Dershin AdaptiveApproach Innovation(15)

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An Adaptive Approachto Managing Innovation- a practical guidefor managers

Harvey DershinPrincipal, Dershin Consulting

by

AIM-article # 004-2011


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AbstractInnovation doesnt have to be a mystery. It can be

organized and managed by people who are not,

themselves, innovators. But managements usual

command and control tools are not sucient or

this task. Innovation emerges rom a system whose

behavior is non-linear. For this reason, management

tools must be fexible and adaptive. This articleapplies ideas rom Complex Adaptive Systems theory

to develop a model or innovation which, in turn,

leads to appropriate methods or its management.

The article applies the general construct o Axelrod

and Cohen to innovation, leading to a clear set o

actions managers can take to advance the process.


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An Adaptive Approach to Managing Innovation - a practical guide for managers

About the Author

hArvey Dershin

Harvey Dershin holds an MS in Engineering rom the University

o Caliornia at Los Angeles and is a Master Black Belt in Lean Six

Sigma. He has been a Senior Executive in companies large and small

and has more than 50 years o experience in engineering, research,

and management in commercial, industrial, non-prot and consulting

organizations. His publications and presentations span the gamut rom

basic engineering research to innovation management.

introDuctionIt is widely recognized that innovation contributes

signicantly to competitive advantage and econom-

ic prosperity or companies and countries world-

wide. While innovation cannot be separated rom

R&D explicitly, in the US total expenditures or

R&D across all economic sectors currently exceed

$300 billion annually, $850 billion or all countries

around the globe (Economist, 2009, US dollars).

Clearly with this much at stake, eective manage-

ment o innovation is essential.

Managing innovation is a subtle and elusive process,

ar dierent rom managing conventional projects

or processes. One never knows where the work will

leadas oten as not up blind alleys. Innovators move

through the process organically, eeling their way

along, looking or unusual or productive connec-

tions, viewing things rom dierent angles, testing

ideas or one or more that seem to t. Insights come

unexpectedly so that projects that seem stalled sud-denly leap ahead while other promising ones stall.

This unusual, sometimes erratic behavior makes

management o innovation quite challenging. In

act, many executives see innovation as unmanage-

able, riddled with risks (Miller and Olleros, 2007).

Much has been written about managing innova-

tion and yet, in the preparation or a recent collo-

quium on creativity, sponsored by Harvard Business

School (Heskett, 2007), managements role emerged

as a major issue. It was reported that many (panel-

ists) eel that traditional management practice has

little to contribute to processes o creation and in-

novation. Similarly, Lane et al. (2009) expressed

surprise that the scientic community has generated

so little understanding o the process o innovation.

In an interview ollowing the Harvard colloquium,

the co-chair, M. Khaire, noted that participants

identied the need to illuminate the role o man-

agers in ostering creativity in organizations as an

agenda item or uture research (Khaire, 2009). Itwas suggested that managers should:

An Adaptive Approachto Managing Innovation- a practical guide for managers


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Foster creativity as gardeners or shepherds

rather than as micro-managers o the process

Recognizetheimportanceoflearningfromfail-

ure and promoting a culture that permits dissent

and ailures

Developorganizationalstructuresconduciveto

creativity.

O course many organizations handle innovation

well [e.g., Apple, Google, Proctor and Gamble, Toy-

ota, and BMW,Business Week

(2006)]. Each suc-cessul company has its own approach, with some

commonalitiesnetworking, collaboration, supplier

involvement, and co-location o developersbut an

overarching management ramework is not apparent.

In summary, while there is global recognition o the

importance o innovation, and many companies are

quite good at it, there still remains a perceived gap

concerning how best to manage the process. This ar-

ticle attempts to close that gap.

ApproAchIt is this authors view that the gap in management

practice, where it exists, results rom a mismatch be-

tween traditional management methods (command

and control) and the unique nature o innovation as a

system having non-linear behavior. This article, based

on an earlier work, Dershin(2010), develops the view

that one can meet the challenges posed by Khaire by

casting innovation as a complex adaptive system.

This approach makes it possible to build on the ideas

o Axelrod and Cohen (2000) regarding complex

adaptive systems in general, applying their rame-

work specically to managing innovation. This per-

mits us to describe the role o managers and speciy

actions they can take to advance the process.

The Axelrod/Cohen ramework is evolutionary and

has been developed so that improvements can be

generated even though every element o a system

may not be ully understood and the outcome maynot be predictable (a good t with innovation).

The resulting management ramework does not yield

deterministic solutions (i.e., do this and that will hap-

pen). Nor is the end game statistical or probabilistic (do

this and the chances are 95% that you will have such

and such an outcome). To the contrary, this road leads

to heuristic solutionsdo these things and you will move

in the direction o an improved innovation process.

bAckgrounDWhy treat innovation as a complex adaptive system?

Theterm complexapplies to innovation becauseo the unpredictability o outcomes, their depen-

dence on initial conditions, and the powerul eect

o interaction and eedback among many variables.

Adaptive appliesbecauseapproaches toan inno-

vation challenge shit and change over time as the

system moves rom one state o knowledge to an-

other.Systemisapplicablebecausecurrentthinking

has progressed rom viewing innovation as relying

on the work o isolated sources to appreciating the

interconnectedness o the various sources.

innovAtion As A system

A number o authors have taken the view that innova-

tion should be seen as a system rather than as one or

more isolated eventsRossi and Russo (2009), Czuchry

et al. (2009), Chen et al. (2008), Smith et al. (2008),

Miller and Olleros (2007), Xu et al. (2007), Fischer

and Frolich (2001) and Janszen (2000). This view en-

tails the realization that the process is complex and has

characteristics that can be described as non-linear.

complex ADAptive systems

As a starting point consider Hollands (1992) de-

scription o a complex adaptive system as any pro-

cess whereby a structure is progressively modied to

give better perormance in its environment. Surely

this applies to innovation. Miller and Olleros (2007)

compare innovation to a series o games. While

not specically using the complex adaptive systems

construct, they describe games that have all the char-

acteristics o such a systemthey are evolutionary,

emergent, indeterminate, open-ended, and path de-pendent, while operating within certain constraints.


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Lane et al. (2006) have modeled innovation directly

as a complex adaptive system using agent-based com-

puter simulation models (ABMs). He compares cases

o no innovation, copying, and innovation accompa-

nied by a leap o aith to achieve goals with added

value and shows that the last out-perorms each o

the ormer. Fischer and Frolich (2001) describe com-

plexity as a multidisciplinary concept that provides

convenient tools or modeling innovation. In this

view, theories o complexity (and evolution) provide

an appropriate ramework or gaining deeper insightinto innovation. Saviotti (2001) describes complexity

theory as being more meaningul or analyzing inno-

vation because it emphasizes uncertainty, qualitative

change, irreversibility, and path dependence, in con-

trast with what he calls the neo-classical view o eco-

nomics, (deterministic, reversible, and predictable).

lineAr AnD non-lineAr processes

Linear processes reach outcomes through a series o

organized steps. Such processes are stable and the out-

comes predictable (within statistical limits), based onthe behavior o each step. Linear processes are scal-

able, particularly i fow is continuous. For example, i

moreorlessmaterialisprovidedasinputtoalinear

process, outputs increase or decrease proportionately.

With non-linear processes, non-additive interaction

makes it impossible to determine the perormance o

a structure rom a study o its isolated parts (Holland,

1992). Non-linear processes oten have signicant

eedback loops, and outcomes are sensitive to chang-

es in driving actors and initial conditions in ways not

always understood. Lorenz (1993) describes a non-

linear system as (one) in which alterations in an ini-

tial state need not produce alterations in subsequent

states. Mapping this thought over to innovation,

one can anticipate, or example, that increasing the

amount o money dedicated to an innovation project

(an input) would not necessarily increase the number

o new products invented (the output).

It is easier to explore the dierence between linear-

ity and non-linearity in physical rather than social

systems since one can use mathematics to visualize

the outcomes. (This is not meant to iner that inno-

vation can be described by a set o equations. Rather,

the purpose o this exercise is to present, graphically,

the implications o dealing with non-linear systems.)

Consider the equations below (rom Lorenz, 1993)

describing deterministic non-periodic fow:

d/ d = -+

d / d = -z+

dz/ d = - z

These equations are non-linear and do not admit to

a general solution. They may be solved numerically,

however, giving rise to the ollowing graphs (Figures

1-4). These depict solutions in two- and three-di-

mensions in linear and non-linear regions.

F 1.Linear relationship between X and Z




6

In the linear regions (Figures 1 and 3) solutions orz fow smoothly rom the variables x and y. Moving

rom any point, in any direction, on the line graph

o Figure 1 or the membrane o Figure 3, one ndsgradual extensions o the initial state. However in

the non-linear regions we see rst the amiliar two-

dimensional gure rom Lorenzs book on Chaos

theory (Figure 2) while in Figure 4, in three dimen-

sions, we see a jagged terrain looking much like whatStacey (1996) called a rugged tness landscape.

F 2.Non-linear region in two-dimensions

F 3.Linear region in three-dimensions

F 4.Non-linear region in three dimensions


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Figures 2 and 4 show what a non-linear environment

might look like. Figure 2 yields multiple solutions in

z or any given x. Figure 4 is intriguing because o

the peaks and valleys arising suddenly in the terrain.

It doesnt take much imagination to see how dicult

it would be to navigate a non-linear system in search

o solutions to an innovation challenge. This dia-

gram truly captures the landscape o innovation.

Negotiating this type o terrain, ull o surprises, is

what it means to innovate.

In summary, recent authors describe the innovation

process as a system that is non-linear and describe

its behavior with models based on complex adaptive

systems theory.

mAnAging innovAtion As Acomplex ADAptive systemI we accept innovation as a non-linear system and

a complex adaptive system as well, this opens doors

to all the thinking and research o the last several

decades in that eld. Much o the work has dealtwith physical or biological systems, but signicant

research has also been carried out on social systems.

Stacey (e.g., 1996) has produced an extensive lit-

erature about the application o complexity the-

ory to organizational design and management.

Plsek and Greenhalgh (2001) and Plsek and Wil-

son (2001) describe how applying theories o

complex adaptive systems could work to benet

large healthcare organizations (specically the UKs

National Health Service). Dershin (1999) examines

the use o complex adaptive systems theory to im-

prove medical care.

Axelrod and Cohen (2000) developed a generalized

frameworkfor,astheytermit,harnessingcomplex

adaptive systems to the design o organizations and

strategies. The key elements o their ramework,

summarized, are variation, interaction, and selec-

tion. With respect to innovation, variation can be

interpreted as the diversity o approaches or strate-

gies brought to a challenge. Interaction sparks the

creative process as ideas intersect, merge, clash, and

evolve. It also drives the selection process, the means

by which better ideas are carried orward and ail-ing ones discarded. Selection implies the key element

o learning rom both success and ailure (the social

equivalent o transmitting DNA down the genera-

tions). The graphic below illustrates these ideas.

A ner grained summary o their ramework, adapt-

ed to innovation, is shown in Table 1, where col-

umns have been added linking Application to Inno-

vation and the Role o Managers.

F 5.The Fundamentals o Innovation

ga a s

la

ia & e




8

ta 1.Central elements o the Axelrod/cohen ramework applied to innovation management.


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To summarize the content o the table in the context o

innovation, various ideas (agents) are created by using

certain idea-generating methods or strategies. Ideas

are grouped into types and converted into populations

o innovations (artiacts). Innovation concepts are

brought into contact with each other through struc-

tured interactions where they have the opportunity to

evolve. Selection criteria are used to choose innova-

tions to be carried orward and identiy those to be

discarded. Emergent knowledge is collected to ben-

et current, uture, and related eorts. Physical andconceptual spaces are dened to acilitate the process.

Taken together, these elements constitute a ramework

or managing complex adaptive systems o innovation.

Managers have an important role to play in this sys-

tem. Decisions have to be made with respect to se-

lection o idea-generating strategies, determining the

rightamountofvariety(e.g.,balancingexploration

with exploitation), and choosing who should be in-

volved in the process. Physical and conceptual spaces

need to be dened and provided or. Structured in-teractions must be dened, scheduled, and managed,

(i.e.,thewho,what,where,andwhen).Managers

need to address which innovations should make up

the population o options to be considered. Decision

rules have to be established and put into place to se-

lect the most promising ideas and innovations and

discard the rest. Emerging knowledge about success-

es and ailures must be captured and shared.

To illustrate how the ramework can be used, con-

sider an innovation challenge (e.g., developing a new

drug or a new type o battery or electric automo-

biles). Managements role would be as ollows:

1 - set up phAse

chooseentitiestobeinvolved

identifyandselectidea-generatingstrategies

denephysicalandconceptualspaces

dene where, when, and how interactions

would take place

developselectioncriteriaanddecisionrules

2 - operAtionAl phAse

turnthesystemlooseandletthecreators,inven-

tors, and innovators work

participateinthecategorizationofideasandin -

novations

denethepopulationforinteractionandselection

scheduleand,possibly,facilitateinteractions

participateinselectionofideasandinnovations

to carry orward and to stop

collectemergingknowledgeandmakeitavail -

able to those addressing the challenge and toothers as necessary

3 - post-operAtionAl phAse

conductreviewstoidentifylessonslearnedfrom

successes and ailures

sharelessonslearned.

Analwordmustbesaidabouttheartofmanaging

innovation. Paraphrasing Stacey (1996), interactions

should be rich but not too rich, variety should pro-

mote innovation but not cause anarchy, and inorma-tion should fow at a pace that matches the evolution

o the process. This requires a manager to be close to

the process as it evolves, using his or her best judg-

ment to steer the process in a positive direction.

supporting eviDenceWhile the innovation management ramework de-

scribed here does not appear as a whole in the lit-

erature, key elements can be ound which support

the approach. Examination o the Toyota product

development system or example (Morgan and Lik-

er, 2006) reveals alignment with several elements

o the ramework. The authors describe the Toyota

system in three categoriesprocess, skilled people,

and tools and technology. Table 2 summarizes the

principles associated with each category. The third

column (Framework) indicates where a particular

principle aligns with a key element o the ramework

proposed here. Explanations ollow.

Several o the Principles (9, 10, and 13) are quiterecognizable as being associated with capture o




10

ta 2.Management ramework and principles o the Toyota product development system.

emergent learning (i.e., part o selection). Prin-

ciples 2 and 4 are techniques or managing varia-

tion. There are two ideas here. The rst is to ac-

commodate increased variety at the ront end o

the process, reducing it as product development

proceeds. The second allows increased variety

throughout the process by building on standardized

product elements where possible. Principle 5 is Toy-

otas way o increasing interaction, implementing

selection, and capturing emerging knowledge since

the chie engineer is a technical systems integratorand leader. Almost the same can be said or Principle

7. Engineers are urged to learn how work is done

and understand how problems are solved by seeing

things rst-hand. This provides interaction and cap-

tures emerging knowledge. Principle 8 is Toyotas

way o integrating supplier knowledge into their

product development system, increasing variation

and interaction and gaining knowledge.

Finally, with Principle 12, Toyota communicates

knowledge, including problem solving, through-

out the organization (analogous to transmitting

newDNAthroughoutapopulation.)Toyotahasalso developed ormal processes or making impor-

tant decisions [the ringi system, Morgan and Liker


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(2006) pp. 264-265]. This aligns with the rame-

works selection criteria element.

Czuchry et al. (2009) describe several cases o suc-

cessully applying an open system approach to in-

novation, aligning with several key portions o the

ramework described here. Dooley and OSullivan

(2007) describe a successul case o structured in-

teraction in a distributed network involving six

organizations conducting research in biotechnol-

ogy (another case o open system innovation).

They point out that the success o the relationship

depended critically on the development o an e-

ective knowledge management inrastructure.

Finally, Verworn et al. (2008) show that requent

interactions between an innovation team and cus-

tomerduringwhattheycallthefuzzyfrontend

o the (product development) process is important

to success. Their research shows that success is also

aided by interactions between the marketing and

technical unctions.

With hundreds o billions o dollars (US) o expen-

diture and the economic well being o companies

and countries worldwide at stake, it is clear that

management has an important role to play in in-novation. The pressing question is what exactly

should managers be doing to move innovation

along? It has been shown here that casting innova-

tion as a complex adaptive system reveals a clear

role or managers and concrete actions that will

move the process orward. But this requires think-

ing about and treating innovation in a non- tradi-

tional way.

The ramework resulting rom applying the Axelrod/

Cohen construct describes specic tasks or manag-

ers, linking them to the development o new orga-

nizational elements, processes, and tools, and keydecisions. It ts well with the concept o open system

innovation. Taken as a whole, this ramework should

allow managers to deal with innovation as a complex

adaptive system. Specic tools and techniques or

each o the elements in the ramework will no doubt

vary rom situation to situation, industry to industry,

and company. These remain to be developed and rep-

resent a research agenda to be carried orward.

Conclusions




12

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References

Welcome to the forumThe InnovationManagementForum.com provides a platorm and channel o

communication exclusively dedicated to innovation management. You are invited

to discuss questions that you might have about how to implement the knowledge

presented in this article in your organization. This is a valuable opportunity to

exchange experiences with the like-minded and with colleagues rom around the

world. The author o this article will also take part in these discussions.

www.innovationmanagementforum.com


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