Perhaps a place to start is why?
My experiences suggest that too much attention is given to the tails of projects, and not enough to the heads.
Project slip at its tail-end is never great: think of the promises made to the sales-force and its customers, the egos at stake perhaps, maybe at worse a dark culture of blame or at best bluntly applied metrics and processes all poised to bias downside and rarely to reward up-side.
However in my experience the down-side of slipping maybe 10% on the time to market (in my experiences it is the few projects, rather than the many, that fall out-side this range) is far outweighed by the chaos and indecision before the product even enters the corporately recognized NPD cycle.
Smith and Reinertsen (1998) in their book Developing Products in Half the Time suggest organizations spend 50% true development time at the fuzzy front-end. Arguably, this period doesn’t represent a similar proportion of the monetary investment; the true impact is the value to market (note value, not, time) lost through delayed revenue and market share as a result of a lengthy front-end.
Particularly if the NPD cycle-times are measured in years.
And typically the fuzzy-front end goes un-measured. So in an industry where an average product cycle time maybe two years from initial investment to commercialization, there may have been at least another two years of preceding fuzziness that went un-noticed.
So what is the fuzzy front end of innovation?
Koen (2001) distinguished five different front-end elements. (However there are many similar examples.)
- Opportunity Identification
- Opportunity Analysis
- Idea Genesis
- Idea Selection
- Concept and Technology Development;
In other words, before you decide to scale-up an idea into a serious NPD investment (or for that matter, any kind of investment), you must decide what you want to develop and for whom.
Not exactly rocket science.
And yet, considering the timescales involved, surely this phase of the innovation cycle deserves more attention than we are prepared to give. Yet in some of my experiences, this phase is governed more by serendipity than by deliberate action.
This is perhaps not surprising.
This phase is rife with complexity, uncertainty and risk; technologies which may not yet be developed fully; collaborators not yet engaged; market trends (let alone specific customer needs) unclear; competitive landscapes rapidly changing; our day-to-day business crises sapping our will to stand back and look at the bigger picture.
However, it is precisely at this stage where “fuzziness” is to be avoided.
Frishammer and Floren (2010) indicate a range of conditions needed during this phase, to which I have added some of my own.
Innovation strategy
This is not the same as strategic innovation. Having a strategy for innovation is so much more than deciding what you want to innovate, no matter how strategically. The innovation strategy is the keystone for innovation. It gets to the core business strategy – who are we and what are we about, and therefore how will we innovate. Some questions may be:
● How open do we want to be? For example do we want to extend our boundaries to encompass external ideation and crowdsourcing? Do we want to foster co-development as our preferred approach? Do we want our brand to reflect this open innovation?
● How critical is innovation to our business? Do we want a pervasive culture of innovation, if so, how would we support and reward this?
● How much do we want to spend? How much should we spend?
● How will we measure success?What behaviours will this foster, will this support our culture?
Ideation
Just googling this term shows there’s a lot out there, much of it describing how to tap into the cognitive processes behind design, such as structured brainstorming, story-boarding different scenarios etc.
True ideation though has to fulfil the scope prescribed by the innovation strategy. What are the boundaries of what we are trying to achieve? Who needs to be involved? How will the ideation process actually work?
All very good stuff, but in my experience, greater potential is to determine who, or what, should feed into the ideation process, rather than the process itself. As the saying goes: garbage in, garbage out. Here are some suggestions:
● Market trend and technology trend scouting. Ideally led by a full-time member of the strategy & innovation team, scouting and horizon scanning should be dispersed across the organization continually, and reviewed continually. The real skill is to have a wide line of sight, never going too deeply too soon, and looking for any adjacencies that may arise. While direction is required, it is used lightly but without major diversion.
● A good picture of who’s who. Who are the key opinion leaders? Somehow we need access to them via their networks. What forums do they participate in, what shows do they go to, what influencing bodies are they a part of?
● Potential collaborators and partners. A big subject, and perhaps worthy of a blog in its own right. Collaborators I find is a lot like career planning…you always have to be active even if you aren’t necessarily looking to fully engage imminently. For sure, as the relationship is cemented, collaboration and relationship management has to be very structured, but seeking the right ones upfront and making the initial enquiries takes time, and the hit rate is by no means 100%. Collaborators will come in all shapes and sizes: academics, customer advisory, technology providers, start-up incubators and accelerators, to name a few.
The right people
I saw a fantastic presentation by Elisabeth Goodman and Lucy Loh of Riverrhee consulting.
Elisabeth and Lucy suggested that there are two key areas of interest when considering personal styles and innovation:
● The influence behind what kinds of ideas people are drawn towards, either incremental or breakthrough.
● The influence behind how the ideas are implemented, either by reaching decisions asap, or to keep options open.
They then went on to say that in a typical innovation process, certain people will gravitate towards the different phases in the innovation cycle:- Define. What is it we are trying to solve? Certain individuals will be drawn towards refining and adapting ideas Discover. The generation of ideas. Individuals drawn to this phase will naturally place emphasis on different and original ideas.
- Decide. Reducing the number of ideas. Here the natural tendency is to adopt new ideas.
- Deliver. Implementation (or decide against). People drawn to this phase favour efficiency and adaptation
Naturally there is no wrong or right, but a need to involve a wide range of personnel in the process…which in itself can present a problem as certain personality types may cluster within industries, hierarchy layers, divisions etc.
Despite this, two key attributes are necessary, regardless of personality.
● The ability to work with strangers
● A positive attitude and the ability to focus.
Matching need with solutions: Connecting the dots, and validation
The key here is validation. Agile validation. Yes, we need ideas screening…some ideas should not get past the ideation stage, however designing a screening tool is relatively simple.
Of more value is to validate whether a solution actually meets a need, we need to enter the world of, among others, Eric Ries, who wrote The Lean Startup (2011)
The basic elements are:
● Minimum Viable Product.
A Minimum Viable Product has just those features that allow the product to be deployed, and no more. The product is typically deployed to a subset of possible customers, such as early adopters that are thought to be more forgiving, more likely to give feedback, and able to grasp a product vision from an early prototype or marketing information.
Much has been ventured about software, particularly app-style UI which is fast to mock up and alter real-time. However, a MVP can be a paper demonstration or story board, more applicable to hardware which has a longer development cycle; added to which the now almost mainstream adoption of 3D printing can get a form-factor in a customer’s hands in hours, not days or weeks.
● Business Model Canvass
Initially proposed by Alexander Osterwalder, it is a visual chart with elements describing a firm’s value proposition, infrastructure, customers, and finances. It assists firms in aligning their activities by illustrating potential trade-offs.
The purpose is that the entire business model is limited to one page, and is therefore easy to view as a whole and adapt very quickly. I have personal experiences of monolithic business plans which are cumbersome and impossible to read fully, let alone keep up to date. Which may be fine when the product requires significant investment (and justification), but does not belong in early stage innovation.
● Pivot
A pivot is a “structured course correction designed to test a new fundamental hypothesis about the product, strategy, and engine of growth
Pivoting is how we learn. Develop the MVP, validate, make actionable changes, amend the Business Model…until we get it right.
And in a way, this agile process should form the front-end methodology in terms of product development. No stage gates, no hefty management reviews, no lengthy development times where customer input is given maybe once or twice during developments (alphas and betas). On a lengthy development cycle, while the team has its head down, the market has moved on.
Attitude to uncertainty & managing risk
In his book, The Other Side of Innovation, Vijay Govindarajan introduces the concept of The Performance Engine (being the existing organization) and the Dedicated Team (the innovators). The Dedicators must work hard to interleave what could be viewed as disruptive into the Performance Engine, and are responsible for successful integration rather than throwing ideas over the wall; in fact members of the Dedicated Team are likely to have come from the Performance engine, and so understand the culture and organizational intricacies.
That said, it must be clear that attitudes to risk must be different up-front, but managed to the point where the level of risk is acceptable when the project passes to the Performance Engine. So by all means have different early risk criteria, faster decision making (based on learning), but ensure that these are transferable in time to a more widely accepted model which won’t implode on scale-up.
And finally we come to funding. To learn, we must fund. There is no out-of-the box right level of funding; it has to be commensurate with the organization’s expectation (go back to the Innovation Strategy). I have heard figures as low as less than 1% R&D funding and as high as 25%. Perhaps another topic for another blog!
