complexity associated with that task. Problem decomposition solves many of these issues. The Heuristic Redefinition Process (HRP) is a powerful method for accomplishing the decomposition of a problem into its constituent elements. HRP is simple enough and the basic steps of the process can be described as follows:

1. Describe the system that contains the problem
2. Define the elements that comprise the system
3. Define the objective of the system
4. Describe how each element achieves the objective of the system
5. Convert each description into a question: "How can we ensure the ELEMENT provides the OBJECTIVE?"
6. Each question then becomes a problem solving or optimization opportunity for the system
7. The problem solver prioritizes these and then begins problem solving at this level (not the original system level)

HRP is a simple approach that can dramatically improve the results of the problem solver's activities. It should be part of every problem solving effort and is an important part of defining the problem.

"Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat." - Sun Tzu -

A great idea is never enough. Excellent execution is not enough. In today's complex business environment, a confluence of expertise must be coordinated in order to assure anything close to victory. A bad idea can be implemented ideally and a great idea can be implemented poorly. Neither will help you win the race to long term survivability. You can also have a great idea that is implemented ideally and still miss the needs of the business. You need the right need identified, you need the right idea that will meet the need, you need the right execution, and on top of all of that, the timing needs to be right.

From this perspective, it is easy to admit that Six Sigma is not enough. There isn't any single method that is enough. Not yet anyway. Competitive Excellence should change the landscape by presenting a holistic approach for business success that can do all that is asked of it.

"Do not repeat the tactics that have gained you one victory, but let your methods be regulated by the infinite variety of circumstances." - Sun Tzu

A business must perform across many domains in order to move from concept to commercialization. The initiating event is the identification of a societal need (SN). A societal need is an unmet desire/need that exists in society. It is the prime mover for the development of an idea (product or service related). Identification of an unmet societal need is the critical step in the commercialization of anything. Society wanted to change the television channel without leaving its seat - the remote control was a response to that need. Society did not want to die from small pox - the small pox vaccination was the response. In order for a business to develop a product or service to meet an identified unmet societal need, the need must be converted to a set of customer requirements (CRs). This translation starts to move the business in the direction of an actionable set of criterion. Responding to an unmet societal need must decompose into action like strategy must decompose into work (Drucker). The customer requirements describe the attributes the product or service must possess in order to meet the societal need. The correlation between the SN and the relevant set of CRs describes the quality of the solution. When a product or service is released to the consumer and the CRs are not met, competition is invited into the space. The leverage that competition has is proportional to the number of unmet (or partially met) CRs. The set of CRs must be converted into a corresponding set of functional requirements (FRs). The identified functions identify a language that the product/service developers use to create a concept. The functions are those necessary to meet the CRs that in turn deliver the SN (SN--CRs--FRs). This decomposition decays the strategic need into a set of necessary functions that the system must provide in order to be a viable response to an unmet SN. The FRs are actionable as they are the drivers for concept generation. The identified concept(s) must provide the functions, that then meet the CRs, which satisfy the SN (SN--CRs--FRs--Concept).

While concept generation is not its own domain, it is the critical transition point from need to response. Once a concept has been generated, design parameters (DPs) must be identified that will deliver the functionality inherent in the concept. The DPs are the language necessary for the reduction of a concept to practice. The DPs must correspond to the FRs and this correspondence strength determines, in part, launch success (SN--CRs--FRs--Concept--DPs). The DPs identify the architecture a product or service must represent in order to provide the necessary functions. These functions meet the CRs, which satisfy the societal need. The DPs must be converted into the language of production in order to be manufactured and/or assembled. This is called the process variable domain (PV). The same correspondence should be preserved: SN--CRs--FRs--Concept--DPs--PVs.

Although there is much that takes place in the commercialization process, these five domains categorize the centers of activity appropriately. Therefore, a business needs competency across these domains. The plot continues to thicken though. Just being able to execute a good idea efficiently is no guarantee of continued success. The business needs to identify the ideal state it seeks to attain. It must also assess what its current state is and identify the gap between the two. The business must identify the appropriate path of action to bridge this gap. These larger strategic issues form the backdrop upon which concept commercialization is played. There is also a larger need for an infrastructure able to support multiple methodologies. All of this works together to create a complex need state for an organization. Also, the strategic alignment will provide a polarizing force that will allow the entire organizational resource pool to pull the organization towards the ideal state.

Without a polarizing force, the organization is in a state of Corporate Brownian Motion (CBM). Brownian Motion is (named after Scottish botanist Robert Brown) is the random movement of microscopic particles suspended in liquids or gases resulting from the impact of molecules of the surrounding medium. Corporate Brownian Motion is the phenomenon where members of a corporation do what they think is best for the organization and also react to each others activities. There is no concerted effort to achieve, just effort to do.

The Battle of Poltava was a resounding victory for Peter the Great. He defeated Charles the XII and 14,000 Swedish cavalry with a superior force of 45,000 Russian soldiers. The battle lasted all day with the outnumbered Swedish soldiers making several valiant efforts against the superior Russian forces. Ultimately the Swedish soldiers had taken too many losses to effectively continue the battle and Charles retreated to Moldavia for five years before he could finally return to Sweden. The captured Swedish soldiers were taken to St. Petersburg and they helped to build the great city.

From October 1941 to January 1942 the Germans attempted the invasion of Moscow and then suffered a counter-attack after the city had been defended. Operation Barbarossa called for the Nazis to capture Moscow in four months but the brutal Russian winter and the lessons of Napoleon were ignored. The Battle of Smolensk slowed the Wehrmacht down and the tide was completely reversed at Moscow.

What are the implications these battles have for the modern corporation? Charles of Sweden underestimated the skills of the new Russian cavalry under the innovative leadership of Peter the Great. Hitler underestimated the Russian resolve and the Russian winter that decimated his blitzkrieg. Corporations also make similar mistakes in judgment. Leaders assume that prior supremacy will sustain the organization even when an upstart challenger enters their field of excellence. Charles assumed that the legacy of the powerful Swedish cavalry could not be matched by the new Russian horse soldiers. The skills of the entrepreneurial organization are not to be underestimated. Peter the Great made this point at Poltava. The power of a discontinuous innovation will propel the upstart to the market space and the mature market share will erode. Customers have proven to be disloyal unless they have become advocates of a particular product or service. Other organizations believe that their superior capital will protect them from any onslaught. They also believe that a swift response to competition will quench any threat. The next generation of an existing product or service remedies all challenges. Or at least they hope this is the case. The fickle and ever-changing competitive landscape can render these advantages useless as the money follows the features and benefits of the function being provided. The prevalent corporate force can find itself unprepared for the challenges ahead as the Third Reich experienced as it forayed into Russia.

If Charles had not underestimated his opponent he would not have brought 14, 000 soldiers to battle 45,000. If Hitler had not assumed that his previous victories would be replicated in Russia without consideration of the famous Russian resolve and the accompanying infamous Russian winter things would have been much different. So too, if the corporate leaders of today will prepare for the foe that is coming after their market share, they will not be defeated. Complacency will be their undoing. Leaders must embrace the science of innovation as they have the sciences of productivity and quality. This is the first step in dodging that exile that Charles found himself facing.

OK not really. However, I am going to describe an approach to innovation that does allow you to leverage the intellectual capabilities of OTHER problem solvers for the generation of solutions to YOUR problems. This can be achieved by utilizing previous problem solving output and adapting it to suit your purposes (what I call Adaptivation). Adaptivation may take place if you will transition your problem statement that is specific to the realm of the abstract. The realm of the abstract allows you to connect with a repository of problem solving information that is found in Contradiction Theory which is a Theory of Inventive Problem Solving (TRIZ) subset. Patents have been analyzed and each specific problem has been affinitized at the abstract level. The same process has been applied to each solution. Therefore, if you convert your problem to the abstract you can match it to an abstraction from Contradiction Theory. Then you will benefit from the resultant solution abstraction. This gives you the necessary kernel that may be used by analogy to create a solution that applies to your specific problem (Adaptivation). This method introduces you to the problem solving thought processes of others who needed a solution and have produced information to that effect. So in a sense, you are benefitting from their knowledge directly. This approach is both open (leverages knowledge you or your organization does not possess) and efficient (not creating a unique solution when an adaptation will work). You can increase your intellectual capacity by using some structured and effective techniques that allow you to benefit from other people's work.

You can read more about this concept in my recent Real Innovation article on Adaptivation titled: "Smart Innovation Adapts to any Problem or Situation."

Ideating takes place in cycles. Cycles of convergence and divergence that is repeated through the definition and resolution phases of problem solving. The process is also iterative. This means that the cycles continue indefinitely as a solution to a problem creates new problems and so on.

The first step is to define the current problem and create the scope for problem solving. This is convergence to a specific problem statement. Then the problem solver needs to diverge to create the identity of the perfect solution (the IFR in TRIZ vocabulary). Once the ideal solution criteria are established you then converge to the acceptable solution criteria. These steps continue until the right problem is scoped and the success criteria established. Then the techniques that are used to create solutions are divergent. The problem solver ideates inside the scope previously identified.

Once solutions are created, convergence to an implementable solution must take place. The process may be represented like this:Converge to problem – Diverge to ideal solution elements – Converge to IFR – Diverge for ideation – Converge to solutionThis process is iterative at any step. Also, the implemented solution may create secondary problems and this then repeats the process.

We are accustomed to solving problems using a linear two-step process. This process is as simple as problem » solution. Our psychological bias is the foundation for our ability to solve problems using this simple process. We have an innate ability to solve problems rapidly that is derived from our automatic intellectual function. This function is a subjective analytic based on the intersection of our education, experiences, and other cultural and environmental bias found in our surroundings.

Psychologists have demonstrated that the automatic intellectual function is ideal for problem solving involving fight or flight scenarios and less than ideal for other problem solving types. We find that the majority of problems that we face are not fight or flight therefore we find ourselves using a problem solving algorithm that is not the ideal most of the time. This is much of the cause for finding ourselves surrounded by products and services that are awash in compromise and mediocrity. These two characteristics allow us to be susceptible to pressure from our competition and the markets we serve. We need to adopt a problem solving process that involves a higher level of intentionality and objective analysis.

Our ability to problem solve will be greatly increased if we incorporate abstraction and analogic thought into a non-linear multi-step problem solving process. This process in its simplest format would be specific problem à generic problem à generic solution à specific solution. We would leverage abstraction for the transition from the specific problem to the generic problem and we would leverage analogic thought to transition from the generic solution to the specific solution. This non-linear abstract and analogic problem solving model defeats the bias innate in the linear two-step model and allows the user to problem solve using the open and potentially adaptive innovative approach. The quality or solutions generated using this approach will be considerably higher. Also, compromise will be minimized thereby reducing competitive opportunities.

Much is to be gained by adopted non-linear processes for problem solving and we can see evidence of this by looking at the non-linear problem solving model found in Six Sigma. Six Sigma uses a model that is comprised of the following key steps: practical problem à statistical problem à statistical solution à practical solution. This model allows for the utilization of abstraction and analogy with a focus on statistics. Statistics allows for the inclusion of qualitative and objective (data based) decision making. We can include these same benefits in problem solving that does not involve optimizing a closed system. We would call this innovative problem solving. Innovative problem solving allows for the generation of solutions that are outside the system (open) as well as the opportunity to adapt someone else's solution to solve your problem. These are critical elements of the science of innovative problem solving and are leveraged when a non-linear problem solving method is adopted.

The International Space Station is a massive undertaking coordinated by five national agencies (US, Russia, Japan, Europe and Canada) with participation from about a dozen other countries. In all, hundreds of companies and 100,000 people have been responsible for the building and operation of the Space Station, planned to be completed around 2016 at a cost of about $130 billion.

Some other facts about the International Space Station:

• It will have a mass of about 500 tons when completely assembled and will measure the length of a football field (361 feet).
• It will provide 46,000 cubic feet of pressurized living and working space-equivalent to the interior volume of one 747 jumbo jet.
• The solar-powered electrical system is connected with 42,000 feet, or about eight miles, of wire.
• The batteries, lined up end-to-end, measure 2,900 feet, more than ½ mile long.
• Electrical and electronic parts include 1,900 different types of resistors, 500 types of capacitors and 150 types of transistors (note that this is not the part count; rather, it is a count of different types of hardware).
• Fifty-two computers will control the systems on the International Space Station. There will be more than 400,000 lines of software for 16 of those computers which, in turn, talk to 2,000 sensors, effectors and embedded "smart" hardware controllers.

It's the complexity of the Space Station – and its associated technical challenges – that initially drew TRIZ into the problem-solving mix. In many cases, typical "compromise" solutions would not meet the strict performance requirements the system demanded, and more creative solutions were needed.

For example, the Space Station is built by adding pressurized and un-pressurized modules to the system over time. Each module in the system exists to perform some specified scientific or research purpose. One such module, the Japanese Experiment Module, is a product of the Japanese Aerospace Exploration Agency, and is scheduled to make its trip to the International Space Station in February 2008.

A pressurized mating adaptor is needed to attach such modules to themselves as the space station is built over time. Just one technical challenge involved in building a pressurized mating adaptor (PMA) is that the heat generated by welding together its structural rings can damage associated sensitive electronics. In TRIZ terms, this is a physical contradiction that required the use of the separation, or isolation, principle to solve the problem of achieving proper welds but not damaging associated systems.

Initially, the electronics were attached to the internal wall of the PMA, and for good reasons that benefited the overall system. But the isolation principle behooved engineers to reconsider the spatial proximity of the electronic components, and decided they could isolate those components from the PMA structure using Fiberite™ bushings at attachment sites.

This worked very well, introduced little cost and complexity, minimized intrusion of the electronics into the internal volume of the PMA, and did not require any changes for the component suppliers.

Also, NASA engineers were challenged by the difficult procedure of attaching modules together in space – a task that required attaching well over a dozen power and signal couplings together. Imagine all those wires hanging or taped to the PMA need to be connected with similar wires on the space modules.

This was a difficult task in space that utilized a several-step, manual, module-interlock system. First two modules are brought together by an astronaut according to a "key system," whereby their respective anodized aluminum fittings align. Next, the astronaut pushes the two fittings together until they snap in place. Finally, the astronaut uses a special aluminum tool to apply torque to the threaded sleeves of the two fittings until they lock down tightly.

Simulating conditions in space by working under water, this process was difficult and posed unacceptable mission risk. The design team, therefore, considered various TRIZ approaches and ultimately decided to use the Physical Contradiction technique.

The physical bi-polarity was this: they wanted the module interlock to ensure proper physical mating and powersignal distribution. At the same time, they didn't want the interlock system because it was so difficult to perform.

The team applied the separation in time principle to compress the mating steps—combine all actions (align, connect, lock) into a single action instead of three. This required a re-design of the locking system whereby, upon pushing two keyed fittings together, the astronaut activates a mechanism that aligns, connects and locks the parts together in a simple, single motion.

This one solution yielded significant cost savings. One part, not many, for each necessary connection were now required. There was no longer a need to design and produce specialized tools. The time and cost of training astronauts was significantly reduced as well. And the new simplicity factor made the procedure safer and lowered overall mission risk to acceptable levels.

Still another formidable challenge was the extreme difficulty and variety of problems around the Space Station's solar power array, a large system with a surface area of 27,000 square feet, or more than half an acre. The shear size of the structure posed extremely difficult problems. The surface finish on each piece of the mirrored tiles had to be near perfect. And assembling each of these panels into the overall structure posed difficulty as well.

During assembly, when panels and tiles were added to the system, adjacent pieces might be damaged, for example by tools or equipment falling on them. By applying the TRIZ principle of the other way around, designers inverted the solar panel structure for the assembly process so installers were looking up at the structure while working on it. This simple action dramatically reduced damage to tiles as others were added.

Also, the biggest reason for panel and tile damage was that many were removed, moved andor placed in other areas of the system; such transportation and turnover increased the opportunity to incur damage, and damage was in fact incurred more frequently than acceptable.

The reason panels and tiles were moved so much was that the system was vulnerable to heat differentials in different areas due to surface aberrations on certain panels and tiles. If such surface variation could be minimized or narrowed to the right extent, then no such heat differentials would occur, and the system would not be compromised or malfunction.

The engineering team measured and studied the problem, moving and replacing tiles in the system according to what the empirical data said. The more they did this, the more the problem just moved around from area to another. This was a serious problem because, in space, surface aberrations can cause one or another part of the solar panel system to overheat – or just create heat differentials that cause panels and tiles to break as they expand or contract according to those differentials.

A TRIZ technique called the System Approach was employed whereby the engineers studied their problem through the lens of the system, sub-system and super-system. It turned out that they had the system and the sub-system well covered in their consideration of solutions theretofore, but they needed to change their paradigm to include the super-system as well.

The System Approach forced the engineers to consider the larger system surrounding the solar panels and tiles, including the power distribution systems as well as the software that controls those systems. Ultimately, the engineers left all the panels and tiles where they were and solved the problem with software.

They determined that they could survey the panel array and record aberration information. They could then write correction algorithms for the analysis software that would adjust differences in performance based on surface imperfections. In other words, the software would make adjustments to how the system drew and distributed power from the various panels and areas of the overall panel assembly – thereby making the system robust to surface aberrations and fluctuations.

There were about 100 other problems encountered and solutions achieved using TRIZ on the International Space Station, each of which demonstrates that innovation is not typically robed in the fancy cloak of One Big Idea, like the Space Station itself. In practice, innovation is the ability to meet specific challenges and solve specific problems with specific tenacity over and over again until they add up to a whole that is greater than the sum of the parts.

(Excerpted from Insourcing Innovation, Francis and Taylor, 2007)

The Seawolf attack submarine was originally commissioned by the US Navy as an advancement in defense against the world's most capable submarine and surface threats. Commissioned for use in 1997, the Seawolf boasts sophisticated electronics for enhanced indications and warning, surveillance and communication.

One of Seawolf's major systems is its passive sonar array – a combination of one array in the fore of the boat and one array in the aft. Passive sonar is important because it converts the pressure caused by sound waves into an electric signal, which is then used to formulate a digital picture of surrounding objects. Passive sonar is a technological advancement because it avoids having to transmit a sound wave outward to discover the presence of surrounding objects.The core of this technology is the sonar array, a huge ball-shaped structure onto which hydrophones are attached. A hydrophone is a specific piezoelectric device that converts sound pressure into an electrical current, even under extremely harsh under-water conditions. Each hydrophone needed to be hermetically sealed, along with all associated wires and cables, to keep it safe from water and other detrimental conditions.

The sonar array is comprised of hundreds of hydrophones combined into a thicker cable, which feeds through a system of signal processing boxes (about one per eight hydrophones), which in turn send signals to a processor that pus all signals together from all hydrophones into the complete picture. A very complex extrusion molding system was used to encase the assembly in polymer, or plastic. Extrusion in this case is the process of pushing hot, molten plastic into a mold, then removing the mold when the plastic hardens. Engineers believed that a consistent extrusion process over the length of the mold would yield the best performance, and this was then kept constant while polymers and general system parameters were optimized using Design of Experiments. Despite these efforts, the polymer extrusion would not adhere to the polymer on the cable assemblies, even tough they were both the same specific form of polymer, polyethylene.

Nevertheless, after numerous failures in test mode, the design was scrapped and engineers went back to the drawing board, looking for a better solution to the problem of keeping the sonar system water tight. Stated as a physical contradiction, the engineers wanted extrusion for water-tightness, but did not want extrusion because of its unreliable results. The separation in space principle sparked a question: What if the extrusion process could be divided into different zones, or instances? Using further experimentation, the design team determined the best way was to slice space in eight zones, or "layers," each with its own different extrusion parameters. With this solution, engineers could control such factors as temperature and pressure during extrusion much more accurately– according to the requirements of each zoned sub-system.

The new solution was implemented, no test failures occurred and the sub was ultimately delivered to its customer, the US Navy.

A joint effort of NASA and the European and Italian space agencies, the Cassini orbiter and Huygens probe were launched In 1997 to make a 934 million mile trip (10 times the distance to the sun) to visit Saturn, its majestic rings and 31 known moons. Having traveled 2.2 billion real miles to enter orbit around Saturn in 2004, Cassini is the most highly instrumented and scientifically capable planetary spacecraft ever flown.

Since its arrival in 2004, the spacecraft has sent a constant stream of images and data back to earth, where more than 250 Cassini scientists strive to answer fundamental questions about our universe and ourselves. Before the end of its prime mission in 2008, Cassini will have orbited Saturn 76 times, 52 of which it will have executed close encounters with seven of Saturn's moons.Among the many Cassini space mission problems requiring innovative solutions was a technical contradiction related to keeping harmful radiation from damaging on-board instrumentation and digital electronics components. If too much radiation gets through to the equipment and electronics, they fail. The details of the technical contradiction were that the improving feature was a reduction in radiation density (parameter 7), while the degrading feature was an increase in system complexity (parameter 39). Cross-referencing these two parameters, the contradiction matrix produced principles number 26 (copying) and 1 (segmentation) as generic solution principles.

Mainly the idea of segmentation provided the impetus for a solution. The design team segmented equipment and components into two distinct layers: outer and inner. Less radiation-susceptible components were re-allocated to the outside of the Cassini structure, where they served their mission purpose while, at the same time, helped shield more sensitive components inside the spacecraft. In some cases, outer-layer equipment was located directly behind structural elements of the spacecraft, thereby adding increased radiation protection.As well, instead of co-locating all the inner components in a single module, they were spatially distributed throughout the vessel to further minimize saturation risk by virtue of their physical non-proximity.

The overall new design did not add significant complexity to the system, and it also reduced the need for very expensive radiation hardening materials formerly used to protect at-risk components.Another problem was the need to isolate the main engines and thrusters from spacecraft instrumentation, as heat, shock and vibration can interrupt or cause instruments to fail. At the same time, all components of Cassini had to be as small and compact as possible, so they could fit into the cone of the launch vehicle on which Cassini would leave earth and enter space.The associated technical contradiction involved improving parameter #8 (volume of a stationary object) and its degrading counterpart, parameter #31 (harmful effect). All Cassini components had to be close and compact (volume of object), but such closeness put instrumentation at risk of damage (harmful effect).Looking in the contradiction matrix, the team encountered inventive principles 30 (flexible thin films), 18 (mechanical vibration), 35 (parameter change) and 4 (asymmetry).

Using the mechanical vibration principle, the engineers reduced vibration frequency by dampening the instrument cases. Instead of mounting instrument cases directly on the Cassini structure, they used rubber mechanisms to isolate each case from the structure - and elegant solution that protected the cases from heat, shock and vibration without expanding their size. Having done this, Cassini's engines and thrusters remained fully effective, yet risk to instrumentation is greatly minimized.TRIZ was further applied on the Cassini frontier by identifying a physical contradiction related to the Huygens probe, which successfully landed on Titan, the largest of Saturn's moons, early in 2005. In the big picture, the probe was desired for its role sending 90-minutes-worth of valuable scientific data to the Cassini orbiter, which in turned relayed that data to earth. Now the Huygens probe sits battery-dead on Titan.

To simplify, the probe was also undesired because it took up space on the orbiter, and this constituted a physical contradiction at the macro-systems level. On the one hand the lander was needed, but on the other it impinged on space constraints. Therefore, the separation in space principle was employed to develop the "center void" that was ultimately used to house the Huygens probe. How could engineers rearrange space to accommodate all the instrumentation and functionality of the Cassini orbiter and the probe without having one impinge on the limited space of the other?The solution was to move some components on the orbiter, thereby creating some space into which part of the probe could fill, while the rest of the probe protruded outside of Cassini . This more effective utilization of space was sufficient to overcome the contradictory requirement of wanting but not wanting the Huygens probe and all its components, instruments and functionality.

Race cars have to perform three functions to be successful. One, they have to accelerate. Two, they have to brake. Three, they have to corner. If a car has any chance of winning, it has to perform all three of these functions better than the others.Yet as important as these functions are, what underlies them?

What is the most basic and crucial aspect of winning a race? Yes it is the perfect coordination of accelerating, braking, and cornering that wins the race, and all the systems and components of a car converge on these three aspects of success. But there is something more fundamental, and that is the ability of each tire to grip the road. It is the coordinated, optimized traction of four patches of rubber as they meet the road that determines how well a car can accelerate, brake, and corner to win a race.It is this same traction a company seeks when it sets out to make innovation common and reliable.

This is the high-level formula by which a company transitions from common sense to uncommon sense when it comes to innovation. This is the point of differentiation between conducting business as usual and conducting business as exceptional. For a corporation, the four patches where the innovation rubber meets the road are culture, infrastructure, methodology, and proficiency. Culture is the ability to shape innovative behavior and practices on a widespread scale. Infrastructure is the technology and management supports that are necessary to grow and reinforce innovation. Methodology is the standard roadmap for implementing innovation projects with a high probability of payoff. Proficiency is the ability to ramp up world-class innovation capability in the shortest possible amount of time.Together, these four cornerstones form the critical success factors of innovation ROI.

As with race cars, each of these factors needs to be coordinated and balanced according to the demands of the day. To be agile, to stop or slow when necessary, to speed up around a hair-pin turn, a race car must distribute power between its four wheels to maximize grip. Similarly, a corporation must a company must constantly distribute power among the four elements of innovation to maximize ROI.If any one element is missing or isn't coordinated with the other elements under changing circumstances, the results are undesirable. At a minimum, the car loses the race and the corporation loses opportunity and money. At a maximum, the car spins out of control, flips over, crashes into a wall and causes a fiery demise for itself and maybe even its driver. The same is true for a corporation.

You know from the field of psychology that behavior is a function of values, so an organization has to value innovation if it is to consistently engage in innovative behaviors. But what if your organization doesn't value innovation like it says it does? What if your organization is like so many that pay lip service to innovation — say they value it — yet don't consistently demonstrate the behaviors necessary to make innovation happen faster, more predictably, and more pervasively?

If you say you value the amount of money in your bank account, surely you have a way to measure and check up on that. This was the whole approach with Six Sigma related to the drive for operational improvement. Although most organizations once paid lip service to quality, Six Sigma forced them to measure quality as they never had before. As a result, they truly came to value quality, and they engaged in behaviors that brought about unprecedented levels of quality.

A Boston Consulting Group report cited in Part One calls for better innovation-related metrics and, surprisingly, points out that a sizeable 49 percent of corporate executives around the world do not carefully track the financial returns associated with each innovation. And seven percent say they aren't sure if their companies track innovation ROI at all. We believe this is a most telling phenomenon: Although most companies are dissatisfied with R&D results, most also don't know how they measure R&D ROI.Still, trying to measure better isn't the whole answer, even though it's a great first step. In the 1980s an interesting business concept called "the cost of poor quality" drove a lot of activity around measuring poor quality and, naturally, improving quality. We're at a similar place today with innovation. Companies are beginning to measure the cost of poor innovation, and they're developing the systems for improving innovation ROI and traceability.The following table gives a summarization of some metrics that could be used to track innovation success. It is not intended to be an exhaustive or universal list, but represents the ways in which companies should think.

Metric category	Some Suggested Metrics
Macro	* Speed-to-change cultural bias * Amount of innovation budget * Ambidextrous index: balance of resource allocation between preservation and evolution (capital, human, technology) * Time to transition from preservation activity to evolution activity Ratio of innovation projects sponsored by executives (disruptive technologies can't occur without senior management sponsorship)
Volume	* Number of innovations made * Number of invention disclosures * Number of patent applications filed * Number of trademarks obtained * Number of people involved systematic problem solving * Number of systematic innovation projects completed * Variance of all the above
Speed	* Time to predict customer/market evolution * Amount of time per innovation * Research cycle time * Product development cycle time * Mean time to solve an innovation problem * Mean time to implement an innovation solution * Variance of all the above
Quality	* Ratio of innovations attempted to innovations made * Time to abandon a poor idea * Degree of discontinuity (level) * Costs avoided * New revenue generated * Costs reduced * Mean Ideality of innovation solutions

Innovation has been practiced in an unintentional and non-focused manner for far too long. The focus on providing structure to a business was placed on other areas of interest. Various focal points have been targeted with great success in the past and resources were applied to these areas with innovation being left to fend for itself. Trial-and-error Edisonian approaches were the way that research and development ideated. With the maturation of leadership skills, team skills, communication skills and technologies, coupled with the systematization of productivity and quality, the constraint has finally come to rest in the innovation domain. Therefore, for the first time in history, a corporation's ability to systematically reduce a concept to practice has outpaced its ability to ideate (develop viable concepts).

This leaves us in the position where something MUST be done in the field of innovation. Someone recently asked the question on the Real Innovation Forum—"…why all this attention on innovation—we did it without focusing on it before…why focus on it now?" We have found ourselves in a position where the field of innovation holds the current constraint in the concept-to-commercialization process. It is time for the systematization of innovation. It is time to move from Edison to Algorithm. It is time to move from fiat to fact. We make decisions that are data-based and it is time to ideate using the same foundation. Without structured evolution in the field of innovation, we are left optimizing (by way of DfLSS, Axiomatic Design, Lean, and Six Sigma) the output of an ad hoc ideation process. Therefore, we are optimizing what is quite possibly a sub-optimal idea. This is why we must now focus on innovation and apply intentionality, method, and structure to it as we have never in the past.

Design for Lean Six Sigma (DfLSS) is a phase-gated integration of world-class methodologies that are utilized in a synergistic fashion to convert the voice of the customer (VOC) and/or the voice of society (VOS) into a commercialized product or service that meets or exceeds requirements, and is manufacturable at the required sigma level. Another way to characterize the VOS, since it is a new term, is to think of it as the "unarticulatable" needs of humans that are such because there is no available knowledge from which to form possibly commercializable ideas, products or processes. For example, while the human community had a need to travel faster prior to Henry Ford's day, the average person had little chance at conceptualizing or coming up with the idea of a combustion engine. Yet society still had a voice that demanded the function that the combustion engine delivered. Therefore, to be most effective in DfLSS, a business must capture both the VOC and the VOS, systematically.

As the organization moves from VOC and/or VOS to the delivered product, there are five key domains of expertise thsat must be exercised. These domains begin with the customer requirement domain (CR). In this domain Quality Function Deployment or Axiomatic Design may be used to capture and analysis the input. The CRs identify the targets of the project. The CRs must be converted into a set of required functions. This is the Functional Requirement (FR) domain. The identified functions are critical for the identification of those outputs that will accomplish the CRs. FRs also identify the input requirement for the third domain - Design Parameters (DPs). Function Modeling and Axiomatic Design may be utilized to identify the necessary functions in the system. Once the functions have been identified, it will be possible to characterize the required design features that will assemble and, with energy provided to the system, generate the necessary functions to meet the CRs. There are many methods that may be utilized in this domain: CAD , CAM, CAE, CFD, FEA, Robust Design, Tolerance Analysis and Axiomatic Design. The final domain characterizes the manufacturing parameters necessary to produce the design features that, when assembled, provide functions that meet customer requirements. The stage-gate reviews ensure the cascade of critical-to-customer requirements through the process.

What I find very interesting is the fact that the impetus for DfLSS is the development of a concept or idea in response to VOC or VOS – and this step is almost completely ignored by current DfLSS methodologies. My proposal is simply to add systematic innovation to the front end of the DfLSS process. Relying on the output of a designer or design team using a body of knowledge (set of CRs), a deadline and an ad-hoc ideation method (fiat, epiphany, brainstorming) is not adequate. This would be analogous to applying DMAIC to optimize what ultimately is a non-value added activity. DfLSS applied to a mediocre idea yields optimized mediocrity. The TRIZ methodology is ideal for rendering an idea-scape that is based on world-class scientific observations on par with the balance of the tools and techniques found in DfLSS. It is my proposition that the infusion of both tactical and strategic TRIZ into DfLSS will increase the density and quality of new product launches. It will also ensure that those resources dedicated to a DfLSS project are optimized.

RSS