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Driving Retail Innovation: The Demand for Digital Capabilities to Transform the Industry


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While many studies on retail innovation analyze the adoption and diffusion of new technologies, there has been no detailed study on the required skillset for driving innovation. Therefore, we would like to determine which are the digital capabilities required for innovation in the retail sector. Using automated latent semantic analysis on 1,087 job advertisements, we identify the contemporary technological trends and required skills associated with innovative business transformations. Our empirical investigation reveals a high level of demand for managerial competence, intensive domain knowledge, and technical skills related to cloud solutions, mobile applications, and business intelligence systems. Based on our findings, we draw conclusions concerning which technologies will be the drivers for future retail innovation.
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With in-memory database management systems, such as SAP HANA, all data and applications are
kept in the computer’s main memory to avoid expensive mechanical hard-drive access, reducing
latency times and increasing the ability to process large data volumes. This approach promises to
enable new possibilities of action in various industries and application areas. In this article, we report
on a recent explorative study among Chief Information Ofcers and technology experts from leading
European retailers. The study elaborates on the business value creation in general and identies ve
specic scenarios how to leverage in-memory technology in retail business, namely: dynamic pric-
ing, ad-hoc couponing, personalized promotions, real-time on-shelf-availability management, and
intraday forecasting and replenishment. Based on these ndings we discuss ve general principles
and challenges which need to be considered in order to leverage business value through in-memory
databases. These principles also apply to a wider audience in diverse areas of business.
Recent technological developments in
the IT sector enable new possibilities for
business process management, service
innovation, and product innovation. Two
examples are ubiquitous computing and
in-memory technology.
Ubiquitous computing aims at leveraging
real-time technologies to collect real-
world data for capturing the status and
environment of a process. The vision is
to “use real-time information from sen-
sors, radio frequency identication, and
other identifying devices to understand
their business environments at a more
granular level, to create new products
and services, and to respond to changes
in usage patterns as they occur” (Daven-
port et al. 2012, p. 43). As a result, great
amounts of rich data can be captured in
nearly real time. Today, not only struc-
tured but many sources of unstructured
data – such as images, audio, and video
– also contain valuable information for
better decision making (vom Brocke et
al. 2011). Researchers and practitioners
have labeled this approach of harvesting
vast amounts of structured and unstruc-
tured data in an integrated fashion as
“Big Data” (Davenport et al. 2012).
Another recent innovation is in-memory
technology. It offers an increased infor-
Results from a Study on Retail Innovation
Clearly, in-memory technology offers increased information processing capabili-
ties, and expectations are rising. What role can it play in terms of business value,
and what does it mean for the retail industry in particular? Learn about 5 scenarios
that might transform the retail business.
by Jan vom Brocke, Stefan Debortoli, Oliver Müller, and Axel Uhl
mation processing capability. For exam-
ple, in-memory Database Management
Systems (subsequently abbreviated with
“DBMS”) may enable a performance in-
crease by a factor of 100,000 (SAP 2012)
compared to traditional relational DBMS.
With in-memory technology, data and
applications are kept completely in the
computer’s main memory to avoid ex-
pensive mechanical hard drive access
(Plattner and Zeier 2011). This approach
allows for an analysis of Big Data in a
timely manner, enables real-time moni-
toring and control of business processes,
and provides an opportunity for new ap-
plications to be developed. Plattner and
Zeier (2011, p. xxxii) claim that in-memo-
ry technology “marks an inection point
for enterprise applications” and that this
innovation “will lead to fundamentally im-
proved business processes, better deci-
sion making, and new performance stan-
dards for enterprise applications”.
Today, while the new technologies for en-
abling real-time and high-performance
computing are available, the business
value they create has not been analyzed
in detail yet. To ll this research gap, the
University of Liechtenstein established
an In-Memory-Technology Competence
Center. In a joint research study with
the Business Transformation Academy
(BTA), interviews with leading authori-
ties of large European retailers, as well
as executives of SAP Retail and the SAP
Future Retail Center were conducted
over a six-month period in 2012. In this
article, application scenarios of in-mem-
ory value creation in retail industry which
were derived from those interviews will
be exemplied. In addition, principles
and challenges will be indicated that
may well be applied to other businesses.
Emerging Technologies
In order to understand how value can be
created through in-memory-technology,
a greater context of emerging technolo-
gies needs to be considered. In gure 1,
the excerpt from the Gartner Hype
Cycle for Emerging Technologies 2012
(Gartner 2012a) illustrates recent de-
velopments. This study has identied
in-memory computing as one of the
top ten strategic technologies for 2013.
Gartner’s denition of a strategic tech-
nology is “one with the potential for sig-
nicant impact on the enterprise in the
next three years” (Gartner 2012b). It can
be observed that a vast amount of the
emerging technologies can be clustered
into four major technology types: (1) Mo-
bile Devices, (2) Internet of Things, (3)
Big Data, and (4) In-Memory Technol-
ogy. These four technology types, which
enable the capture, storage, and analy-
sis of business events in real time, are
explained briey in table 1.
While each of the above-mentioned
technologies is powerful on its own, a
certain power lies in their combination.
The increased information processing
capacity provided by in-memory tech-
nology seems to serve as an enabler for
more innovative application scenarios.
In the next section, we will briey outline
the key technological characteristics of
in-memory technology.
Fig. 1: Excerpt
from the Hype
Cycle for Emerg-
ing Technologies
2012 (adapted
from Gartner
Peak of
Trough of
Slope of
Plateau of
as of July 2012
Internet of
In-Memory Database Management Systems
In-Memory Analytics
Social Analytics
Text Analytics
NFC Payments
Mobile OTA Payments
Media Tablets
Predictive Analytics
In-Memory Database Management
The technological foundations of in-
memory computing were developed in
the mid-1980s, but it is recent develop-
ments in the area of computer hardware
that have made the use of these tech-
nologies economically feasible for many
companies, primarily by increasing the
main memory sizes and the computing
power at affordable prices. As a result
of these developments, many enterprise
software vendors have begun building
in-memory technology into their applica-
tion systems, for example SAP with their
in-memory technology database appli-
ance SAP HANA.
In-memory DBMS can be described in
terms of ve primary technical charac-
The whole operational and/or ana-
lytical database is stored entirely
in Random Access Memory (RAM),
avoiding expensive performance
loss of disk I/O (Word 2012). Ac-
cessing data in main memory is up
to 100,000 times faster than access-
ing data on a traditional hard disk,
resulting in increased information
processing capabilities (Berg and
Silvia 2012).
Multiple multi-core CPUs can pro-
cess parallel requests in order to
fully use the available computing re-
sources (Word 2012).
Instead of the row-based-only, trans-
action-focused approach that is im-
plemented in traditional relational
DBMS, a hybrid row- and column-
oriented storage approach is applied
in SAP HANA. The column-oriented
storage approach provides signifi-
cant advantages for modern CPUs
as it improves data compression and
allows for massive parallel process-
ing and efficient memory access,
which is required for analytic pur-
poses (Plattner 2009).
Innovations in the hybrid row- and
column-storage approach allow data
(1) Mobile Devices: The diffusion of mobile technologies, such as smart phones and tablet comput-
ers, has accelerated since the turn of the century (Ladd et al. 2010). Mobile computing has the po-
tential to signicantly alter the interactions of individuals, groups, organizations, and societies. Stand-
alone applications or remotely-accessible thin clients enable on-demand and on-the-go access to
various business applications of the enterprise system.
(2) Internet of Things (IoT): The IoT is a concept belonging to ubiquitous computing where each
real-world object is equipped with sensor- and communication-devices, enabled by recent develop-
ments in technology: the physical size of wireless network enabled devices has drastically decreased
and their computation power, memory, network speed, and throughput have increased. This progress
has led to an almost innite number of new applications in many business domains. The vision is to
fully connect the real world with the digital world.
(3) Big Data: Recently, this term has been used to describe the data sets in applications that are so
large (from terabytes to exabytes) and complex (from sensor to social media data) that the current data
management technologies cannot cope with them anymore. They require advanced and unique data
storage, management, analysis, and visualization technologies (Chen et al. 2012). New technologies, like
in-memory DBMS, offer a solution for including large and complex data in the results of various queries.
(4) In-Memory Technology: This technology breaks away from the traditional architectural principles
of relational Database Management Systems (DBMS) and stores data permanently in the main mem-
ory of the underlying system instead of the physical hard drives. Because of growing main memory
capacities and affordable prices, it is possible to shift whole databases into the main memory of
servers. Such systems are called “in-memory DBMS”. The most profound implication of this change
in storage is the performance gain of the database systems since the access time for main memory is
orders of magnitude less than for disk storage.
Table 1: Four
types of emerging
compression ratios between 5 and
10 on the raw data (Word 2012).
In-memory DBMS implement an
insert-only approach (Berg and Sil-
via 2012). This means that the data-
base does not allow applications to
execute low-performing updates or
deletions on physically stored tuples
of data.
As we will see in the following paragraphs,
these ve technical characteristics of in-
memory DBMS are the keys to a signi-
cant performance increase of data man-
agement, enabling improved business
processes, better decision making, and
new performance standards for enter-
prise applications.
Value Creation through In-Memory
In-memory-based application systems
are increasingly gaining relevance for
decision makers in the eld of enterprise
information systems. Still, their specic
benets in terms of economic value cre-
ation are not yet well understood. Chief
Information Of cers are not sure to what
extent their organization’s information
systems may benet from an investment
in in-memory technology. The same
holds true for software vendors.
Considering value creation, one has to
be aware that, put simply, technology
alone does not generate business value.
The same applies to in-memory technol-
ogy. Rather it may enable changes in
business processes, which ultimately
lead to business value (see g. 2).
Based on this understanding, business
application scenarios, in which in-memo-
ry technology can enable process change,
need to be identied. Drawing from our
investigation into emerging technologies,
we also tried to envision combinations
of in-memory technology with Big Data,
Internet of Things, and mobile comput-
ing that might offer particular potential for
process innovation. To learn about such
potentials, we conducted interviews with
four leading experts in the eld of retail
and retail information systems.
The purpose of this joint research project
is to provide exemplary value-generating
application scenarios through process
and service innovation in the retail sec-
tor and then to derive general principles
for leveraging the potential of in-memory
technology in an enterprise context.
Based on a series of in-depth interviews
with retail industry experts, ve promising
in-memory technology based application
scenarios were identied. In this section,
we will present the ve exemplary sce-
narios in depth; discuss the technical
and organizational prerequisites, as well
as the need for in-memory technology. A
summary of the application scenarios will
be presented in table 2.
Scenario 1: Dynamic Pricing
The benets of dynamic pricing models
have long been known in service indus-
tries, such as airlines, hotels, and utilities,
where the product or service capacity is
perishable and xed in short-term (El-
maghraby and Keskinocak 2003). The
increased availability of demand data led
to an increasing adoption of this policy
in other sectors as well (e.g., Sport Tick-
ets, Online Shops, etc.). The idea behind
dynamic pricing in retail is to regularly
adjust the prices of goods depending on
various factors, such as real-time inven-
tory levels, current demand, time of the
day, quality of perishable goods, indi-
vidual purchase history, as well as pur-
chases of other customers.
In-memory technology facilitates this
scenario with its ability to capture and
store large amounts of data in a timely
Fig. 2: Business
value creation
(adapted from
Bakos 1987)
manner, e.g., inventory levels, current
demand, or quality of perishable goods.
The analysis of these data must be per-
formed close to real time in order to pro-
vide information on a product’s current
From a technical perspective, various
prerequisites need to be fullled. On
the one hand, qualitative and quantita-
tive data on the goods must be avail-
able. Evaluating the quality of perish-
able goods requires new technologies,
e.g., sensor networks as an integrated
part of the Internet of Things, measur-
ing environmental parameters, such as
temperature, humidity, and luminance. In
order to monitor current inventory levels
and demand, all transactions need to be
processed and analyzed in real time. On
the other hand, the collected data must
be processed accordingly. The constant-
ly changing prices must be visible to the
customer. In the case of a supermarket,
this means that printed price labels must
be replaced by electronic price tags to
cope with this rst challenge. Regularly
recalculating the prices requires real-
time communication between the point
of sales (POS) and the corresponding
in-memory back-end system (e.g., SAP
HANA) to guarantee that the correct up-
to-date prices are displayed.
However, beyond the technical prereq-
uisites, the organizational prerequisites
also need to be addressed, and those
are more crucial for the success of dy-
namic pricing. On the one hand, dynamic
pricing policies must be in line with the
business strategy meaning that the com-
pany needs to be willing to start price
discrimination. On the other hand, it is
very important that the customer trust
is preserved. If there are no discernible
strategies for price transparency, the risk
of putting customers off is very high.
Scenario 2: Ad-hoc Couponing
Ad-hoc couponing relates to printing per-
sonal coupons on receipts depending on
individual factors such as the customer’s
current shopping basket and the respec-
tive individual purchase history, as well
as the purchases of other customers.
Thus each customer receives highly indi-
vidual up- and cross-selling offers. Since
the coupon is printed on the receipt, the
customer has to return to the retail store
in order to cash the voucher. This allows
for better customer retention.
Implementing ad-hoc couponing re-
quires capturing and analyzing huge
amounts of data in a timely manner, and
in-memory technology provides the nec-
essary capacity and speed.
Real-time information ow between the
POS and the back-end system is based
on a two-way communication. First, in-
formation about the current shopping
basket needs to be transmitted to the
“coupon calculation engine”. Second, the
calculated offer has to be transmitted
back to the POS for printing the receipt.
This requires constant up-to-date sales
data availability in the back-end system
as well as some means of customer iden-
tication in order to access the personal
purchase history. The most common
technique for customer identication is
the distribution of loyalty cards, which
need to be presented for each purchase.
As in the dynamic pricing application sce-
nario, ad-hoc couponing also requires
the company’s willingness to give away
coupons. Depending on the rm’s strate-
gy, such a price-based instrument might
not t the marketing concept, which then
could make this scenario not applicable.
Scenario 3: Personalized Promotions
In this scenario, personalized promo-
tions are pushed onto a customer’s
smartphone when he or she is, e.g., near
a specic shelf or even driving or walking
by a retail store. This enables customer-
specic up- and cross-selling possibili-
ties, and may lead to an increased cus-
tomer retention, which in turn leads to
increased sales volumes.
The continuous collection and storage of
user location data as well as quick analy-
ses of these records and of correlations
with historical sales data require informa-
tion processing capabilities which only
in-memory technology can offer.
Locating, identifying, and reaching one’s
customer is a crucial prerequisite in this
scenario. Today’s smartphones already
offer all required functionalities and the
current smartphone penetration is rap-
idly rising. In addition to the data of one
specic customer, information about the
buying behavior of other customers is
of equal importance for recommending
possibly relevant items. As it is with all
cross- and up-selling activities, the orga-
nization’s strategy must include the will-
ingness to do such promotions.
Scenario 4: Real-Time On-Shelf-
Availability Management
Real-time on-shelf-availability manage-
ment enables an instantaneous detection
of out-of-shelf situations based on time
series analysis. The sales data are col-
lected at every POS and directly trans-
mitted to the corresponding back-end
system in real time. When an out-of-shelf
situation is detected, a replenishment no-
tication trigger gets sent to the employ-
ee in charge (e.g., pop-up in the enter-
prise system, e-mail, text message, etc.).
In contrast to the previously introduced
scenarios, this application scenario does
not require any additional hardware solu-
tion and, thus, enables great benets for
little costs.
In this scenario, having the sales data
available in one central database in a
timely manner is a crucial factor for be-
ing able to quickly react to dropping
sales volume caused by an out-of-shelf
situation. However, there is an even more
time-critical part of this application: the
constantly running analysis of the data
and the recognition of out-of-shelf situa-
tions based on distinctive sales patterns.
In-memory databases fulll both prereq-
uisites for implementing this application
Besides the important real-time com-
munication between the POS and the
corresponding back-end system, the
identication of relevant sales patterns
constitutes the most challenging techni-
cal prerequisite in this scenario. However,
various data mining techniques already
exist which can serve as a basis for
further developments. From an organi-
zational point of view, the only prereq-
uisite for this application scenario is the
in-stock availability at the warehouse of
the goods to be replenished. In some
cases, the scenario which is described
next might support this accomplishment.
Scenario 5: Intraday Forecasting and
Intraday forecasting and replenishment
means predicting out-of-stock situations
and re-supplying stores with the needed
goods multiple times a day. This does
not only include the supply from a cen-
tral warehouse to various branches, but
also the transfer between the stores. As
a result, the amount of inventory and,
therefore, idle capital can be reduced
The constant gathering of sales data from
the POS and the continuous monitoring
of inventory data require a database
optimized for inserting large amounts
of records. Additionally, the continuous
analyses of data to detect out-of-stock
patterns as well as the route-planning for
the next delivery of supplies are predes-
tinated tasks for in-memory databases.
While real-time communication between
the POS and the back-end system, as
well as up-to-date inventory data are
essential technical requirements, the or-
ganizational prerequisites pose a bigger
challenge, including agile supply chains
and sufcient margins on the goods.
Therefore, this scenario is not feasible for
all types of merchandise since the addi-
tional transportation costs must be cov-
ered by higher margins. This scenario is
a balancing act between having enough
inventory and the occurring costs for the
required extended logistics network, i.e.,
an agile supply chain.
Lessons Learned
Based on our introduction chapter and
learning from the ve scenarios dis-
cussed above, we can derive ve prin-
ciples for value-creation through the in-
memory database innovation.
First, in-memory technology alone does
not create business value per se.
Value creation does not come automati-
cally by choosing in-memory DBMS.
Rather it enables changes to business
processes, which then may lead to sub-
stantial business value creation. Oppor-
tunities need to be identied and busi-
ness transformations based on these
opportunities need to be successfully
managed. For example, all application
scenarios presented in our study require
Scenario Description Technical Prerequisites Organizational
Dynamic Pricing Adjusting prices dynami-
cally depending on
inventory levels
current demand
time of the day
quality of perishable
individual purchase
purchases of other
Electronic price tags
Real-time communication
between POS and back-
end system
Up-to-date sales and
inventory data
Customer’s purchase
history (e.g., loyalty card)
Internet of Things
Willingness for price
Customer trust must
be preserved
Printing personal coupons
on receipts depending on
current shopping bas-
individual purchase
purchases of other
Real-time communication
between POS and back-
end system
Up-to-date sales data
Customer’s purchase
history (e.g., loyalty card)
Willingness to give
away coupons
Pushing personalized
promotions onto custom-
er’s smartphone while
walking close by a
specic shelf
driving/walking by a
retail store
Access to customer’s
smartphone (e.g., mobile
Customer’s exact
geographic location
Customer’s purchase
history (e.g., loyalty card)
Purchase data of other
Willingness to do
Real-Time On-
Detecting out-of-shelf
situations instantly via
POS time series analysis
Real-time communication
between POS and back-
end system
Typical sales patterns
Additional products
must be on stock
Forecasting and
Predicting out-of-stock
situations and re-supply-
ing stores multiple times
a day (incl. stock trans-
fers between stores)
Real-time communication
between POS and back-
end system
Up-to-date sales and
inventory data
Agile supply chain
Sufcient margins
Table 2: Applica-
tion scenarios
new processes and algorithms to be
implemented in the enterprise system.
None of them would work right away by
swapping the enterprise system data-
base with an in-memory DBMS.
Second, high value potential lies in the
combination of in-memory technology
with other supporting technologies.
Examples for such complementary tech-
nologies are mobile devices (e.g., smart
phones, tablet computers) and sensing
devices. These technologies are re-
quired as data sources and data sinks to
incorporate contextual data. An example
is the “personalized promotion” scenario
in which location-based services are
used extensively. Nevertheless, data pri-
vacy and security needs to be ensured
at every point of the process in order to
guarantee its wide acceptance among
the users.
Third, a mature IT and process land-
scape is required in order to unfold the
full potential of in-memory technology.
In this context, mature stands for (1) high
data quality, (2) harmonization of diverse
internal data sources, and (3) sharing in-
formation along the supply chain. Oth-
erwise, in-memory technology can only
be used to support existing processes
or applications (e.g., reporting) without
capitalizing its distinguished capabilities.
For example, in the case of the “intraday
forecasting and replenishment” scenario,
information from outside the company’s
system boundaries (e.g., from a supplier)
are required. If these three prerequisites
are not fullled the increase in informa-
tion processing capacity can only work
locally in single transactions. The entire
supply chain must be t enough to le-
verage the performance increase by in-
memory technology; otherwise bottle-
necks might limit the value creation.
Fourth, existing planning and forecasting
models have to be extended to incorpo-
rate the larger set of parameters that can
be taken into account.
As more data can be included for gener-
ating the underlying mathematical model,
in-memory technology promises to pro-
vide better prediction results. However,
the challenge is to come up with an ap-
propriate mathematical model based on
the business requirements. For example,
in the scenarios of “dynamic pricing” and
“ad-hoc couponing”, the core functional-
ities are the price/recommendation algo-
rithms. The success of these scenarios
heavily depends on the quality of the re-
sults of the implemented business logic.
And nally, leveraging the potential of in-
memory technology challenges, beyond
technical and methodological challenges,
also issues related to people, strategy,
and compliance need to be considered.
The described application scenarios
show that – apart from the application
landscape – also diverse stakehold-
ers involved in the business processes
need to support the process changes fa-
cilitated by in-memory technology: Cus-
tomers need to cope with the amount of
new information which is being actively
pushed to them. Retailers need to nd
the right amount of additional informa-
tion (e.g., personalized promotion) for
customers as an information overload
might lead to a negative impact on the
expected results. Since organizations
are socio-technical systems, strategies
of business transformation need to be
selected and applied carefully.
The authors would like to thank the following experts for
sharing their thoughts and experiences as interviewees for
our study:
August Harder, Chief Information Ofcer, Coop
Dr. Reinhard Schütte, former Chief Financial Ofcer and
Chief Information Ofcer of EDEKA AG
Pascal Hagedorn, Future Retail Center, SAP Co-Innova-
tion Lab, SAP
Jörg Wagner, Global Head Trading Industries Consulting
Services, SAP
In summary, this article aimed at giving
an overview of the business value of
in-memory technology in an enterprise
context. We focused on the retail indus-
try and, based on the conducted inter-
views, we identied ve specic applica-
tion scenarios. These ndings allowed
us to derive ve more general principles
to consider for leveraging business value
through in-memory technology. Since
we consider these principles relevant
for a wider audience in diverse areas of
business, we hope that these ve sce-
narios will inspire others to apply the
presented principles to their sector. At
the same time it is clear that the applica-
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Word, J. (2012). SAP HANA Essentials (2nd ed.). Epistemy Press.
tion of in-memory technology in an en-
terprise context is still at an early stage.
Our ndings have a preliminary charac-
ter and call for further investigation.
As one of our interviewees stated, we are
just at the beginning of a new era of en-
terprise systems, which is comparable to
the time when decision support systems
were introduced a few decades ago. Our
journey of studying the business value of
in-memory technology will also continue
intensively. Our further plans encompass
comparing and contrasting the use of in-
memory technology in several industries,
in different parts of the value-chain, and
in various types of applications.
Prof. Dr. Jan vom Brocke is Hilti Chair of Business Process Management (BPM) and
Director of the Institute of Information Systems at the University of Liechtenstein. Jan
has more than 15 years of experience in IT and BPM projects and he has published more
than 200 peer-reviewed papers in renowned outlets, including Management Information
Systems Quarterly (MISQ). Jan is author and editor of 17 books including Springer’s
International Handbook on Business Process Management. His work is widely recog-
nized e.g. by the Financial Times Germany. He is an invited speaker and trusted advisor
on IT and BPM around the globe.
Stefan Debortoli is a PhD student at the Hilti Chair of Business Process Management
(BPM) at the University of Liechtenstein. His doctoral studies focus on the creation of
business value through real-time and high-performance computing, including phenom-
ena and innovations like Big Data and In-Memory Technology. Stefan has over ve
years of working experience in the eld of software engineering and IT project manage-
ment across various industries, including electrical engineering, educational software
and nancial services.
Dr. Oliver Müller is Assistant Professor at the Hilti Chair of Business Process Manage-
ment (BPM) at the University of Liechtenstein. Before joining the team, he worked as a
Researcher at the European Research Center for Information Systems (ERCIS) at the
University of Muenster, Germany. In 2011, Oliver nished his PhD thesis about customer
decision support systems. Prior to his academic career, he gained industry experience
as a professional consultant for supply chain management and as a visiting researcher
at SAP Research. Oliver’s research interests are business process management, IT for
creativity & innovation, and decision support systems.
Prof. Dr. Axel Uhl is head of the Business Transformation Academy at SAP. He has
been a professor at the University of Applied Sciences and Arts Northwestern Swit-
zerland (FHNW) since 2009. Axel Uhl received his doctorate in economics and has a
master in business information systems. He started his career at Allianz and worked
for DaimlerChrysler IT Services, KPMG, and Novartis. His main areas of research are
strategy and IT innovation, leadership, and business transformation management.
In collaboration with
Business Transformation Academy (BTA)
c/o University of Applied Sciences and Arts Northwestern Switzerland (FHNW)
School of Business (HSW), Institute for Information Systems (IWI)
Peter Merian-Strasse 86
CH – 4002 Basel
The Business Transformation Academy (BTA) is a joint research project of the University of Applied Sci-
ences and Arts Northwestern Switzerland (FHNW) and SAP AG. The BTA is a Swiss non-prot associa-
tion. It is registered with the Commercial Register of the Canton of Basel-Stadt under the name “Business
Transformation Academy” and under the number CH- (legal nature: association).
Authorized representatives: Prof. Dr. Axel Uhl, Lars Alexander Gollenia, Prof. Dr. Rolf Dornberger, Nicolas
Steib, Prof. Dr. Jan vom Brocke, Paul Stratil.
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However, the BTA does not accept any responsibility for topicality, correctness, and completeness of the
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