One of the most common flaws of current innovation process models is when the first stage is initiated by the spark of new, brilliant ideas. What most innovation managers don’t seem to apprehend is that brilliant ideas rarely come from inspiration – so called “Eureka moments” – they come from hard work and perspiration.
A deliberate and purposeful innovation process should never start with the assumption that great ideas will just suddenly appear. They require hard efforts, dedication and thorough analysis to become breakthrough. Yes, they may evolve out of thin air as a flash of brilliance – it does happen. But an innovation management system cannot be left to chance, it needs to be structured to give optimal preconditions for success. As one of our ever most superior inventors so elegantly put it:
“Genius is one percent inspiration and ninety-nine percent perspiration.”
– Thomas Alva Edison
We require more invention in the innovation process
First note that not every innovation must build on an invention, regardless if the innovation is a product-, service-, process-, business model innovation or other. But in most cases, at least when we are striving for breakthrough innovation, it probably should. To better clarify the logic behind this reasoning let me first distinguish between invention and innovation.
“Invention is the generation of newness or novelty, while innovation is the derivation of value from that novelty.”
– Szmytkowski, 2005
So by this logic, to create innovations that are novel and breakthrough rather than incremental, the process needs to be more “invention-like”. As such it requires an R&D like approach, because in R&D thorough analysis and deep understanding of the problem is (almost) always a mandatory tactic. To ensure that you are providing a prominent solution to your problem you have to shed some light on two very crucial questions.
- Am I solving the right problem?
- Am I providing the best (most innovative) solution to this problem?
These questions highlight the importance of preparation before execution (execution in this case refering to “problem-solving” or “idea generation”) because bringing the best possible solution to the wrong problem will only take you so far. Or as former CEO of InnoCentive Dwayne Spradlin puts it in a Harvard Business Review article: “We now know that the rigor with which a problem is defined is the most important factor in finding a suitable solution”. Several prominent people other than Spradling and Edison have emphasized this as well.
“If I were given one hour to save the planet, I would spend 55 minutes defining the problem and five minutes resolving it.”
– Albert Einstein
“Give me six hours to chop down a tree and I will spend the first four sharpening the axe.”
– Abraham Lincoln
Albert Einstein strikes an extremely vital chord as he portrays the magnitute of putting more focus on the problem than on the solution. His point is concise; if we do not have complete insight into the problem we are facing we might just be wasting our time presenting irrelevant resolutions. This is one of the reasons why we call it the problem-solving phase and not the solution-suggestion phase – the task at hand is to solve the problem. And to solve the problem we have to understand the problem. Never forget that.
So how about existing innovation process models?
In a comprehensive review of the various generations of innovation process models from the 1930:s until today Du Preez et al find that most of the innovation process models involve a pattern of the following steps or stages: (a) idea generation and identification, (b) concept development, (c) concept evaluation and selection, (d) development, and (e) implementation (Du Preez et al, 2006). So, as you can see, historically there has been little spotlight on problem-solving as the pre-cursor to idea generation. Regardless if we look at the Minnesota Innovation Research Program (MIRP) innovation process model, the Stage-Gate process model, the Innovation Systems model, or the Open Innovation Model they all start with a phase of “idea generation”, “discovery” or “concept development”. None of them have an initial phase for finding, analyzing and defining problems, the core component of innovative problem-solving. One exception is the Collaborative Innovation (CI) process model developed by United Technologies Research Centre (UTRC) that utilizes the Quality-Function Deployment method to understand the relationship between customer needs and ways of satisfying those needs and Problem-Formulation Modeling to document the rationale behind the existing (baseline) product or process design. The CI model uses several TRIZ techniques in a logically structured way that improves the model’s credibility, since TRIZ is a well-known methodology for pairing problems with solutions through proper analysis.
But generally the problem portion of the reigning innovation process models is severely neglected and would need to be reintroduced and further emphasized. As we will now see, it is hard to be purposefully innovative when you are unsure if you are addressing the root cause.
Open ideation decreases the accuracy of solutions
And it seems the neglection of truly understanding the real, underlying problem has evolved with open innovation, and then especially with customer-driven ideation, as most of the responsibility of analyzing the problem at hand is handed over to the customer – who is less likely to have access to all the relevant information. (This goes double for open-ended ideation compared to corporate ideation campaigns, of course.) The principle of crowdsourcing ideation is similar to that of open source – or as Linus’s Law states it: “given enough eyeballs, all bugs are shallow”. What this principle refers to is the theory that if given a large enough co-developer base, almost every problem will be characterized quickly and the fix obvious to someone. So for innovation management this would translate into skipping the process of defining the problem and instead relying on asking enough people for Eureka solutions. Metaphorically this is the same thing as trying to shoot a fly with a shotgun instead of a sniper rifle; you invest more in chance than you do in accuracy. Sure, innovation does involve a good portion of chance, but innovation management should focus on providing accuracy.
And with open innovation the “fuzzy front-end” experiences some another severe problems with submitting non-analytic solutions via ideation services, and that is those of psychological inertia, problematizing solutions, and assumptions, all leading to incremental, non-breakthrough innovations.
- Problematizing solutions happens when the ideator directly presents the most obvious solution and then tries to retrofit the solution to solve the requested problem.
- Assumptions are when we are locked in to expectations and think that certain solutions are not plausible in our context, because “that’s not how it is done here”.
- Psychological inertia becomes present when we are comfortable with the way things are and are not open to make any (major) changes.
Understanding the relationship between problems and opportunities
And as for the two crucial questions we are trying to shed light on – once we have understood the problem, we need to find opportunities that can resolve the problem. Let us first distinguish the difference between the concepts of problems and opportunities. Generally a problem is a situation that is undesirable, while an opportunity is a possibility to improve a situation to a state that is more desirable. So technically, behind every problems lies an opportunity, because every situation that is in an undesirable state can be improved and is thus an opportunity.
And to grasp the vitality of a problem one must understand its context. In Systems Thinking the first step is to “recognize and formulate” the problem. This comes with a certain conundrum, because to recognize a problem one must be aware of its existance. And to be aware of a problem there needs to be a sensory system indicating errors, faults, incidents, and other undesirable situations. In practical terms this means that there needs to be channels to gather these signals of undesirable situations and raise red flags when they become too many or too severe. When the flags are raised is the point when we should really start probing the problem.
Understanding the underlying cause of undesirable situations
In Systems Thinking they do not point out a certain “problem”, they consider an entire “problematic situation”, because to fully understand the cause of the problem one must understand the factors that lead to the effect that is perceived as a problem. Every problem exists in a context consisting of different components and stakeholders. There is always a network of cause-and-effect sequences that influence each other, and to comprehend why a certain undesirable action takes place the entire network system must be mapped out and the trace of poor results be backtracked until we fully apprehend what leads up to the problem. This is the point when we can really start solving the problem and not until then.
A very compelling method to conduct this procedure with is called Root Cause Analysis where techniques for locating the root cause is applied to terminate the source of the problem. A root cause is considered such if removal thereof from the problem-fault-sequence prevents the final undesirable event from recurring. This is a relatively simple and practical method (often applied and visualized with fishbone diagrams) that can be easily encapsulated into an innovation process.
So you have probably started to see that it will be more fruitful for organizations to apply these techniques as the initial sequence of any innovation process model. Thorough innovation processes should start with locating problems through various channels and then analyze them using RCA techniques to find the root cause and its context. These results should then be submitted as part of ideation campaigns to provide ideators with the full picture of the underlying problem to increase the likeliness of breakthrough thinking.
This is how a good innovation process model is built – with proper analysis rather than chance.
- Du Preez N.D., Louw L., Essmann H. (2006): An Innovation Process Model for Improving Innovation Capability. Journal of High Technology Management Research 2006: 1-24.