Tag Archives: Ikujiro Nonaka

Deliberate perturbation at Levi's

Last April, I received an email from Erik Joule, Senior Vice President of Merchandising and Design at Levi’s, asking to speak with my colleagues and me about our paper “Wellsprings of Creation“.  Erik and Levi’s were setting out on a “massive cultural transformation project” to renew the organization’s capacity for innovation and creativity.  After speaking with us about our research, Erik decided to use our theory of deliberate perturbation as his conceptual frame for the transformation project.  Subsequently, Erik and I have met a few times to discuss the project, and he invited me to meet with some people at Levi’s to learn more about what they’ve been up to.  On Thursday, I took him up on the offer and interviewed two people from the strategy group.  Here’s their perspective on how Levi’s has been changing.

Our conversation began with a recently launched new product development initiative.  The new product targets a market segment that has not been a priority for the company, and thus represents a significant, high level perturbation.  As Brad, Mike and I explain in our paper, the hallmark of a perturbation is that it jolts an organization away from a stable, predictable equilibrium trajectory.

The initiative incorporated a variety of lower-level perturbations designed to throw the organization out of balance.  For example, working together with a design firm, Levi’s conducted a series of three “salons”, off-site workshops lasting two or three days.  Each of these salons brought together about twenty people from functions across the organization to develop ideas around the new product theme.  One salon was conducted in Memphis, where the company rented a space and recruited local consumers to interact directly with the Levi’s team.  Levi’s wanted quick feedback about their ideas, so they sewed clothes on the spot, had consumers try them out, and got immediate reactions.  The salons were about “ripping you out of your day to day job” and getting into a different frame of mind–an image that captures the essence of perturbation.

The distribution of exploration within the company has also changed.  Previously, the company separated exploitation (day-to-day business) and exploration (innovation), concentrating the latter in a dedicated innovation group.  As with many other companies that have attempted this approach, the innovation group proved unable to drive exploration throughout the company.  Within the organization, it was seen as a “group of people off on the side”, isolated from the market and customer needs, and not taken very seriously.  Now the group is gone, and “innovation is something you do every day.”

Several concepts and phrases came up repeatedly during the interview: rapid prototyping, direct feedback from customers, the need to pursue “progress over perfection”.  The changes have been disorienting for the organization.  Putting rough prototypes in front of real consumers has been discomforting for an organization accustomed to perfecting their products before letting them see the light of day.  Projects staffed with cross-functional teams have disrupted traditional boundaries, as when members of the strategy team (without any specialized training, just a fifteen-minute orientation) participated in a consumer shop-along project that would previously have been performed exclusively by staff from the consumer insight group.  This disorientation is, of course, a sign that perturbations are occurring–that established processes are being knocked off balance.

From an organizational learning perspective, I’m intrigued by the emphasis on collecting people from across the organization and creating opportunities for them to experience their customers–shop-alongs, field trips, even a mock retail environment in a conference room at headquarters with real consumers invited in to “try it out”.  According to Ikujiro Nonaka’s theory of knowledge creation, such shared experiences enable the development of tacit knowledge.  Employees who participate in shop-alongs or see consumers walking around a mockup of a store acquire knowledge beyond what they can articulate in words.  Shared tacit knowledge provides a common frame of reference and thus facilitates communication.

Our conversation raised at least two provocative questions.

First, although employees involved in these efforts have adopted the mantra “failure is an option, fear is not”, what will happen when a significant failure occurs?  Indeed, a senior manager “jumped all over” this phrase when it was presented to him: we have to succeed!  One of my interlocutors reflected, “I don’t think we tolerate failure well … if something fails, it will be the grumbling around the water cooler” that casts a pall over the initiative.  So how should management handle failures?

Second, Erik’s openness to new ideas creates another challenge: which to pursue?  Before, the default response to a new idea was no; Erik’s default response is yes.  “Erik loves ideas”.  One project came up with a list of one thousand ideas.  The theory of deliberate perturbation posits that too few perturbations lead to stagnation, but if perturbations are too numerous or not complementary, they result in chaos and decreased performance.  So how to manage the flow of ideas and ensure that perturbations are as productive as possible?

Knowledge: A Short Essay and an Annotated Reading List

One of my colleagues asked me what to read to learn about knowledge. The answer requires a bit of explanation.

There are two approaches to knowledge. On one hand, there are the epistemologists. The epistemologists have spent many centuries developing criteria for evaluating whether a belief qualifies as knowledge. On the other hand, there are the computer scientists and organization theorists, who tend to focus on how knowledge affects the performance of problem solving systems (i.e., humans, computers, organizations). These two approaches can be reconciled as follows.

For the computer scientists and organization theorists, knowledge is anything that improves the performance of a problem solving system, except for information processing capacity. If two systems execute the same number of symbolic operations but one system gets a better answer, it must know something the other system doesn’t.

Epistemologists, by contrast, want perfect knowledge that will never lead a problem solving system to act in ways that betray its own goals. Such perfect knowledge is difficult to obtain, and perhaps even more difficult to define. After a few millennia, epistemologists still haven’t come up with a satisfactory definition. This is not to say that the field has failed: epistemology can help us evaluate the quality of knowledge and acquire better knowledge.

A short example may help clarify the matter. To a computer scientist, “what goes up, must come down” is a reasonably good piece of knowledge. It tells a problem solving system not to throw a water balloon straight up in the air. To an epistemologist, this isn’t knowledge at all, because it isn’t true. If I launch a rocket into space, it doesn’t need to come down. In fact, up and down are not even valid except within very limited frames of reference. The computer scientist has a tolerant, inclusive philosophy of knowledge, while epistemologists have an exacting, exclusive philosophy of knowledge.

For those of us concerned with understanding the performance of problem solving systems, the problems raised by epistemologists are not of primary importance.We are better served with an inclusive definition of knowledge that asks not whether the knowledge is true, but whether it is useful. Those interested in this view of knowledge may find the following books and articles useful.

How organizations represent and exploit knowledge

March, J. G. and H. A. Simon. Organizations. 2nd ed. Cambridge, MA: Blackwell, 1993.

Simon, H. A. The Sciences of the Artificial. 2d ed. Cambridge, MA: MIT P, 1981.

Organizations and The Sciences of the Artificial are essential introductions to the science of problem solving systems (equivalently, symbol systems or information processing systems). Chapters 6 and 7 of Organizations are especially important, because they describe the functioning of performance programs (equivalently, routines), which are one of the most important ways that problem solving systems represent knowledge. Make sure to get the second edition, which has useful commentary after each chapter. Read these books several times.

How organizations learn

Mukherjee, A. S. and R. Jaikumar. “Managing Organizational Learning: Problem Solving Modes Used on the Shop Floor.” 1992.

Bohn, R. and R. Jaikumar. “The Structure of Technological Knowledge in Manufacturing.” Working paper. 1992.

Clark, K. B., R. Henderson, and R. Jaikumar. “A Perspective on Computer Integrated Manufacturing Tools.” Boston, MA, 1988.

Jaikumar had a wonderfully precise grasp of how knowledge, learning, and problem solving interact and drive system performance. The first paper describes the mechanics of unstructured problem solving, which is closely related to learning. The second paper demonstrates how theoretical models can be used to investigate the way knowledge functions. The third paper sheds light on how computers influence learning. Although the studies focus on manufacturing, the principles generalize. Unfortunately, these excellent papers are not easily available

Darr, E. D., L. Argote, and D. Epple. “The Acquisition, Transfer, and Depreciation of Knowledge in Service Organizations: Productivity in Franchises.” Management Science 41, no. 11 (1995): 1750-62.

Edward Feigenbaum often says that knowledge usually comes in thousands of grains of gold dust rather than in large nuggets. This elegant empirical study beautifully captures this aspect of organizational knowledge, and provides an example of one technique for quantitatively analyzing knowledge, learning, and knowledge decay.

Epistemological perspectives

Newell, A. “The Knowledge Level.” AI Magazine 2, no. 2 (1981): 1-20, 33.

Nonaka, I. “A Dynamic Theory of Organizational Knowledge Creation.” Organization Science 5, no. 1 (1994): 14-37.

Lenat, D. B. and E. A. Feigenbaum. “On the thresholds of knowledge.” Artificial Intelligence 47, no. 1-3 (January 1991): 185 – 250.

These three articles by leading experts on problem solving and knowledge provide theoretical foundations for the inclusive, computer science/organization theory approach to knowledge. None provides a complete theory, but when read together they provide a great deal of insight. The significance of the ideas cannot be grasped without considerable reflection. It may help to read them repeatedly, perhaps interspersed with the other readings on the list.

Mechanics of knowledge systems

Davis, R, H. Shrobe and P. Szolovitz. “What Is a Knowledge Representation.” AI Magazine, Spring (1993), 17-33.

Feigenbaum, E. A., B. G. Buchanan, and J. Lederberg. “On Generality and Problem Solving: A Case Study Using the DENDRAL Program.” In Machine Intelligence, edited by B. Meltzer and D. Michie, 165-90: Edinburgh UP, 1971.

Feigenbaum, E. A. “Knowledge Engineering: The Applied Side of Artificial Intelligence.” Proc. of a symposium on Computer culture: the scientific, intellectual, and social impact of the computer. New York Academy of Sciences, 1984.

To understand the mechanics of knowledge, one must dig into questions of representation and inference. Davis’s article provides a useful overview of the issues involved in representation. Feigenbaum’s articles on DENDRAL and knowledge engineering describe the nuts and bolts of working with knowledge.