Lean and Agile Innovation Ecosystems: Part 1

Yond Cassius has a lean and hungry look,
He thinks too much; such men are dangerous.

William Shakespeare, Julius Caesar Act 1, scene 2

Before there were lean startups there was lean manufacturing. Lean manufacturing, which seeks to eliminate all expenditures which do not support value for the customer, was developed by Toyota in the 1950s and was in part responsible for the Japanese auto industry becoming the US auto industry’s fierce competitor two or so decades later. Agile software development, introduced in the 1990s was influenced by ideas and methods from the lean manufacturing. Its purpose is to make software usable, adapt to changes, and allow people to excel according to their strengths, rather than according to the system. More recently, lean startup methodology has become popular, intended to shorten product development cycles by iteratively creating products and integrating user feedback.

As noted in last month’s blog: A tale of Two Quotes http://innovationrainforest.com/2014/06/30/a-tale-of-two-quotes/ Rick Dove in his book on agile enterprises, Response Ability: The Language, Structure, and Culture of the Agile Enterprise. John Wiley and Sons, Inc., 2001, introduced the concept of “Response Ability.” He notes that “The agile enterprise can respond to opportunities and threats with the immediacy and grace of a cat prowling its territory” and goes on to explain that “response-able” components can be designed into enterprise ecosystems. These ideas are closely related to those of re-usable components within a framework (see my October 2013 blog: Create early, use often: Lego™ blocks, learning objects, and ecosystems. Part 2 http://innovationrainforest.com/2013/10/13/create-early-use-often-lego-blocks-learning-objects-and-ecosystems-part-2/).

While much of the focus of agility has been in manufacturing and software development, let’s see if any of the “response-able” components concepts illuminate how innovation ecosystems may become agile; an ability to adapt rapidly to system environment changes. After all, we have already introduced the idea of self-organization in a complex adaptive system, which implies agility. How can analyzing agile manufacturing systems help us in building agile innovation ecosystems able to self-organize and respond effectively to external shocks?

Why should we make comparisons between systems? What new understanding might emerge? Comparisons only makes sense if we can learn more about system B by comparing it with system A, and then only if any similarities are more than just coincidence. A cloud in the sky may look like a face, but I doubt we will learn anything enlightening about how faces grow from studying how clouds form.

History shows benefits of comparisons; our understanding of economic systems has been improved, some would argue, by the study of thermodynamics, and innovation flow may be helpfully compared with biological flow.

Manufacturing cell

The results of Rick Dove’s extensive research on systems such as the manufacturing cell illustrated above indicate that principles of “response–able” systems include components with certain characteristics such as (I’m simplifying considerably as this is only an introduction):

  1. Components of response–able systems are distinct, separable, self-sufficient units cooperating towards a shared common purpose.

In innovation ecosystems the function and activities of each stakeholder and the strength of their cultural alignment should be clear to other stakeholders as well as all cross-functional and collaborative activities and existing supportive and incentive policies. This also applies to stakeholders outside the community. Without alignment towards common purposes “friction” between components can be destructive.

  1. Components of response–able systems share defined interaction and interface standards; and they are easily inserted or removed.
  2. Components within a response–able system communicate directly on a peer-to-peer relationship; and parallel rather than sequential relationships are favored.

For innovative innovation ecosystems this means efficient communications to keep transaction costs low. The application of parallel rather than sequential relationships will be discussed in Part 2 of this blog.

  1. Component relationships in a response–able system are transient when possible; decisions and fixed bindings are postponed until immediately necessary; and relationships are scheduled and bound in real time.

This is not a recommendation for procrastination, rather avoidance of decision making with insufficient information which may fix an ecosystem component which later turns out to be a mistake (e.g. building a new business incubator before a reliable deal flow is apparent).

  1. Components in response–able systems are directed by objective rather than method; decisions are made at a point of maximum knowledge; information is associated locally, accessible globally, and freely disseminated.
  2. Component populations in response–able systems may be increased and decreased widely within the existing framework.
  3. Duplicate components are employed in response–able systems to provide capacity right – citing options and failed – soft tolerance; and diversity among similar components employing different methods is exploited.
  4. Component relationships in response–able systems are self-determined; and component interaction is self-adjusting or negotiated.

In previous blogs we discussed the phenomenon of emergence in complex adaptive ecosystems. Emergence is an outcome of self-organization, without centralized control (#5, #8) in the form of a new level of order in the system that comes into being as novel structures and patterns which maintain themselves over some period of time. Innovation springs from emergence. Emergence may create a new entity with qualities that are not reflected in the interactions of each agent within the system. Emergent organizations are typically very robust and able to survive and self-repair substantial damage or perturbations.

  1. Components of response–able systems are reusable/replicable; and responsibility for ready reuse/replication and for management, maintenance, and upgrade of component inventory are specifically is designated.
  2. Frameworks of response–able systems standardize into component communication and interaction; defined component compatibility; and are monitored/updated to accommodate old, current, and new components.

Reusability was discussed at some length October 2013 as referenced at the top of this blog. However, this topic will be further explored in Part 2 of this blog.

Shakespeare might be surprise to learn that his opinion of thinking men (sic) was wrong; one way the US auto industry responded to the competitive challenge of higher quality Japanese imports in the 1980s, which led to agile manufacturing concepts among other changes, was to enable more thinking among assembly line workers.

Next time: Lean and Agile Innovation Ecosystems: Part 2


A Tale of Two Quotes

“I don’t like using words like ecology to explain in shorthand a rich and useful organizational concept for business. For one, these soft edged metaphors turn off a lot of hard edged business people who occupy a large portion of the organizational power structures, especially in operations and manufacturing.. For another, nature has the patience and resilience to absorb a lot of failed or marginal experiments that would terminate a business enterprise…. Simply referencing the metaphorical links and then postulating a new business paradigm doesn’t appear successful in communicating with most people who have operational concerns.” Rick Dove, Response Ability: The Language, Structure, and Culture of the Agile Enterprise. John Wiley and Sons, Inc., 2001, p 134.

A Harvard business school alumnus responding to the intra-Harvard debate between Jill Lepore, an historian, and Clay Christensen, a business school professor, about theories of disruptive technologies is quoted as saying “We don’t learn laws of business. We learned stories.” John McDermott, Career Advice from Marina Keegan, Financial Times (US), June 26, 2014.

Rich Dove’s book is about agile manufacturing but also much more. In the next blog in this series I shall introduce a few of Rick’s concepts and discuss whether they can throw more light onto how innovation ecosystems may become agile; an ability to adapt rapidly to changes and shocks.

Meanwhile, let’s (1) gently dissect these two quotes, and (2) suggest what practical results the complex adaptive systems theory of innovation ecosystems predicts which will be of value to the most skeptical operations person. We only have space to begin here, and will continue next time.
In the above “.. throw more light onto..” is itself a metaphor; we are not literally going to use a flashlight. Francis Thompson (1859-1907) in his poem Contemplation uses the metaphor which nudges us into a sense of contemplation.

“This morning so I, fled in the shower,
The earth reclining in a lull of power”

Much has been written by philosophers about how the hearer decides to seek a nonliteral meaning in a metaphor, makes us attend to some likeness between two things, conveying an idea to open different frames of mind beyond the more straightjacketed analogy (A is like B, freshness after a rain shower is like the earth resting).

Thus, we are saying that a metaphor can help express a theory, but first we should be sure that we have some common ground as to what is a theory. Thomas Kuhn, a philosopher of science, set out criteria (although not necessarily precise ones) to help chose a theory or chose from competing theories. He stated that a theory should be:

1. Accurate, in that it empirically adequate with experimentation and observation.
2. Consistent, namely internally consistent, but also externally consistent with other theories.
3. Broad Scope, with consequences extending beyond the phenomena it was initially designed to explain.
4. Simple, using the principle that the simplest explanation is usually the better one.
5. Fruitful, in that any theory should predict new phenomena or new relationships among phenomena.

Others might add one more requirement, that of “falsifiability” or proving a theory to be wrong by making an observation or conceiving an argument which proves a theory statement to be false.

Or, put more succinctly, a theory must explain and predict. Without prediction a theory is worthless. I suggest we should hold stories and other narrative forms to Kuhn’s five-test scrutiny. Narratives have become a popular (as the Harvard graduate stated), and effective, metaphorical explanation of events – for example, in complex adaptive systems, where a mathematical description is not possible. Can narrative predict as well as explain? Let’s begin to investigate by applying Kuhn’s tests to complex adaptive systems concepts introduced in recent blogs in this series. For example:

Emergence
Emergence is an outcome of self-organization in the form of a new level of order in the system that comes into being as novel structures and patterns which maintain themselves over some period of time. Innovation springs from emergence. Emergence may create a new entity with qualities that are not reflected in the interactions of each agent within the system. Emergent organizations are typically very robust and able to survive and self-repair substantial damage or perturbations.

Kuhn’s Tests
Kuhn’s tests 1 through 3 are easily satisfied, whereas #4 might be more problematic – depending on how we define ‘simple.” Complex adaptive systems theory has been especially fruitful (test 5) as we described in April’s blog Games of chance? Cause and effect in innovation ecosystems Part 2 http://innovationrainforest.com/2014/04/21/games-of-chance-cause-and-effect-in-innovation-ecosystems-part-2/ which reported the work of Sharon Zivkovik on social entrepreneurship. Prof. Zivkovik reports on how complex adaptive systems theory predicts, under certain conditions ” interactions between independent agents produce system-level order as agents interact and learn from each other, change their behavior, and adapt and evolve to increase their robustness. Empirical research has shown large complex systems such as communities require enabling conditions to be created in order to maintain the coordination required for emergence self-organization and adaptive capability.”

In T2VC’s recent innovation ecosystems work with Medellín, Colombia, similar behavioral changes and adaptations occurred by adjusting certain conditions (this case example will appear in a future blog).

Another concept is:
Stabilizing feedback
If new emergent order is creating value it will stabilize or legitimize itself, finding parameters that best increase its overall sustainability in the ecosystem. Stability results by slowing the non-linear process that led to the amplification of emergence in the first place.

Kuhn’s Tests
We don’t have space this month to go into detail, but discussions of empirical studies on networks scattered among several previous blogs, such as the stabilizing effects of weak links, could be shown to meet all five requirements.

I hope readers of this blog series will now at least be beginning to understand that Rainforest innovation ecosystems are complex adaptive systems, and that the Rainforest metaphor expands our thinking. Philosophers have postulated that “even a quite definite speaker intention does not finally determine the meaning of a metaphor’ and that “the interpretation of the light the metaphor sheds on its subject may outrun anything the speaker is thought explicitly to have in mind.” An Irenic Idea about Metaphor, Philosophy, Vol.88, No.343, p 25. In the Rainforest case the metaphor in fact preceded the more detailed analysis of the complex adaptive systems model. The metaphor worked.

Next time, more on agile innovation ecosystems and more tests of theory and predictions.


Games of chance? Cause and effect in innovation ecosystems Part 2

Notes on the practice of innovation and technology commercialization

“That is why, according to Viktor Mayer-Schönberger and Kenneth Cukier’s book, Big Data, ‘causality won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning’. But a theory-free analysis of mere correlations is inevitably fragile.”
Big data: are we making a big mistake? Financial Times, March 28, 2014

“You have on each table cardboard, drinking straws, glue, string, balloons, paper cups, and other bits and pieces. Use these materials to build a model your local innovation ecosystems.” These were the instructions given to multiple groups of five or six from among those of us who participated in the recent Global Innovation Summit in San Jose California. An undisclosed prize, based on unexplained criteria (a slice of innovation humor?) was to be given to the winner. This modeling game was a lot of fun, provided insight to some, and also raised the question of what kinds of models might represent innovation ecosystems?

We usually build system models to simplify the world around us in order to better understand it – and hope that in such simplified models we have included the important features of the actual system. For example, it is not possible to include what may be large numbers of possible causes producing observed outcomes.

Image

A model may be a physical structure, as in the picture above of one team’s product from the Global Innovation Summit, or in the form of mathematical equations (in some cases this may have been the way an actual system was designed), computer simulations, or even a set of stories. Using narrative to understand the dynamics of innovation ecosystems will be explored in a future blog in this series.

One difficulty is that the more we expand and generalize models to take into account wider circumstances the more unmanageable they become and usually we have to make additional simplifying assumptions or model small subsets of a system.

At this point it’s important to make a distinction between complex and complicated systems. Complex (adaptive) systems are what we have been discussing in the past few blogs. We noted, for example, that in such systems the same inputs may not always yield the same outputs and the whole is more than the sum of its parts. Complicated systems may be broken down into smaller and smaller constituent parts (superposition principle); the whole is the sum of its parts and behaviour is completely predictable. An economy is complex. An modern passenger aircraft is complicated. Both systems are composed of a system elements connected in a system structure. Both kinds of system perform specific system functions in its system environment. Both systems may have a permeable system boundary allowing inputs from, and outputs to, the external environment.

The difference is that complicated systems can be fully modeled whereas complex systems are inherently resistant to modeling.

My colleague Henry Doss, is his series of Forbes blogs on leadership, put the issue well “We live and work in a world that wants specificity and predictability, but we live and work in systems that defy predictability…  A strong leader of complex systems knows this truth about systems, and understands that oftentimes judgment, intuition and commitment are more important than measurements, projections and predictions.  Knowing that systems are resistant to predictive models, and are rich in unforeseen, often positive, outcomes is a powerful foundation for effective leadership.  And it’s an awareness that will make for more informed and nuanced decision-making.” Does Synergy Really Mean Anything? http://www.forbes.com/sites/henrydoss/2014/03/24/does-synergy-really-mean-anything/

Do difficulties relating effects to causes mean that we cannot model complex adaptive ecosystems? In fact no, even without predictive capabilities progress can be achieved. Sharon Zivkovik at the University of Adelaide, Australia, in her article Addressing Society’s Most Pressing Problems by Combining the Heroic and Collective Forms of Social Entrepreneurship http://www.emes.net/uploads/media/ECSP-R11-27_Zivkovic.pdf notes “According to complex adaptive systems theory, under certain conditions interactions between independent agents produce system-level order as agents interact and learn from each other, change their behavior, and adapt and evolve to increase their robustness. Empirical research has shown large complex systems such as communities require enabling conditions to be created in order to maintain the coordination required for emergence self-organization and adaptive capability.”

These communities may be said to be engaged in ‘collective entrepreneurship’ by integrating knowledge and resources from different, and sometimes diverse, parts of the ecosystem, capitalizing on properties of far-from-equilibrium complex adaptive systems such as self-organization, and using the resulting resources to address difficult problems.

Dr. Zivkovik further notes “The aim of interventions at the point of self-organization is to enable community system members and their resources to recombined into new patterns of interaction and working arrangements that improve the functioning and performance of the community system and displace the old way of thinking.”

It is this recombining into new patterns of interaction or moving from one ‘basin of stability’ to another in a Rainforest innovation ecosystem that allow us to build, if not complete ecosystem models, then at least some predictability around these stable regions. I use the word “moving” and this indicates what’s missing in our discussion so far; a model must be dynamic and include flows such as those of knowledge, and capital in the innovation ecosystem. These are relatively new ideas in the context of innovation ecosystems and present considerable challenges to the modeler. However, it does seem that we should be able to model such systems beyond string and Styrofoam™.

The first part of this Blog is at: Games of chance? Cause and effect in innovation ecosystems Part 1
http://innovationrainforest.com/2014/03/11/games-of-chance-cause-and-effect-in-innovation-ecosystems-part-1/

Next month: A review of the 14 blogs so far in this series, their connections, and what I hope we have learned.


Games of chance? Cause and effect in innovation ecosystems Part 1

Notes on the practice of innovation and technology commercialization

As cousin Zeb spreads his money on the table, ready to play poker with Cuthbert J. Twillie (played in the movie by W.C. Fields) he excitedly asks, “Is this a game of chance?”

“Not the way I play it, no,” comes Twillie’s reply.

My Little Chickadee (1940) movie starring W.C. Fields and Mae West.

Is poverty a cause of crime, did my forgetting to change the oil in my car cause the engine to seize, was the lack of funding for patenting inventions in my university the cause of low technology commercialization compared to peer institutions, what was the cause of sudden rise in value of my company stock? Chance, probability, cause and effect are so embedded in our daily lives that may give scant thought to the mechanisms of causality – what cause produces what effect, either immediately or at a later time. In this and the next blog we shall show that time is the critical feature of causality in both “plantation” and “rainforest” innovation ecosystems (The Rainforest: The Secret to Building the Next Silicon Valley http://www.therainforestbook.com/ ).

If, in building ecosystems, we propose interventions that adjust the ecosystem’s sub-systems and, especially their connections, we need to know what the effect of these actions are likely to be. For example, in one case, improving communications and shared goals among universities, incubators and accelerators, resulted in improved efficiency and ‘fitness’ of the innovation landscape  (see February 2014 blog The Gardener’s Dilemma http://innovationrainforest.com/2014/02/08/the-gardeners-dilemma-2/ for the concept of fitness).

Philosophers have been debating cause and effect for millennia. Aristotle identified four basic causes and stated that “we do not have knowledge of a thing until we have grasped its why, that is to say, its cause.” David Hume, whom we met in the January blog, made his readers think about whether we are justified in using inductive reasoning to understand events. Ludwig Wittgenstein’s, quoted in the March 2013 blog in this series, with his usual ability to both enlighten and confuse, dropped in the idea that “outside logic everything is accidental.”

Causality would seem to imply that we can create simple models such as event A causes B and in turn action B may be the cause of an effect C. For example the diagram below depicts a causal model relating price and demand, for which algebraic equations can be written. Q is the quantity of household demand for a product, P is the unit price, I is household income, W is the wage rate for producing the product, and U1, U2 are unmodeled error factors effecting quantity and price.

Image 

From Causality by Judea Pearl, Cambridge
University Press, 2000.

But wait a moment. In previous blogs we discussed how innovation ecosystems are non-linear complex adaptive systems where the same inputs don’t always produce the same outputs,  where the behavior of a system is not the sum of its individual parts, where there are disruptions and emergence, and where effects occur in far-from-equilibrium states. Surely then, complex adaptive systems make a mockery of simple causation?  So, what should we do to get a hold of cause and effect in complex adaptive innovation ecosystems? If we cannot, then we have lost our way completely.  It is to these questions we shall now turn our attention.

Writers in several disciplines including biology, physics, economics, and sociology continue to add to theories and applications. However, it’s clear that there is much still to be explained. The remainder of this blog and the next will survey and summarize what is known about solving practical problems of cause and effect in complex adaptive innovation ecosystems. Much of what is discussed next is taken from the thought provoking work of David Byrne and Emma Uprichard.

Deterministic equations cannot be written for complex systems as they can for the linear system diagramed above. Other means are needed. A valuable concept is the ‘causal narrative’ – descriptions or cases, which may contain both text and numbers, that help to explain why some event happened in a complex system and how the state of the system came about. Such narratives are reconstructions of events similar to case examples and studies familiar from education, although here we are typically talking about short narratives and maybe reusable knowledge facets as introduced in the October 2013 blog in this series Create early, use often: Lego™ blocks, learning objects, and ecosystems. Part 2 http://innovationrainforest.com/2013/10/13/create-early-use-often-lego-blocks-learning-objects-and-ecosystems-part-2/.

In practice preparation of a roadmap for an innovation ecosystem demands an understanding of causality. A roadmap is a trajectory over time which might show what actions or projects are recommended and when they should begin and when they should be completed. In this situation causality means causality by comparison – comparison with the trajectories of systems which have similarities, or in the language of complexity those which are ‘near neighbors.’  Knowledge of what happened to produce an existing state can enable choices to be made of which actions – causes – can produce future expected results – effects. The late Fritz Ringer, professor emeritus of history at the University of Pittsburgh, described this as “the kind of causal analysis that will explain why the course of historical development ultimately led to the explanandum* in question, rather than to some other outcome.”

In this introduction to cause and effect in complex systems I have a feeling of having laid myself open to charges of either over simplifying issues or making them vague. Part 2 will attempt to correct either situation and look at the practical consequences of trajectories in complex innovative ecosystems and all their component parts. W.C. Fields was correct, it’s not a game of chance.

* “explanandum”  is not a familiar word to many of us, but quite a handy one. An explanandum is a phenomenon that needs to be explained, and its explanans is the explanation of that phenomenon.

Next time: Cause and effect in innovation ecosystems Part 2


The Gardener’s Dilemma

Notes on the practice of innovation and technology commercialization

A colleague at T2VC posed this question:

  1. We know that if everyone is an entrepreneur, a society will not function. 
  2. We know that if everyone is a producer, a society will wither. 
  3. Thus, is it possible to determine the exact proportion of innovators versus producers to maximize the productivity of an ecosystem? 

This question is, I think, part of a broader one: in an innovation ecosystem is it possible to adjust all the various elements so that the ecosystem optimize its outcomes (measured in some way)? These elements may be universities, technology commercialization systems, public sector financing funds, and so forth – more about this later. You may remember that in the February 2013 Imperfect Works http://innovationrainforest.com/2013/02/06/imperfect-works/ blog in this series we talked about optimizing which means “make something as good as possible within a whole system” although each individual element may not be operating in the best way it can.

In their paper Chaos prevailing on every continent: Towards a new theory of decentralized decision-making in complex systems, http://www.temple.edu/lawschool/dpost/chaos/chaos.htm researchers David G. Post and David R. Johnson present a method of finding optimal configurations of elements in complex adaptive systems. The authors pose the Gardener’s Dilemma:  how can a gardener find the best or at least a good configuration of a collection of plants whose overall “fitness” (for example total yield) is dependent upon the behavior of all the other plants? You may begin to see how the garden is an analogy for an innovation ecosystem. We shall see shortly if this analogy is helpful.

In this imaginary garden there are plants of different species. The gardener would like to obtain the most luxuriant overall growth. The gardener must decide for each individual plant: should it be pruned or not? How can the best combination of pruned and un-pruned plants be created that will produce the greatest yield for the whole garden?

As always, we need to make some assumptions, which for this garden are:

(1)   The relationship between an individual plant’s pruned or unpruned state and its growth is different for each plant. For some plants growth will be increased by pruning, for other plants pruning will reduce their growth.

(2)   Each individual plant’s growth can be affected by the growth of other plants, for example as one plant grows it might block sunlight reaching another plant that needs it. Kauffman calls these spillover effects although I prefer interactions.

Even with such simplifying assumptions it turns out that this problem, and many similar ones, are “computationally intractable” or in other words incapable of true solutions by any known analytical methods. A little thought may convince us why this is the case for our garden. Suppose there are only three plants each of which may be pruned (we will call this state 0) or unpruned (we will call this state 1). We can use the tree diagram below to help figure that 8 possible configurations exist, namely 2 x 2 x 2 = 23.

ImageA little further arithmetic will show for 4 plants there are 16 possible configurations, namely 2 x 2 x 2 x 2 = 24 and so on. To generalize this pattern we can say that in any system with N elements, each of which can take one of S possible states, there are SN different system configurations. This SN can rapidly become a very large number. For 10 plants the number of configurations to be tried is already 1,024. Hard work for the gardener!

Is this gardening knowledge of any practical use for those of us developing optimal innovation ecosystems? If this this ecosystem analogy is computationally intractable, why am I wasting your time discussing it?  Stay with me for just another moment and I will demonstrate its practical use – but first one final research result.   

When a problem cannot be solved by mathematical analysis computer modeling may help, even greatly oversimplified models can be used as long as their simplifying assumptions are not forgotten when applying the results to the real world. Stuart Kauffman and his colleagues have developed a family of computer models and problem-solving algorithms for complex interconnected systems, known as “NK models,” for studying various forms of the Gardener’s Dilemma in, for example, evolutionary biology and cyberspace law. We don’t have space for details (which are in Johnson and Post’s publication) but essentially the method consists of modelling interactions between ecosystem elements. The elements in the garden ecosystem are sub-divided into any number of non-overlapping but interacting self-optimizing parts called patches, like a patchwork quilt. 

ImageTo quote Post and Johnson “The result is a fairly remarkable one: It is by no means obvious that the highest aggregate fitness of the system will be achieved if it is broken into quilt patches, each of which tries to maximize its own fitness regardless of the effects on surrounding patches. Yet this is true. It can be a very good idea, if a problem is complex and full of conflicting constraints, to break it into patches, and let each patch try to optimize, such that all patches co-evolve with one another.”

A typical innovation ecosystem has elements (S) such as universities and research institutes with  their technology transfer offices (TTOs), new business incubators and accelerators, some form of central support organization to assist TTOs with issues such as market intelligence and so forth, financing programs such as early-stage R&D grants and seed and venture funds, economic development organizations, science and technology park, a contract research organization, and sometimes miscellaneous organizations which were formed for different times but are still functioning.  All these can certainly exist in many states so N will be much larger than 2 as in our garden.

Applying what we have learned, it is good practice to divide these ecosystems into patches, for example four possible patches could be:

Universities and research institutes with  their technology transfer offices (TTOs)

Central support organization to assist TTOs

Early-stage R&D grants and seed and venture funds

New business incubators and accelerators

Contract research organization

Science and technology park

Economic development organizations

Miscellaneous organizations which were formed for different time

Having the elements trying to maximize their fitness within their patch improves trust and communications between them – and as a result – decreases transaction costs.  Furthermore, this should break down the rigid hierarchical structure from which some ecosystems suffer. Other practical applications of the Gardener’s Dilemma can be found, such as explaining the familiar S-shaped curves of technology growth and disruption. Climbing around the fitness landscape searching for peaks and valleys will be discussed in future blogs.

Your response to all this may be “but this is obvious, I don’t need computer models.” In complex adaptive systems what appears to be obvious is not always so (occasionally disastrously not so). Personally I’m comforted by having a foundation theory.

Next time: Games of chance? Cause and effect in innovation ecosystems.


Dialogs concerning natural ecosystems: A New Year’s whimsy

The setting: a New Year’s eve postprandial table. The diners:

Francis Bacon, 16 – 17th century philosopher and statesman, and inventor of the frozen chicken.

David Hume, 18th century philosopher and author of An Enquiry Concerning Human Understanding, and who believed that political discourse should always be polite (hmm…)

Cleisthenes, a politician from an antique land.

Dinner party host.

Host: “Francis, David – if I may be allowed to be informal in the presence of such empiricist greatness – we had a superb dinner and an enlightening debate about the scientific method, deductive reasoning, innovation, and Rainforests. Perhaps we can wrap up our conversation now?

We talked about the agriculture model of cultivated rationality with the goal of maximizing final output that I know resonates with you both. It’s a model that works for the mass production of plants, but blatantly fails when it comes to creating new ones. In this case, the better method is the Rainforest [http://www.therainforestbook.com/], which despite, or thanks to, its apparent non-rationality is the environment for innovation and the emergence and spread of new species. The Rainforest model also has the advantage of lowering transaction costs.”

Bacon: “What do you mean by transaction costs?”

Host: “Forgive me Francis, I forgot it was carelessness about cash transactions and the calumnies of your groupies that abruptly ended your political career.”

Hume: “As you know, I believe in deductive reasoning. How can we have such reasoning and demonstrate causal relationships in so patently a complex and, may I say, disorganized, model of innovation ecosystems?”

Bacon (who during the prior dinner conversation about innovation clearly believes Google to be the new 21st century ecclesiastical authority): “you may remember in my Essays I described time as the ‘great innovator’ but Google gave 115 million entries for definitions of innovation; it would seem the word ‘innovation’ has, in my opinion, been debased. However, the point I want to make is that innovation must include a time element”

Hume: “I have written that ‘all men of sound reason are disgusted with verbal disputes …it is found that the only remedy for this abuse must arise from clear definitions.’ Surely ‘innovation’ used to mean true breakthroughs, disruptions like the telephone, jet engine, internet, DNA structure, and so forth?”

Host: “David, innovation as such is a topic for another discussion. What I want to raise here is that it may not always be easy to grasp some of the Rainforest fundamentals so the purpose of these blogs is to apply Rainforest concepts to real issues, questions, and problems in innovation. In so doing we will use these principles to describe, through concrete examples, how to build innovation ecosystems in developed and developing countries.

Cleisthenes: “As an ancient fellow, I’m not sure what that these items are which Francis apparently knows about – although as a Greek I recognize tele and phone, so I assume telephone means someone shouting from a great distance. But, as a politician I don’t see so far how any of our discussions enable me to address the problems I have with delivering economic and job growth (we already have too many public intellectuals lolling around all day talking). These are my immediate problems: I’m not saying ‘I want to create an effective Rainforest ecosystem’ because I don’t yet see how this will help me. Just tell me how and I’ll go away and do it. Is that too much to ask?”

Host: That’s a fair point. Remember the reason I invited you to this New Year’s Eve dinner – and I’m grateful to you all for coming so far in time and space – is to seek your advice on future blogs in this series.

Let me survey what I believe to be the evidence for how to develop effective innovation ecosystems, summarize how this will be presented in more detail in future blogs, and get your comments.

Cleisthenes: “I’m sorry to be repetitive but I want to be sure I’m not misunderstood; this philosophical discussion is quite exciting – intellectually, but you still have not helped me. I don’t need to know about bounded rationality and such like we discussed over our meal, I want something that works. Before my government commits money to building Rainforest ecosystems, how will I know if we are on the right course without waiting for three, five years, or longer, by which time there will be a new government and new politicians to take the credit if success emerges, or blame the previous administration’s mistakes if failure; and of course the ‘expert’ consultants we used will be long gone to challenges new?”

Bacon: I realize that, unlike in my day, one cannot know about everything. My Google search showed me that innovation involves so many ideas from different disciplines, which have all been researched in depth. The readers of these blogs will not have time to study original sources. These blogs should bring results from research and application to the reader in a manageable form for practical use.

Host: If I understand your advice then in future blogs we should show how in Rainforest complex ecosystems we should, inter alia: solve problems; make decisions; express causality where possible; build innovation capacity; create roadmaps; show leadership; communicate.

Bacon: Don’t forget time. Time means telling an evolving story. Time as an element implies narrative. I believe in the efficacy of narrative to present what I have called elsewhere a ‘globe of precepts.’

Hume: Convince me that deductive reasoning and causality are not discarded in the complex systems of which you speak. As I have noted elsewhere, ‘we must endeavor to render all our principles as universal as possible, by tracing up our experiments to the upmost, and explaining all effects from the simplest and fewest causes.’

Cleisthenes: I concur with Hume’s last statement; by the way David, you sound like a Austrian economist.  Keep it practical, easy to understand, and applicable to finding solutions to specific problems.

Host: Splendid! We will follow your sage guidance. Let’s reconvene next New Year’s Eve and review progress. Perhaps I’ll also invite Wittgenstein to talk about complex systems and reducing statements at a higher level to statements at a lower level, and maybe invite …..

Next time: Solving problems and growing gardens in the Rainforest.


Let’s all have a good argument

Notes on the practice of innovation and technology commercialization

 “I suspect that the fate of all complex adapting systems in the biosphere – from single cells to economies – is to evolve to a natural state between order and chaos, a grand compromise between structure and surprise.”  Stuart Kauffman, At Home in the Universe: The Search for the Laws of Self Organization and Complexity (1995).

In this blog we continue, from the last blog Fury and Adrenaline http://innovationrainforest.com/2013/11/20/fury-and-adrenaline/ looking into the basic analytical infrastructure which underpins commercialization activities and the development of supportive ecosystems – with an emphasis on what is known about complex systems. I believe that more rigor leads to better explanation and prediction (the mark of a good theory is that it must not just explain but also predict) and thence to improved application by practitioners.

It was a warm cloudless summer morning in Central Asia as the glum looking group slid slowly but resolutely into the conference room to be appraised of new government initiatives to support R&D and technology commercialization.  Several in the front rows sat, arms severely folded, questioning – sans words – the veracity of my colleague and I, and, by extension, that of the government’s sincerity.

A “presentation” was clearly not going to impress. Let’s have a debate instead I thought – with not entirely flawless logic – had not Frederick Engels, co-revolutionary with Karl Marx whose philosophy had once dominated this land, believed in the negation of the negation to deliver the future? A noisy dispute followed between audience and presenter (me, intervening only when the volume exceeded a decibel level sufficient to attract those in adjoining offices) and among audience members. One scientist became especially upset but was restrained by his colleagues from walking out in high dudgeon.

However, in the course of the bruising arguments which followed something exciting emerged out of the session’s flotsam; new thinking and agreements among the previously hostile audience which had taken the opportunity to vent against the government and their foreign consultants, and then moved on to a constructive deliberation.

The last blog introduced the idea of dis-equilibrium state (also called a far from equilibrium state); one in which it is definitely not business-as-usual and events are occurring which push a system into a highly dynamic and unstable state. Quite an accurate description of the Central Asian event.

Complexity science shows that when systems are in a dis-equilibrium state, small actions and events, “perturbations” in the system, can be amplified through a positive feedback cycle of self-reinforcement. This effect has been predicted and observed in evolutionary biology and also in studies of leadership in groups of people.

To show the theory behind all this we need to introduce the concept of a phase space (also called a state space).  The term was originally use for substances which can exist in several phases or states. For example, water can exist in solid (ice), liquid, or vapor (steam) phases. The formal definition is “a multidimensional space in which each axis corresponds to one of the coordinates required to specify the state of a physical system, all the coordinates being thus represented so that a point in the space corresponds to a state of the system.” Note that, but don’t be too alarmed, we are using “space” as a mathematical term not as in everyday usage.

As an example, when you are riding your bicycle the physical space you inhabit is the familiar 3-dimensional space. However, your phase space is a 2-dimension one whose axes are position and velocity (remember velocity = speed and direction, such as 5 miles/hour due north). In economic systems, the phase space variables could the inflation rate, the interest rate, the national debt, and the unemployment rate, for example.

The Rainforest Canvas, created by T2VC, is a set of questions to help map an innovation ecosystem in a region by looking at factors such as: Leaders, Stakeholders, Frameworks, Resources, Activities, Engagement, Role Models, Infrastructure, Culture, and Communications. (The Rainforest: The Secret to Building the Next Silicon Valley http://www.therainforestbook.com/ ). In the Rainforest view of the world the system variables are these headings in the Rainforest Canvas. Parameters or constraints are the sub-questions under each heading. Changes in these parameters move the ecosystem to a new point in its phase space. As we can imagine, but cannot draw, a phase space diagram with these 10 variables let’s just consider three ecosystem variables a, b, and c, represented along the horizontal and vertical axes in the three dimensional phase space diagram (below).

Cusp 1Path 1 represents for example a big jump to a new business model, or a new set of resources, or possibly some disruptive innovation, whereas Path 2 represents continuous transformation: maybe a gradual culture change, or breaking down a problem or opportunity into manageable pieces and sequentially tackling each one. Diagram adapted from J. Goldstein et al. A Complexity Science Model of Social Innovation in Social Enterprise. Journal of Social Entrepreneurship, Vol 1, Mo 1, p.109, March 2010.

We will further build on this diagram in future blogs.

Once a complex system, of individuals in this case, is pushed to a far from equilibrium state, the more its leaders and members surface conflict and create controversy, the more likely that the system will generate novel opportunities and solutions. The more that leaders and members encourage rich interactions, the more likely that amplifying actions will be present in the system.

On the subject of leadership, my polyhistor colleague, Henry Doss, has written in his Forbes blog about organization’s which mistakenly focus on training leaders to lead people, rather than training leaders to build and lead systems: Why Your Innovation Leadership Training Will Fail http://www.forbes.com/sites/henrydoss/2013/06/06/why-your-innovation-leadership-training-will-fail/ Henry introduced The Innovation Syllogism:

Innovation is a product of culture (not individuals).
Culture is an emergent factor of systems (not individuals).
Therefore, systems drive innovation (not individuals).

“If the logic and assumptions of this syllogism hold, then you may find that the most critical aspect of building an innovative organization – systems – is absent from your training and development planning.” We will discuss emergence as a phenomenon in complex adaptive systems in a future blog.

Oh yes, what happened in the Central Asian meeting? Standing applause at the end, and the person who seemed most pleased with what emerged – you guessed it – was the scientist who had been the most voluble!

 

 


Fury and adrenaline

Notes on the practice of innovation and technology commercialization

No one descends with such fury and in so great a number as a pack of hungry physicists, adrenalized by the scent of a new problem.”  D. Watts. Small Worlds: The dynamics of networks between order and randomness. Princeton University Press, 1999.

I’m postponing the promised discussion on learning from agile manufacturing (see my October blog Create early, use often: Lego™ blocks, learning objects, and ecosystems. Part 2 http://innovationrainforest.com/2013/10/13/create-early-use-often-lego-blocks-learning-objects-and-ecosystems-part-2/ in order to talk about where my blogs are heading since I wrote the first one last February.

The point I’m attempting to make in these pages is that, at least in my experience, most analyses of technology commercialization are rather surface and have little depth or theoretical underpinnings. By comparison, economics, engineering, and law, each have an analytical base. Maybe this lack of depth is because it’s only been some 30 years since Paul Romer recognized technology as an endogenous growth factor. Of course, we are all aware of problems caused by physicists “descending” on economics and in some cases giving the practice an unjustifiable mathematical rigor based on unsustainable assumptions. I speak as a physicist myself – not an economist. I also speak not as an academic or researcher but as a practitioner seeking insights into practice.

In my work I find that technology commercialization methodologies for developing countries are frequently being unnecessarily re-invented, and reusable knowledge is not sufficiently shared (the topic of my last two blogs). My hypothesis is that this is because the basic analytical infrastructure which underpins commercialization activities in disparate regions of the world is not well understood, and that which is understood is not efficiently available to practitioners.

Through these blogs, In a small way, I’m trying to create a more complete basis together with tools, based on theory and practical experience, for use by practitioners.

So, having set this task, what does this “analytical base” look like – and where does JMW Turner’s 1805 Shipwreck painting (below) come into the story?Image

Previous blogs in this series have discussed, in addition to reusable knowledge objects, the role of networks and links, decision making and problem solving, transaction costs, imperfect optimization, and complexity. It is this latter topic – complexity – which will be the rationale for the next few blogs which will attempt to at least approach an analytical foundation. We shall begin to see how these ideas relate to issues in The Rainforest: The Secret to Building the Next Silicon Valley http://www.therainforestbook.com/ by T2VC’s founders Greg Horowitt and Victor Hwang and how the book’s principles can be applied.

We will try to collect results from researchers and practitioners applicable to technology commercialization and, more broadly, creating the architecture of economic development ecosystems. This will not be a simple task as thousands of research papers, blogs, and articles are published each year on this subject.

At two ends of the application spectrum, Jean Boulton talks about the world as a complex system So the world is a complex system – what should aid agencies do differently? http://www.oxfamblogs.org/fp2p/?p=9645  whereas Ian McCarthy applies ideas of complex adaptive systems to new product development New Product Development as a Complex Adaptive System http://itdepends4.blogspot.ca/2012/09/new-product-development-as-complex.html?goback=.gde_76119_member_194199540

We live in a complex and non-linear ecosystem usually in far from equilibrium situations so I begin by briefly explaining these terms, in the present context, along with related concepts.      

There are many definitions of a complex system (which reflects the fact that we have an incomplete understanding of such systems). An informal definition is a large network of relatively simple components with no central control, in which emergent complex behavior is exhibited.  We will return to “emergence” in a later blog. The term “adaptive” is often appended to complex systems and means a system which is capable of learning.

This definition can be extended by saying that a complex system is a system which has heterogeneous smaller parts, each carrying out some specialized function, not necessarily exclusively, which then interact in such a way as to give integrated responses. In a complex system, as opposed to a complicated one, the function of the whole in a complex system cannot always be guessed from the function of its parts, and the reassembly of the parts does not always give back this function. This extended definition is based on work by David Snowden, which we shall discuss in future blogs.

Colonies of ants, our immune system, flocks of birds (below), and the World Wide Web are examples of such systems.Image

The notion of nonlinearity is important here: the whole is more than the sum of the parts. Innovation is considered to be such a system which also exhibits another property of nonlinearity, namely, where the same input may not always yield the same output. This means that to understand a complex system we have to study the system as a whole; different from the “reductionist” methodology of decomposing systems into their individual components to see how they work.

Some results from research in complex systems we might say are common sense (which is another way of expressing our personal experience) such as the theory behind the emergence of new ideas from a group of people arguing with each other. Other results may be counter-intuitive, such as how small changes in initial conditions may produce large effects later on.

And Turner’s Shipwreck painting?  His magnificent brush strokes show a Far from equilibrium state. That is, one in which it is definitely not business-as-usual and events are occurring which push a system into a highly dynamic and unstable state. Much more about this concept later.

Next time: If all of life is a dispute (according to Nietzsche), let’s argue – a case example of a far from equilibrium state of affairs in technology commercialization.


Create early, use often: Lego™ blocks, learning objects, and ecosystems. Part 2

Notes on the practice of innovation and technology commercialization

Aristotle (384-322) in his Nicomachean Ethics, Book III, in writing about a person’s voluntary and involuntary acts, distinguishes between (i) who is acting, (ii) what is the act, (iii) the circumstances of the act, (iv) the instrument or tool, (v) the aim of the action, and (vi) the manner of doing the act; for example quickly or slowly.  

As we shall see, it turns out Aristotle  had rather good advice for applying the reusable knowledge tools introduced in my last blog Create early, use often: Lego™ blocks, learning objects, and ecosystems. Part 1 http://innovationrainforest.com/2013/09/19/create-early-use-often-lego-blocks-learning-objects-and-ecosystems-part-1/ in which I speculated about the appealing simplicity of building technology commercialization programs, and, more broadly, supportive ecosystems, by plugging Lego™ blocks of learning into each other.

Well, can we? Let’s see how far we can go. If you’ve read my other blogs you’ll remember that “context” is a perennial theme. We meet it once again here.

Actually, the Lego™ block idea has been discussed extensively by educators and found to be an imperfect analogy. The problem is that the metaphor’s assumption suggests any learning object should be combinable with any other learning object; this is not always the case as we shall see – because of our old acquaintance: context.

It’s not that you cannot reuse these learning objects (we will call them ‘tools’ from now on) because contexts are never the same, it’s that context must be fully understood. We can think of every tool as being embedded in its own contextual cloud. For example, the tool to decide whether to license a technology or create a spin-off company which was introduced in an earlier blog Solving the Right Problem: Part 1 http://innovationrainforest.com/2013/03/24/solving-the-right-problem-part-1/ has a rather small context cloud; it is largely context independent. By contrast, a tool for use in developing intermediaries Network holes and how to plug them http://innovationrainforest.com/2013/07/31/network-holes-and-how-to-plug-them/ has a large context cloud; it is highly context dependent. This is mostly because “… no two knowledge intermediaries are the same; their work is entirely context specific, which means that, while it is possible to draw general lessons as to how they [a user] could chose to act, it is impossible to develop a standard set of rules as to how they should act.” Jones, Harry, et al (2012) Knowledge Policy and Power in International Development: A Practical Guide. The Policy Press. Chapter Five: Facilitation knowledge interaction, p 123. The authors also caution that (p. 135) … “it will not be possible to anticipate how the information will be used [by those seeking solutions] and its likely effects.” 

I have not been able to come up with any tool which is entirely context free.

We next need to introduce the concept of ‘contextual qualifiers’ which are those pieces of knowledge that allow a user to assess whether a given policy or practice, implemented elsewhere, is truly relevant or applicable to the user’s environment. Conditional qualifiers are statements, which refer to knowledge Lego™ blocks (documents, videos, etc.) which ‘qualify’ the knowledge presented as being dependent on certain conditions. In more detail:

  1. Contextual qualifiers are those knowledge fragments (also called “facets”) that allow a user to assess whether a given policy or practice, implemented elsewhere, is truly relevant or applicable to the user’s environment.
  2. Contextual qualifiers are statements, which refer to knowledge sources (documents, videos, etc.) which qualify the knowledge presented as being dependent on certain conditions.
  3. Contextual qualifiers acknowledge that “one size doesn’t fit all” and that a tool is needed that helps knowledge users to better appreciate the influence of contextual factors.
  4. Contextual qualifiers facilitate the presentation and comprehension of context when locating potentially relevant resources.
  5. Reusable knowledge products may include embedded contextual qualifiers.
  6. Users should be able to contribute contextual qualifiers such as “how to use” or “when to use,” or “when not to use” based on their experience.

Contextual qualifiers are important not only when selecting, designing and implementing actions – what we might call ‘formulating solutions’ – but also in diagnosing where the failures and bottlenecks lie – what we might call ‘identifying problems’.  

Identifying problems brings us back full circle to knowledge reuse in innovation and commercialization. Studies on knowledge reuse for innovation from NASA’s Jet Propulsion Lab at Caltech cited in Knowledge reuse – the process of knowledge reuse in radical innovation situations http://knowledgeputeri.wordpress.com/articles/knowledge-reuse-the-process-of-knowledge-reuse-in-radical-innovation-situations/ found that users were motivated to reuse others’ ideas if:

  1. They are confronted with an insurmountable problem with their current knowledge and resources.
  2. They re-conceptualized the problem and approach to require an ambitious new perspective.
  3. They believed that they can find useful existing ideas elsewhere.

Are not these common technology commercialization challenges, and again, more broadly, challenges of developing supportive innovation ecosystems?  Additional motivating factors found in the studies included:

  1. Work processes that optimize exposure to diverse knowledge sources.
  2. Use of extensive personal knowledge bases with both weak and strong ties (for more about weak and strong ties see my blog:  The ties that bind us: networks, strong links, weak links, and expanding our knowledge http://innovationrainforest.com/2013/06/30/the-ties-that-bind-us-networks-strong-links-weak-links-and-expanding-our-knowledge/)
  3. Culture within the project which encourages reuse.
  4. Availability of flexible ways to assess credibility of potentially reusable knowledge.
  5. Ability to scan for fit.
  6. Ability to quickly determine malleability of reusable knowledge.

Or, as some time earlier, Aristotle might have expressed the stages:

(i) Person seeking a solution to a problem, (ii) applying the solution, (iii) context, (iv) tool, (v) re-conceptualizing the problem, (vi) short-term or long-term impacts.

 Next time: Response Ability: Learning from agile manufacturing.


Create early, use often: Lego™ blocks, learning objects, and ecosystems. Part 1

Notes on the practice of innovation and technology commercialization

“Mankind are so much the same, in all times and places, that history informs us of nothing new or strange in this particular. Its chief use is only to discover the constant and universal principles of human nature.”
David Hume (1711–76).  An Enquiry Concerning Human Understanding.

A handful of years ago a colleague of mine from the World Bank and I were having coffee in Washington DC with visitor from South Africa. She was listing the skills and tools that she believed South Africa needed to improve technology transfer and commercialization, especially from universities. At that time I had never worked anywhere in Africa, but I was stunned by the realization that some 90% of the items in our visitor’s list of wants and needs were identical to those of the countries in Eastern and Central Europe where I had more experience. Since then this commonality of needs has been verified by working in many other countries from Colombia to Kazakhstan.

In this blog I argue that there is unnecessary and frequent reinvention in creating technology commercialization systems, especially in developing countries, resulting in unnecessarily high transaction costs and less than optimum efficiency.  Reusable knowledge tools, analogous to more general reusable knowledge or learning objects, can reduce reinvention of known processes, lower transaction costs, and increase technology commercialization efficiency. This is important because more and more developing countries are attempting to build ecosystems around technological innovation. To be clear, when I use the terms ‘learning object’ I mean a digital resource that can be reused to facilitate learning. In the application discussed here it’s helpful to think of a learning object for what it does (an agent) rather than what it is (its properties).

Wait a moment. Am I going against what I was preaching in an earlier blog about context and cutting and pasting solutions without paying attention to context? To be honest, maybe – a little. In Solving the Right Problem: part 1, March 24, 2013. http://innovationrainforest.com/2013/03/24/solving-the-right-problem-part-1/ I stated “Solving the right problem is all about context. A problem comes embedded in its own context; apparently similar problems in different contexts may have very different solutions. Likewise, solutions have their own contexts.” In the first part of that blog I looked look at one way of identifying a problem which may also bring out its context and suggest possible solutions. In Part 2 of the blog, Fallibility and the Making of Good Decisions, problem solving and decision making in Rainforest ecosystems was discussed. http://innovationrainforest.com/2013/04/30/fallibility-and-the-making-of-good-decisions-solving-the-right-problem-part-2/ Let’s see what, if anything, has changed.

I always have fun playing with my granddaughter’s Lego™ blocks, and the Lego™ block analogy is used frequently whenever knowledge is being collected and assembled from disparate resources. Mary Adams and Michael Oleksak in their 2010 book Intangible Capital: Putting knowledge to work in the 21st century organization speak about using Lego™ blocks to build models of a knowledge factory such as Google search or a medical device company (see the video “You can grow like Google”  http://www.youtube.com/watch?v=brBwWqiSg8g)

Wouldn’t it be great if we could build technology commercialization programs, and more broadly supportive ecosystems, by plugging Lego™ blocks of learning into each other? There is an appealing simplicity. In the second part of this blog we will use some examples to see how far we might go, discuss the limitations of the Lego™ block analogy, and suggest that a reluctance to apply reusable knowledge tools to problems arises from a misunderstanding of the role of context.

We will also introduce the concept of ‘contextual qualifiers’ which are those pieces of knowledge that allow a user to assess whether a given policy or practice, implemented elsewhere, is truly relevant or applicable to the user’s environment. Conditional qualifiers are statements, which refer to knowledge Lego™ blocks (documents, videos, etc.) which ‘qualify’ the knowledge presented as being dependent on certain conditions.

As I was preparing this blog a Harvard Business Review article was published: Consulting on the Cusp of Innovation by Clayton Christensen, Dina Wang, and Derek van Bever (HBR October 2013, pp. 106 – 114) which discusses how incumbent consulting firms are being eroded by technology and other forces. The authors note that “only a limited number of consulting jobs can be productized but that will change as consultants develop new intellectual property. New IP leads to new toolkits and frameworks, which in turn lead to further automation and technology products.” In this new business model, consultants may not always re-invent solutions; a move away from work where value depends primarily on “consultants’ judgment rather than repeatable processes.” The authors call this “value-adding process business” in which “processes are usually repeatable and controllable.”

I’m guilty of raising several issues and leaving them hanging. Next time these will be pulled together and some conclusions drawn about the feasibility of reusable knowledge tools in technology commercialization.


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