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Integrating organizational networks, weak signals, strategic radars and
scenario planning
Paul J.H. Schoemaker ⁎, George S. Day, Scott A. Snyder
Wharton School, University of Pennsylvania, Philadelphia, PA, USA
article info abstract
Article history:
Received 18 December 2011
Received in revised form 7 October 2012
Accepted 9 October 2012
Available online 20 December 2012
As firms become more networked they greatly expand their points of contact with the outside
world. This cangreatly help the detection of early signs of threats or opportunities emergingat the
periphery. But a major challenge for firms scanning the periphery of their networks is how to
manage the explosion of information. How do they avoid undue distraction while spotting useful
signals amid an avalanche of data? We discuss how strategic radars can be used to integrate
outside networks, weak signals, sense making, strategic dialog and scenario planning. A brief case
study illustrates how a strategic radar system was actually developed and deployed by a large
government agency in order to enhance its adaptive capability for coping with increasing external
change.
© 2012 Elsevier Inc. All rights reserved.
Keywords:
Weak signals
Scenario planning
Peripheral vision
Organizational networks
Signal scanning
Strategic radar
Sense making
Information processing
1. Introduction
Many organizations are narrowly focused on their immedi-
ate environment —a familiar landscape of core products and
knowncustomers,aswellastraditionalcompetitors,regulators,
technologies and stakeholders [1]. Psychological research on
attention shows, however, that any sharp focus risks missing
key signals from the periphery,asinthefamousmissedgorilla
experiments by [2]. The periphery is defined as the area outside
of where a person or organization is focusing. In business, strong
peripheral vision involves monitoring remote markets, new
competitors, emerging technologies, novel business models, and
much seemingly tangential data. All this entails much more than
sensing incipient change. It is especially about knowing where
to look more carefully for clues, how to interpret weak signals,
and when to act on faint or ambiguous stirrings [3].Weak signals
are defined here as seemingly random or disconnected pieces of
information that at first appear to be background noise but
which can be recognized as part of a larger pattern when viewed
through a different frame or by connecting it with other pieces
of information [4,5].
A study of more than 300 global senior executives found that
80% felt that their organizations had less capacity for peripheral
vision than they would need [3]. Thus, it is no surprise that two
thirds of corporate strategists in another study admitted their
organizations had been surprised by as many as three high-
impact competitive events in the past five years [6].Moreover,
97% of respondents said their companies lacked an early warning
system to prevent such surprises in the future. Organizations
routinely run through red lights and in hindsight people wonder
how the signals could have been missed [7]. Well publicized
examples include the 9/11 terrorist attacks, corporate scandals
seen too late, the sub-prime mortgage meltdown, mega-
lawsuits against drugs companies, BP's oil spill in the Gulf of
Mexico, tectonic uprisings in the Middle East, and Japan's nuclear
disasters.
In spite of formal risk management procedures, including
business analytics and scanning systems, organizations contin-
ue to be blindsided. This is increasingly costly in an era of
Technological Forecasting & Social Change 80 (2013) 815–824
⁎Corresponding author. Tel.: +1 610 525 0495.
E-mail address: schoemak@wharton.upenn.edu (P.J.H. Schoemaker).
0040-1625/$ –see front matter © 2012 Elsevier Inc. All rights reserved.
http://dx.doi.org/10.1016/j.techfore.2012.10.020
Contents lists available at SciVerse ScienceDirect
Technological Forecasting & Social Change
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open innovation, increasing uncertainty and changing global
playing fields. An often overlooked resource for improving on
organization's early warning system is its extended network of
partners, collaborators, suppliers, customers, and other exter-
nal contacts. Such sources have long been used to compete
more effectively in the marketplace, but they can also be used to
accelerate learning about changes in the external environment.
A large organization may have thousands of touch points with
many parts of its environment. But seldom is this promising
source of information mined systematically or even recognized
as part of a potentially powerful strategic radar system.
The central argument of this paper rests on three proposi-
tions. First, that many organizations lack a systematic and
repeatable capacity for detecting and acting on the weak signals
of threats or opportunities from their periphery. Second, that
their network relationships offer the possibility of extending
the reach of their early warning systems, albeit at the risk of
overloading their capacity to absorb and interpret signals. Third,
that advances in business analytics, knowledge systems and
visualization technologies, when guided by scenario analysis,
can help these organizations improve their ability to anticipate
and respond to the most relevant signals. Our paper does not
test these propositions empirically but rather builds on them
conceptually by integrating diverse research streams within a
scenario planning framework.
The paper begins with a review of the extant research on
extended networks that proliferate the potential external
touch points of the organization. Next we describe the design
and implementation of a strategic radar system. We adopt the
radar metaphor to connote a methodology for detecting the
position and movement of objects that are over the horizon,
while improving the signal-to-noise ratio. A strategic radar in
our context is a scenarios-based system for integrating the
scanning as well as monitoring of external signals, the assess-
ment of possible strategic responses, and any follow-up probes
to amplify interesting signals. We illustrate the salient features
of such a system with a real-life application in the US Defense
Logistics Agency that started by developing multiple future
scenarios. We conclude the paper with observations about the
crucial role of vigilant leadership.
2. Research on extended networks
There is little formal literature on the role of external
networks to improve an organization's peripheral vision.
Various applied papers connect the roles of learning, networks
and the periphery in case studies. For example, Boersma [8,9]
describes how in the early history of Philips and General
Electric, their R&D departments were greatly helped by being
deeply embedded in external government, academia and
customer networks, as well as being connected to other parts
of the organization internally. Dupree [10] found similar results
for Merck and Co., showing historically how their innovations
on biological compounds were related to a “series of complex,
evolving networks of scientific, governmental and medical
institutions.”Nosella and Petroni [11] describe how an Italian
company's international success in developing space system
applications relied on four strategic networks, with a special
focus on the leadership and coordination challenges encoun-
tered by the lead firm.
External networks can also play a critical role in areas
other than R&D. For example, Nohria [12] examined how a
Boston area networking organization called “128 Venture
Group”helped technology entrepreneurs and investors find
overlapping areas of interest. Likewise, Cachia et al. [13] offer
various case studies showing how online social networks like
Twitter and Facebook can provide important insight into
emerging social trends. Apart from such case studies, little
formal literature exists that directly connects external organi-
zational networks, strategic radars and scenario planning. How-
ever, sociologists have extensively examined how information
gaps or blind spots can result from structural holes in external
networks [14,15].
Helpful insights also come from studies of internal networks
within large, global firms because their dispersed structures
bear much resemblance to external networks. John-Cramer et
al. [16] analyzed the network structures within a global con-
sumer electronics company and found that cultural differences
played a singular role in enabling or denying information
sharing, especially collaboration across geographies and prod-
uct divisions. These findings align with Ahuja [17] who found
that structural holes, such as disconnects between network
elements, degrade innovation performance. Likewise, Hansen
[18] found that the strength of network ties across operating
units within a firm directly influences both the search for useful
information and the transfer of complex knowledge in a timely
fashion.
More generally, there exist extensive literatures on (i) social
networks, (ii) innovation clusters, (iii) knowledge spill-overs
and (iv) scanning models. The social networks literature
highlights the crucial role of personal relationships, organiza-
tional culture, professional affinity groups and physical prox-
imity. The latter aspect gave rise to a rich literature on the role
of regional clusters in innovation, which in turn prompted
formal attention to the role of knowledge spill-over among
firms, especially within research communities. Lastly, the field
of scanning has developed its own literature, from competitor
intelligence to the search for innovations [19,20].
3. Types of external intelligence networks
An organization's external networks can come in multiple
guises. Their common denominator is that they extend the
peripheral vision of the organization by expanding its social
boundaries and external information exchange. As illustrated
below, different networks tap different zones of the periphery
and naturally lend themselves to essential intelligence man-
agement tasks.
3.1. Scanning tasks
These networks are purpose-built to scan the periphery,
by bringing together people from diverse fields to identify
events and patterns with implications for their clients. For
example, the Mack Center for Technological Innovation at the
Wharton School serves as a network for leading companies
and academics in scouting emerging technologies and new
business models. Newer, internet-enabled networks, such as
FutureMonitor, are designed to capitalize on the wisdom of
crowds to surface trends. Insights from these trend spotters
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are commonly aggregated and distributed by blogs such as
the Business Innovation Factory.
3.2. Sharing of intelligence
Industry trade associations have long served an important
networking role. Participants in narrow industry verticals, or
wide agglomerations such as the Business Round Table, the
Conference Board or the Association of National Advertisers,
are drawn by shared interests to exchange information or
obtain new insights. Because these associations often func-
tion as affinity groups, there is some risk that they merely
reinforce existing beliefs and mental models. In some cases,
they become echo-chambers where like-minded managers
reconfirm each other's beliefs and biases. To combat this
tendency toward conformity, small groups of non-competing
companies often gather in peer networks to exchange infor-
mation and learn vicariously from the experiences of outside
peers [21].
Sharing information within large corporations is also
critical [22]. Customer insights are often widely dispersed or
conversely remain isolated within a single research group.
Companies are usually not configured to obtain data at the
point where segments, channels and categories intersect, nor
can they develop a coherent picture of customers. One solution
is to create an insights network that integrates internal data
with expert third parties, as well as key suppliers or customers
who can provide data on say regional or store level competitor
intelligence.
3.3. Opening the innovation process
A variety of intermediaries have recently emerged to help
firms search for ideas and help inventors find markets for their
innovations [23,24]. Networks like Innocentive and Nine Sigma
are designed to find solutions by posing problems to a global
community of scientists. Procter & Gamble similarly uses an
extended network of individuals and institutions to identify
products, ideas and solutions to technical problems [25].By
reaching far and wide, through advisory boards, web-based
auctions, and scouting expeditions, these firms improve their
peripheral vision, and thus see sooner what is happeningat the
edges of their business. Such an external orientation, enabled
by easier search and faster communication across the globe,
stands in stark contrast to more traditional inside-out innova-
tion models.
3.4. Connecting to an eco-system
An open innovation approach naturally leads to an open-
systems view of business, in which the firm tries to benefit
from, and even orchestrates, an external environment that
functions as an eco-system for its own business model. An
exemplar is Li & Fung which generated over $8 billion per year
in garments, toys and other products without owning a single
factory [26]. Similarly, Apple Computer connects to over a
million software developers, thousands of accessory makers,
and a myriad of content providers including TV networks and
record labels. Most such eco-systems are designed to support
specific business strategies, from supply chains to delivery
channels, and in many cases become an essential part of the
firm's business model [27]. However, such networks can often
be deployed also as a strategic radar aimed at picking up weak
signals. For example, a supply chain might serve just its
intended purpose such as procuring inputs in a timely fashion
or a wider purpose as well, namely gathering intelligence about
industry dynamics, competitor behavior, etc.
One consequence of greater organizational participation in
extended networks—where each node in the network is
connected to others—is a rapid expansion in the number of
weak signals received. This expansion is very likely to outpace
and eventually overwhelm the capacity of the organization to
absorb and make sense of a trove of potential intelligence—a
kind of organizational indigestion. For example, unique infor-
mation (such as private customer data) is growing at over 50%
per year per person while information consumption per person
is growing at slightly less than 18% per year. It is not clear that
traditional business analytics can handle this growing gap [28].
The more networked an organization becomes, the more it pays
to frame this network as a strategic radar and ask how it can be
improved.
4. Role of scenario planning
How should organizations design and implement a strategic
radar system that can integrate and interpret many diverse
signals? On one hand, such a system needs good inputs from
multiple sources and on the other hand, the proliferation of
signals must be filtered and interpreted to extract new insights.
Scenario planning can serve valuable functions on the input side
as well as in terms of sense making. Within strategic planning,
scenarios refer to script-like narratives of possible futures that
may emerge beyond the firm's control (for a historical overview,
see Bradfield et [29]). A few scenarios usually suffice to define a
broad range within which the exogenous part of the future
might unfold. However, the scenarios should truly reflect a
wide range of viewpoints from inside as well as outside the
organization, so that they jointly depict a broad spectrum of
future possibilities [30]. In addition, each scenario should
present more than an end-state description, but highlight the
dynamic logic of its basic story line (akin to a Hollywood
storyboard). This dynamic aspect is especially important when
tracking scenarios over time.
Scenarios as such are not states of nature (they are seldom
exhaustive) nor probabilistic predictions but rather illustrative
narratives of what could happen [31]. The focus is not on
forecasting the future, or fully characterizing key uncertainties
via complete probability distributions, but rather on bounding
the uncertainty range of the future. The intended benefit of
scenarios is that they provide structured frameworks for
managerial discussions, which help stretch as well as focus
people's thinking [32,33]. For this reason, scenario building is a
natural integrative activity for connecting weak signals from
outside networks with a forward looking strategic radar system
that can help inform strategic decisions.
For most managers, the mental anchors used to encode weak
signals about future changes are the present or recent past. But
often the past is a highly misleading guide to the future,
especially after major discontinuities have occurred such as
deregulation, tax changes, new technologies, etc. The financial
community in London, for instance, may have been seriously
hampered cognitively by its long stable regulatory past in coping
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with the sudden deregulation of the financial markets in 1987
(i.e., the ‘Big Bang’). Similarly, the newly formed Bell Operating
companies in the U.S. were significantly handicapped in 1984 by
having functioned for many decades as a regulated monopoly.
The idea of having to compete for customers or embrace
innovation was alien to those with decades of experience inside
Ma Bell. One way to shift people's mental filters beyond the
present is to create scenarios that supplant the past as the main
sense making framework. Properly interpreting weak signals
may require a shift in mental models, which is often hard to do in
groups and especially organizations [34–36].
The scenario approach differs both in orientation and
method from more traditional extrapolative oriented planning,
including myopic dashboards. The focus is less on numbers and
more on world views, mental models and strategic dialog [37].
Since uncertainty is so central in scenario planning, a purely
statistical approach diverts attention to computational com-
plexity rather than conceptual analysis. For example, if we were
to cross-classify nuncertainties with moutcomes each, the
number of possibilities explodes quickly (i.e. m
n
). Each of these
combinations can be represented as points in an n-dimensional
space defined by the nrandom variables, as shown in Fig. 1.It
depicts an uncertainty cone for the period 2004 to 2014 within
which the future is likely to evolve. Scenarios A, B, C, and D
describe a limited number of outcome clusters at the boundary
of cone in 2014. If done properly, these boundary cases reflect
numerous early warning signals that were initially picked up
from the firm's extended networks.
The aim in scenario planning is to sketch the boundaries of
the cone rather than fully characterize all possible outcomes.
The objective is to challenge people's thinking about various
future narratives prior to defining and structuring problems
analytically. In project planning, for example, scenarios may
help upfront in identifying which uncertainties are most
important for a subsequent decision tree analysis. In broader
contexts, scenario planning can help set the agenda for deep
dialog or prepare the organization for major change [38].
Also, scenarios can help stress test an existing strategy and
future-proof it further where needed. Once robust strategies
have been formulated, the organization is ready to devise
scanning and monitoring frameworks to track how the future is
unfolding and whether elements of the strategy need to be
fine-tuned or perhaps fundamentally revised. This is one main
purpose of the strategic radar in our model, as described next.
5. Designing strategic radars
An organizational Strategic Radar (SR) is an integrated
framework that uses scenario planning, business analytics and
dashboard technologies for two main purposes: (1) to monitor
and scan for important signals from the external environment
and (2) to trigger strategic and operational adjustments in
response to these changes as needed. Ideally, the radar system
is continuously fed by organizational sensors that monitor
known indicators as well as by scanning for unexpected signals.
These diverse inputs can come from inside the firm, from its
extended networks, or from external information providers.
Unlike traditional corporate planning systems, SR entails a
continuous monitoring and synthesis process, built around a
scenario-based planning model that fully respects the highly
uncertain environment most firms operate in. Fig. 2 depicts the
basic monitoring and scanning functions of SR, with various
details explained in the next section as well as the Appendix.
There are many ways to design an SR depending on the firm's
strategy, existing planning process, organizational configuration
and the allocation of decision rights. We describe here a generic
system that is scenario-based, in order to fully recognize the
complexity and uncertainty of a changing external environment.
The main challenge is how to interpret weak signals from the
periphery through diverse mental models efficiently. The overall
Fig. 1. The uncertainty cone.
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intent is to provide managers with a timely view of how their
external environment is changing, and then offer suggestions
about how to adjust or inform various tactical as well strategic
decisions.
In general, a complete SR system consists of three major
functions supported by extended networks and linked together
by a scenario-based planning framework:
1. Monitoring external signals by providing periodic updates
about pre-specified forces shaping the business environ-
ment. This means tracking a specific force (such as increased
use of RFID technology) and examining it through multiple
scenario lenses. In addition to updating and projecting the
future direction(s) of a specific force, SR also estimates the
band of uncertainty around any future projections in light of
the scenarios examined.
2. Assessment of strategic actions based on the above updates
about the external environment. A periodic reprioritization of
the top strategic capabilities needed for future success may be
called for, in some or all of the strategic segments the
organization operates in. More operationally, it may entail a
revised ranking of the attractiveness of existing strategic
projects and real options underlying the current strategy
(within a portfolio context).
3. Scanning for additional weak signals that could impact the
future external environment. Unlike (1) above, where the
variables to be monitored are pre-selected, this step entails a
search for unexpected signals within broadly defined cate-
gories. For example, it may require probing for potentially
disruptive technologies in say air transportation and then
scoring the potential impact of these newly discovered
signals for the current strategy.
The role of scenario planning is critical in SR since it deals
with early warning signals that are often incomplete and thus
subject to multiple interpretations. Rather than jump to
premature conclusions, scenarios help the organization explore
the deeper uncertainties surrounding the signal. For example, a
few decades ago a major US newspaper company picked up a
signal that Xerox had just introduced a new service to deliver
customized newspapers electronically to hotels and other
locations, allowing users to print out tailor-made content.
Travelers could now get their local news delivered remotely or
read a leading national newspaper in their native language. But
the announcement could also mean that hotel guests in the
future might never again hear the familiar thump of a newspaper
outside their doors, since Xerox's move would lead to digital
media e-reading.
The weak signal of remote printing required a deeper
analysis of its true meaning since much depended on the future
scenarios the newspaper leaders had in mind. In a “business as
usual”scenario, this new service would represent a niche market
(the traveler's segment) and could be viewed as a welcome
alternative channel of distribution besides the physical delivery
of newspapers. It could create new opportunities for newspapers
to move beyond their limited geographic areas and further
enhance customer loyalty. In a “cyber-media”scenario, however,
where electronic channels are adopted rapidly, this initial foray
into customized printing in hotels would be the start of
customized home printing of newspapers. Such a development
could render a newspaper's physical assets obsolete (such as its
expensive printing presses and fleet of delivery trucks). By
looking at this single weak signal through multiple lenses, the
newspaper's managers were better able to explore its potential
implications and monitor its development over time [39].
In addition to monitoring a known issue of concern, such as
remote printing developments in the above historical example,
continuous scanning for unfamiliar or unstable categories is
critical as well. A scanning library should be developed to
collect and store signals obtained from both internal and
external networks. The challenge here is to look widely for
relevant signals within broad categories, without knowing yet
what exactly to look for (in contrast to the monitoring function
which is sharply focused). Once internal and external data
sources reveal potentially relevant scanning items, they can be
evaluated for their potential relevance via a combination of
methods including expert panels, prediction markets, and
Repeated for Each Cycle
Setup
Stage
Research Monitor Analyze Publish
Review Scenario
Planning result &
Information Audit
Define Forces,
Signals, Mapping
Research Signal
data sources
Research Meta-
Category sources
Adjust
Signal Data
Collection
Meta-Category
Scanning and
Publishing
Prepare Cycle
Draft content
(Dashboard,
Analysis)
Conduct team
Produce final
results:
Dashboard,
Analysis Report,
Presentation
Identify signal
sources
Identify Expert
Network
Identify Meta-
Categories
Monitoring setup
(if necessary)
Publishing
Data Analysis
Revise Expert
Network and
Participant
Surveys
Conduct Cycle
content review
and analysis
Material
Conduct
Sponsor Team
Review
Stage Elements
Define Reports /
Refine Dashboard
Managerial Control Elements - Project, Issue, Risk, Communications, Funding
Web Survey
Background Support Services – Expert Network development and maintenance,
Industry standard Force and Signal management, Industry Meta-category development,
Review Meta-category materials for new potential forces, Develop new signals.
Fig. 2. Strategic radar system setup.
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crowd-sourcing. The scanned items can come in the form of
news clippings, research reports, grapevine conversations at
the water cooler, or as weak signals from the organization's
extended networks.
6. Application to a government agency
The deeper challenge of SR is to align the design compo-
nents of its extended scanning network with the human side of
the organization. The Defense Logistics Agency (DLA), which
implemented an SR, provides an instructive example. DLA
supplies the US armed forces with all kinds of critical supplies,
from clothing, food and medicines to spare parts, tools, fuel,
and building materials. It operates in many ways like a large
private sector firm in the supply and logistics field. DLA sits
above the armed forces (Army, Navy, Air Force, Marines, etc.)
and procures nearly all supplies needed except for weapon
systems and ammunition. For example, DLA ships more fuel
through the Rotterdam harbor than any other organization.
Given the global complexity of DLA's far flung operations, it
embraced SR as a means to enhance its strategic planning
process and peripheral vision. We briefly describe some of the
benefits achieved and lessons learned from DLA experiences,
while respecting the highly confidential nature of some of its
field operations.
After conducting a planning exercise to explore possible
future scenarios relating to logistics and defense operations
around the globe, DLA started to use its very extensive networks
to monitor for early signals of change. These networks included
vendors, customers, partners as well as current and former
employees. Initially, DLA was focused on capturing information
about pre-specified trends and uncertainties, using various kinds
of business analytics as part of its well staffed monitoring
function. Over time, however, these networks started to scan for
new “weak”signals that could result in significant change in the
scenarios themselves, especially if the signals fell outside the
range of the scenarios. Potentially disruptive “meta-categories”
were scanned including Energy Availability, Defense Spending,
Outsourcing, Range of Military Operations, Supply Chain Tech-
nology, Geopolitical Risk, and Defense Policy Changes.
For example, the Department of Defense (DoD) and NATO
had moved to Radio Frequency Identification (RFID) as the
primary technology platform for logistics tracking. Signals about
standardization and new mandates governing commercial ship-
ping involving RFID could significantly impact the economics
and long term availability of this technology for DLA. During the
monitoring process, various signals revealed a slow-down in
commercial RFID deployments and an increase in alternative
wireless sensor networks. Thanks to its extended networks,
plus an SR-type system to integrate disparate weak signals, DLA
picked up these early warnings about RFID's decline much
sooner. Likewise, the system flagged increasing energy prices,
coupled with possible limits on energy consumption within
DoD, as important signals that could impact its current operating
model. In both of these examples, early identification and
interpretation of weak signals helped DLA adjust its strategies
sooner than otherwise would have been the case. Fig. 3 depicts
how various RFID-related signals for DLA prompted updates of
the external scenarios and changes in the relative priority
assigned to various strategic capabilities and initiatives.
In DLA's case, data was routinely collected in 5 meta-
categories and these signals, plus any additional scanning inputs,
provided the basis for each quarterly update. About 15 experts
were polled to evaluate the relevance of 20 or so candidate
signals within each mega category, meaning that about 100
external signals were processed in each quarterly cycle. This was
about 50% more than would normally be reviewed. The full list of
100 would be narrowed down by the experts to the 20–30 most
relevant signals. This translates into 1–4 signals per meta-
category and some of these would truly spark deep debate
among the experts and managers. One example was the
emergence of crime syndicates in certain supply chains; another
was when the evolution of wireless sensors might replace
DLA's deeply embedded RFID technology. Some of the signals
discussed would directly connect with the original scenarios
developed, such as the impact of trade restrictions on moving
goods around the world. But other signals were related more
indirectly and some weak signals were missed entirely. For
example, the development of smart phones was detected too
slowly at a time when Blackberries still reigned supreme.
Linking signals picked up from monitoring or scanning
activities to the scenarios requires considerable interpretation.
First, a new signal would be assessed in relation to a pre-
specified force shaping the future, such as “Technology Impact
on Logistics”. Second, the impact of this force would then be
judged in light of the existing scenarios, either to revise them or
to re-assess their relative likelihood. In DLA's case, each force
would be assessed by 15 individual experts for the time period
2004 to 2014, using a scale of 1–5 with 5 denoting Extreme
Impact and 1 Negligible Impact. Once the adjusted estimates
were calculated for all forces, the scenario weights would then
be updated by comparing the new force estimates with the
previous force values in place (see Appendix for details).
In addition to expert polling, DLA used web-based, interac-
tive executive dashboards to help visualize changes in external
forces (see the example in Fig. 4). These dashboards would alert
leaders that certain strategic or tactical decisions might need
revision due to significant changes in the external environment.
Without the aid of a customized SR dashboard, it would be hard
to manage the information overload that wide network scanning
typically induces. However, technology alone is not enough.
Ultimately, the human side of the organization is critical in
enhancing peripheral vision, sense-making, and strategic action.
Since weak signals are by definition ambiguous, they need to be
interpreted through deep dialog in order to discover their
deeper meaning. SR aimed to do this by integrating weak signals,
scenarios, strategic planning and dashboard or similar visualiza-
tion technologies (Appendix gives further detail).
7. The leadership challenge
It takes leadership to orchestrate the kind of SR conver-
sations that help an organization see around the corner
sooner than its rivals. As William Gibson observed, the future
is already here but unevenly distributed. Few organizations
have learned to harness these uneven signals from the edges
of their business and see the future more clearly. Most
companies lack the ability to mine their extended network
and to mind the weak signals in strategic ways. Leaders may
become so overwhelmed with external information that they
miss or ignore important warning signals, akin to distracted
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drivers running their car through a red light. The various
warning signs of impending disaster may be clear to others,
but the organization is often too preoccupied to notice.
Whether a firm is willing and able to effectively deploy an
extended intelligence network to see external change sooner
depends greatly on the vigilance of the leadership team,
especially their willingness to engage in scenario planning. The
problem in most organization, is that managers pay attention
to that what is most pressing and operationally relevant at the
moment. In contrast to such operational orientations, truly
vigilant leaders tend to be externally focused, curious, and open
to diverse perspectives. They are willing to listen to a wide
array of sources and foster broad social as well as professional
networks. By inclination, vigilant leaders have a network
mind-set [40] and are comfortable with ambiguity [1].
Vigilant leaders in turn can mobilize the rest of their organi-
zation to pay attention to the periphery, by tapping extended
networks and other sources. They foster an inquisitive approach
to strategy making that alerts the organization to possible
challenges and opportunities lurking at the edges of business.
Also, they will make the necessary investments in knowledge
systems and analytical support, while assigning clear account-
ability for detecting, tracking and sharing weak signals. Scenario
planning can help promote vigilance and discovery, especially in
those organizations that use their web of networks to extract
better insights from their widened periphery. Fortunately,
various supporting technologies—such as dashboards and
mobile communication - are now available to enhance an
organization's absorptive capacity [41], and thus its peripheral
vision and collective sense making, within a scenario context.
Fig. 4. Scenario dashboards.
New Market
Driver/Force
Identified
Network Scanning
Identifies Weak Signals Scanning Item:
“Commercial
RFID mandates
slip ” and
“Growth in
Wireless Sensor
Networks ”
New signal impact
assessed by Experts in
Network
Managed ForcesManaged Forces
Important Weak
Signal Flagged
in System
Market Force:
“Technology Impact on
Logistics ”
2012 Assumption =
Medium Impact
Pattern Matching:
“High - tech
Socialism
Consolidated
Drivers/Forces
Analyzed via
Experts in
Network
Options Portfolio:
“Protect RFID
standards” , “Expand
Cooperative/Open
R&D related to
sensor networks
Market Force
Changes
Translated to
Business
Impacts
“
”
Market Force
Changes
Translated to
Business
Impacts
Fig. 3. Strategic radar in practice.
821P.J.H. Schoemaker et al. / Technological Forecasting & Social Change 80 (2013) 815–824
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Acknowledgements
The authors thank the Mack Center for Technological
Innovation at the Wharton School for research support, and
Decision Strategies International for use of the DLA case and
associated charts.
Appendix A. Technical Aspects of the SR System
The Signals Function of the dashboard represents the in-
telligent front-end of the SR. It collects and processes signals
from the external environment using multiple sources as shown
in Fig. 5. A web-based expert network was used to identify as
well as interpret potential new signal candidates. Structured
questions were posed around known signals (such as oil price
changes) versus open ended questions around new potential
signal areas (such as unexpected political turmoil in an African
nation). The inputs of these experts can be aggregated and
adjusted for difference in expertise as well as correlations among
experts [42]. The goal of each revision is to get a new projection
for each signal in terms of magnitude and rate of change. The
revision is then added to the Signals function vector so that it
includes a history of each signal value from initialization through
the latest input. Over time, each experts' inputs can be scored in
terms of probability calibration and adjusted for affective or
cognitive biases influencing their predictions [43].
The Scenarios Function of the dashboard transforms inputs
from the signal function to re-estimate the weight of each
scenario. It assumes that relevant scenarios are already available
for the issues of interest. The DLA scenario weights reflected
likelihood as well as its potential impact of each scenario, and
these were prominently displayed in the SR dashboard. As
showninearlierFig. 4, there were four DLA scenarios with the
titles as listed in the dashboard.
The process of updating the scenario weights, after new
signal values have been gathered, starts by representing the
input signals and force estimates as a vector. Next, we would
judge to what extent the end state scenarios approximate the
input signal vector. This approach can be expressed more
formally as follows:
Historic
Signal
Database
Projected
Value
Extended
Expert Net
Signal 1
Signal 2
Signal 3
Signal k
.
.
.
Public Sources
External Experts Web survey
Monitoring
Predictive
Analytics
Expert
Prediction
Fig. 5. Signal collection and assessment of force values.
F
new1
F
new2
.
.
.
F
newn
Fnew
A B C D
F
A1
F
A2
.
.
.
F
An
F
B1
F
B2
.
.
.
F
Bn
F
C1
F
C2
.
.
.
F
Cn
F
D1
F
D2
.
.
.
F
Dn
W
i
= RSS (F
new
-F
i
)
Sig
1
Sig
2
.
.
.
Sig
n
Signals Force
Estimate
Scenario Weights
FE
i
=1/n*{∑E
ij
*C
ij
}+ η
J=1
n
Scenario Reference Values
Eij = Estimate of Force i for expert j
Cij = Confidence Level for Force i
estimate by expert j
η= Adjustment based on new
info/bias correction
W
A
W
B
W
C
W
D
Legend: Sig = Signal; F = Force; A, B, C, and D refer to four different scenario respectively;
W = weight of scenario; RSS = Root Sum S
q
uared; n = number of forces monitored.
822 P.J.H. Schoemaker et al. / Technological Forecasting & Social Change 80 (2013) 815–824
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More sophisticated pattern matching or learning techniques
could be used such as neural networks and Bayesian
updating. As a simpler alternative, users could manually es-
timate each scenario's likelihood using expert judgments. In
our SR model, the scenarios function displays the updated
importance weight of each scenario along with its previous
weight, as shown in the example in Fig. 4.
The DLA scenarios shown here were constructed in 2004
and included over 10 high impact forces. As described earlier,
one of those forces was “Technology Impact on Logistics”.
Once the adjusted estimates have been calculated for all
forces, new scenario weights can be estimated by comparing
the new force estimates (or vector) with the previous force
values (or vector). The updated scenario weights are then
calculated by looking at the vector difference (using Root
Sum Squared) between the updated force vector and each
scenario vector, and then weighing the scenarios based on
the inverse of this vector difference.
The technical aspects of the SR system are far less important
however than the type of discussions they engender among key
stakeholders. By relating signals back to the scenarios in a
structured way, a deeper conversation ensues [38]. Also, it
allows many signals to be processed jointly which is especially
important when triangulating ambiguous data. Periodically
updating the scenarios with newly spotted radar signals serves
a valuable integration function. Over time, SR will enhance an
organization's sense making and adaptive capability, especially
when facing increased uncertainty.
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Paul J. H. Schoemaker serves as Research Director of Mack Center
for Technological Innovation at the Wharton School of the University of
Pennsylvania, where he teaches strategy and decision making. Schoemaker
spent an extended sabbatical from the Univ. of Chicago with the scenario
planning group of Royal/Dutch Shell in London. He is the founder and
chairman of Decision Strategies International, Inc, a consulting and trai ning
firm specializing in strategic planning, executive development and multi-
media software (see www.decisionstrat.com). Schoemaker has written
over 100 academic and applied papers, and is the (co)-author of several
books including Decision Traps,Decision Sciences,Wharton On Managing
Emerging Technologies,Winning Decisions,Profiting from Uncertainty,
Peripheral Vision and Brilliant Mistakes.
George S. Day is the Geoffrey T. Boisi Professor and a Professor of Marketing
at the Wharton School of the University of Pennsylvania, where he codirects
the Mack Center for Technological Innovation. His many books include
Market Driven Strategy, Wharton on Managing Emerging Technologies (edited
with Paul Schoemaker) and most recently Strategy from the Outside In (with
Christine Moorman).
823P.J.H. Schoemaker et al. / Technological Forecasting & Social Change 80 (2013) 815–824
Author's personal copy
Scott A. Snyder, PhD is the President and Chief Strategy Officer of
Mobiquity, a leader in delivering innovative wireless solutions for enter-
prises. He has over 24 years of experience in Fortune 500 companies and
start-up ventures. Dr. Snyder is the author of The New World of Wireless:
How to Compete in the 4G Revolution (Wharton Publishing in July 2009)
and authored chapters in The Network Challenge: Strategy, Profit, and Risk
in the Interlinked World (Wharton Publishing, 2009) and Inside the Minds:
Small Business Growth Strategies: Goals for Successful CEOs (Apatore
Books, December 2007). Dr. Snyder is a Senior Fellow in the Management
Department at the Wharton School and an Adjunct Faculty Member in the
School of Engineering and Applied Science at the University of Pennsylvania.
824 P.J.H. Schoemaker et al. / Technological Forecasting & Social Change 80 (2013) 815–824
