This document provides an overview of different areas of business intelligence (BI) that are being examined as part of the FIMECC S4Fleet project. It summarizes nine different BI topics: 1) BI Framework, 2) BI Architecture, 3) Competitive Intelligence, 4) Customer Intelligence/Sales/CRM, 5) Financial Intelligence, 6) Fleet Management, 7) HR Intelligence, 8) R&D Measurement, and 9) Supply Chain Measurement. For each topic, it outlines the research areas of interest and expected outcomes. The overall goal of the project is to develop a BI framework to facilitate real-time strategic decision making for solution providers.

1. 2015
FIMECC S4Fleet
Business Intelligence for
Real-Time Decision Making

2. 2

3. Table of contents
Business Intelligence Framework ����������������������������������������������������������������������� 5-6
Business Intelligence Architecture ������������������������������������������������������������������������7
Competitive Intelligence �������������������������������������������������������������������������������������� 8-9
Customer Intelligence, Sales and CRM �����������������������������������������������������10-11
Financial Intelligence ........................................................................................12-13
Fleet Management ............................................................................................14-15
HR Intelligence ..................................................................................................16-17
R&D Measurement ...........................................................................................18-19
Supply Chain Measurement ���������������������������������������������������������������������������20-21
Strategic Decision Making ������������������������������������������������������������������������������22-23
3

4. “FIMECC S4Fleet BI
sub-project, is developing
a BI framework to facilitate
real-time strategic
decision making
of solution providers”
4

5. Business Intelligence Framework
5

6. Areas of interest
Business Intelligence Framework for real-time
strategic management
New theory for real-time strategic management
Measurement framework and measures for data
collection
Utilization of BI knowledge in solution provider
organizations
Outcomes
BI framework for improved real-time decision making
BI practices and capabilities for solution providers
Identification of key BI measures
Marko Kohtamäki
Professor
Department of Management
marko.kohtamaki@uva.fi
+358(0)44 971 0432
Business Intelligence
Framework
Contacts
Where the companies are collecting and storing more infor-
mation than ever, the question of development, utilization and
implementation of the knowledge has become particularly
relevant. This sub-project focuses on developing a framework
utilized for collecting, developing utilizing and implementing
BI knowledge in the top management team and business
unit levels.
Where the existing research on strategic decision making
provides a vast amount of knowledge on the processes and
contents of company strategy, much of this knowledge has
been developed before or during the early days of digitaliza-
tion and the internet. The traditional strategy theory does not
provide necessary frameworks, practices nor tools for real-
time strategic decision making. New theory development is
needed for fast strategy utilizing the newest solutions for BI,
and big data analytics.
BI User Interface theme concentrates on developing a fra-
mework for Business Intelligence at the top management
team level and a new approach to real-time strategic decision
making.
“The traditional strategy theory does
not provide necessary frameworks,
practices nor tools for real-time
strategic decision making.”
6

7. Business Intelligence Architecture
7

8. Competitive
Intelligence
Areas of interest
The role of CI tools and practices during the
service transition.
How does CI support strategy formulation/
implementation, transform information into
knowledge, and ensure knowledge sharing and
organizational learning?
How does CI impact the speed and accuracy of
problem/opportunity identification, the exploitation
of new opportunities, and the reconfiguration of
organizational routines?
Competitive intelligence (CI) uses processes, tools,
and advanced skills to collect, interpret, and internally
share and exploit the intelligence related to customers,
suppliers, competitors and government. Therefore, CI
is critical for manufacturers moving toward customer
solutions. The CI theme of our project focuses on how
external information is collected, analyzed and transfor-
med to actionable intelligence through BI applications.
Outcomes
How CI data collection and knowledge
utilization enables strategic decision making
at the top management team level (TMT).
Delineating links between firm performance
and CI practices.
Identify advantages and shortcomings of the key
CI practices, and attempt to offer new measures
for potential process design.
Contacts
Rodrigo Rabetino
Assistant Professor
Department of Management
rodrigo.rabetino@uva.fi
+358(0) 29 449 8451
8

9. “CI is critical for
manufacturers moving
toward customer
solutions.”
9

10. Customer Intelligence,
Sales & CRMCustomer Relationship Management (CRM) is a mana-
gement approach that aims to create, develop, and
enhance relationships with strategically targeted cus-
tomers. CRM is utilized in order to maximize customer
value, corporate profitability, and thus, shareholder
value.
From a strategic viewpoint, CRM is not merely an IT
solution. CRM involves a synthesis of strategic vision:
A corporate understanding of the nature of customer,
the utilization of the appropriate CRM applications and
information management as well as high-quality ope-
rations and service.
CRM theme of this project aspires to increase the kno-
wledge on customer data gathering and quality as well
as the factors that enable companies to do reliable
forecasting.
Areas of interest
CRM & Sales measures used by management
level
The role of Customer Relationship Management
practices in leading industrial companies
Customer data gathering & usage in sales
processes and the fit and implementation of
CRM systems
Outcomes
Increased understanding on the CRM
related knowledge collection and utilization in
sales processes
Increased understanding on firms way of
managing sales processes and CRM activities
Identification of key CRM -measures
Ossi Cavén
Project Researcher
Department of Management
ossi.caven@uva.fi
+358(0) 29 449 8698
Customer Intelligence,
Sales & CRM
10

11. “CRM theme of this project aspires to increase
the knowledge on customer data gathering
and quality as well as the factors that enable
companies to do reliable forecasting.”
11

12. Financial
Intelligence
Financial intelligence refers to collecting and
assimilating financial information thorough the
organization to enable rigor and accurate real-time
decision making in executive board level. The role
of finance has traditionally been strategic as CFO is
typically also corporation’s executive vice president.
However, the nature of financial information has
been changing from the historic focus towards future
orientated. Thereafter, understanding the root causes
and predicting the future have been highlighted
recently. On the other hand, manufacturer’s transition
from products to solutions has forced companies to
critically review their established financial KPIs as the
new business and earning logics emphasize different
factors such as customer profitability and retention rate
and life-cycle profits and costs over revenues, scale
advantages and fixed costs.
The financial intelligence theme highlights how
industrial solution providers use BI systems to collect,
assimilate, analyze and exploit financial information
for improved real-time decision making. Moreover,
the theme focuses on how financial information can
enable executive board to sense and seize business
opportunities and reconfigure company’s resources
more effectively.
Contacts
Tuomas Huikkola
Project Researcher
Department of Management
tuomas.huikkola@uva.fi
+358(0) 29 449 8448
“Theme focuses on how
financial information
can enable executive
board to sense and seize
business opportunities
and reconfigure
company’s resources more
effectively.”
12

13. Areas of interest
The role of BI systems to collect and
assimilate financial data
How BI systems can be utilized in predicting
and sketching future scenarios?
How BI systems facilitate solution provider’s
dynamic capabilities i.e. how BI systems
facilitate firm’s ability to sense, seize and
reconfigure its resources?
Outcomes
What type of BI systems solution providers
apply to increase their financial intelligence in
case companies
How financial intelligence is orchestrated
across the organization effectively in case
companies
How BI systems are utilized in implementing
strategy in case companies
What financial KPIs solution providers use to
measure their organizational performance
13

14. Fleet Management
Remotely collected fleet data has been used for
various purposes in manufacturing companies for more
than two decades. During the on-going decade, the
industrial internet megatrend has brought the topic
into mainstream business discussion. Technology has
developed to facilitate, and is no longer a bottleneck
for, the development of innovative industrial services
and solutions.
Areas of interest
How remotely collected fleet/environment data
is utilized in operational/strategic decision making
in industrial companies at the moment?
What kind of manually collected fleet data is
utilized at the moment?
What kinds of development needs exist: How
remotely and manually collected fleet data
can be combined to provide optimal support for
both supplier and customer decision making?
Outcomes
Holistic understanding about how remote data and
manually collected qualitative data can be combined
effectively to be utilized for strategic and operational
decision making in both supplier and customer
companies.
Contacts
Mathias Hasselblatt
Project Researcher
Department of Management
mathias.hasselblatt@uva.fi
+358(0)44 712 3030
Remote data enables radical innovations in
industrial service business, but to fully ex-
ploit its potential, combining it with manually
collected qualitative data is needed. The fleet
management research in this project aims to
create a holistic understanding how combined
fleet information can be utilized more effective
manner in strategic and operational decision
making in both supplier and customer compa-
nies.
14

15. “Remote data enables radical
innovations in industrial
service business, but to
fully exploit its potential,
combining it with manually
collected qualitative data
is needed.”
15

16. HR
Intelligence
Areas of interest
Strategic human resource measures
utilized by the top management team
The role of strategic human resource
management practices in leading industrial
companies
Talent, competence and performance
management practices affecting work force
engagement and various forms of performance
Jesse Heimonen
Project Researcher
Department of Management
jesse.heimonen@uva.fi
+358(0) 29 449 8550
Contacts
The role of human resource (HR) related knowledge uti-
lization in the top management team decision making is
recognized as a growing area of interest in the search
for alternative sources for competitive advantage.
Especially in industries shifting from pure product busi-
ness into more service oriented offerings, firms are forced
to re-evaluate and develop the existing human resource
base. Advanced abilities to measure and interpret human
capital related knowledge contain potential to further
develop and effectively manage valuable internal assets
that are difficult to imitate by competitors.
HR Intelligence as one of our central themes in the ove-
rall BI framework focus on HR practices that drive firm
performance.
Outcomes
Increased understanding on the advanced HR
related knowledge collection and utilization enabled
strategic human resource management
Increased understanding on firm performance driving
talent, competence and performance management
practices
Identification of key HR related measures
16

17. “In industries shifting
from pure product
business into more service
oriented offerings, firms
are forced to re-evaluate
and develop the existing
human resource base.”
17

18. Research and development (R&D) activities have direct
impact on the competitive advantage for technology
firms. It has been widely recognized that strategic
investments in internal R&D functions as well as colla-
boration with external R&D partners has become critical
in developing successful product innovations. On the
other hand, developing innovations can be risky and
costly; research has shown that only one of four R&D
projects is successful. Due to the high failure rate of
product innovation as well as increasing investments
in R&D, measuring R&D performance is essential for
managerial decisions.
Due to its complex and knowledge intensive nature,
performance and cost efficiency of R&D work is far
more difficult to estimate or measure than production
cost. However, to analyze performance and success
of R&D investments, relevant metrics that can be used
as managerial basis to obtain relevant and objective
information for strategic decision making concerning
R&D function are essential.
Contacts
R&D
MeasurementAreas of interest
Research on success factors and
performance metrics in new product
development and success
Developing metrics for R&D in terms
of efficiency, performance, competences,
resources and external dependence
Outcomes
Identification of key performance measures
(KPIs) for R&D function and product
innovation success
Practical and objective tools to support
decisions related to R&D outsourcing,
insourcing or offshoring decisions in the
firms.
Iivari Bäck
Assistant Professor
Department of Management
Iivari.back@uva.fi
+358(0) 44 712 3127
18

19. “To analyze performance and success of R&D
investments, relevant metrics that can be used as
managerial basis to obtain relevant and objective
information for strategic decision making
concerning R&D function are essential.”
19

20. Supply Chain
Measurement
Ilkka Sillanpää
Assistant Professor
Department of Management
ilkka.sillanpaa@uva.fi
+358(0) 40 777 7167
The main purpose of supply chain (SC) measurement
is to get information for top management’s needs,
but also several kinds of SC measures are needed at
every management and operational level. SC should
be measured because of the management interest in
measuring how efficient SC is. Measuring is also nee-
ded when supply chain management (SCM) is going
to be developed. Developing SCM needs a qualified
measurement system for measuring SC performance.
SC performance measurement systems play important
role in manufacturing firms in operations and in busi-
ness strategy implementation. Performance measure-
ment system provides information for the monitoring,
control, evaluation and feedback functions for opera-
tions management. It can be a driver for motivation,
management action, continuous improvement and the
achievement of strategic objectives.
Contacts
20
Areas of interest
There should be several kinds of measures used
in SC performance metrics: balanced approach,
strategic, tactical and operational levels and financial
as well as non-financial measures. SCM could be
measured at various management or operation levels.
The main research questions are:
How can the performance of the supply chain be
measured?
With which indicators can the performance of the
supply chain be measured?
How do the indicators selected represent the
supply chain?
Outcomes
The goal is to deepen knowledge in supply chain
performance measurement in manufacturing
industry and to develop supply chain performance
measurement framework for companies.

21. 21

22. “This research theme
sheds light on strategic
decision making,
strategic cognition
and cognitive models
in in Finnish solution
provider firms.”
22
Strategic
Decision Making
The existing strategy research is mostly concentrating
on processes and contents of strategy work. Far less
attention has been placed to real-time strategic deci-
sion making processes, which is knowledge acquisi-
tion, decision making and knowledge implementation.
Relevant measures, tools and processes are at the
center of absorptive capacity and organizational agility.
This research theme sheds light on strategic decision
making, strategic cognition and cognitive models in
Finnish solution provider firms by utilizing both qualita-
tive and quantitative research designs. Focusing on the
cognitive factors of decision makers, the study intends
to understand the utilization and implementation of
knowledge in decision making in different organizatio-
nal levels. In short, researchers search for practices,
processes and tools that facilitate strategic learning
in organizations.
Areas of interest
How BI knowledge is utilized in strategic decision
making?
How strategic knowledge is implemented in the
organizations?
How managerial cognition frames strategic
decision making?
Outcomes
a) Enhanced understanding of strategic decision
making in different organizational levels
b) Increased understanding of utilization of BI
information in strategic decision making
c) Knowledge on strategic cognition, cognitive maps
and mental models influencing strategic decision
making
Suvi Einola
Project Researcher
Department of Management
suvi.einola@uva.fi
+358(0) 40 563 0507
Contacts

23. 23

24. University of Vaasa
Wolffintie 34
PL 700, 65101 Vaasa
tel. 029 449 8000
uva.fi
facebook.com/vaasanyliopisto
youtube.com/UniversityOfVaasa
twitter.com/univaasa
instagram.com/univaasa
pinterest.com/univaasa
Interested in
collaboration
with us?
Project
Lead
Customer Intelligence,
Sales & CRM
Fleet
Management
Production / Project
Measurement
Supply Chain
Measurement
Competitive
Intelligence
Financial
Intelligence
HR
Intelligence
R&D
Measurement
Strategic Decision
Making Processes
Marko Kohtamäki
Professor
Department of Management
marko.kohtamaki@uva.fi
+358 (0) 44 971 0432
Ossi Cavén
Project Researcher
Department of Management
ossi.caven@uva.fi
+358 (0) 29 449 8698
Mathias Hasselblatt
Project Researcher
Department of Management
mathias.hasselblatt@uva.fi
+358 (0) 44 712 3030
Valeriia Boldosova
Project Researcher
Department of Management
valeriia.boldosova@uva.fi
+358 (0) 40 448 9153
Ilkka Sillanpää
Assistant Professor
Department of Management
ilkka.sillanpaa@uva.fi
+358 (0) 40 777 7167
Rodrigo Rabetino
Assistant Professor
Department of Management
rodrigo.rabetino@uva.fi
+358 (0) 29 449 8451
Tuomas Huikkola
Project Researcher
Department of Management
tuomas.huikkola@uva.fi
+358 (0) 29 449 8448
Jesse Heimonen
Project Researcher
Department of Management
jesse.heimonen@uva.fi
+358 (0) 29 449 8550
Iivari Bäck
Assistant Professor
Department of Management
iivari.back@uva.fi
+358 (0) 44 712 3127
Suvi Einola
Project Researcher
Department of Management
suvi.einola@uva.fi
+358 (0) 40 563 0507