ENABLERS AND BARRIERS OF SMART DATA-BASED … · · 2017-12-21MANAGEMENT SERVICES IN INDUSTRIAL BUSINESS NETWORKS Toni Ahonen1, ... Enablers And Barriers Of Smart Data-Based Asset

ENABLERS AND BARRIERS OF SMART DATA-BASED ASSET

MANAGEMENT SERVICES IN INDUSTRIAL BUSINESS NETWORKS

Toni Ahonen1, Jyri Hanski1, Matti Hyvärinen2, Helena Kortelainen1, Teuvo

Uusitalo1, Henri Vainio2, Susanna Kunttu1, Kari Koskinen2

Abstract Recent academic research has paid particular attention to how digitaliza-

tion disrupts current business models and business environments. Furthermore,

servitization has gained significant attention. However, so far only a fraction of

the wide range of opportunities related to digitalization has been realized. In this

paper we aim to better understand the drivers, limitations and stakeholder expecta-

tions in different industrial business environments.

In the proposed paper, we address digitalization in the area of engineering asset

management from the following perspectives: 1) enablers and barriers of digital-

ized asset management service business, 2) availability and use of data for deci-

sion-making support, and 3) changes for business models. We also further con-

template which decision-making situations need to be supported by digital asset

services. The paper is based on data received from a company workshop and a lit-

erature review.

1 Introduction

Digitalization can be defined as the use of digital technologies to change a

business model and provide new revenue and value-producing opportunities; it is

the process of moving to a digital business (Gartner 2016). Digitalisation has al-

ready transformed many sectors of industry and society, for instance media, bank-

ing and communications. However, in the future, it is expected to transform the

industries and society even further (OECD 2015, World Economic Forum 2015).

Opportunities for asset management services offered by digitalization are dis-

cussed extensively in recent research publications (e.g. World Economic Forum

2015, Tihinen et al. 2016, Popescu 2015, Yoo et al. 2012). In practice, for in-

1 Toni Ahonen (), Jyri Hanski, Teuvo Uusitalo, Helena Kortelainen, Susanna Kunttu

VTT Technical Research Centre of Finland, Ltd., Tampere, Finland

e-mail: [email protected]

2 Matti Hyvärinen, Henri Vainio(), Kari Koskinen

Tampere University of Technology, Tampere, Finland

e-mail: [email protected]

2 T. Ahonen – Enablers And Barriers Of Smart Data-Based Asset Management Services In Industrial Business Networks

stance, digitalization enables better management of life cycle information and bet-

ter availability of sensor, equipment and process information for different stake-

holders. Therefore, new value potential for asset management services could be in

real-time optimisation and predictive maintenance.

An increasing number of product and service providers aim to gain a competi-

tive advantage through new smart data-based asset management services. It is

acknowledged that the business potential is significant (Manyika et al. 2013) and a

change is called for, however, Porter and Heppelmann (2014) state that it is a dan-

gerous oversimplification to suggest that the Internet of Things “changes every-

thing”. They note that the rules of competition and competitive advantage still ap-

ply. Companies also need to have a clear strategy for how to use data and analytics

to compete, and how to deploy the right technology architecture and capabilities

such as analytics tools (Barton & Court 2012). Thus, companies need to find their

own perspective for the Industrial Internet and develop solutions that fit the con-

crete needs of their customers. Further information is required to understand the

enablers and barriers related to different types of data-based asset management

services.

In this paper, we address digitalization in the area of engineering asset man-

agement from the perspectives of enablers and barriers of digitalized asset man-

agement services. The paper is based on data received from a company workshop

and a literature review.

From the managerial perspective, this paper provides decision support for the

development of asset management services by identifying different types of ser-

vices and analysing the enablers and barriers for the different types. From the sci-

entific perspective, this paper this paper analyses the effects of digitalization on

the asset management, a classification of smart asset management services and po-

tential enablers and barriers for the development and delivery of asset manage-

ment services.

2 Smart data-based asset management services

Manufacturing companies are increasingly investing in developing industrial

services and seeking new opportunities in a more customer value-oriented way.

However, companies are in different positions and levels of preparedness with re-

spect to the digitalisation and adoption of new technologies in asset management

service business. Early adopters have started creating solutions for predictive

maintenance and remote asset management, and new means for improving worker

productivity, safety and working conditions. Maintenance services are a typical

example of digitalized asset management services. One example of maintenance

service is ‘SKF Rotation For Life’ provided by SKF, a global supplier of bearings,

spindles and seals (SKF homepage). The service combines bearing technology,

T.Ahonen - Enablers And Barriers Of Smart Data-Based Asset Management Services In Indus-

trial Business Networks 3

failure detectability and reliability services into an integrated package. Digital so-

lutions can provide tools and methods to collect and analyze data to detect emerg-

ing failures in early phase and to give case-relevant repair instructions to field

though e.g. mobile solutions. This allows for doing the right actions, on the right

time, and correctly.

Another example of digitalized services is CIP (clean in place) wash optimiza-

tion service, offered by a company called TTS-Ciptec (TTS-Ciptec homepages).

The optimization of the CIP washes are conducted through remote monitoring of

washing liquids and predictive analytics services related to these monitoring re-

sults. In this case, digitalized solutions reduce CIP times and so the CIP wash can

be conducted much faster. CIP optimization includes also viewpoints from the re-

sources savings, in terms of water, energy and chemical consumptions.

Figure 1 presents a stepwise approach for holistic asset management services,

with four service types; namely product-related services, production support ser-

vices, asset optimization and risk sharing, and asset and risk management.

Figure 1. Classification of smart asset management services.

Product-related services exploit the sensor and ICT technologies to digitalize

the current offering, including e.g. preventive maintenance services and spare

parts management. Production support services cover the services with digital

tools for preventive maintenance, failure diagnostics and information-based over-

all equipment efficiency optimization integrated with expert consultation. The CIP

wash optimization service described above is an example of a service of this cate-

gory. Asset optimisation and risk sharing includes utilisation of wide installed

base data to support operation and maintenance, using methods such as bench-

marking and predictive maintenance. The SKF Rotation for Life service is an ex-

ample of a service of this category. Asset and risk management integrates holistic

Product-relatedservices

ProductionSupportServices

AssetOptimisation &

Risk Sharing

Asset & RiskManagement

Service provider’s role and requirements for capabilities in managing the assets increaseCo

mp

lexi

tyo

f th

ese

rvic

esan

d

bu

sin

ess

mo

del

sin

crea

se


digital services and intelligent products, resulting in highly automated decision-

making.

3 Methodology

The study follows hermeneutic worldview and qualitative research methodolo-

gy. The goal of the research is to understand the barriers and enablers for digital

transformation in the asset management services and construct new hypotheses

based on the findings. The data for was collected by a literature review regarding

enablers and barriers for data-based asset management services and in a workshop

using world café method.

Two workshop groups consisting of altogether 6 researchers from VTT and

TUT and 8 company representatives from five companies discussed digitalization

opportunities, barriers and enablers. Selective sampling was utilised in the study.

All the participants are involved in a national research project aiming at support-

ing the development of new digital asset management services. All the participants

represent small or medium sized companies that are currently actively involved in

developing or offering digital asset management services.

Researchers, who also wrote down all the ideas, facilitated the discussions. The

generated ideas were discussed and enriched by the other group in an iterative

process. At the end of the world café, the facilitators presented the opportunities,

enablers and barriers and the workshop participants commented and further en-

riched them.

In the first phase of analysis, the data from the literature review and the work-

shop were analysed separately. In the second phase, the data from the different

sources were compared. Identified enablers and barriers were divided using the

framework that includes three crucial stages for service development; business

models, decision-making support and data analytics. In the final stage of analysis,

the identified key barriers and enablers were analysed through the lenses of the as-

set management services types presented in Figure 1.

4 Results

We apply the following three areas as the parts of the framework for our analy-

sis in the next section:

1) Business models

2) Decision-making support

3) Data analytics



These areas together compose the path from data to development of profitable

business. Evans & Annunziata (2012) have stated that Industrial internet is a com-

bination of the following key elements: intelligent machines, advanced analytics

and people at work. Here, we address intelligent machines and advanced analytics

together and address the people at work aspect by particularly considering the de-

cision-making support for different processes, actions and decision-making situa-

tions at different levels of work. In addition, we consider the business model level

as an integrating entity in which significant changes are expected.

4.1 Enablers and barriers of digital asset management services

The recent literature discusses the barriers for digital transformation (Kane et

al. 2015, Sommarberg 2016, Tihinen et al. 2016). In addition, barriers for digital

asset management services were identified in a workshop setting described in

chapter 3. Table 1 introduces some of the barriers for digital asset management.

Table 1. Barriers for digital asset management services.

Per

spec

-

tive

Barriers identified from the litera-

ture review

Barriers identified in the company

workshop

Bu

sin

ess

mod

el

- No strong business case

- Investment in current infrastruc-

ture

- Industry specific financial KPI’s

- Industry beliefs

- Legacy competences, processes

and systems and equipment

- Return on continuous improve-

ment

- Data security risks and ownership

- Incumbents active resistance

- Lack of trust in virtual environ-

ments

- High barrier industry

- Rivalry between existing competi-

tors

- Cost driven customers

- High entry barrier industry

- Long established stakeholder or-

ganizations

- Oligopolistic market

- Heightened complexity of the in-

- Selling value instead of a product

challenging

- Customers’ focus too much on the

acquisition price instead of the

value throughout the asset lifecy-

cle

- New collaboration and partnership

models and value creation of ser-

vices are not easily understanda-

ble for customers, in particular

procurement organisations

- Earning logic: if service is given

free of charge to customers, cus-

tomers might not see the value

- Identifying the specific digitaliza-

tion opportunities for a specific

company

- Technology-driven development

efforts of companies

- Companies afraid of cannibalizing

their own current business

- Internal opposition for change


novation process - Inadequate communication of the

examples, benefits, opportunities

and also discussion of the risks re-

lated to digitalization

- Stepwise change, no time for in-

cremental learning by doing

- Fear of losing job

- Management lacks understanding

of the nature of change in skills

and competencies

- Customers do not recognise the

potential for digital services when

they see the supplying company

only as a machine supplier

- Risk of losing own analytics com-

petences, when using external ma-

chine learning systems

- Marketing digital services is un-

derdeveloped, need for clear mes-

sages and stories

- Maintenance and version man-

agement problems due to tailoring

products and services for custom-

er needs

- Making right technology and plat-

form choices

Dec

isio

n-m

ak

ing

- Insufficient technical skills

- Lack of interoperability of existing

systems

- Uncertain risks brought by invest-

ment in new technologies

- Immature or untested technologies

- Too many competing priorities

- Lack of an overall strategy

- Security concerns and data privacy

- Lack of organizational agility

- Lack of entrepreneurial spirit, will-

ingness to take risks

- Lack of collaborative, sharing cul-

ture

- Lack of employee incentives

- Cultural barriers

- Legacy leadership and manage-

ment (leadership from the past)

- Risk adverse business culture

- New, diverse know-how and skills

are needed for the new digital ser-

vices, but the current employees

may not necessarily have it

- Lack of change leaders in compa-

nies



- Lack of absorptive capacity

- Next quarter pressure

- Well established dominant logic

- Complex organisations

- Incrementalism

- Hierarchical organisations

- Impatience

- Inexperienced management

- Missing financial track record

Data

An

aly

tics

- Lack of relevant history data

- Data security

- Data ownership confusion and

conflict of interest

- Management beliefs

- Lack of competences

- Privacy concerns

- Lack of systemic understanding

- Cost and availability of data

transmission

- Non sensored installed fleet

- Lack of data governance rules

- Lack of relevant data and infor-

mation for specific decision-

making situations

- Access to customer’s system is

not always easy

- Lack of integration of data col-

lected systems from different

manufacturers

- Insufficient knowhow to analyse

the available data efficiently

- Environmental features and insuf-

ficient sensor technology cause

challenges for data acquisition

Enablers for digital transformation based on World Economic Forum (2015)

and Sommarberg (2016) and the company workshop are depicted in Table 2.

Table 2. Enablers for digital asset management services.

Per

spec

-

tive

Enablers identified from the litera-

ture review

Enablers identified in the company

workshop

Bu

sin

ess

mo

del

- Digital entrants to traditional in-

dustries

- Substitutive business models

- Value driven customers

- Value driven suppliers

- Influential early adopters

- Suppliers passing middle-men

- Platform supply

- Business model diffusion

- Consumer business behaviour into

B2B

- Partnerships in development of

services, products and compe-

tences

- The pilots and development pro-

jects should be at sites where the

fluent communication is guaran-

teed and the needs can therefore

be understood well

- A combination of short and long

term objectives for the develop-

ment guarantees both the ade-

quate ambition level of the whole


4.2 Analysis

The division of the identified enablers and barriers into the three categories was

challenging as some of the enablers and barriers could be placed into two or even

three categories.

The main barriers identified by the workshop participants are related to value of

services, business perspective and customer needs, readiness for transformation,

supporting technologies, and competencies in service delivery, risk of losing com-

- Software intensity

- Access to crowd knowledge

- Influential Early Adopters

- New level of customer insight

- Powerful development tools

- Quantum leap in productivity

- Real time opportunity

- Asset light

- Slow growth of existing business

- Start-up emergence

- Crowdfunding

- Common approach to addressing

security concerns

- The convergence of standards to

support better interoperability

- Collaboration to create technology

testbeds, such as for testing tech-

nology interoperability

- Bandwagon and Hype

- Degree of external engagement

(crowds and users)

efforts but also practical results to

justify the business cases

- Marketing new digital services to

existing customers

- The pioneers act as encouraging

examples since when a larger

company takes the first steps to-

wards digital services first, it

makes easier for the smaller play-

ers to follow

- Emphasising the business per-

spective and customer need in the

development of digital service

Dec

isio

n-

mak

ing

- Management consulting focus

- Management rotation

- Emerging digital strategy frame-

works

- Experiment culture

- Transparency and commitment

- Internal information sharing and

marketing, creating a clear mes-

sage and narrative

Data

An

aly

tics

- General digital technologies

- Reducing human errors

- Scalability of knowledge

- Analysis speed

- Advances in cognitive computing

- Users opening their data

- Platform tools

- Agile testing to complement long-

term development

- Education for the whole organisa-

tion

- Cloud based solutions

- Companies should offer frame-

works/platforms where digital

services can be easily used



petences, access to information required for services, marketing and communica-

tion and R&D. The enablers identified by the participants are related to partner-

ships, R&D, marketing and communication, supporting technologies and compe-

tencies, references, business perspective and customer need.

Main barriers related to business models identified in both literature and by the

workshop participants are cost-driven customers, selling value instead of product,

and earning logic (lack of strong business case). Common barriers in decision-

making include lack of new diverse know-how, skills and leadership. Regarding

the data analytics, common barriers include lack of relevant data, lack of capabili-

ties to produce information for decision-making (e.g. lack of competencies in ana-

lytics), and data access and ownership issues.

Business model related enablers identified in the literature and by the workshop

participants are partnerships (collaboration and external engagement), testbeds and

pilots, customer need emphasis (new level of customer insight) and influential ear-

ly adopters (pioneers). Common decision-making related enablers are not identi-

fied. Common enablers related to analytics include utilisation of new digital tech-

nologies, such as cloud and platform solutions.

5 Discussion

Figure 1 presented a classification of smart asset management services, which

suggests that, when moving towards asset and productivity management, service

providers are expected more capabilities to manage complexity and take a signifi-

cant role in creating valuable new knowledge to manage customer’s asset in a ho-

listic manner. The emphasis and role of different enabling and hindering factors

changes according to the service types.

There are enablers and barriers for digitalised asset management that have been

emphasised in both literature and in our research. In Table 3, we present these

most essential enablers and barriers identified for digitalised asset management.

We address them with respect to four asset management service types presented in

Section 2 and present how the enablers and barriers are differently emphasised in

different services.


Table 3. The most significant enablers and barriers for asset management service types.

It is identified that the transformation from product-related services towards more

holistic service types calls for new sort of capabilities for more collaborative rela-

tionships and continuous communication as well as increased capability to pro-

duce data for specific situations and utilise the available data efficiently. While the

technological capabilities can be seen relevant for all types of asset management

services, issues related to customer and value understanding, capabilities to inte-

grate to customers’ decision-making processes and collaborative business models

are more emphasised in the more holistic service types.

6 Conclusions

Current paper proposes a framework for the classification of asset management

services. Furthermore, the paper discusses the enablers and barriers of smart asset

management services with a service classification as a framework, which seems

rather little addressed in the scientific debate.

Product-

related

services

Production

support

services

Asset

optimisation

and risk

sharing

Asset and

productivity

management

Enablers

Barriers

Customer value-driven development activities

Utilisation of new technologies as an integrated and supporting part in a novel business model

Lack of tools to communicate and understand the value

Lack of readiness for transformation

Security and data ownership

Lack of capabilities to produce information for decision-making

Partnerships and collaborativedevelopment

Agile testing and effectiveutilization of existing technologies

Agile testing alongside with long-termdevelopment

Cost-driven customers

Pioneers as examples

Lack of relevant data for specificsituations



Our results suggest that to answer to the challenges and opportunities of digital-

ization, companies need new capabilities at business model, technology, manage-

ment and process and method levels. Significant part of the emerging industrial

services rely on multi-source data. Large amounts of data are already collected,

however, in many occasions only a fraction of the data is effectively exploited.

Therefore, the utilisation of the available data and data analytics is an area where

more know-how and competences would be needed. However, even more im-

portantly, understanding of the customer and phenomena related to production and

business environment will be a prerequisite for developing services that meet the

customer needs.

As the managerial implication, the service classification framework and the en-

ablers and barriers identified provides supporting knowledge for practitioners to

plan their own route to digitalized services. We also conclude that the enablers and

barriers specifically related to how data are converted into customer value need to

be studied further.

Acknowledgements

The authors gratefully acknowledge the Finnish Funding Agency for Technol-

ogy and Innovation for funding the research project and the companies involved in

the research. We also wish to thank the colleagues from VTT and Tampere Uni-

versity of Technology for their valuable comments.

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ENABLERS AND BARRIERS OF SMART DATA-BASED … · · 2017-12-21MANAGEMENT SERVICES IN INDUSTRIAL BUSINESS NETWORKS Toni Ahonen1, ... Enablers And Barriers Of Smart Data-Based Asset