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Electronic copy available at: http://ssrn.com/abstract=2237631
Trend Scanning, Scouting and Foresight Tech-
niques
René Rohrbeck
1. Introduction
The front end of innovation has earned the adjective ‘fuzzy’, particularly as it is
considered unstructured, non-linear, and highly iterative (Khurana and Rosenthal
1998; Koen et al. 2001; Verworn et al. 2008). But this should not be misunder-
stood as a need to rely on hope or chance encounters to drive innovation.
Beating competition in the innovation game will require developing the abili-
ties to innovate on the basis of early signals in trends, involve internal and external
partners in discussing insights into the future, and to build an organization that is
able to grasp opportunities in a timely manner.
This is by no means easy for any firm, and to make matters worse, building
foresight capabilities involves working partly against organizational reflexes that
are useful and critical. For example, the critical ability to focus on the current
business can easily be damaged if the firm engages excessively in scanning its
environment and entering new fields of business. Thus, building corporate fore-
sight capabilities will always imply an important balancing act.
Corporate foresight comprises all activities that are aimed at identifying chang-
es, creating a consolidated future outlook, and using these insights into the future
in ways useful to the organization. These activities include developing a strategy,
creating innovations, managing risk, and exploring new markets (Rohrbeck
2010a; Slaughter 1997).
If successful, a corporate foresight activity fulfills a dual role: it creates useful
insights into the future and at the same time triggers and facilitates organizational
response. The second role is crucial, as innovating in new business fields is partic-
ularly challenging for established firms (Chandy and Tellis 2000). New product
categories often cannibalize existing ones, and while innovations in current prod-
ucts are blessed with a high level of planning certainty, innovating in new product
categories requires organizations to deal with a high level of uncertainty (Nijssen
et al. 2005). Using traditional techniques of evaluating innovations, such as dis-
counted cash-flow analysis, to decide which new innovation project to fund would
practically always lead to the ones in established product categories being chosen
over the ones in new product categories.
Trend&Scanning,&Scouting&and&Foresight&Techniques&
Rohrbeck, R.
In: Front End of Innovation: Managing the Unmanageable Fuzzy Side, Oliver Gassmann, Fiona Schweitzer (Editors)
Electronic copy available at: http://ssrn.com/abstract=2237631
2
To discuss the challenge and the solutions proffered by foresight at the front end
of innovation, this chapter is structured along a generic process model of corporate
foresight (Daft and Weick 1984):
• Scanning: How to detect signals of future change?
• Interpretation: How to facilitate the discussion about the impact of changes?
• Action: How to trigger new innovation initiatives?
2. Detecting future changes
Many firms still believe that relying on a list of the mega trends (i.e., trends with
high impact and longtime horizons) makes them sufficiently oriented towards the
future. They consider such mega trends sufficient for guiding innovation efforts
towards promising future markets. The first bit of bad news for these firms is that
the majority of their competitors will most likely already have done the same. The
second bit of bad news is that by betting on mega trends, they can easily end up
innovating in an area where uncertainty, and therefore the number and size of
business opportunities are low. Thus firms willing to outcompete rivals on the
basis of innovation face a first challenge:
1st challenge: How to detect signals of change that still yield a competitive
advantage?
The first answer to that challenge lies in focusing on trends that are not as easy to
perceive as mega trends. In the 1970s, Igor Ansoff proposed not to wait until eve-
ryone can spot changes, but to start by detecting telling ‘weak signals’ (Ansoff
1975). This is, however, challenging in itself as such a search is like searching for
a needle in a haystack, without knowing what one is looking for. At the outset,
every weak signal could – but in most cases will not – develop into a major
change that can be the basis of a promising innovation. When scanning their envi-
ronments for weak signals, firms should therefore only spend an appropriate
amount of time and money to assess a potential trend. That appropriate amount is
small at the beginning and increases only if the change proves relevant in the first
phase of the analysis. Choosing the appropriate amount of effort is, however, very
difficult as the potential relevance of a given signal is not known a priori.
Translated into practice, this means that, in the first step, a weak signal should
only be identified and recorded. After an initial screening, only the weak signals
that give signs of potential relevance should then be investigated further and/or be
discussed with internal experts. This way the firm will only commit a significant
amount of resources to a limited amount of candidates for relevant future changes.
Assigning the scanning task to experienced employees can further enhance this
process of detecting weak signals. Employees with a long track record in a given
industry, a strong internal and external network, and a broad, rather than narrow,
3
educational background are much more likely to reduce the number of errors in
the detection process. In this context, two kinds or errors are relevant (see Fig. 1).
&
Fig. 1 Errors in detecting signals of change
The false negatives result in the firm’s inability to respond to a relevant change.
This would open the door to competitors who may grasp the innovation opportuni-
ty. Or, in case of a change that carries risk, the firm will be taken by surprise and
would suffer the consequences. But equally bad are the false positives errors. They
carry the important negative effect that the firm wastes valuable resources on
chasing after ghosts, i.e. opportunities that in the end do not materialize, or threats
that never occur.
The second answer to the challenge of detecting change relevant to a firm’s
competitive position lies in using a broader scanning scope. As discussed above,
most firms focus only on high-impact trends with a long timeframe. This might be
justified for firms in industries with slow clock speed, but for firms in fast-
changing markets, scanning the environment for trends with short time horizons is
at least equally important.
The fashion business is an exemplary industry, where many firms have mas-
tered the art of spotting changes in trend-defining subcultures and translating these
into products in the market within three to six months. But also technological
change can be swift, and, for example, many consumer electronics firms have
built excellent sensors that generate insights into emerging technologies and new
customer needs in the last decades.
False
negative
Correct
positives
False
positives
Correct
negatives
Detection and assessement
by foresighter
no yes
Relevance of change
no yes
4
Today, scanning for such change can often be partly automated through intelli-
gent data mining (also called bibliometric) approaches. Online social networks
provide an abundant amount of data on consumer behavior and needs and are easy
to scan. In this context, both global and broad social networks as well as niche
communities can be equally relevant, depending on the individual need of the
firm. For instance, a company working in medical devices would be much more
interested in scanning a community of medical doctors than a broad social net-
work.
Data mining can be performed in different ways, ranging from a keyword-
based search for monitoring a certain development, to exploring new changes
through scanning for associated terms. The latter way of gathering data is particu-
larly useful to identify innovation opportunities in converging industries.
For example, if 15 years ago, a firm dealing in cameras had searched for terms
associated with digital photography, it might have been surprised to find a relation
to the term ‘mobile phone’. By identifying that link, the firm might have been in a
position to proactively initiate a partnership with a manufacturer of smart phones
and to profit from jointly innovating on image capture, storage and viewing.
These two ways of searching, i.e., monitoring (directed search) and scanning
(broad search), can be used in the same way for technology change. The most
relevant data sources in this field are still publication and patent databases. But
more recently, social networks have also been used for identifying and contacting
key experts in technological domains. Such techniques often build on the pyramid-
ing principle, where the leading expert is identified through a series of contacts
with experts in the field in that each expert is asked to name another expert whom
they would regard as more knowledgeable than themselves (von Hippel et al.
2008).
Fig. 2 Expert search through pyramiding in the web 2.0
Basic
Knowledge
Specialized
Knowledge
Expert
Knowledge
Increasing value of
knowledge „Pyramiding“ in web 2.0
! Authors of high-authority blogs are
a good starting point
! Online social networks enable a
detailed criteria search
! But: Estabilshing valuable contacts
requires often still a personal contact
through introduction and face-to-face
meetings
Information seeker
Leading expert
5
Fig. 2 shows how pyramiding can enable an information seeker to identify the
leading experts in a field in a few steps. This technique allows the information
seeker to efficiently work through basic and specialized knowledge to reach the
true expert knowledge level. The new user generated content services on the inter-
net further facilitate the search for experts, for example through enabling the iden-
tification of influential blog authors as starting points for the expert search. In
addition, social networks can be accessed and searched by keywords to identify
experts with the sought-after knowledge and skills.
Choosing an approach that puts people (scouts/foresighters) center stage, rather
than databases, also helps to tackle another challenge:
2nd challenge: How to detect change when terminology is unclear and patchy?
Relying on experts rather than databases for identifying change makes dealing
with immature terminology easier. Humans are able to update their terminology as
it develops around them. To illustrate the challenge, let us assume that a firm has
obtained information on a new phenomenon that involves user generated content
and the phenomenon that people are increasingly willing to share their knowledge
for free on the internet. At the outset, it would have been difficult to define good
keywords to analyze this new phenomenon. For example, starting with
‘crowdsourcing’ and ‘user generated content’ would have provided good insight
into part of the overall phenomenon at best.
However, through talking to experts, a scout might have been able to quickly
link ‘crowdsourcing’ and ‘user generate content’ to other associated terms such as
‘blogs’, ‘wikis’, ‘social networks’, etc. This would have resulted in a much more
comprehensive and rich understanding of the phenomenon, when compared with
database search based on keywords alone.
This is also why in fast moving environments many firms are building their
foresight activity on networks of scouts that gather information through direct
communication with the persons that are leading the change. These can be re-
searchers who develop certain technologies in leading companies, universities,
and research institutes. But scouting works equally well with scouts that gather
their information from leading thinkers that are behind socio-cultural changes.
Fig. 3 shows a generic scout network, where scouts work like neuronal nodes in
the brain, connecting external experts with internal stakeholders. However, equal-
ly important is the network between the scouts that provides a platform for early
validation of signals and triangulation to ensure that a signal points at a relevant
change.
&
6
&
Fig. 3 Generic scouting network (Rohrbeck 2010b)
An additional advantage of scouting networks is that they facilitate the follow-
up steps right up to developing the innovation. The personal contact they afford
allows the gathering of additional information, if required to convince colleagues
of the importance of the change. Also, experts’ statements often carry more weight
for decision makers than a good database analysis would. Going through multiple
iterations with an expert and collecting valuable statements is further supported by
the trusted relationships that the scouts normally maintain with their sources.
As scouting is only one possible method of establishing foresight, it points at
another question that arises when designing foresight approaches:
3rd challenge: How to choose a foresight method that is appropriate for a
given task and in a given context?
To guide this choice, two types of methods can be distinguished, i.e., methods that
are more suitable for an exploration (finding options, new approaches, new cus-
tomer needs, etc.) and methods that are more suited for a prediction/planning task
(Porter et al. 2004; Reger 2001). Regarding the context, the dominant factor that
guides choice is the level of uncertainty. Uncertainty includes complexity and
volatility of the firm’s environment.
A further distinction should be made between foresight methods suitable for
exploring the future on the market side and methods more suitable for the technol-
ogy aspect.
On the market side (see Fig. 4), at least 16 important methods can be identified
that offer support in the front end of innovation. Methods such as ethnographical
Scout
Scout
Scout
Scout
Scout
Network
Company
boundaries
Information flow Internal stakeholder Sources Scout Scouts (internal/ external)
7
studies are particularly useful for exploring new customer needs even in product
domains that are unknown to the firm and have a high level of uncertainty.
&
Fig. 4 Market foresight methods
On the other end of the spectrum, methods such as lead market analysis or sim-
ulations can be used, but only if the factors that influence the market are known
and information about direction and rate of change is available. Thus, they can
only be employed in low uncertainty environments. At the center of the portfolio
are methods that can be employed for either prediction or exploration. These
methods also include expert interviews or scouting networks. The outcome can be
determined by the way in which the interviews are conducted. Roadmapping is
also an example of a method that can be used for either exploration or planning.
Some firms use it primarily as an internal planning tool to ensure collaboration
between product and technology planning. But roadmapping can also be used as a
central method in a workshop for identifying and discussing innovation opportuni-
ties.
On the technology side (see Fig. 5), classical methods such as S-curve analysis
(where performance increase of a technology is plotted against time) are still being
employed with the aim of predicting the right moment for switching from one
technology to the next. An increasing usage of the internet for bibliometric analy-
sis and targeted expert search can, however, also be observed in this context.
Context
(level of uncertainty)
low high
Tas k
Predicting/planning Exploration
1 Anologies
Market foresight methods
2 Bibliometrics (Publications, Patents, …)
3 Cross-impact analysis
4 Delphi analysis/prediction markets
5 Ethnographical studies
6 Expert interviews/scouting
7 Lead user analysis
8 Lead market analysis
9 Long wave analysis
10 Monitoring
11 Roadmapping/backcasting
12 Scenario analysis
13
Stakeholder analysis
14
Simulation/gaming
15
16
Trend extrapolation
Socio-cultural current analysis
1
2
3
5
6 7
8 9
10
11
12
13
14
15
16
4
8
&
Fig. 5 Technology foresight methods
Other methods, such as TRIZ, are used only by few firms, even though they yield
a high potential for exploring development trajectories or identifying the next
technology generation for a given product. Traditional trend extrapolation is in-
creasingly supplemented by other methods of assessing emerging concepts.
Such a method is the Gardner hype cycle, which is a model that proposes that
the popularity of a given technology follows a certain typical development cycle
(see Fig. 6). This cycle is composed of five phases. In the first phase, the technol-
ogy gains popularity fast as the media quickly catches on to the promised perfor-
mance gains as compared to those of current technologies. In the second and third
phases, the technology fails to deliver on the performance its inventors have prom-
ised or on the performance the media has described and consequently loses popu-
larity as fast as it gained it in the first phase.
If the technology survives this ‘trough of disillusionment’ and investments in
technology development retain a sufficient level, it has the opportunity to reach
the ‘slope of enlightenment’, where technology developers and potential users
have aligned their expectations and the technology starts to deliver according to
these expectations. In the final phase, the technology reaches the ‘plateau of
productivity’, where it remains until it is substituted by another technology.
Context
(level of uncertainty)
low high
Tas k
Predicting/planning Exploration
1
Technology foresight methods
2
Bibliometrics (Publications, Patents, …)
3
Cross-impact analysis
4
Delphi analysis/prediction markets
5
6
Expert interviews/scouting
7
8
9
Long wave analysis
10
Monitoring (continuous)
11
Roadmapping/backcasting
12
Scenario analysis
Technology assessment/s-curve
Trend extrapolation
1
Simulation/gaming
TRIZ
2
3 4 8
5
6
7
9
10 11
12
13 Gardner hype cycle
13
9
Fig. 6 Gardner hype cycle 2012 (Source: Gardner Newsroom, 16 August 2012)
Some firms use the framework of the hype cycle, but do not rely on the as-
sessment by Gardner and instead form their own judgment through internal expert
panels. Such an assessment is typically made through workshops and thus pro-
vides the additional benefit of triggering an internal discussion about the potential
of future technologies and innovations. Such a participative approach also pro-
vides strong support for later development of the innovation.
3. Interpreting the Impact of Future Changes
After a potential future change has been detected, the firm needs to establish
whether it is relevant and how it might impact its business. This is referred to as
organizational interpretation or translation into the organizational discussion.
Much of the interpretation work can be facilitated by corporate foresight methods.
For example, cross impact analysis applied to technologies can reveal which key
enabling technology needs to be developed to allow the building of given product
families. Other changes, for example those that have been identified through a
bibliometric analysis, often need a dedicated translation effort, for example in the
form of a workshop, where foresighters and innovation managers work together to
identify innovation opportunities.
10
Before linking foresight to innovation is discussed, it should be clarified what
is meant by change. For the interpretation phase, and also later in the response
phase, it is important to know what kind of future change an organization is deal-
ing with. In this context, the distinction between trends and uncertain dynamics
applies.
Trends are characterized by a high level of certainty at least about the direction
of change, and in many cases also good information on the likely rate of change.
Most mega trends fall into this category. For example, that society is aging can be
predicted rather confidently, as young high-school graduates entering the work-
force take at least 17 years to ‘develop’. This means that the current number of
births predetermines the number of graduates that will enter into apprenticeships
in 17 years. At the same time, the number of elderly persons is predetermined to
an even stronger extent and can thus be predicted easily. In addition, history sug-
gests that birth rates are difficult to alter, particularly in a stable external environ-
ment. These factors of predetermination make the aging society a trend which is
particularly predictable.
By contrast: Who would be comfortable making predictions about the future of
the European monetary policy? Questions, such as, ‘Will the EURO still be the
primary currency in Europe?’, or ‘What will be the role of the EURO in the inter-
national monetary system?’, are questions to which the answers are notoriously
difficult to predict. Therefore, it can be concluded that the uncertainty about direc-
tion (increase or decrease in the number of participating countries) and rate of
change (speed of growth or decline of memberships) gives this change an uncer-
tain dynamic.
Of these two categories, trends are much more popular in innovation planning.
The simple reason is that most firms rely on management systems that work with
plans, which in turn are built on assumptions about future states of influencing
factors. Uncertain dynamics are highly inconvenient for such management sys-
tems, as they would require developing multiple plans for the different possible
states of uncertain dynamics in the future. This leads to the following challenge:
4th challenge: How to plan and innovate on the basis of uncertain dynamics,
where direction and rate of change are unknown?
The first answer to this challenge lies in thinking in scenarios. While working with
good, medium and worst case scenarios is standard practice in financial planning,
this useful technique has not spread much in innovation management. The central
proposition is that instead of trying to predict uncertain factors and making matters
worse by combining these into predictive models, firms should rather
acknowledge the uncertainty and identify a number of possible and consistent
scenarios.
11
Managerial action on the basis of scenarios can than follow three routes
(Gausemeier et al. 1998):
• Planning-oriented: Actions based on the premise that the most likely scenar-
io will occur
• Responsive: Combine actions intended to move towards the scenario that
yields the greatest opportunity and actions meant to avoid the scenario with
the greatest risk
• Trend-setting: Actions designed to create a desired scenario, for example by
influencing key actors in a given market to take joint actions
All three routes can lead to success. Whether one route is more preferable depends
on the strategy of the firm. For example, if the firm aims to achieve innovation
leadership, the trend-setting route would be most appropriate. If a company strives
to limit its exposure to risk, the responsive stance would be preferable. For large
firms that need to ensure that all relevant internal units act in an orchestrated fash-
ion, the plan-oriented route is often favored.
But even if a firm plans and acts on the basis of a most likely scenario (thus
choosing to abandon the plurality of futures in its planning), it will have benefited
from having engaged in scenario thinking. Identifying scenarios and discussing
alternative routes of development trigger both explicit and implicit contingency
planning and will make the firm or the individual innovation projects more re-
sponsive and robust.
4. Triggering new Innovation Initiatives
In this context, the broader question arises whether planning is the only way to
trigger innovation. Research has shown that innovations often go through phases
of stagnation until a new insight or individual creative idea unlocks the barriers
that have previously prevented the further development (Backman et al. 2007;
Gemünden 2001). Therefore, the assumption that all innovation can be planned
through enough pre-development analysis should be abandoned. A better assump-
tion would be that organizations will always lack some future insights (i.e., emerg-
ing promising market and technology opportunities) at a given moment and that
they will be able to enhance their innovation in a later phase when more insights
have been gathered. This is true throughout the whole innovation process, but
particularly important in the front end of innovation. Therefore, an additional
challenge should be taken into account when designing foresight approaches:
5th challenge: How to use foresight methods in the highly iterative front end
of innovation?
By studying serial innovators (individuals that have repeatedly and successfully
12
driven innovations in large firms), Griffin et al. (2007) find that these innovators
follow two cycles until a satisfactory outcome is achieved. Of these two cycles,
only invention and validation are of relevance in the context of the front end of
innovation. The second cycle, which deals with bringing the invention to market,
is less relevant in the present discussion. The first cycle consists of three phases:
• finding the right problem
• understanding the problem
• inventing and validating
This cycle can be triggered by any of the phases. For example, it might start
with the discovery of an unfulfilled future need detected by using an ethnograph-
ical consumer research method, such as a 24-hour stay in a user family. Such stays
can uncover tasks that customers want to perform, but cannot with their current
products. For example, one might have discovered that families would like to
synchronize their weekly timetables. A large pin board mounted in the kitchen
might currently be the preferred method.
But such a solution might be judged unsatisfactory, particularly if schedules are
updated regularly and sometimes at short notice. Such an initial unfulfilled need
could trigger the search for a better understanding of the ‘problem’, or a search for
initial ideas for the invention. In this case, it could have been discovered that smart
phone usage is spreading increasingly to the ‘young consumer’ segment, giving
rise to the idea that a successful invention might involve using electronic devices
and the internet to synchronize the calendars.
The first prototypes of such devices might then be validated through lead user
workshops, leading to the discovery that features such as push messages to par-
ents, when the children’s appointments of the next day are being altered, would be
extremely useful. That would lead to a better understanding of the ‘problem’ and
trigger a new round in the three-step cycle, leading to an enhanced innovation
concept.
This example illustrates how foresight methods can be selected and deployed
throughout an iterative process of generating innovation. The ideal selection and
deployment will depend on the firm and the context in which it operates.
The usefulness of foresight methods, however, goes beyond triggering innova-
tion initiatives (Rohrbeck and Gemünden 2011). Successful innovations often
emerge from the interplay of an innovator (working on and promoting his/her
innovation) and an opponent, who through challenging the innovation ensures
continuous enhancement and finally a superior product quality. Consequently, two
roles of foresight can be distinguished in the front end of innovation, i.e. the role
of the innovator and that of the opponent. Both roles are illustrated in Fig. 7. It
also shows which elements the two roles fuel the front end of innovation. For the
illustration, the conceptualization of the front end by Koen et al. (2001) has been
used.
13
&
Fig. 7 The two roles of corporate foresight in the front end of innovation
To institutionalize an opponent role, a firm can create specific foresight units that
engage in workshops with the project teams of ongoing innovation initiatives. This
should be done regularly, for example every three months (Rohrbeck and
Gemünden 2011). It is, however, important to ensure that such workshops have a
constructive nature, for instance by emphasizing that the foresight team is not only
expected to identify weaknesses of the current concept, but is also required to
bring insights (e.g. on alternative technologies) to the table.
In addition, it is important to ensure that the workshops are taken seriously and
that they can have real consequences. One way of doing this is to implement a
process by which the innovation initiatives can be terminated if they fail to
demonstrate that they will produce a state-of-the-art product.
A successful way of institutionalizing such a termination process is illustrated
by the 3M so-called ‘death parties’. They are real parties to which all R&D em-
ployees are invited to celebrate the termination of an R&D project. Organizing
such events to create a positive and joyful atmosphere ensures that every employ-
ee understands that terminating a project is not a failure, but rather a liberation of a
team on its way to developing an unsuccessful innovation. At the same time the
death parties also mark a new beginning as the project staff and the budget can
now be deployed to explore and develop a more promising innovation opportuni-
ty.
5. Value Creation of Corporate Foresight in the Front End
The value contributed by corporate foresight could be classified into 13 potential,
positive impacts (Rohrbeck and Schwarz 2013). Of these, four relate to strategic
management outcomes and will thus not be discussed here. The remaining impacts
can be clustered into three categories: (1) those related to perception, (2) those
related to interpretation and triggering actions, and (3) those that contribute to
Opponent role
! Challenge basic assumptions
(customer needs, technolo-
gical development, political
and regulatory issues)
! Challenge state-of-the-art of
current R&D projects
! Scan for disruptions that might
endanger current and future
innovations
Initiator role
! Identify new needs and latent
customer needs
! Identify emerging
technologies
! Identify competitor‘s concepts
early
! Monitor change and change
drivers
! Identify tipping points
Engine
Idea
Genesis
Idea
Selection
Opportunity
Analysis
Opportunity
Identification
Concept &
Technology
Development
14
overall value. For this chapter, some value contributions have been rephrased to fit
the perspective of the front end of innovation. The result can be seen in Table 1.
The perception category is the most obvious. By applying foresight methods,
firms are able to channel more future insights into their front end of innovation
and thus increase the likelihood of discovering interesting opportunities. In addi-
tion, a continuous scanning activity contributes to the discovery of potentially
disruptive technologies that might endanger ongoing innovation initiatives (Reger,
2001).
To maximize the value created in the perception phase, it should be continuous-
ly monitored and fine-tuned. This is best achieved by monitoring the innovation
candidates. This facilitates assessing how many of the opportunities discovered
fall into the four categories:
• Correct negatives
• Correct positives
• False negatives
• False positives
If, for example, the firm discovers that it misses important opportunities (high
number of false negatives) it could encourage its foresighters to discard opportuni-
ties only after a more thorough search has been completed, or it could organize
additional interdisciplinary review panels that screen the future insights before
they are judged to be irrelevant.
Group
Potential value contribution of strategic foresight
Perception
Gaining insights into changes in the environment
Contributing to a reduction of uncertainty (e.g., through identification of
disruptions)
Interpretation and
triggering action
Identifying new key and disruptive technologies
Enhancing the understanding of customer needs
Identifying potential customers
Enhancing the understanding of the market
Identifying opportunities and threats regarding the product and technol-
ogy portfolio
Reducing the level of uncertainty in R&D projects
Overall
Facilitating organizational learning
Shaping the future (e.g., by influencing other parties, such as politics
and other companies)
Table 1 Value creation from corporate foresight in the front end of innovation
In the second category, success could be monitored by simply counting the num-
ber of new key technologies or technologies that yield a disruptive potential for
the firm. An analysis of this number over time shows whether the ‘fitness level’ of
15
a scanning activity increases or decreases. The same is true for the market-related
scouting successes, such as new customer needs, new customer (groups), or new
insights into changes in the market or industry. In addition, the opportunities and
threats identified that have an impact on the level of existing product and technol-
ogy portfolios could be counted. These elements of value creation can also be
classified as throughput measures that monitor how well the foresight system
operates.
The second category also contains one output measure that is primarily related
to the opponent role. This measure indicates if the foresight activities related to
challenging ongoing R&D projects are successful in reducing the overall risk level
of the R&D project portfolio.
The third category consists of two firm-level value drivers that have only an in-
direct link to the front end of innovation. They are, however, of high importance
for the firm and can thus provide good arguments for the funding of the foresight
activities as a whole.
The facilitation of organizational learning is important to mention, but unfortu-
nately difficult to measure. It relates to the ability of a firm to break away from old
mindsets and routines. This ability to embrace new business opportunities, create
new organizational structures and processes is particularly important for respond-
ing to highly disruptive changes in the environment. Foresight methods can be
expected to contribute to this ability in various ways, for example by opening the
debate about multiple, possible futures, based on a scenario analysis.
A second value that could be created in this third category is an enhanced abil-
ity to influence others in order to shape the future. This is particularly important in
cases when a firm aims to develop a systemic innovation, where multiple firms
and sometimes also governmental and societal actors need to work together to
create a new market.
An example of such an innovation is electric mobility. In this field, car compa-
nies are developing technologies jointly with their suppliers, while governmental
bodies and interest groups develop the regulatory framework and a platform
framework through which the battery charging infrastructure is built and operated.
Corporate foresight can help in such systemic innovation developments with joint
scenario development, joint roadmapping, and collaborative business modeling
(Rohrbeck et al. 2013).
6. Future Outlook
Overall, the usage of foresight techniques in the context of business firms is rising.
This can be ascribed to two main causes. First, many industries have recently
experienced disruptive change, which has led to the bankruptcy of dominant firms
that had previously been perceived as too big to fail. In the respective industries,
this created a strong motivation to build foresight capabilities to avoid such a fate.
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Second, firms whose competitors have built foresight capabilities are interested in
following suit as they struggle to respond to change as fast as their more capable
peers.
These two effects have led to the increasing popularity of corporate foresight
practices and the creation of practitioner cycles and will eventually result in corpo-
rate foresight being established as an academic field and a recognized practice
domain. The coming years will show how many firms implement foresight tech-
niques, build foresight units, and design tailored foresight processes.
Most likely corporate foresight will take a similar development route, as did in-
novation management 30 years ago. At the beginning, it was implemented as part
of other firm functions, and later it grew independent into a function in its own
right.
In addition to the overall growth of corporate foresight, the practice of corpo-
rate foresight is expected to develop in three directions in the future:
• Corporate foresight and open innovation
As an increasing number of firms is exploring ways to innovate with external
partners, interest in teaming up for corporate foresight is also growing.
Deutsche Telekom, for example, is exchanging future-related information
with both value adding partners and competitors to enhance the company’s
future outlook (Rohrbeck et al. 2009). Others are using foresight techniques to
support open innovation by identifying promising external technologies to be
implemented in their own internal innovations (Veugelers et al. 2010).
• Corporate foresight and Web 2.0/Web 3.0
The internet and particularly the emergence of the Social Web (2.0) have ena-
bled instant expert identification and supported scouting approaches by
providing powerful tools to discuss insights and jointly create knowledge
(Gordon et al. 2008). It can be expected that the emergence of the Semantic
Web (3.0) will yet again enhance foresight capabilities, for example by facili-
tating more intelligent patent analysis (Bergmann et al., 2008) or enhancing
the ability to identify systemic patterns that open up innovation opportunities.
• Corporate foresight and systematic exploration of new business fields
Large firms in particular are under increasing pressure to move to new busi-
ness fields, as the time in which firms can enjoy innovation-leader premium
profits is decreasing. The iPhone provides a clear example of where Apple is
struggling to counteract the fast approaching threat of other vendors, such as
Samsung, and the time in which Apple was able to enjoy high margins ap-
pears to be running out. Therefore, firms are increasingly looking towards
corporate foresight to propose approaches that integrate multiple methods and
allows the systematic exploration of new business fields (Heger and 2012).
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7. Checklist
To sum up, the following checklist may serve as a guideline for successfully inte-
grating foresight into the fuzzy front end of product innovation:
• Build additional sensors to identify weak signals of change. Use a mixture
of bibliometric and people-centric search mechanisms.
• Experiment with methods and processes until an approach has been found
that works for the given task, the context and the company.
• Provide foresight capabilities that can be integrated on demand in the itera-
tive process of the front end of innovation.
• Ensure a high level of interdisciplinarity to ensure that the full extent of the
impact of a change is perceived and that enough complementary perspectives
can contribute to defining the innovation idea.
• Use methods that enable a systemic observation of change (such as scenario
technique), rather than relying on methods that build on linear cause-effect re-
lationships.
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