TY - BOOK
T1 - Fleet service creation in business ecosystems - from data to decisions
T2 - Fleet information network and decision-making
AU - Kortelainen, Helena
AU - Hanski, Jyri
AU - Kunttu, Susanna
AU - Kinnunen, Sini-Kaisu
AU - Marttonen-Arola, Salla
PY - 2017
Y1 - 2017
N2 - Digitalization and the industrial internet are
transforming business as technologies enable the
gathering, processing and utilization of increased
amounts of data. At the same time, technology companies
have shifted their focus from product delivery to
life-cycle services which are knowledge-intensive by
nature. Companies are interested in developing global
fleet-based industrial services, which could be seen as
means to increase competitiveness. The targets of these
fleet services, the customers' asset fleets, are
typically complex and expensive assets, and they are
characterized by high profitability, efficiency and
safety demands.
In order to provide value-adding fleet services, these
knowledge-intensive services require data collection from
globally distributed fleets. Technical and economic
life-cycle data are often fragmented in business
ecosystems and the full business potential of data is
rarely utilized. Therefore, innovative technological and
business model solutions are needed to upgrade the
accumulated data into business knowledge that can be used
to support decision-making and service delivery. This
offers new opportunities not only for typical fleet
services such as maintenance but also to support
different types of fleet decision-making situations
regarding different types of fleets.
This publication presents and discusses the findings from
the project "Fleet information network and
decision-making", which is a part of the DIMECC Service
Solutions for Fleet Management (S4Fleet) research
program. The project addresses this issue by developing
ways to upgrade the accumulated fleet data into valuable
business knowledge that can be used in decision-making
both on the level of individual companies and the whole
ecosystem. The research is conducted in collaboration
with companies involved in the research project, and the
findings result from multifaceted cooperation with the
case companies over a three-year period. The project
results in a Data to Business Knowledge (D2BK) model, and
documents pathways for industrial ecosystems to implement
the model. The main ambition is to increase understanding
on fleet data based services and develop ways to create
value from fleet data at ecosystem level.
AB - Digitalization and the industrial internet are
transforming business as technologies enable the
gathering, processing and utilization of increased
amounts of data. At the same time, technology companies
have shifted their focus from product delivery to
life-cycle services which are knowledge-intensive by
nature. Companies are interested in developing global
fleet-based industrial services, which could be seen as
means to increase competitiveness. The targets of these
fleet services, the customers' asset fleets, are
typically complex and expensive assets, and they are
characterized by high profitability, efficiency and
safety demands.
In order to provide value-adding fleet services, these
knowledge-intensive services require data collection from
globally distributed fleets. Technical and economic
life-cycle data are often fragmented in business
ecosystems and the full business potential of data is
rarely utilized. Therefore, innovative technological and
business model solutions are needed to upgrade the
accumulated data into business knowledge that can be used
to support decision-making and service delivery. This
offers new opportunities not only for typical fleet
services such as maintenance but also to support
different types of fleet decision-making situations
regarding different types of fleets.
This publication presents and discusses the findings from
the project "Fleet information network and
decision-making", which is a part of the DIMECC Service
Solutions for Fleet Management (S4Fleet) research
program. The project addresses this issue by developing
ways to upgrade the accumulated fleet data into valuable
business knowledge that can be used in decision-making
both on the level of individual companies and the whole
ecosystem. The research is conducted in collaboration
with companies involved in the research project, and the
findings result from multifaceted cooperation with the
case companies over a three-year period. The project
results in a Data to Business Knowledge (D2BK) model, and
documents pathways for industrial ecosystems to implement
the model. The main ambition is to increase understanding
on fleet data based services and develop ways to create
value from fleet data at ecosystem level.
KW - fleet management
KW - data management
KW - industrial services
KW - decision making
M3 - Report
T3 - VTT Technology
BT - Fleet service creation in business ecosystems - from data to decisions
PB - VTT Technical Research Centre of Finland
ER -