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

4 Comments on “Lean and Agile Innovation Ecosystems: Part 1”

  1. […] quest to understand the science of innovation ecosystems. First is agility. In July’s blog (http://innovationrainforest.com/2014/07/22/lean-and-agile-innovation-ecosystems-part-1/) we introduced the notion of agility in innovation ecosystems and looked at some principles of […]

  2. […] July’s and August’s blogs about agile innovation ecosystems suggest that there is a need for rapid diffusion, spread, or flow, of information (knowledge, learning, innovations) if such networks are to be responsive. It is to this feature we shall turn our attention in this blog – with two caveats. […]

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