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Digital transformation success
under Industry 4.0: a strategic
guideline for manufacturing SMEs
Morteza Ghobakhloo
School of Economics and Business, Kaunas University of Technology,
Kaunas, Lithuania, and
Mohammad Iranmanesh
School of Business and Law, Edith Cowan University, Joondalup, Australia
Abstract
Purpose –The digital transformation under Industry 4.0 is complex and resource-intensive, making a
strategic digitalization guideline vital to small and medium-sized enterprises’success in the Industry 4.0
transition. The present study aims to provide manufacturing small and medium-sized enterprises (SMEs) with
a guideline for digital transformation success under Industry 4.0.
Design/methodology/approach –The study first performed a content-centric literature review to identify
digital transformation success determinants. The study further implemented interpretive structural modeling
to extract the order at which the success determinants should be present to facilitate the SMEs’digital
transformation success optimally. The interpretive model and interpretive logic knowledge base matrix were
also used for developing the digital transformation guideline.
Findings –Eleven success determinants are vital to SMEs’digital transformation efforts. For example, results
revealed that external support for digitalization is the first step in ensuring digital transformation success
among SMEs, while operations technology readiness is the most inaccessible success determinant.
Research limitations/implications –The study highlights the degree of importance of the 11 success
determinants identified, which magnifies each determinant’s strategic priority based on its driving power and
dependence power. Theorizing the dependent variable of “digital transformation success”and quantitatively
measuring the extent to which each success determinant contributes to explaining “digital transformation
success”offers an exciting opportunity for future research.
Practical implications –Digital transformation success phenomenon within the Industry 4.0 context is
significantly different from the digitalization success concept within the traditional literature. The digital
transformation under Industry 4.0 is immensely resource-intensive and complex. Smaller manufacturers must
have specific capabilities such as change management and digitalization strategic planning capability to reach
a certain degree of information, digital, operations and cyber maturity.
Originality/value –The digital transformation success guide developed in the study describes each success
determinants’functionality in relation to other determinants and explains how they might contribute to the
digital transformation success within the manufacturing sector. This guide enables smaller manufacturers to
better understand the concept of manufacturing digital transformation under Industry 4.0 and devise robust
strategies to steer their digital transformation process effectively.
Keywords Digitization, Critical success factors, Advanced manufacturing technology, Strategic planning,
Small and medium-sized enterprises, Industry 4.0
Paper type Research paper
1. Introduction
The fourth industrial revolution, labeled Industry 4.0, represents a new chapter in the
management and control of the industrial value chain. Industry 4.0 was first born in the
manufacturing industry, marrying physical manufacturing technologies and digital
technologies such as big data, artificial intelligence (AI) and cloud computing (Fatorachian
Digital
transformation
success
This research has been a part of a project that received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 810318. The opinions expressed in the
paper are of the authors only and in no way reflect the European Commission’s opinions.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1741-038X.htm
Received 13 November 2020
Revised 16 February 2021
18 March 2021
Accepted 30 March 2021
Journal of Manufacturing
Technology Management
© Emerald Publishing Limited
1741-038X
DOI 10.1108/JMTM-11-2020-0455
and Kazemi, 2018;Thoben et al., 2017). Industry 4.0 nowadays expands beyond the
manufacturing industry boundaries, encompassing the digital transformation of any
industrial value chain (Culot et al., 2020). Surprisingly, manufacturing companies, small and
medium-sized enterprises (SMEs) in particular, find themselves in a disadvantaged position
compared to other industrial communities when it comes to the Industry 4.0 transition (Kane
et al., 2017). Industrial reports reveal that, despite their early lead in the digitalization and
automation of internal processes, most manufacturers lag in digital transformation under the
Industry 4.0 agenda, particularly compared to the tourism, transportation, retailing,
construction and energy industries (Wellener et al., 2018).
Industry 4.0 transition and the underlying digital transformation is believed to provide
manufacturing SMEs with valuable advantages vital to their future competitiveness and
survival, such as manufacturing productivity, reduced operating costs, improved product
quality and product innovation (Chen, 2019;Moeuf et al., 2018). Industrial reports, however,
indicate that the rate at which manufacturing SMEs have implemented the digital
technologies of Industry 4.0 is worryingly low (Klitou et al., 2017). For example, Won and
Park’s (2020) empirical study of Korean manufacturing SMEs showed that less than 5% of
surveyed SMEs are classified as smart manufacturing adopters. Similarly, Ghobakhloo and
Ng (2019), in their study of two Asian countries, showed that the adoption rate of Industry 4.0
technologies such as artificial intelligence or simulation falls below 20% among
manufacturing SMEs. Frank et al.’s (2019) study showed that the adoption rate of Industry
4.0 technologies among the Brazilian manufacturers surveyed is significantly low. Digital
Economy and Society Index DESI (2020) also argues that many SMEs across Europe struggle
with digitalization and adjusting to the data-driven economy. This report also indicates that
the digitization of larger businesses is somewhat promising, yet the vast majority of SMEs
are not taking advantage of digital technologies in Europe. The low rate of digital
transformation among SMEs has been, very recently, attributed to the complex and
unpredictable nature of Industry 4.0 as well as the unique characteristics of smaller firms
such as risk aversion, resource limitation and the overall lack of technical competencies
(Horv
ath and Szab
o, 2019;Masood and Sonntag, 2020). Under the disruptive force of the
digital industrial revolution, the manufacturing industry is moving toward digitalization,
global integration and customer orientation at full throttle, positioning any manufacturer
lagging behind at significant risks of losing its competitive position (Calabrese et al., 2020).
The COVID-19 pandemic incident show-cased how uncertainty and market turbulence can
severely impact manufacturing SMEs’survival and how digitally enabled organizational
flexibility and agility can be a saving grace for businesses in these dire times (Guo et al., 2020).
Despite the importance of benefits and competitiveness opportunities that Industry 4.0
appears to offer, the complexity, vagueness and knowledge intensity of digital
transformation force SMEs to be over-cautious in their digitalization decision processes
(Horv
ath and Szab
o, 2019;Masood and Sonntag, 2020). Under such circumstances, SMEs
profoundly need a strategic digitalization guideline that enables them to better access or
develop the necessary steps, tools, methods and know-how to facilitate their transformation
journey toward Industry 4.0 (Colli et al., 2019;M€
uller et al., 2018). Unfortunately, the academic
and industrial literature review reveals that SMEs generally lack the necessary knowledge
and understanding of the strategic importance of Industry 4.0 to plan the underlying digital
transformation strategically (Chauhan et al., 2021;Stentoft et al., 2020).
The present study strives to address this issue by developing a guideline that methodically
explains how SMEs can enjoy a higher degree of manufacturing digital transformation success.
To this purpose, the present study attempts to accomplish the following research objectives:
(1) To conduct a content-centric qualitative review of the literature and identify the
determinants of digital transformation success among manufacturing SMEs and
JMTM
(2) To apply interpretive structural modeling to map the interrelationships among
success determinants identified and develop a model guiding manufacturing SMEs
for successful digital transformation.
2. Industry 4.0 and digital transformation success
Defining “digital transformation success”has been a challenging task within the information
system (IS) and operations management literature, mainly due to the novelty and scope of the
Industry 4.0 phenomenon. To address this gap, the study first provides a concise description
of Industry 4.0 and the underlying digital transformation in this section. The study further
continues by presenting a background of the current understanding of digital transformation
success and its determinants.
2.1 Industry 4.0
The Fourth Industrial Revolution was first thought of as the digital revolution occurring
across the manufacturing industry, yet, Industry 4.0 nowadays is narrated as the digital
transformation of industrial value chains in their totality (Culot et al.,2020). The
digital transformation under Industry 4.0 is characterized by implementing certain digital
technologies (Indri et al., 2018) and developing valuable design principles (Frank et al., 2019;
Hermann et al., 2016). Figure 1 provides the Industry 4.0 archetype and explains how
technology trends and design principles of Industry 4.0 interact within the manufacturing
context to materialize the hyperconnected manufacturing chain concept.
The technology trends of Industry 4.0 are low- or high-tier modern digital innovations or
advanced manufacturing technologies that enable the digital industrial revolution (Ciffolilli
and Muscio, 2018;Kumar et al., 2020). Smart sensors, industrial robots, smart wearables and
machine controllers are examples of low-tier technology trends of Industry 4.0, which can be
acquired and implemented as discreet digitalization projects within the industrial
environment (Frank et al., 2019). The higher tier technology trends of Industry 4.0, such as
industrial Internet of Things (IIoT), Cyber-physical Production Systems (CPPS) or digital
twins, are built upon the integration of various low-tier digital and operations technologies
such as networking infrastructure, sensors, machinery and even connected human
components (Boyes et al., 2018;Drath and Horch, 2014). Design principles of Industry 4.0,
as the building block of digital transformation, are necessary conditions that enable
industrial value chain members to achieve advantages promised by the Industry 4.0
transition (Dev et al., 2020;Indri et al., 2018). Previous studies have introduced a diverse
collection of Industry 4.0 design principles (Hermann et al., 2016). Horizontal integration,
vertical integration, real-time capability and customer orientation are among the most widely
accepted design principles of Industry 4.0 (Dev et al., 2020).
Addressing the opportunities and challenges of Industry 4.0 for SMEs has been highly
popular among scholars (e.g., Masood and Sonntag, 2020;Moeuf et al., 2018). Another popular
stream of research involves assessing SMEs’behavior in implementing the technology trends
of Industry 4.0 (Agostini and Nosella, 2019). The strategic management of digital
transformation (Cimini et al., 2020;M€
uller et al., 2018), business value creation strategies
for competitiveness (Chen, 2019) and digitalization maturity assessment (Mittal et al., 2018;
Pirola et al., 2019) have been among the most popular streams of research within the SME-
Industry 4.0 literature.
2.2 Manufacturing digital transformation success
The traditional IS literature has been mainly concerned with the concept of “digitalization
success,”which has been frequently referred to as “IS success.”Recognizing the complexity
Digital
transformation
success
of the success concept, the IS literature suggests that IS success is multidimensional,
measured through a series of interdependent variables. The taxonomy of IS success
developed and introduced by Delone and Mclean (1992,2003) is, arguably, the most widely
accepted definition of IS success within the traditional IS literature (Petter et al., 2013). Delone
and Mclean (D&M) success taxonomy explains that IS success is multidimensional,
comprising five interrelated variables: information quality, system quality, system usage,
user satisfaction and net benefits (DeLone and McLean, 1992;2003). Therefore, drawing on
the IS success taxonomy and a wide range of IS success determinants within the traditional IS
and operations management literature deems an obvious starting point for measuring
Industry 4.0 digital transformation success
- Real-time production flow monitoring
- Real-time equipment management
- Capital investments evaluation
- Intelligent stakeholder management
- Customer integration
- New busin ess opportunity discovery
Simulaon and
Modelling (Digital twin)
Smart prod ucts
- Human error reduction
- Maintenance a ssistance
- Safety management
- Design and visualization
- On-the-job training
- Individualized-mass production
- Reduced time -to-market
-ycneiciffeygrenegnirutcafuna
M
- Informed de cision-
making
- Customer behavior
prediction
- Market sensing
- Shorter cycl e times
- Reduced wast e
- Increa sed safety
-Improved quality
- energy efficiency
-Smart contracting
- Financial t ransaction security
- Assets history management
- Energy accounting
- Disaster recovery
-Scalability
-Data accessibi lity
- Cost-effectiveness
- Data securit y and cyber risk
management
- Dependability improvement
- Real-time production monitoring
- AI-powered a utonomous diagnostic
- Higher value creation
-Anomaly proactiveness
-Continuous energy consumption
monitoring
-Product
-as-a-service
business model
- Product utili zation
optimization
- Higher value creation
- Improved customer
engagement
- customer support
- customer monitoring
- Improved machine repeatability
- Informed de cision-making
-Real-time competitive edge
-Real-time production monitoring
- AI-powered a utonomous
diagnostic
-Improved OEE
-ytivitcudorpdevorpmI
-noitaziminimksiR
-Improved customer servi ce
-Improved production reliabi lity
Hyperconnected manufacturing management level
Smart suppliers
Digital supply ne twork lev el
Vertical and
horizontal integration
Virtualization Decentralization Product and service
indivi dual ization
Customer
orientation
Real-time
capability
ModularityInteroperability
Automaon and
indus trial ro bocs Big dat a analyc s Blockc hain
Addive/Advanced
manufacturing
Augmented and
virtual reality
Cloud data and
compung
Industrial internet of
things
Internet of people Internet of services Cyber-physical
producon systems
Cybersecurity
Smart stakeholders Smart logiscs
Design principles of Industry 4.0
levelyrotcaftramS
Figure 1.
Industry 4.0 archetype
JMTM
Nevertheless, the IS success concept within the classic literature mostly addresses the
digitalization success associated with a simple or multifaceted IS project. The digital
transformation under Industry 4.0, as shown in Figure 1, is starkly different. In general,
organizations embark on IS implementation to undertake various types of digitalization
projects (Hughes et al., 2020;Petter et al., 2013). However, the digital transformation under
Industry 4.0 is not a technological innovation that businesses can implement as an IS project
(Culot et al., 2020). The digital transformation under Industry 4.0 is a strategic business
transformation that relies on the institutionalization and integration of various combinations
of modern information and digital technologies (IDT) such as AI, data analytics, digital twins,
industrial robots and blockchain. This process also involves adopting agility, customer
orientation and product individualization as core competencies (Fatorachian and Kazemi,
2018;Kagermann, 2015). The traditional IS success variables, such as system use or net
benefits, can, to some extent, indicate the success of digital transformation within
manufacturers. However, as proposed in Figure 2, the traditional IS success measures
should be complemented with the fundamental design principles of Industry 4.0, such as
virtualization, value system integration, interoperability and real-time capability as more
robust proxies of digital transformation success. Based on the framework proposed in
Figure 2, manufacturing digital transformation success involves selecting the right Industry
4.0 technology to adopt, correctly implementing the technologies, ensuring the satisfaction of
technology end-users, consistently and purposefully using the technologies in support of
strategic business objectives and gaining value from the technologies implemented. The
value of digitalization itself may involve various operational and financial metrics such as
employee productivity, market sensing, cost-saving, process reliability, resource efficiency
and improved decision-making. Since digital manufacturing transformation success involves
novel dimensions not addressed within the classic IS success models, conducting a
comprehensive review of Industry 4.0 literature to identify the determinants of digital
transformation success is indispensable.
2.3 Determinants of digital transformation success
To identify the determinants of digital transformation success, the present study followed the
procedure introduced by Petter et al. (2013) and Webster and Watson (2002) and conducted a
content-centric qualitative literature review. This procedure enables the present study to
identify and evaluate qualitative- and quantitative-related research within the Industry 4.0
digital transformation domain. The content-centric literature review was initiated by the full-
text search within several online databases (e.g. EBSCOhost, Scopus and ProQuest) using
several combinations of keywords related to Industry 4.0 (including “Industry 4.0”,“Industrie
4.0,”and “fourth industrial revolution”and manufacturing digital transformation success
related keywords (including “digital transformation,”“success,”“manufacturing,”“value
Design Principles of Industry 4.0
Technology Trends of Industry 4.0
Opportunies enabled by Industry
4.0 transion
(IIoT, AI, AV R, CPS, ...)
(Value chai n integration, interoperability,
real -ti me ca pabi lity , .. .)
(Improved productivity, Innovation
opportunities, customer satisfaction,
business model innovation, enhanced
flexibility, resources efficiency, .. .)
Digital transformation under Industry 4.0
Manufacturing Digital Transformation under Industry 4.0
Success Determi nants
Organizational
factors
Technological
factors
Environmental
factors
Tradional IS success dimensions
(Information quality, system quality,
system use, …)
Figure 2.
The framework of
digital transformation
success under
Industry 4.0
Digital
transformation
success
creation,”“growth,”“benefit,”“advantage”and “competitiveness”). The initial advanced search
using various search strings resulted in identifying 536 empirical and conceptual papers. At the
next step, each of the 536 documents identified was subjected to the following exclusion criteria:
(1) The document is not an article published in reputable scientific journals (e.g. book
chapters, special issue reviews or conference proceedings);
(2) The article does not have its main body of text written in English;
(3) The article uses keywords such as Industry 4.0 or digital transformation merely
within the title, abstract, keyword or references section(s);
(4) The article uses keywords such as Industry 4.0 or digital transformation only as a
cited expression;
(5) The article does not offer any form of a practical or theoretical contribution to the
concept of digital transformation success;
(6) The article only targets large organizations and methodically ignores SMEs.
The elimination process based on subjecting the 536 documents identified to the exclusion
criteria yielded 78 journal articles for content analysis. The review process further continued
by conducting the two complementary article identification steps. First, the backward review
process was conducted within which the reference sections of the 78 shortlisted articles were
analyzed in detail. Second, the study conducted the forward review process using the Google
Scholar and Web of Science platforms to identify which papers have cited the 78 shortlisted
journal articles. The documents specified in the backward and forward review processes were
first subjected to the keyword search strings, as a result of which 182 potentially related
documents were identified. Subjecting the 182 newly identified documents to the exclusion
criteria yielded 29 additional studies for content analysis, turning the final pool of shortlisted
journal articles to 107 (78 þ29) studies for content analysis.
Following Petter et al. (2013) and Tranfield et al. (2003), two assessors independently
performed the qualitative content analysis of the 107 journal articles to address the question
of “what are the determinants of manufacturing digital transformation success among
SMEs?”Each of the assessors first tabulated the scope, objectives, methodology and
contribution of shortlisted articles. The assessors further extracted the potential success
determinants independently without any preconceived idea of how many success
determinants would emerge and how to categorize them structurally. To build consensus,
the assessors attended a series of meetings and shared findings, appraised conflicting ideas
and reached an agreement about categorizing success determinants. After cross-checking
extraction patterns of determinants identified and unifying determinants under a
synonymous concept, the assessors identified 11 success determinants, namely: business
partner digital maturity, cybersecurity maturity, change management competency,
digitalization readiness preassessment, external support for digitalization, information and
digital technology expertise, information and digital technology readiness, management
competency for digital transformation, manufacturing digitalization strategic roadmapping,
operations technology readiness and resource availability. The description of these
determinants is as follows.
Business partner digital maturity (BPDM): With the advent of Industry 4.0, manufacturing
SMEs operate in a hypercompetitive environment characterized by the reduced time to
market and ever-increasing product complexity (Masood and Sonntag, 2020). Therefore,
SMEs’survival nowadays relies on their capabilities to open up their boundaries to the value
chain partners and realize the digital supply network (DSN) concept (Queiroz et al., 2019). DSN
relies on smart machinery, equipment, assets, sensors and execution system across
JMTM
manufacturing chains to interconnect and communicate with each other in real-time (Ivanov
and Dolgui, 2020). It means digital transformation success under Industry 4.0 entails the
digitalization of individual manufacturers at the micro-level and the macro-level
digitalization of business partners simultaneously (Chen, 2019;M€
uller et al., 2018).
Cybersecurity maturity (CSM): Adding advanced digital technologies to the existing
operation technologies (OT) require SMEs to have a certain degree of cyber capabilities to
secure critical manufacturing systems and operations (Moeuf et al., 2020). Unfortunately, the
rate at which cybersecurity attacks target SMEs is expediting (Kabanda et al., 2018).
Therefore, the success of manufacturing digitalization efforts depends on SMEs having
cybersecurity as a central corporate strategy (Ghobakhloo and Ng, 2019). Regardless of the
company size, each manufacturer seeking digitalization should have comprehensive
cybersecurity initiatives tailored to the particularities of its IDT infrastructure,
collaboration channels and processes (Ani et al., 2017). Thus, even among SMEs,
cybersecurity solutions are generally complicated, expensive and knowledge-intensive
(Lezzi et al., 2018). Accordingly, cybersecurity maturity is an undeniable component of the
digitalization success among manufacturing SMEs.
Digitalization readiness preassessment (DRP): Organizations often fail to embrace
innovative business models enforced by new digital technologies (Bibby and Dehe, 2018).
Manufacturing digitalization under Industry 4.0 generally entails implementing disruptive
business models by manufacturers, including SMEs (M€
uller et al., 2018). Manufacturers
usually do not fully comprehend the scope and characteristics of Industry 4.0 and the
underlying digital transformation (Moeuf et al.,2020;Mittal et al., 2018). Smaller
manufacturers generally struggle with positioning themselves regarding Industry 4.0 and
are incapable of developing the necessary tools to measure their Industry 4.0 maturity before
embarking on the digitalization journey (Castelo-Branco et al., 2019;Pirola et al., 2019).
Digitalization readiness preassessment can help manufacturing SMEs realize whether they
have the required competencies in taking the preliminary steps, such as digitalization cost-
benefit analysis, strategic roadmapping or IDT-OT maturity assessment, toward Industry 4.0
transition (Bibby and Dehe, 2018).
External support for digitalization (ESD): Industrial reports indicate that the adoption
rate of Industry 4.0 digital technologies among manufacturing SMEs is still low (Castelo-
Branco et al.,2019). This issue is mainly attributable to the complex, expensive and
knowledge-intensive nature of Industry 4.0 technologies and SMEs’inherent limitations
regarding financial resources, human capital and skill gaps (Mittal et al., 2020).
Governments are following various supporting policies to promote the digitalization of
SMEs. Some countries, such as Spain, follow the market-based approach by providing
direct final support in tax incentives or loans (Klitou et al., 2017;Morisson and Pattinson,
2019). Providing information and implementation consultancy for Industry 4.0 or funding
industry-academia collaboration are other examples of governmental policies for
promoting manufacturing digitalization among SMEs (Ciffolilli and Muscio, 2018).
Regardless of the supporting policy type, SMEs in countries with an Industry 4.0-
supporting government have enjoyed a higher manufacturing digitalization (Klitou
et al., 2017).
Information and digital technology expertise (IDTE): Digital transformation under
Industry 4.0 entails implementing advanced digital and manufacturing technologies, the
fusion of virtual and physical worlds, elimination of information silos and seamless
integration (Frank et al., 2019). Therefore, software engineering, data analytics, business
modeling, cybernetics and mechatronics skills are necessary for ensuring manufacturing
digital transformation success (Kamble et al., 2018;M€
uller et al., 2018). However, most SMEs
suffer from the lack of expertise necessary for implementing advanced IDTs, which roots in
the SMEs’informal skill development strategies, lack of financial resources and restrictions
Digital
transformation
success
in employing internal or external IT talents (Horv
ath and Szab
o, 2019;Stentoft et al., 2020).
SMEs’skill gap is complemented by the fierce competition in the talent market, making it
even harder for lagging SMEs to build the necessary expertise for successful implementation
and utilization of digital manufacturing technologies (Krishnan and Scullion, 2017).
Consistently, the empirical evidence indicates that SMEs with a higher degree of IDT
expertise have enjoyed higher digitalization (Maroufkhani et al., 2020).
Information and digital technology readiness (IDTR): Under Industry 4.0, digitalization is
not merely limited to implementing advanced digital technologies. It depends on the
manufacturers’capability to seamlessly integrate the new digital solutions into the existing
legacy networks, IT/OT infrastructure and processes to create a cohesive digital ecosystem
capable of a seamless and real-time stream of data all across the manufacturing processes
(Fatorachian and Kazemi, 2018;Shi et al., 2020). In reality, not all manufacturers have the
readiness to leverage digital technologies, IDT resources, talents and existing OTs
strategically (Stentoft et al., 2020). Developing digitalization readiness is gradual and
tremendously resource-intensive, thus, less viable to smaller manufacturers (Mittal et al.,
2018). SMEs with higher digitalization readiness are better fit to effectively prioritize their
digital manufacturing investment, perform digitalization risk management and strategically
align IDT resources for integration (Moeuf et al., 2018;Pirola et al., 2019).
Management competency for digital transformation (MCDT): Smaller manufacturers
generally have limited financial resources, risk resilience, bargaining power with technology
providers and skillsets (Masood and Sonntag, 2020). Thus, top management in SMEs should
have the necessary strategic vision to better guide manufacturing digitalization efforts
(Agostini and Nosella, 2019). The literature explains that SMEs with poorly developed
management best practices have been less successful in implementing digital innovations
and developing competitiveness (Snider et al., 2009). Therefore, manufacturing digitalization
success in SMEs depends on the management team having the necessary strategic vision and
competency to allocate necessary resources, define the decision-making processes, implement
the appropriate digitalization strategies, address the digitalization skill gap and attract
external support (Moeuf et al., 2020). More importantly, top management in SMEs should
have the competency to promote the employees’sense of ownership and trust throughout the
digitalization process and promote the culture of interconnectedness and information
transparency (Kumar et al., 2020;Stentoft et al., 2020).
Manufacturing digitalization strategic roadmapping (MDSR): Under the Industry 4.0
scenario, SMEs need to achieve the utmost digitalization level, including the vertical
integration of each manufacturing function at the plant level and the horizontal integration
for seamless data interchange with business partners and customers (Mittal et al., 2020).
Therefore, SMEs must replace redundant legacy IDT infrastructure, implement a unified
enterprise-wide and interoperable manufacturing execution and planning system, develop
the necessary digital connectivity among various components of the digital manufacturing
environment, enhance the man-machine interaction, revise the human resource strategies and
develop an innovative business model for the effective integration with business partners
(Cimini et al., 2020;Frank et al., 2019). This level of complexity compels manufacturers to
develop a strategic roadmap for manufacturing digitalization that creates a time-based action
plan that defines the current digitalization status of the organization and simulates the future
digitalization landscape (Colli et al., 2019). Empirical studies propose that when the
digitalization strategic roadmap addresses the critical digital transformation domain such as
IDT governance, supply chain integration and IDT knowledge domains, a higher degree of
manufacturing digitalization can be observed among SMEs (M€
uller et al., 2018).
Operations technology readiness (OTR): Industry 4.0 and the underlying design principles
entail integrating advanced IDTs into the existing OT to enable the communication pyramid
of integrated manufacturing (Papazoglou et al., 2015). At the smart connection level, sensor-
JMTM
equipped OTs collect and exchange signals with machine controllers (Lu et al., 2016). At one
communication level higher, smart execution systems translate the data collected at the
control level into meaningful information for the intelligent resource planning modules and
vice versa (Li et al., 2018). To achieve the desired level of integration, SMEs need to upgrade
the digitizable OTs or replace the existing equipment and machinery that lack the necessary
functionality to adhere to technical digitalization requirements (Schlechtendahl et al., 2015).
To SMEs, however, this process tends to be vastly resource-intensive. SMEs should have
adequate OT readiness in evaluating the capabilities of existing OTs, measuring the
integrability of existing equipment and machinery, devising a technological roadmap for
equipping existing OTs with sensors and intelligent controllers and identifying the most
suitable replacement for the legacy redundant OTs (Ghobakhloo, 2020;Stentoft et al., 2020).
Change management competency (CMC): Radical organizational changes always render a
depression in performance. Due to the disruptive force of Industry 4.0, change management is
a strategic priority for manufacturing digitalization success. The digital manufacturing
transformation for SMEs entails reshaping the conventional business models and
revolutionizing how organizations operate (Dowell and Muthulingam, 2017). It means
Industry 4.0 digital transformation equally depends on focusing on the people’s side of the
change process while implementing the digital transformation strategies (Sivathanu and
Pillai, 2018). Therefore, the Industry 4.0 change management policies should also manage the
human resource side of change, so employees are empowered, willing and knowledgeable
enough to be a part of the digital change process (Kagermann, 2015). A competent change
management policy can also support SMEs’digital transformation by reducing bureaucratic
practices and promoting hyperconnectivity, innovative-thinking and knowledge sharing
(Moeuf et al., 2018;Quinton et al., 2018).
Resource availability (RSA): Industry 4.0 and the underlying manufacturing digitalization
processes are dynamic and incredibly resource-intensive (Telukdarie et al., 2018), requiring
constant capital investment and human resource allocation (Frank et al., 2019;Tortorella and
Fettermann, 2018). Even large organizations are struggling with financing the Industry 4.0
digital transformation (Raj et al., 2020). SMEs venturing into Industry 4.0 digitalization must
afford capital and human resource for various tasks and processes such as technology
roadmap development, digital maturity assessment, digital skillset development, OT
modernization, legacy equipment replacement, digital technology acquisition, cybersecurity
measures and software licensing (Agostini and Nosella, 2019;Moeuf et al., 2018). Consistently,
SMEs seeking a successful transition into digital manufacturing should have the necessary
resources to continuously support various digitalization activities throughout the adoption,
implementation and institutionalization stages (Kumar et al., 2020;Mittal et al., 2020).
3. ISM methodology
ISM is a graph-theoretic decision-making tool that analyzes the relationships between determinants
(variables) of a phenomenon and synthesizes the causal relationships identified into a meaningful
graphical model (Warfield, 1982). ISM offers a systematical approach for transforming the subjective
resources concerning a tangible system, such as experts’opinions and judgments, into a structured
causal model that can provide significant theory-building opportunities in exploratory studies (Purohit
et al.,2016). ISM has been a popular modeling technique within the information and operations
management discipline. Examples of use cases of ISM includes identifying IS project success factors
(Hughes et al.,2020), determining technological capabilities for supply chain resilience (Rajesh, 2017),
identifying the enablers of green lean six sigma (Kaswan and Rathi, 2019) and modeling the success of
food logistics system traceability (Shankar et al.,2018). Applying ISM in this study involves the seven
steps presented in Figure 3. This figure has been developed based on the standard procedures widely
accepted within the ISM literature (Hughes et al.,2020;Kaswan and Rathi, 2019;Warfield, 1982).
Digital
transformation
success
3.1 Collecting expert opinion
Using expert opinion for identifying the relationships among variables of interest is an integral
part of ISM. Consistently, the present study followed the expert selection standard procedure
(e.g. Hertzum, 2014) and applied a two-step expert selection approach to ensure the validity of
ISM outputs. The study first identified a pool of 21 experts highly experienced in various aspects
of digital transformation and Industry 4.0 transition within the manufacturing industry,
particularly among the European SME sector. The study further applied an expert retrieval
model using multiple tools such as semi-structured interviews and assessed the accessibility,
willingness and knowledgeability of the 21 experts. The research team formally approached the
21 experts identified, explained the objectives of the present study and invited them to declare
whether they are interested in participation. Out of the 21 experts identified, six experts did not
agree to participate, while two experts did not reply to the initial invitation and follow-up efforts.
Consistently, 13 experts declared their interest in participation. The research team developed a
semi-structured questionnaire to ensure that the remaining experts have the necessary
conversational competence as well as Industry 4.0, digitalization and technology management
knowledge to participate in the meetings. The study followed the widely accepted guidelines for
designing and executing semi-structured interview questions. For example, the questions were
phrased in the way experts had to provide detailed answers, instead of “Yes”or “No”replies.
Out of the 13 experts who participated in the selection interviews, one expert was excluded
due to the lack of conversational competence. Two experts were excluded as they showed a
lack of familiarity with the Industry 4.0 concept and the industrial implications of digital
technologies. As a result, ten experts were shortlisted for participating in the Nominal Group
Technique (NGT)-based meetings after the screening procedures. One expert withdrew
before the NGT meetings. Thus, out of 21 experts initially identified, nine experts were
shortlisted to participate in the NGT-based meetings. Table 1 lists the demographic
properties of participating experts. Experts refined and validated the success determinants
within the first two meetings. They further decided on the relationships among each pair of
determinants across meetings 3 to 4.
NGT is a robust and widely accepted collaborative decision-making and knowledge
sharing technique (Harvey and Holmes, 2012). The research team held four NGT sessions via
Zoom Meetings to capture the opinion of experts. The study also strictly followed the NGT
Collecting the experts’ views and
opinions on the success
determinants
Developing the contextual
relationship (Xij) between every pair
of success determinants
Developing the Structural Self-
Interaction Matrix (SSIM)
Developing the Initial Reachability
Matrix (IRM)
Developing Final Reachability Matrix
(FRM)
Establishing the hierarchy level of
success determinants
Replacing attribute nodes with
relationship statements
Removing the transitivity from the
digraph
Graphically presenting the
relationship statement identified into
the model of Manufacturing Digital
Transformation Success
MICMAC analysis and assessment of
driving power and dependence power
of determinants of manufacturing
digital transformation success
Identifying determinants of manufacturing digital transformation success via content-centric review of literature
Development of strategic guideline for
SMEs to ensure the success of
manufacturing digital transformation
2petS
Step 3
Step 4
1petS
Step 5
Step 7
Step 6
Figure 3.
Steps for application
of ISM
JMTM
execution steps introduced by Ghobakhloo (2020) to ensure the validity and reliability of NGT
session outcomes. In the preparation step, the moderator first prepared the NGT questions
that clarified the objective of each meeting. Across the silent idea generation step, each expert
silently generated ideas and opinions when applicable. As the third step, each expert engaged
in the round-robin feedback sessions, and each opinion was recorded concisely. In the fourth
step, the opinions regarding the primary theme of each meeting were discussed thoroughly
among experts. Consistently, and after a moderated discussion on each relationship, experts
reached a shared consensus on the relationships identified.
3.2 Identifying the contextual relationships
The study follows the ISM literature (Hughes et al., 2020) and applies the following coding
system to develop the Structural Self-Interaction Matrix (SSIM). Consistently, Table 2
presents the SSIM of the study, which has been developed based on the experts’opinion. For
example, the (BPDM, RSA) entry in the SSIM is presented by symbol O, which means experts
collectively believe that these two success determinants are causally independent.
V: Success determinant icauses success determinant j
A: Success determinant iis caused by success determinant j
X: Success determinants iand jcause each other
O: Success determinants iand jare independent
No Country Job title Academic degree Field of expertise
1 Italy Technology and
integration engineer
BEng. Technology
(industrial and
management engineering)
implementation and
integration of industrial IT
systems
2 Italy Associate professor of
industrial engineering
Ph.D. Management
economics and industrial
engineering
Digital maturity assessment
and business strategy
development
3 Netherlands Professor of engineering
systems foundations
Ph.D. Technology
management
Methodologies, tools, and
approaches in smart
manufacturing
4 France Senior manufacturing
digitalization consultant
MSc. Engineering
management
Digital and autonomous
supply chain and
digitalization options
5 Spain Digital productivity and
transformation manager
BSc. Manufacturing
engineering and
management
Process digitalization and
digital transformation
strategic planning
6 Sweden Associate professor of
industrial engineering and
management
Ph.D. Industrial
engineering and decision
analytics
Digital manufacturing
ecosystem strategy
7 Sweden Smart manufacturing
manager
Ph.D. Industrial and
system engineering
Manufacturing strategy
analysis and digitalization
strategic planning
8 Germany Associate professor of
technology management
and work science
Ph.D. Operations
management
Technology assessments
and sustainable business
planning
9 Denmark Director of technology
innovation
MSc. Sustainable systems
engineering
Digital business strategy
and IT strategic partnership
Table 1.
Demographic
properties of
participating experts
Digital
transformation
success
3.3 Developing the initial reachability matrix
Table 3 presents the Initial Reachability Matrix (IRM) of the study. The IRM in Table 3 has
been developed based on the following conversion rules, commonly accepted within the ISM
literature (Kaswan and Rathi, 2019):
When the (i, j) entry in the SSIM is V, entry (i, j) in the IRM is set to 1, while entry (j, i) is set
to 0.
When the (i, j) entry in the SSIM is A, entry (i, j) in the IRM is set to 0, while entry (j, i) is
set to 1.
When the (i, j) entry in the SSIM is X, both (i, j) and (j, i) entries in the IRM are set to 1.
When the (i, j) entry in the SSIM is O, both (i, j) and (j, i) entries in the IRM are set to 0.
3.4 Developing the final reachability matrix
The fourth step in applying ISM in this study involves developing the Final Reachability
Matrix (FRM) and calculating the driving power and dependence power of each digital
transformation success determinant. Table 4 presents the FRM of the study, developed based
on applying the Transitivity rule to the IRM. According to the transitivity rule, if success
determinant Xdirectly causes success determinant Y, and success determinant Ydirectly
Factors BPDM CSM CMC DRP ESD IDTE IDTR MCDT MDSR OTR RSA
BPDM 1 1 0 0 0 1 0 0 1 0 0
CSM 0100001 0 000
CMC 0010010 0 000
DRP 0011000 0 100
ESD 1001110 1 101
IDTE 0 1 1 0 0 1 1 0 1 1 0
IDTR 0 1 1 0 0 0 1 0 0 0 0
MCDT 0 0 1 1 0 0 1 1 1 0 0
MDSR 0 0 1 0 0 1 0 0 1 1 0
OTR 0000000 0 010
RSA 0101011 0 111
Factors RSA OTR MDSR MCDT IDTR IDTE ESD DRP CMC CSM BPDM
BPDM O O V O O V A O O V –
CSM A O O O X A O O O –
CMC O O A A A X O A –
DRP A O V A O O A –
ESD V O V V O V –
IDTE A V X O V –
IDTR A O O A –
MCDT O O V –
MDSR A V –
OTR A –
RSA –
Note(s): BPDM, business partner digital maturity; CSM, cybersecurity maturity; CMC, change management
competency; DRP, digitalization readiness preassessment; ESD, external support for digitalization; IDTE,
information and digital technology expertise; IDTR, information and digital technology readiness; MCDT,
management competency for digital transformation; MDSR, manufacturing digitalization strategic
roadmapping; OTR, operations technology readiness; RSA, resource availability
Table 3.
The IRM for success
determinants
Table 2.
The SSIM for success
determinants
JMTM
Factors BPDM CSM CMC DRP ESD IDTE IDTR MCDT MDSR OTR RSA Driving power Ranking
BPDM 1 1 1* 0 0 1 1* 0 1 1* 0 73
CSM 0 1 1* 0 0 0 1 0 0 0 0 37
CMC 0 1* 1 0 0 1 1* 0 1* 1* 0 64
DRP 0 0 1 1 0 1* 0 0 1 1* 0 55
ESD 1 1* 1* 1 1 1 1* 1 1 1* 1 11 1
IDTE 0 1 1 0 0 1 1 0 1 1 0 64
IDTR 0 1 1 0 0 1* 1 0 0 0 0 46
MCDT 0 1* 1 1 0 1* 1 1 1 1* 0 82
MDSR 0 1* 1 0 0 1 1* 0 1 1 0 64
OTR 0 0 0 0 0 0 0 0 0 1 0 18
RSA 0 1 1* 1 0 1 1 0 1 1 1 82
Dependence power 291041992 892
Ranking 5 2 1 4 6 2 2 5 3 2 5
Table 4.
The FRM for
determinants of
manufacturing digital
transformation success
Digital
transformation
success
causes success determinant Z, determinant Xshould be regarded as a direct cause to
determinant Zwithin the FRM.
3.5 Establishing the hierarchy level of success determinants
To establish the hierarchy level for the determinants of digital transformation success, the
study uses the values within the FRM and develops the reachability, antecedent and
intersection sets for each success determinant. For a given determinant, the reachability set
consists of the success determinant itself and other success determinants that it causes.
However, the antecedent set includes the success determinant itself and other success
determinants that it is caused by Kaswan and Rathi (2019). The intersection between the
reachability set and antecedent set for a given success determinant constructs the intersection
set (Rajesh, 2017). In this step, establishing the hierarchy level takes place iteratively and
through applying the extraction process. In each iteration, the success determinants with
matching reachability and intersection sets are identified and extracted. After removing the
extracted success determinants from remaining reachability, antecedent and intersection
sets, the extraction process is repeated iteratively until the hierarchic level of each remaining
success determinant is identified. Table A1 (in Appendix) presents the hierarchy level for
determinants of manufacturing digital transformation success.
3.6 Developing the final structural model
Using the extraction levels identified in Table A1, positioning the 11 success determinants
consistent with the iterations 1 to 5, demonstrating the causal relationships with vector
arrows and removing the transitivities among them, the ISM model of manufacturing digital
transformation success among SMEs is developed and presented as Figure 4. Consistent with
the number of iterations, the success determinants in the ISM model are position across five
placement levels. Following the ISM methodology (Hughes et al., 2020;Purohit et al., 2016;
Warfield, 1982), only the direct causal relationships between consecutive placement levels are
represented graphically by vector arrows while ignoring the transitivity rule. External
support for digitalization →Business partner digital maturity relationship is the only
exception simply because business partner digital maturity is not directly caused by any of
the success determinants positioned at the second placement level.
External support
for digitalization
Management
competency for
digital
transformation
Resource
availability
Business partner
digital maturity
Digitization
readiness
preassessment
Information and
digital technology
expertise
Change
Management
Competency
Manufacturing
digitalization
strategic
roadmapping
Information and
digital technology
readiness
Cybersecurity
maturity
Operations
technology
readiness
Placement level 1 Placement level 2 Placement level 3 Placement level 4 Placement level 5
Figure 4.
The ISM model of
manufacturing digital
transformation success
among SMEs
JMTM
3.7 MICMAC analysis
MICMAC analysis is the final step in ISM, which involves assessing driving power and the
dependence power of each success factor. MICMAC is an indirect classification method for
the comparative evaluation of each success determinant and its relational scope (Purohit et al.,
2016). MICMAC involves classifying success factors into the following quadrants;
(1) Autonomous quadrant, including success determinants with weak driving power and
weak dependence power;
(2) Driver quadrant, consisting of success determinants with strong driving power and
weak dependence power;
(3) Linkage quadrant, including success determinants with strong driving power and
strong dependence power;
(4) Dependent quadrant, consisting of success determinants with weak driving power
and strong dependence power.
The driving power and dependence diagram for the determinants of manufacturing digital
transformation success is presented in Figure 5, which has been developed based on the
driving power and dependence power values available within the FRM. Figure 5 explains that
digital readiness preassessment is the only autonomous success determinant in this study.
External support for digitalization, management competency for digital transformation,
resource availability and business partner digital maturity are categorized as driver success
determinants. Change management competency, information and digital technology
expertise and manufacturing digitalization strategic roadmapping, placed at the top-right
side of Figure 5, are classified as the linkage success factors. As expected, the highly
dependent success determinants of cybersecurity maturity, information and digital
technology readiness and operations technology readiness fall within the dependence
quadrant.
1234567
7
6
5
2
1
3
4
LinkageDependent
Driver
suomonotuA
8910
10
9
8
11
Dependence power
11
rewopgnivirD
III
IV
I
II
*
IDTE
*
CSM
*
CMC
*
DRP
*
ESD
*
IDTR
*
MCDT
*
MDSR
*
OTR
*
RSA
*
BPDM
Note(s): BPDM, business partner digital maturity; CSM, cybersecurity maturity; DRP, digitalization
readiness preassessment; ESD, external support for digitalization; IDTE, information and digital
technology expertise; IDTR, information and digital technology readiness; MCDT, management
competency for digital transformation; MDSR, manufacturing digitalization strategic roadmapping; OTR,
operations technology readiness; CMC, change management competency; RSA, resource availability
Figure 5.
MICMAC analysis
matrix
Digital
transformation
success
4. Discussion
ISM results and the manufacturing digital transformation success model developed as
Figure 4 explain that external support for digitalization is the stepping stone for ensuring
Industry 4.0 transformation success within the SME sector. This success determinant’s
favorable presence would enable SMEs to have a higher chance of developing other
determinants of digital transformation success, particularly resource availability,
management competency for digital transformation and business partner digital maturity.
Figure 4 also explains that resource availability and management competency for digital
transformation are the most critical success determinants that SMEs can develop and,
subsequently, gain the competency to perform a comprehensive digitalization readiness
preassessment. If favorably present, business partner digital maturity, complemented by the
digitalization readiness preassessment capability, would enable SMEs to develop the more
intermediate success determinants of change management competency, information and
digital technology expertise and manufacturing digitalization strategic roadmapping. These
three success determinants, located at the linkage quadrant of the MICMAC matrix (Figure 5),
have high driving power and dependence power, meaning they play an essential role in
transferring the value of the driver success determinants into the dependent success
determinants. Cybersecurity maturity, information and digital technology readiness and
operations technology readiness are the three dependent success determinants positioned at
the most right side of the ISM model of manufacturing digital transformation success,
making them the most challenging and complex success determinants to develop.
The digital transformation success model presented in Figure 4 follows the ISM rules and
demonstrates the order in which each success determinant should be developed to ensure the
highest degree of digital transformation success among SMEs. The ISM model only provides
direct causal relationships among variables placed at the successive placement levels. To
scrutinize the precedence relationships among various success determinants, the study
develops a detailed roadmap for manufacturing digital transformation success among SMEs
and presents it as Figure 6, mainly through borrowing from the Total ISM technique and
constructing the interpretive logic knowledge base according to the experts’inputs. The
roadmap presented in Figure 6 maps all the direct relationships among the 11 success
determinants and explains in what way each success determinant is influenced by its
predictors. The combination of the ISM model presented in Figure 4 and the manufacturing
digital transformation success roadmap shown in Figure 6 may serve SMEs as a valuable
guide to understanding how and in which order the success determinants should be pursued
and developed. External support for digitalization is, obviously, the primary and most crucial
success determinant. The external support mostly involves supportive governmental policies
for Industry 4.0 transformation, the development of which logically falls outsides the SMEs’
boundaries. Nonetheless, manufacturing SMEs must be aware of the existing supportive
policies, programs and incentives to develop an extensive plan to take advantage of them.
Figure 6 explains that external support for digitalization makes a massive contribution to
manufacturing SMEs’digital transformation success by directly facilitating the development
of six different success determinants. For example, external support for digitalization can
enable resource availability among SMEs, in the form of governments offering goal-based
financial incentives or supportive loans for higher-risk profile companies pursuing
manufacturing digital transformation. These supports can further lead to the overall
digital maturity of industry value chains, thus, increasing the digital maturity of business
partners for focal SMEs. Figures 5 and 6, collectively, explain that achieving manufacturing
digital transformation success relies on a very complex and intertwined network of success
determinants. The journey toward digital transformation success among SMEs starts with
developing success determinants with the highest driver power, positioned at the left side of
Figure 6. Some success determinants belonging to specific placement levels are independent
JMTM
External support
for digitalization
Management
competency for
digital transformation
Resource
availability
Business partner
digital maturity
Digitalization
readiness
preassessment
Information and
digital technology
expertise
Change
Management
Competency
Manufacturing
digitalization strategic
roadmapping
Information and
digital technology
readiness
Cybersecurity
maturity
Operations
technology
readiness
●
spohskrow0.4yrtsudnI
●
gniniarttnemeganamnoitazitigiD
●
noita
z
i
t
i
g
idgn
i
rutcafunaM
secivresgnitlusnoc
● Subsidized IDT training
for employees
● SME-friendly regulaon
for IDT service providers
●
Employee empowerment
●
Employee involvement
●
Change resist ance
management
●
Agile workplace culture
● Digizaon project
management
● I DT leveraging competency
●
IDT vendor selecon
● ITD integraon knowledge
●
Funding employee training
●
Supplying necessary technology
●
Timely resource
allocaon
●
Funding the necessary
planning and analycs tools
●
IDT resource
Management
●
Digizaon
forecasng and
planning
●
Recruing top talent
●
Funding legacy hardware upgrade
●
Supplying necessary technology
●
Funding security program development,
cybersecurity risk assessments, firewalls,
and monitoring services.
●
Effecve communicaon
●
Progress measurement
and performanc e analysis
●
Informed decision-making
●
IDT resource
alignment
●
ITD skill development
strategies
● Risk ma nagement proacve ness
●
Digizaon conngency strategies
● Digizaon Management
process simplificaon
● I mproved inter-team
communicaon
●
Secure communicaons
●
Informaon s haring security
●
Cybersecurity measure
effecveness
●
Cybersecurity awareness
●
IDT resilience
●IDT technical assistance
●
IDT integrability
● Collaborave digizaon
planning
● I ntegrated capability-
building ecosystem
● Cybersecurity skill development
● Cybersecurity policy enforcement
●
Cybersecurity emergency planning
●
Idenfying digital talent gaps
● Measuring d igital competences
● IDT/OT shortcoming assessment
● Change-related cost/risk/benefit
assessment
● Digital culture (e.g., digital
leadership, digital atude, IDT
competency, and digital
thinking) assessment
●
Change priority idenficaon
●
Increased interoperability of
OTs
●
Enhanced integrability of OTs
● Methodical OT readiness
assessment
●
OT maturity priorizaon
Figure 6.
The strategic roadmap
for digital
transformation success
among SMEs
Digital
transformation
success
of each other, thus, their development can be planned as relatively independent
organizational projects. By moving toward the right side of Figure 6,the
interdependencies among success determinants drastically increase, making it somewhat
impossible to develop the intermediate success determinants independently. The CMC →
IDTE →IDTR →CMC chain of precedence relationships in Figure 6 is an exciting
example of the interdependency loop, highlighting the importance of meticulously planning
the steps for developing these success determinants as successive, yet interdependent
organizational projects.
5. Conclusions
Manufacturing SMEs are nowadays striving to capitalize on Industry 4.0. Yet, smaller
organizations are at a disadvantage when it comes to embarking on digital transformation.
The present study attempted to address this issue by exploring the determinants of digital
transformation success among manufacturing SMEs under the Industry 4.0 agenda. The
study conducted a comprehensive content-centric review of the literature on Industry 4.0 and
identified 11 success determinants, which was complemented by ISM to exploring the
interdependencies among them. These efforts resulted in developing the ISM model of digital
transformation success for SMEs. The ISM model and the resulting guideline are expected to
offer valuable implications for the theory and practice.
5.1 Implications
The digital transformation success process under Industry 4.0 is a novel and complex
phenomenon. The present study addressed the determinant part of the digital transformation
success process and identified 11 success determinants relevant to the SME sector. The study
thoroughly explained the functionality of each success determinant. The study further
explained how success determinants might contribute to the digital transformation success
among SMEs. Findings showed that external support, mainly from the government side, is
where the journey toward digital transformation starts among SMEs. Overall, digital
transformation under Industry 4.0 is immensely resource-intensive and complex. SMEs
usually lack the necessary competencies to embark on the Industry 4.0 transition. Not only
should governments adopt large-scale Industry 4.0 policies to increase the digitalization
competencies of SMEs but they should ensure that SMEs are aware of these policies and the
necessary delivery channels for services and incentives are also in place.
Findings also demonstrated that the digital transformation success phenomenon and the
underlying success determinants within the Industry 4.0 context are starkly different from
the digitalization success concept within the traditional IS-SME literature. SMEs must have
novel, yet dynamic capabilities such as change management and digitalization strategic
planning competencies to reach a certain degree of IDT, OT and cybersecurity maturity as
the indispensable prerequisites of the Industry 4.0 transition. Developing these capabilities is
noticeably resource (capital, time and knowledge) intensive. Any SME seeking digital
transformation should conduct a digitalization readiness preassessment of some sort to
understand whether the necessary competencies and resources for developing vital
capabilities such as IDT maturity are in place. The digitalization readiness preassessment
is essential to SMEs’survival in the Industry 4.0 era because immature digitalization
decisions in the hypercompetitive business environment can be disastrous to any
organization. SMEs need to understand that digital transformation under Industry 4.0
expands beyond focal firms’boundaries. The implementation of advanced manufacturing
technologies such as additive manufacturing, AR, robotics and HPC-CAD are an integral part
of manufacturing digitalization under Industry 4.0. However, manufacturing value chains are
JMTM
shifting toward hyperconnected manufacturing, a circular information-based ecosystem
where IoT, AI and big data facilitate value-network integration across manufacturing
processes, production lines and execution systems throughout supply chains. Therefore, the
digital transformation in SMEs, to some extent, is tied to the overall digital maturity of
business partners, making the digitalization partnerships with value chain members a
strategic priority for SMEs.
Finally, the digital transformation success model developed in the present study tends to
serve manufacturing SMEs as a baseline for having a structured overview of successful
digital transformation and its basic requirements under Industry 4.0. Undoubtedly, there is
no one-size-fits-all strategic roadmap to serve the digitalization needs of all manufacturing
SMEs. The digitalization success model developed in this study should be regarded as a
baseline. Each manufacturer can tailor this model to its strategic priorities, core capabilities,
weaknesses and values while progressing toward digitalization.
5.2 Limitations and future directions
The study identified 11 determinants of digital transformation success and specified the
order in which they should be present in support of the Industry 4.0 transition. The degree of
importance for success determinants presented in this study merely magnifies the strategic
priority based on the driving power and dependence power. For example, the study called
“external support for digitalization”as the most important success determinant, mainly
because it facilitates other success determinants uncontestedly. Yet, it has not been
conceivable to theorize the dependent variable of “digital transformation success”and
quantitatively measure the extent to which each success factor contributes to explaining
“digital transformation success.”Theorizing the dependent variable of “digital
transformation success,”developing a robust measurement instrument for it and
measuring the contribution of each success determinant identified in this study to the
digital transformation success construct, mainly via an empirical survey of the industry, can
be an exciting direction for the future research. The present research is primarily targeted at
the manufacturing SME and caution should be exercised when generalizing the present
findings to the broader business audience. We expect larger organizations to face different
sets of issues during the Industry 4.0 transition. They might rely less on external support for
digitalization or experience more severe digitalization change management challenges.
Future research is cordially invited to address this issue. Finally, the empirical validation of
the model presented in this study can be the next logical step in advancing the knowledge of
Industry 4.0 digital transformation success. Overall, ISM is exploratory, and the present
study could not conceivably assess the validity of the proposed model via confirmatory
analytical methods. Thus, validating the ISM-based models of digital transformation success
proposed via a confirmatory approach such as cross-sectional survey and structural equation
modeling analysis can be an exciting avenue for future research.
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Appendix
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Morteza Ghobakhloo can be contacted at: morteza_ghobakhloo@yahoo.com
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Factors Reachability set Antecedent set Intersection set Level
Iteration 1
BPDM BPDM, CSM, CMC, IDTE, IDTR,
MDSR, OTR
BPDM, ESD BPDM
CSM CSM, CMC, IDTR BPDM, CSM, CMC, ESD, IDTE,
IDTR, MCDT, MDSR, RSA
CSM, CMC, IDTR I
CMC CSM, CMC, IDTE, IDTR, MDSR, OTR BPDM, CSM, CMC, DRP, ESD,
IDTE, IDTR, MCDT, MDSR, RSA
CSM, CMC, IDTE,
IDTR, MDSR
DRP CMC, DRP, IDTE, MDSR, OTR DRP, ESD, MCDT, RSA DRP
ESD BPDM, CSM, CMC, DRP, ESD, IDTE,
IDTR, MCDT, MDSR, OTR, RSA
ESD ESD
IDTE CSM, CMC, IDTE, IDTR, MDSR, OTR BPDM, CMC, DRP, ESD, IDTE,
IDTR, MCDT, MDSR, RSA
CMC, IDTE, IDTR,
MDSR
IDTR CSM, CMC, IDTE, IDTR BPDM, CSM, CMC, ESD, IDTE,
IDTR, MCDT, MDSR, RSA
CSM, CMC, IDTE,
IDTR
I
MCDT CSM, CMC, DRP, IDTE, IDTR, MCDT,
MDSR, OTR
ESD, MCDT MCDT
MDSR CSM, CMC, IDTE, IDTR, MDSR, OTR BPDM, CMC, DRP, ESD, IDTE,
MCDT, MDSR, RSA
CMC, IDTE, MDSR
OTR OTR BPDM, CMC, DRP, ESD, IDTE,
MCDT, MDSR, OTR, RSA
OTR I
RSA CSM, CMC, DRP, IDTE, IDTR, MDSR,
OTR, RSA
ESD, RSA RSA
Iteration II
BPDM BPDM, CMC, IDTE, MDSR BPDM, ESD BPDM
CMC CMC, IDTE, MDSR BPDM, CMC, DRP, ESD, IDTE,
MCDT, MDSR, RSA
CMC, IDTE, MDSR II
DRP CMC, DRP, IDTE, MDSR DRP, ESD, MCDT, RSA DRP
ESD BPDM, CMC, DRP, ESD, IDTE, MCDT,
MDSR, RSA
ESD ESD
IDTE CMC, IDTE, MDSR BPDM, CMC, DRP, ESD, IDTE,
MCDT, MDSR, RSA
CMC, IDTE, MDSR II
MCDT CMC, DRP, IDTE, MCDT, MDSR ESD, MCDT MCDT
MDSR CMC, IDTE, MDSR BPDM, CMC, DRP, ESD, IDTE,
MCDT, MDSR, RSA
CMC, IDTE, MDSR II
RSA CMC, DRP, IDTE, MDSR, RSA ESD, RSA RSA
Iteration III
BPDM BPDM BPDM, ESD BPDM III
DRP DRP DRP, ESD, MCDT, RSA DRP III
ESD BPDM, DRP, ESD, MCDT, RSA ESD ESD
MCDT DRP, MCDT ESD, MCDT MCDT
RSA DRP, RSA ESD, RSA RSA
Iteration IV
ESD ESD, MCDT, RSA ESD ESD
MCDT MCDT ESD, MCDT MCDT IV
RSA RSA ESD, RSA RSA IV
Iteration IV
ESD ESD ESD ESD V
Table A1.
Hierarchy level for
determinants of
manufacturing digital
transformation success
JMTM
