ROTMAN Magazine
To
stimulate novel thinking, try looking at a complex
problem through five particular ‘lenses’.
by Olivier Leclerc and
Mihnea Moldoveanu
ROB
MCEWEN HAD A PROBLEM. The chairman and
CEO of Canadian mining group Goldcorp knew
that its Red Lake site could be a money-spinner — a mine nearby was thriving;
but no one could figure out where to find high-grade ore. The terrain was
inaccessible, operating costs were high, and the unionized staff had already gone on
strike. In short,McEwenwas lumbered with a gold mine that wasn’t
a goldmine.
Then,
inspiration struck. Attending a conference about recent developments in IT,
McEwen was smitten with the open source revolution. Bucking fierce internal
resistance, he created The Goldcorp Challenge: the company put Red Lake’s
closely guarded topographic data online and offered $575,000 in prize money to
anyone who could identify rich drill sites. To the astonishment of players in
the mining sector, upward of 1,400 technical experts based in 50-plus countries
took up the problem.
The
result? Two Australian teams, working together, found locations that have made
Red Lake one of the world’s richest gold mines. “From a remote site, the
winners were able to analyze a database and generate targets without ever
visiting the property,” McEwen said. “It’s clear that this is part of the
future.”
McEwen
intuitively understood the value of taking a number of different approaches
simultaneously to solve a difficult problem.
A decade later, we find that this mindset is ever more critical. Business
leaders are operating in an era when forces such as technological
change and the historic rebalancing of global economic activity from developed
to emerging markets have made the problems increasingly complex, the tempo
faster, markets more volatile, and the stakes higher. The number of variables
at play can be enormous, and free-flowing information encourages competition,
placing an ever-greater premium on developing innovative, unique solutions.
This
article presents an approach for doing just that. How? By using what we call ‘flexible objects for
generating novel solutions’, or flexons,
which provide a way of shaping difficult problems to reveal innovative solutions
that would otherwise remain
hidden. This approach can be useful in a wide range of situations and at any
level of analysis, from individuals to organizations to industries.
To be
sure, this is not a silver bullet for solving any problem whatsoever; but it is
a fresh mechanism for representing ambiguous, complex problems in a structured
way to generate more innovative solutions.
The
Flexons Approach
Finding
innovative solutions is hard. Precedent and experience push us towards familiar
ways of seeing things, which can be inadequate for the truly tough challenges
that confront senior leaders. After all, if a problem can be solved before it
escalates to the C-suite, it typically is. Yet we know that teams of smart people
from different backgrounds are likely to come up with fresh ideas more quickly
than individuals or like-minded groups do. When a diverse range of experts —
game theorists to economists to psychologists — interact, their approach to
problems is different
from that of individuals. The solution space becomes broader, increasing the
chance that a more innovative answer will be found. Obviously, people do not
always have think tanks of PhDs trained in various approaches at their
disposal. Fortunately, generating diverse solutions to a problem does not
require a diverse group of problem solvers. This is where flexons come into
play.
While
traditional problem-solving frameworks address particular problems under
particular conditions — creating a compensation system, for instance, or
undertaking a value-chain analysis for a vertically integrated business — they
have limited applicability. They are, if you like, ‘specialized lenses’.
Flexons offer ‘languages’ for shaping problems, and these languages can be adapted
to a much broader array of challenges.
To
accommodate the world of business problems, we have identified five flexons, or
problem-solving languages. Derived from the social and natural sciences,
flexons can help users understand the behaviour of individuals, teams, groups,
firms, markets, institutions, and whole societies. We arrived at these five through
a lengthy process of synthesizing both formal literatures and the private
knowledge systems of experts, and trial and error on real problems. We don’t
suggest that these five ‘languages’ are exhaustive — only that we have found
them sufficient, in concert, to
tackle very difficult problems. While serious mental work is required to tailor
the flexons to a given situation, and each retains blind spots arising from its
assumptions, multiple flexons can be applied
to the same problem to generate richer insights and more innovative solutions.
Let’s examine each in turn.
1.
THE NETWORKS FLEXON
Imagine
a map of all of the people you know, ranked by their influence over you. It
would show close friends and vague acquaintances, colleagues at work and
college roommates, people who could affect your career dramatically and people
who have no
bearing on it. All of them would be connected by relationships of trust,
friendship, influence and the probabilities that they will meet. Such a map is
a network that can represent anything from groups of people to interacting
product parts to traffic patterns within a city — and therefore can shape a
whole range
of business
problems.
For
example, certain physicians are opinion leaders who can influence colleagues
about which drugs to prescribe. To reveal relationships among physicians and
help identify those best able to influence drug usage, a pharmaceutical company
launching a product could create a network map of doctors who have co-authored
scientific articles. By targeting clusters of physicians who share the same
ideas and (one presumes) have tight interactions, the company may improve its
return on investments compared with what traditional mass-marketing approaches would
achieve.
The
networks flexon helps to decompose a situation into a series of linked problems
of prediction (how will ties evolve?) and optimization (how can we maximize the
relational advantage of a given agent?) by presenting relationships among
entities. These
problems are not simple, to be sure; but they are well-defined and structured —
a fundamental requirement of problem solving.
2.
THE EVOLUTIONARY FLEXON
Evolutionary
algorithms have won games of chess and solved huge optimization problems that
overwhelm most computational resources. Their success rests on the power of
generating diversity by introducing randomness and
parallelization into the search procedure
and quickly filtering out sub-optimal solutions.
Representing
entities as populations of ‘parents’ and ‘off-spring’ subject to variation,
selection and retention is useful in situations where businesses have limited
control over a large number of important variables and only a limited ability
to calculate the
effects of changing them, whether they’re groups of people, products, project
ideas or technologies. Sometimes, you must make educated guesses, test and
learn.
But
even as you embrace randomness, you can harness it to produce better solutions.
That’s because not all ‘guessing strategies’ are created equal. We have crucial
choices to make: generating more guesses (prototypes, ideas or business
models), spending more time developing each guess, or deciding which guesses will
survive.
Consider
a consumer-packaged-goods company trying to determine if a new brand of
toothpaste will be a hit or an expensive failure. Myriad variables — everything
from consumer habits and behaviour to income, geography and the availability of
clean water — interact in multiple ways. The evolutionary flexon may suggest a
series of low-cost, small-scale experiments involving product variants pitched
to a few well-chosen market segments (for instance, a handful of representative
customers high in influence and skeptical about new ideas). With every turn of
the evolutionary-selection crank, the company’s predictions will improve.
3.
THE DECISION-AGENT FLEXON
To the
economic theorist, social behaviour is the outcome of interactions among
individuals, each of whom tries to select the best possible means of achieving
his or her ends. The decisionagent flexon takes this basic logic to its limit
by providing a way of representing teams, firms and industries as a series of
competitive and cooperative interactions among agents.
The
basic approach is to determine the right level of analysis — firms, say. Then
you ascribe to the decision agent beliefs and motives consistent with what you
know (and think they know), consider how their payoffs change through the
actions of others, determine the combinations of strategies they might
collectively use, and seek an equilibrium where no agent can unilaterally
deviate from the strategy without becoming worse off.
Game
Theory is the classic example, but it’s worth noting that a decision-agent
flexon can also incorporate systematic departures from rationality:
impulsiveness, cognitive shortcuts such as stereotypes and systematic biases.
Taken as a whole, this
flexon can describe all kinds of behaviour, rational and otherwise, in one
self-contained problem-solving language whose most basic variables comprise
agents (individuals, groups, organizations) and their beliefs, payoffs and
strategies.
For
instance, financial models to optimize the manufacturing footprint of a large
industrial company would typically focus on relatively easily-quantifiable
variables such as ‘plant capacity’ and ‘input costs’. Taking a decision-agent
approach, you would assess the payoffs and likely strategies of multiple
stakeholders —including customers, unions and governments — in the event of plant
closures. Adding the incentives, beliefs and strategies of all stakeholders to
the analysis allows you to balance the trade-offs inherent in a difficult
decision more effectively.
4.
THE SYSTEM-DYNAMICS FLEXON
Assessing
a decision’s ‘cascading effects’ is often a challenge. Making the relations between variables of a
system — along with the causes and effects of decisions — more explicit enables
you to understand their likely impact over time.
A
system-dynamics lens shows the world in terms of flows
and accumulations of money, matter (i.e., raw
materials and products), energy (i.e., electrical current, heat,
radio-frequency waves), or information. It sheds light on a complex system by
helping you develop a map of the causal relationships among key variables,
whether they are internal or external to a team, a company or an industry; subjectively or objectively measurable; or
instantaneous or delayed in their effects.
For
example, consider the case of a deep-sea oil spill. A source (the well) emits a
large volume of crude oil through a sequence of pipes (which throttle the flow
and can be represented as inductors) and intermediate-containment vessels (which
accumulate the flow and can be modeled as capacitors). Eventually, the oil
flows into a sink (which, in this case, is unfortunately the ocean.) A pressure
gradient drives the flow rate of oil from the well into the ocean. Even an
approximate model immediately identifies ways to mitigate the spill’s effects,
short of capping the well. These efforts could include reducing the pressure
gradient driving the flow of crude, decreasing the loss of oil along the pipe,
increasing the capacity of the containment vessels, or increasing or decreasing
the inductance of the flow lines. In this case, a loosely defined phenomenon
such as an oil spill becomes a set of precisely posed problems addressable
sequentially, with cumulative results.
5.
THE INFORMATION-PROCESSING FLEXON
When
someone performs long division in her head, a CEO makes a strategic decision by
aggregating imperfect information from an executive team, or Google
servers
crunch Web-site data, information is being transformed intelligently. This
final flexon
provides a lens for viewing various parts of a business as ‘information-processing
tasks’, similar to the way such tasks are parceled out among different
computers. It focuses attention on what information is used, the cost of
computation, and how efficiently the computational device solves certain kinds
of problems. In an organization, that
device is a collection of people, whose processes for deliberating and deciding
are the most important explanatory variable of decision-making’s effectiveness.
Consider
the case of a private-equity firm seeking to manage risk. A retrospective
analysis of decisions by its investment committee shows that past bets have
been much riskier than its principals assumed. To understand why, the firm
examines what
information was transmitted to the committee and how decisions by individuals
would probably have differed from those of the committee, given its standard
operating procedures. Interviews and
analysis show that the company has a bias toward riskier investments and that
it stems from a near-unanimity rule
applied
by the committee: two dissenting members are enough to prevent an investment.
The insistence on near-unanimity is counterproductive because it stifles
debate: the committee’s members (only two of whom could kill any deal) are
reluctant to speak first and be perceived as an ‘enemy’ by the deal sponsor. And the more senior the sponsor, the more
likely it is that risky deals will be approved. Raising the number of votes
required to kill deals, while clearly counterintuitive, would stimulate a richer
dialogue.
Putting
Flexons to Work
We
routinely use these five problem-solving lenses in workshops with executive
teams and colleagues to analyze particularly ambiguous and complex challenges.
Participants need only a basic familiarity with the different approaches to
reframe problems and generate more innovative solutions.
Following
are two examples of the kinds of insights that emerge from the use of several
flexons, whose real power emerges in their combination.
1:
REORGANIZING FOR INNOVATION. A large biofuel manufacturer
that wants to improve the productivity of its researchers can use flexons to
illuminate the problem from very different angles.
NETWORKS.
It’s possible to view the problem as ‘a need to design a
better innovation network’ by mapping the researchers’ ties to one another
through co-citation indices, counting the number of e-mails sent between them,
and using a network survey to reveal the strength and density of interactions
and collaborative ties.
If coordinating different knowledge domains is important to a company’s
innovation productivity, and the current network isn’t doing so effectively,
the company may want to create an ‘internal knowledge market’ in which financial
and status rewards accrue to researchers who communicate their ideas to co-researchers.
Or the company could encourage cross-pollination by setting up cross-discipline
gatherings, information clearinghouses, or wiki-style problem-solving sites
featuring rewards for solutions.
EVOLUTION.
By describing each lab as a self-contained population of
ideas and techniques, a company can explore how frequently new ideas are
generated and filtered and how stringent the selection process is. With this
information, it can design interventions to generate more varied ideas and to
change the selection mechanism.
For instance, if a lot of research activity never seems to lead anywhere, the
company might take steps to ensure that new ideas are presented more frequently
to the business development team, which can provide early feedback on their applicability.
DECISION
AGENTS. We can examine in detail how well the interests of individual
researchers and the organization are aligned.
What financial and non-financial benefits accrue to individuals who
initiate or terminate a search or continue a search that is already under way?
What are the net benefits to the organization of
starting, stopping, or continuing to search along a given trajectory? Search
traps or failures may be either Type I (pursuing a development path unlikely to
reach a profitable solution) or Type II (not pursuing a path likely to reach a
profitable solution). To better
understand the economics at play, it may be possible to use industry and
internal data to multiply the probabilities of these errors by their costs.
That economic understanding, in turn, permits a company to tailor incentives
for individuals to minimize Type I errors (by motivating employees to reject
apparent
losers
more quickly) or Type II errors (by motivating them to persist along paths of
uncertain value slightly longer than they normally would).
2:
PREDICTING THE FUTURE. Now consider the case of a multinational
telecommunications service provider that operates several major broadband,
wireless, fixed and mobile networks around the world, using a mix of
technologies (such as 2G and 3G.) It wants to develop a strategic outlook that
takes into consideration shifting demographics, shifting technologies for
connecting users with
one another and with its core network (4G), and shifting alliances—to say
nothing of rapidly evolving players from Apple toQualcomm.
This problem is complicated, with a range of variables and forces at work, and
so broad that crafting a strategy that contains significant blind spots is
easy. Flexons can help.
Each
view of the world described below provides valuable food for thought, including
potential strategic scenarios, technology road maps and possibilities for
killer apps. More hard work is needed to synthesize the findings into a
coherent worldview, but the different perspectives provided by flexons
illuminate potential solutions
that might otherwise be missed.
DECISION
AGENTS. Viewing the problem in this way emphasizes the incentives for
different industry players to embrace new technologies and service levels. By
enumerating a range of plausible scenarios from the perspective of customers
and competitors, the network service provider can establish baseline
assessments of
future pricing, volume levels and investment returns.
NETWORKS.
This lens allows a company or its managers to look at the
industry as a pattern of exchange relationships between paying customers and
providers of services, equipment, chips, operating systems and applications,
and then to examine the proper-ties of each exchange network. The analysis may
reveal that not all innovations and new end-user technologies are equal: some would
provide an opportunity for differentiation at critical nodes in the network;
others would not.
SYSTEM
DYNAMICS. This flexon focuses attention on data-flow bottlenecks in
applications ranging from e-mail and voice calls to video downloads, games, and
social-networking interactions. The
company can build a network-optimization map to predict and optimize capital
expenditures for network equipment as a function
of expected demand, information usage and existing constraints. Because cost
structures matter deeply to annuity businesses (such as those of service
providers) facing demand fluctuations, the resulting analysis may radically
affect which services a company believes it can and cannot offer in years to
come.
In
closing
As
indicated herein, flexons can help turn chaos into order by representing
ambiguous situations and predicaments as well-defined, analyzable problems of
prediction and optimization. They allow
us to move up and down between different levels of detail to consider
situations in all their complexity. Perhaps most important, flexons bring
diversity to the thinking of the problem solver, offering more opportunities to
discover counter-intuitive insights, innovative options, and unexpected sources
of competitive advantage.
Olivier
Leclerc is a principal in McKinsey’s Los Angeles office.
Mihnea
Moldoveanu is Associate Dean of the Full-Time MBA program and Desautels
Professor of Integrative Thinking at the Rotman School of Management.
This article
originally appeared in McKinsey Quarterly (mckinseyquarterly.com).
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