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Industry 4.0 is considered a new industrial stage in which vertical and horizontal manufacturing processes integration and product connectivity can help companies to achieve higher industrial performance. However, little is known about how industries see the potential contribution of the Industry 4.0 related technologies for industrial performance, especially in emerging countries. Based on the use of secondary data from a large-scale survey of 27 industrial sectors representing 2,225 companies of the Brazilian industry, we studied how the adoption of different Industry 4.0 technologies is associated with expected benefits for product, operations and side-effects aspects. Using regression analysis, we show that some of the Industry 4.0 technologies are seen as promising for industrial performance while some of the emerging technologies are not, which contraries the conventional wisdom. We discuss the contextual conditions of the Brazilian industry that may require a partial implementation of the Industry 4.0 concepts created in developed countries. We summarize our findings in a framework, that shows the perception of Brazilian industries of Industry 4.0 technologies and their relations with the expected benefits. Thus, this work contributes by discussing the real expectations on the future performance of the industry when implementing new technologies, providing a background to advance in the research on real benefits of the Industry 4.0.
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The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
The expected contribution of Industry 4.0 technologies for
industrial performance
Lucas Santos Dalenogare1
Guilherme Brittes Benitez1
Néstor Fabián Ayala2
Alejandro Germán Frank1**
1 Organizational Engineering Group (Núcleo de Engenharia Organizacional NEO), Department
of Industrial Engineering, Universidade Federal do Rio Grande do Sul, Brazil.
2 G-SCOP Laboratory, Grenoble Institute of Technology (INPG), France
**Corresponding author:
Prof. Alejandro G. Frank. Address: Av. Osvaldo Aranha 99 - Sala LOPP 508 - 5º andar. Escola de
Engenharia. Universidade Federal do Rio Grande do Sul, Centro, CEP 90035190 - Porto Alegre,
RS Brazil. Telephone: + 55 51 3308-3490. E-mail: frank@producao.ufrgs.br
Acknowledgments
The authors thank the Brazilian National Council for Scientific and Technological Development (CNPq
Conselho Nacional de Desenvolvimento Científico e Tecnológico) (Process n. 305844/2015-6), the
Research Council of the State of Rio Grande do Sul (FAPERGS, Fundação de Amparo à Pesquisa do Estado
do Rio Grande do Sul) (Process n. 17/2551-0001) and the Research Coordination of the Brazilian Ministry
of Education (CAPES), for the financial support received to conduct this research.
Authors´ biography
Lucas Dalenogare, is a researcher at the Organizational Engineering Group (NEO Núcleo de Engenharia
Organizacional) of the Department of Industrial Engineering of the Federal University of Rio Grande do
Sul Brazil. He has a bachelor degree in Civil Engineering of the same university. His main research is
concerned with the use of advanced technologies for the Industry 4.0
Guilherme Brittes Benitez is a Ph.D. candidate at the Department of Industrial Engineering of the Federal
University of Rio Grande do Sul Brazil. He is member of the Organizational Engineering Group (NEO
Núcleo de Engenharia Organizacional) at the same university. His research is concerned with the creation
of ecosystems for Industry 4.0.
Néstor Fabián Ayala, Ph.D. is a post-doctoral research fellow at the Institute Polytechnic of Grenoble,
France. He is member of the G-SCOP laboratory at the same university. His main research interests include
strategic management, strategic and operations management.
Alejandro Germán Frank, Ph.D. is an Associate Professor of Industrial Organization at the Department of
Industrial Engineering of the Federal University of Rio Grande do Sul (UFRGS) - Brazil. He is the executive
director of the Organizational Engineering Group (NEO Núcleo de Engenharia Organizacional) at UFRGS.
His main research interests include strategic and operations management, industrial organization,
industrial performance, servitization and new product development.
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
The expected contribution of Industry 4.0 technologies for industrial
performance
Abstract
Industry 4.0 is considered a new industrial stage in which vertical and horizontal manufacturing
processes integration and product connectivity can help companies to achieve higher industrial
performance. However, little is known about how industries see the potential contribution of
the Industry 4.0 related technologies for industrial performance, especially in emerging
countries. Based on the use of secondary data from a large-scale survey of 27 industrial sectors
representing 2,225 companies of the Brazilian industry, we studied how the adoption of
different Industry 4.0 technologies is associated with expected benefits for product, operations
and side-effects aspects. Using regression analysis, we show that some of the Industry 4.0
technologies are seen as promising for industrial performance while some of the emerging
technologies are not, which contraries the conventional wisdom. We discuss the contextual
conditions of the Brazilian industry that may require a partial implementation of the Industry
4.0 concepts created in developed countries. We summarize our findings in a framework, that
shows the perception of Brazilian industries of Industry 4.0 technologies and their relations with
the expected benefits. Thus, this work contributes by discussing the real expectations on the
future performance of the industry when implementing new technologies, providing a
background to advance in the research on real benefits of the Industry 4.0.
Keywords: Industry 4.0; digitization; advanced manufacturing; industrial performance; emerging
countries.
1 Introduction
Industry 4.0 is understood as a new industrial stage in which there is an integration between
manufacturing operations systems and information and communication technologies (ICT)
especially the Internet of Things (IoT) forming the so-called Cyber-Physical Systems (CPS)
(Wang et al., 2015; Jeschke et al., 2017). This new industrial stage is affecting competition rules,
the structure of industry and customers’ demands (Gilchrist, 2015; Bartodziej, 2017). It is
changing competition rules because companies business models are being reframed by the
adoption of IoT concepts and digitization of factories (Dregger et al., 2016; Lasi et al., 2014;
Wang et al., 2015). From the market point of view, digital technologies allow companies to offer
new digital solutions for customers, such as internet-based services embedded in products
(Ayala et al., 2017; Coreynen et al., 2017). From the operational perspective, digital
technologies, such as CPS, are proposed to reduce set-up times, labor and material costs and
processing times, resulting in higher productivity of production processes (Brettel et al., 2014;
Jeschke et al., 2017).
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
Several countries have recently created local programs to enhance the development and
adoption of Industry 4.0 technologies. In Germany where this concept was born this program
was called “High-Tech Strategy 2020”, in the United States was established the “Advanced
Manufacturing Partnership”, in China the “Made in China 2025” and in France the “La Nouvelle
France Industrielle” (Kagermann et al., 2013; Rafael et al., 2014; Wahlster, 2013; Zhou, 2017;
CNI, 2013; Liao et al., 2017). In Brazil, the program called “Towards Industry 4.0” (Rumo à
Indústria 4.0) was created by the Brazilian Agency for Industrial Development (ABDI Agência
Brasileira de Desenvolvimento Industrial) together with other initiatives of the Ministry of
Industry, Foreign Trade and Services (MDIC Ministério da Indústria, Comércio Exterior e
Serviços) (ABDI, 2017). All these programs, in both developed and emerging countries aim to
disseminate the Industry 4.0 concepts and technologies in local firms.
Nevertheless, it is well-known that the adoption of advanced technologies can be more
challenging for emerging countries (Hall and Maffioli, 2008; Kumar and Siddharthan, 2013).
Since the economies of emerging countries have been historically more focused on the
extraction and commercialization of commodities, companies in these countries are frequently
behind in terms of technology adoption, when compared to their counterparts in developed
countries (Castellacci, 2008). Other factors such as ICT infrastructure, culture, level of education
and economic and political instability can also interfere in the value perception and in the
consequent level of investments in advanced technologies (Frank et al., 2016). Thus, even when
the Industry 4.0 related technologies are presented by the literature as beneficial for firms, given
the particular characteristics of developing economies, an important question emerges: what is
the perception of industries in developing countries about the benefits of Industry 4.0 related-
technologies for industrial performance?
We aim to answer this question by analyzing the potential benefits for product development,
operations and side-effects aspects expected by the Brazilian industry when implementing the
Industry 4.0 related technologies. We analyze secondary data from a large survey recently
applied in Brazil by the National Confederation of the Industries (Confederação Nacional das
Indústrias CNI), which comprises a sample of 2,225 companies from different industrial
segments of this emerging country. Our findings indicate that only some of the Industry 4.0
related technologies are expected as beneficial by the Brazilian industry and that it depends on
the focus of the industrial sectors, i.e. focus in differentiation or cost. We also discuss some
unanticipated findings regarding advance technologies with negative expected results on
industrial performance.
The remaining sections of this paper are structured as follows. In Section 2, we provide the
theoretical background for Industry 4.0 technologies and the expected benefits of their
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
implementation, as well as their usefulness in emerging countries. Section 3 introduces the
research method where we discuss the secondary data source and our methodological
procedures for the data treatment and analysis. The results are presented in Section 4, followed
by the discussions of the findings in Section 5 and the conclusions in Section 6.
2 Theoretical background
2.1 Industry 4.0 and the international technology diffusion-adoption theories
Some scholars and practitioners have considered four main industry changes throughout the
history, while the Industry 4.0 is the last one and an ongoing industry transformation (Qin et al.,
2016). The steam machine between 1760 and 1840 characterized the first industry
revolution; the second was defined by the utilization of electricity in industrial processes in the
end of the XIX century; the third revolution started in the decade of 1960 with the use of ICT
and industrial automation. The fourth industrial revolution or Industry 4.0 emerged from
several developed countries and it was consolidated in a German public-private initiative to
build smart factories by the integration of physical objects with digital technologies (Brettel et
al., 2014; Hermann et al., 2016). The key element that characterizes this new industrial stage is
the deep change in the manufacturing systems connectivity due to the integration of ICT, IoT
and machines in cyber-physical systems (CPS) (Kagermann et al., 2013; Schwab, 2016). As a
result, the Industry 4.0 can be considered nowadays as a new industrial age based on the
connectivity platforms used in the industry (Lasi et al., 2014; Parlanti, 2017; Reischauer, 2018).
It considers the integration of several different dimensions of the business, with a main concern
on manufacturing issues, based on advance manufacturing technologies (Saldivar et al., 2015;
Fatorachian and Kazemi, 2018). In such a sense, Industry 4.0 can be understood as a result of
the growing digitization of companies, especially regarding to manufacturing processes
(Kagermann, 2015; Schumacher et al., 2016).
Following this concept, Industry 4.0 can be seen as a matter of technology diffusion and
adoption. Emerging technologies of this new industrial age have been conceived in developed
countries such as Germany, which is nowadays leading the diffusion of the concept to other
countries interested in its adoption (Arbix et al., 2017; Bernat and Karabag, 2018). However, the
diffusion-adoption process tends to be slow and it usually flows from developed countries to
developing countries (Phillips et al., 1994; Eaton and Kortum, 1999; Comin and Hobijn, 2004).
Therefore, different behavior patterns could be seen when analyzing digital technologies in an
emerging country such as Brazil comparing to the leading countries on this issue such as
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
Germany. According to the diffusion-adoption theories, different aspects can produce such gaps
between economies. Barriers to the diffusion and adoption are frequently present (Parente and
Prescott, 1994) and the competitive environment of both the supplier side and the adopter
industry also create differences (Robertson and Gatignon, 1986). As a consequence, emerging
countries can have a different value perception of the diffused technologies (Alekseev et al.,
2018; Luthra and Mangla, 2018) which may be based on different needs compared to developed
countries (Kagermann, 2015).
Our study is based on the fact that the perceived value of technologies can be different in
emerging countries, which can also change their adoption of these technologies (Castellacci,
2008; Castellacci and Natera, 2013). Instead of studying the technology diffusion-adoption flow,
as previously done by several other scholars (e.g. Phillips et al., 1994; Comin and Hobijn, 2004),
we focus on the current adoption and its expected benefits in the Brazilian industry. We first
address the general benefits proposed by those enthusiastic on Industry 4.0. Second, we
consider the Brazilian industrial context and the possible difficulties for the implementation of
Industry 4.0 concepts. Then, we use empirical data to investigate the adoption levels and the
expected benefits. We use the diffusion-adoption theory in order to understand better our
findings.
2.2 Industry 4.0 and its expected benefits
The Industry 4.0 concepts are proposed to enable companies to have flexible manufacturing
processes and to analyze large amounts of data in real time, improving strategic and operational
decision-making (Kagermann et al., 2013; Porter and Heppelmann, 2014; Schwab, 2016). This
new industrial stage has been possible due to the use of ICTs in industrial environments
(Kagermann et al., 2013) and due to the cheapening of sensors, increasing their installation in
physical objects (Brettel et al., 2014; Porter and Heppelmann, 2014; Bangemann et al., 2016).
The advancements in these technologies allowed the development of embedded and connected
systems (Jazdi, 2014; Kagermann et al., 2013; Brettel et al., 2014). These systems aim to monitor
and control the equipment, conveyors and products through a cycle of feedbacks that collect a
great quantity of data (big data) and update the virtual models with the information of the
physical processes, resulting in a smart factory (Wang et al., 2015; Wang et al., 2016; Gilchrist,
2015). Therefore, since the development of digital manufacturing in the 1980s, different
technologies have emerged and have been applied in production systems, such as cloud
computing for on-demand manufacturing services (Yu et al., 2015), simulation for
commissioning (Saldivar et al., 2015), additive manufacturing for flexible manufacturing systems
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
(Kagermann et al., 2013; Wang et al., 2016), among others. Table 1 presents a list of ten types
of technologies frequently associated to the Industry 4.0 concept (CNI, 2016; Gilchrist, 2015;
Jeschke et al., 2017).
Table 1: Technologies of the Industry 4.0
Technologies
Definition
Computer-Aided Design and
Manufacturing [CAD/CAM]
Development of projects and work plans for product and
manufacturing based on computerized systems (Scheer, 1994).
Integrated engineering systems
[ENG_SYS]
Integration of IT support systems for information exchange in
product development and manufacturing (Kagermann et al.,
2013; Bruun et al., 2015; Abramovici, 2007).
Digital automation with sensors
[SENSORING]
Automation systems with embedded sensor technology for
monitoring through data gathering (Saldivar et al., 2015).
Flexible manufacturing lines
[FLEXIBLE]
Digital automation with sensor technology in manufacturing
processes (e.g. radio frequency identification RFID in product
components and raw material), to promote Reconfigurable
Manufacturing Systems (RMS) and to enable the integration and
rearrangement of the product with the industrial environment
in a cost-efficient way (Brettel et al., 2014; Abele et al., 2007).
Manufacturing Execution Systems
(MES) and Supervisory control and
data acquisition (SCADA)
[MES/SCADA]
Monitoring of shop floor with real time data collection using
SCADA and remote control of production, transforming long-
term scheduling in short term orders considering restrictions,
with MES (Jeschke et al., 2017).
Simulations/analysis of virtual
models
[VIRTUAL]
Finite Elements, Computational Fluid Dynamics, etc. for
engineering projects and commissioning model-based design of
systems, where synthesized models simulates properties of the
implemented model (Saldivar et al., 2015; Babiceanu and Seker,
2016).
Big data collection and analysis
[BIG_DATA]
Correlation of great quantities of data for applications in
predictive analytics, data mining, statistical analysis and others
(Gilchrist, 2016).
Digital Product-Service Systems
[DIGITAL_SERV]
Incorporation of digital services in products based on IoT
platforms, embedded sensors, processors, and software
enabling new capabilities (Porter and Heppelmann, 2014).
Additive manufacturing, fast
prototyping or 3D impression
[ADDITIVE]
Versatile manufacturing machines for flexible manufacturing
systems (FMS), transforming digital 3D models into physical
products (Weller et al., 2015; Garrett, 2014).
Cloud services for products
[CLOUD]
Application of cloud computing in products, extending their
capabilities and related services (Porter and Heppelmann,
2014).
The technologies presented in Table 1 support the three main advantages that characterize
Industry 4.0: vertical integration, horizontal integration and end-to-end engineering (Kagermann
et al., 2013; Wang et al., 2015). The vertical integration refers to the integration of ICT systems
in different hierarchical levels of an organization, representing the integration between the
production and the management levels in a factory (Kagermann et al., 2013). On the other hand,
the horizontal integration consists in the collaboration between enterprises inside a supply
chain, with resource and real time information exchange (Brettel et al., 2014). End-to-end
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
engineering is the integration of engineering in the whole value chain of a product, from its
development until after-sales (Kagermann et al., 2013; Brettel et al., 2014; Gilchrist, 2016).
The extant literature has suggested that this integration achieved by digital technologies can
promote several benefits to the industry (Kagermann et al., 2013). For business operations, the
communication between machines and products enables reconfigurable and flexible lines for
production of customized products, even for small batches (Brettel et al., 2014; Wang et al.,
2016). In addition, with the CPS for information processing, companies have more support for
decision-making processes and have faster adaptation for several kinds of events, like
production line breakdowns (Schuh et al., 2017). Therefore, these systems can increase the
productivity of the companies, with better efficiency of resources utilization, through the
combination of production with smart grids for energy savings, for example (Ali and Azad, 2013;
Jeschke et al., 2017). Industry 4.0 also has opportunities and benefits for business growth.
Through the horizontal integration concept, collaborative networks among enterprises combine
resources, divide risks and quickly adapt to changes in the market, seizing new opportunities
(Brettel et al., 2014). Collaboration is extended to customers also, through digital channels and
smart products that integrate the firm with the customers, allowing also the delivery of higher
value to the latter (Kiel et al., 2016; Porter and Heppelmann, 2014). Using additive
manufacturing technology, enterprises can co-design products with customers, resulting in
highly customized products, increasing their perceived value (Weller et al., 2015). Finally, with
the service orientation of Industry 4.0 (Gilchrist, 2016) and horizontal integration, new business
models can be developed, with new ways to deliver and capture value from customers
(Kagermann et al., 2013; Chryssolouris et al., 2009).
From a socio-technical perspective (Hendrick and Kleiner, 2001), it is acknowledged that the
adoption of the aforementioned emerging technologies of the Industry 4.0 are not supported
by themselves. There are at least three complementary socio-technical dimensions to the
technological one to consider the digitization process towards the Industry 4.0 implementation
(Frank et al., 2015): (i) organization of work - new technologies need to rethink how the
organization will operate (Brettel et al., 2014); (ii) human factors new technologies require
new competences and skills from the workers (Ras et al., 2017; Wei et al., 2017); and (iii) external
environment adoption of new technologies are dependent of the maturity where they are
implemented (Schumacher et al., 2016). We focus on two of them, the technological
opportunities and its relation with a specific external environment (i.e. an emerging country).
Human factors and the organization of work can be enablers that potentialize the benefits of
these technologies for business performance, as previously shown in the broader literature of
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
technology management (Westerman et al., 2014). Thus, we consider only the first step, which
is to verify the expected contribution of the technologies for industrial performance, being
aware that such technologies may need a complementation of these other dimensions in a
specific context.
2.3 Industry 4.0 in the context of emerging countries
As stated, Industry 4.0 was born in developed countries, where prior industrial stages are
already mature regarding automation and ICT usage, two concepts of the third industrial
revolution that converge in the Industry 4.0 (Kagermann et al., 2013). In this sense, emerging
countries may face an important gap for the Industry 4.0 adoption due to the low maturity of
prior industrial stages (Krawczyński et al., 2016; Guan et al., 2006). In the case of Brazil, the ICT
adoption has significantly grown improving work productivity (Mendonça et al., 2008; 2009;
Cortimiglia et al., 2012). However, as shown in the findings of Frank et al. (2016) in a large-scale
survey of Brazilian industry, the investments on software acquisition has not leaded to good
results in terms of market benefits or internal manufacturing process improvement. The authors
suggest that companies are investing in software acquisition simply to automatize their
operational routines instead of seeking advanced ICT tools that could give them a real
competitive advantage in innovation development (Frank et al., 2016).
On the other hand, regarding manufacturing technologies, the same work of Frank et al. (2016)
shows that machinery and equipment acquisition strategy resulted in poor results for innovation
outcomes when compared to other innovation activities of industries in Brazil. As argued by
these authors, one of the reasons is that most of the companies do not acquire leading
technologies as those from the Industry 4.0 , but only those basics to update old industrial
equipment, which is also in line with other prior works in emerging markets (e.g. Franco et al.,
2011; Zuniga and Crespi, 2013). In this sense, the work of Nakata and Weidner (2012) showed
that most population in emerging countries has lower incomes than in developed countries,
what implies that the most consumed product are low cost, making lower price a more relevant
factor in competitiveness than innovativeness. This market behavior can clearly influence
technology investments. Usually, firms in developing countries are focused on making
investments in well-established technologies for the increase of productivity than in advanced
technologies for the differentiation of products, as evidenced in prior studies, cited above. Thus,
the two main pillars of Industry 4.0 processing technologies and ICT still seems weak in order
to advance toward the fourth industrial revolution.
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
In addition, there are structural challenges that emerging economies may face and that can be
a barrier for the Industry 4.0 establishment. One of them is that emerging economies growth
are based on the low-cost workforce, especially for manufacturing activities, and it can
discourage or delay investments in automation and other technologies, which usually are more
expensive in these countries (Castellacci, 2008; Ramani et al., 2017). The supply chain of the
manufacturing industry may be another constraint, which tend to be less integrated when
compared to developed countries (Marodin et al., 2016; Marodin et al., 2017b). Besides, the few
investments in R&D (Olavarrieta and Villena, 2014), added to the economic and political
instabilities and low quality of education and research institutions (Hall and Maffioli, 2008;
Crisóstomo et al., 2011; Frank et al., 2016), configure a hard scenario for the adoption of Industry
4.0 technologies.
Finally, based on this prior research, it is clear that challenges for the adoption of Industry 4.0
technologies in emerging countries are different from those in developed countries, as it is
proposed in the technology diffusion-adoption literature (Phillips et al., 1994). As the concept of
Industry 4.0 is relatively new, there is a high uncertainty and lack of knowledge about the real
impact and contribution of the Industry 4.0 related technologies in the context of emerging
countries in general. In order to fill this gap, our study focuses on the contribution of these
technologies in the Brazilian industry, as one representative of the emergent economies which
has significantly increased the industrial activities in the recent years (Frank et al., 2016). Few
studies have been conducted in this country on Industry 4.0 initiatives, while most of them come
from consulting research and presents only descriptive information of this scenario. One of them
is the survey conducted by Price Waterhouse Coopers (PWC) in 32 Brazilian industries (PWC,
2016), which shows a low level of digitization in several business processes. However, despite
the low level of digitization, this survey shows that Brazilian enterprises expect bigger
investments in digital technologies for the next years, with return in efficiency improvement,
reduction of operational costs and additional business income (PWC, 2016). Other important
source of information is the industrial survey conducted by the National Confederation of the
Industry of Brazil (CNI, 2016), where a set of Industry 4.0 related technologies were considered
and analyzed in the Brazilian industry. This survey shows that the level of implementation is still
low, but that there are already some industrial sectors investing in these technologies and that
an important part of the industry is concerned with this issue and is expecting new benefits from
such investments. Following this last survey, we aim to deepen such analysis by investigating the
association between the considered technologies and expected benefits in the CNI (2016) large-
scale survey.
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
3 Research method
3.1 Sampling and measures
Our study focuses on a secondary data analysis of the dataset collected by the Special survey
on Industry 4.0 in Brazil, conducted by the National Confederation of the Industries (CNI, 2016).
CNI is an entity that represents the Brazilian industry and comprises 1,250 employers’ unions
and almost 700,000 industrial businesses affiliated. CNI promotes the interests of the industry
in Brazil and as well as research and development studies
1
. This large-scale industrial survey had
the purpose of obtaining a current technological overview on Industry 4.0 in Brazilian industry.
CNI elaborated a questionnaire and sent it by e-mail to operations managers of 7,836 companies
random selected from the population. The population of the survey is composed only by
companies related to production activities (i.e. extractive and transformation sectors). The total
amount of useful responses obtained was 2,225 which represents a response rate of 28.39%
(CNI, 2016). The final sample represents 40,8% small, 36,6% medium and 22,6% large industrial
companies from 27 sectors in Brazil (see demographic details in Table 2). Given the demographic
distribution of the complete responses (questionnaires) regarding companies’ size, the
industrial sectors, and the regional distribution of the data collected (which included all the
industrialized States of the country), we have no reasons to believe the existence of biased
patterns when compared to the incomplete responses, which were not included in the final
sample (Hair et al., 2009, p.42-45). However, such level of details is not provided in the available
secondary data from (CNI, 2016).
Table 2 Demographic characteristics of the industrial sectors considered in the sample
Industrial
sectors
considered
in the study
Mining
Rubber products
Food products
Plastics produtcs
Beverages
Non-metallic mineral products
Textiles products
Basic metals
Wearing apparel
Metal products (not machinery and equipment)
Leather and related products
Computers, electronics and opticals products
Footwear and parts
Electrical equipment
Wood products
Machinery and equipment
Pulp and Paper
Motor vehicles, trailers and semi-trailers
Printing and recorded media
Other transport equipment
Coke and refined petroleum products
Furniture
Chemicals
Repair and installation
Soap and detergents
Other manufacturing
Chemicals and pharmaceuticals
Sample
distribution
Total of companies in the 27
sectors: 2,225
Large companies: 500 (22.6%)
Medium companies: 815 (36.6%)
Small companies: 910 (40.8%)
1
Information source http://www.portaldaindustria.com.br/cni/en/about/about-cni/
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
The questionnaire used in the survey is composed by six group of main questions
2
: (i) Key-
technologies: a list of 11 digital technologies related to the Industry 4.0 where the companies
indicate the technologies that they consider the most potential to enhancing the
competitiveness of the Brazilian industry in the next five years; (ii) Adopted technologies: the
same list of technologies where the companies indicate those technologies they are already
using (iii) Expected benefits: a list of benefits expected from digital technologies where the
companies indicate up to five benefits they expect to obtain with the technologies adopted; (iv)
Internal barriers: a list of internal barriers the companies face in order to acquire digital
technologies; (v) External barriers: a list of external barriers the companies face in order to
acquire digital technologies (vi) Industrial policy: a list of possible actions the government should
make to accelerate the digital technologies adoption by the Brazilian industries. For the purpose
of this paper, we used data from the questions (ii) and (iii) of this survey, i.e. the digital
technologies adopted and the expected benefits. Question (ii) asks: Indicate the digital
technologies that your company already uses”. For this question, a list of 11 digital technologies
are provided (see Section 3.2). Question (iii) asks “Indicate the main benefits that your company
expects to obtain by adopting digital technologies: (Indicate up to five items)”. Here, a list of 14
benefits are provided (see Section 3.2). For both set of variables, the scale provided by the CNI
database is in percentage (0% to 100%), representing the relative amount of companies of each
industrial sector that have adopted a specific technology (Question ii) or that are expecting a
specific benefit (Question iii).
3.2 Variables Selection
Since our main purpose is to understand the expected benefits of Industry 4.0 related
technologies for industrial performance in Brazil, we defined as independent variables the
technologies of Industry 4.0 adopted by the industrial sectors and as dependent variables the
benefits expected by industrial sectors that are applying these technologies, which are both
provided by the CNI (2016) survey. As presented in Table 3, the Industry 4.0 technologies are
represented by 11 technologies and the expected benefits by 14 main benefits aligned with
those highlighted in the literature. From the independent variables of our regression model, we
did not include two technologies that are considered in the CNI survey. The first one was ‘digital
automation without sensors’, that was excluded because it is exclusively related to the classic
automation of the third Industrial Revolution. The second variable excluded was
Simulation/virtual models [VIRTUAL], because it did not follows a normal distribution in the data,
2
The complete questionnaire is available at http://www.portaldaindustria.com.br/estatisticas/sondesp-
66-industria-4-0/
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
presenting a high value of Kurtosis (4.269) (Hair et al., 2009), although it is directly related to the
Industry 4.0 and was considered in Table 1. The data for the considered variables of our study
are provided by CNI (2016) at an aggregate-level, as the percentage of companies in each
industrial sector that indicated the adoption of a specific technology and the expectation for a
specific benefit. Therefore, our study considers the analysis at the industrial sector level. Besides
these variables, we also included two dummies as potential control variables in order to
represent the three levels of technology intensity of the 27 industrial sectors under analysis (low,
medium and high). These technological intensity levels are described in the CNI (2016) report.
Table 3 summarizes the dependent and independent variables used in our regression model.
Table 3 - Technologies and expected benefits considered in the research model
Technologies
(Independent Variables)
Computer-Aided Design integrated with
Computer-Aided Manufacturing [CAD/CAM]
Integrated engineering systems [ENG_SYS]
Digital automation with sensors [SENSORING]
Flexible manufacturing lines [FLEXIBLE]
MES and SCADA systems [MES/SCADA2
Big data [BIG_DATA]
Digital Product-Services [DIGITAL_SERV]
Additive manufacturing [ADDITIVE]
Cloud services [CLOUD]
1 These dependent variables were deleted from the model during the PCA procedure of variables
reduction as explained in Section 3.3.
3.3 Variables reduction for regression analysis
To understand how the different Industry 4.0 related technologies are seen as beneficial for the
industrial performance, we kept all Industry 4.0 technologies (Table 1) as single variables (not
constructs) in order to differentiate the association of each of them to the expected
performance outputs. We tested multicollinearity using the Variance Inflator Factor (VIF) to
avoid potential mulicollinearity among these independent variables in the regression model. On
the other hand, we synthesized the 14 expected benefits presented in Table 3 (i.e. industrial
performance) into main categories using a Principal Component Analysis (PCA)
3
. PCA technique
3
PCA has been proposed as suitable also for small sample sizes (aggregated data, in our case), when the
validation tests and the outputs are robust enough as those obtained in our results. For more details see
MacCallum et al. (2001) and Dochtermann and Jenkins (2011).
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
allowed us to obtain broader performance metrics based on the partial contribution of different
but correlated measures (Hair et al., 2009). Such a strategy was also used in other prior works
in the operations management field (e.g. Marodin et al., 2017a) and innovation field (e.g. Frank
et al., 2016). This helped us to study the potential contribution of the technologies for the
benefits of overall performance metrics when strong correlated outputs are considered. Based
on Hair et al. (2009), we divided this procedure in two steps, the validation of PCA adequacy to
the sample and the reduction of variables by means of the PCA technique, as explained next.
We used three criteria to evaluate the adequacy of the data to the PCA technique: the Kaiser-
Meyer-Olkin (KMO) test for measure of sampling adequacy, Bartlett’s test of sphericity, and the
measure of sampling adequacy (MSA)
4
(Hair et al., 2009). All these tests suggested that the
dependent variables can be reduced using PCA, since the KMO test was 0.501 (i.e. it equals the
threshold value recommended), while the Barlett’s test of sphericity presented a p-value < 0.001
(i.e. lower than the suggested p < 0.05 significance level) and the MSA test indicated that 75%
of the variables had values higher than 0.5, as required by this test (Hair et al., 2009).
Then, we performed the PCA for the dependent variables (Table 4). We used a Varimax
orthogonal rotation factor solution in order to reduce ambiguities often related to non-rotated
analysis and achieve clearer and more meaningful factor solution from the PCA (Hair et al. 2009).
We followed an iterative process to achieve the optimized solution where the optimal number
of components were selected based on the eigenvalues, which should be higher than 1.0 (latent
root criterion) and on the the percentage of variance criterion, which considers that the optimal
number of components are those that exceed 60% of the total variance and ideally more than
70%; in our case we used the latter percentage (Hair et al., 2009). In the initial solution, 6 of the
14 output variables (Y2, Y3, Y5, Y8, Y9 and Y14) showed no relation to any principal components
(these variables are indicated in Table 3). Therefore, they were deleted from the outputs. Then,
the PCA with Varimax was performed again for the eight remaining dependent variables, which
were represented in three components that explain 75.49% of the variance, as shown in Table
4. The three main components were defined according to the variables with high factor loading
(>0.5) represented in them. The factorial scores for these new three outputs were obtained by
means of the Thurnstones’ method. Table 4 also shows the reliability analysis of the three
constructs using Cronbach’s alpha, being all them above the threshold value of 0.7 (Hair et al.,
2009). Hence, the final three factors are: Product expected benefits [PRODUCT], Operational
4
The statistical tests for both PCA and regression analysis were performed by using IBM® SPSS® Statistics
version 20.
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
expected benefits [OPERATION] and Side-effects expected benefits [SIDE-EFFECTS]. The first one
(PRODUCT), includes all benefits regarding the product offered, measurement of customization,
quality and launch time as dimensions of the product performance. The second construct
(OPERATION) considers all the metrics regarding the internal industrial activity of the factory,
including costs, productivity and process control of the factory.
Lastly, we called the third component as Side-effects expected benefits [SIDE-EFFECTS] because
it considers the collateral effects related to the use of digital technologies of Industry 4.0. In this
third component, two benefits are included: the improvement in sustainability (or reduction of
externalities) and the reduction of labor claims. Despite the main goal of Industry 4.0, which is
to increase productivity, the initiative aims to reach this goal with more efficient resources
utilization, possible by the use of technologies such as additive manufacturing (Kagermann et
al., 2013; De Sousa Jabbour et al., 2018). In addition, labor claims can be reduced due to different
reasons in this initiave, as this new paradigm relies less on the human force (i.e. fewer workers
with potential claims) and also because some technologies aims to help workers to perform their
taks (i.e. workers more assisted to do their job), e.g. human-machine collaboration systems
(Gilchrist, 2016; Wang et al., 2015). Both benefits, improving sustainability and reducing labor
claims, can be related into one component as they are usually not the primary objectives
expected from industries when investing in digital technologies, so these benefits can be seen
as derivative from the expected primary benefits from the Industry 4.0 (CNI, 2016). Table 5
presents the correlation matrix of the final set of variables used in our analysis. This table also
shows the descriptive statistics such as mean, standard deviation and the skewness and kurtosis
test to verify normality of the data.
Table 4 - Rotated Factor-Loading Matrix from PCA procedure
List of expected benefits from the Industry 4.0
Factor loadings (a)
PRODUCT
OPERATIONAL
SIDE-
EFFECTS
Commu-
nalities
Improvement of product customization
0.797
0.251
-0.171
0.727
Improvement of product quality
0.766
0.167
-0.309
0.711
Reduction of operational costs
0.306
0.865
0.026
0.843
Increase productivity
0.461
0.609
0.071
0.588
Reduction of product launch time
0.868
0.028
0.202
0.796
Improving of sustainability (externalities)
0.079
-0.076
0.935
0.886
Increase of processes visualization and control
-0.035
0.818
0.06
0.675
Reduce of labor claims (worker satisfaction)
-0.311
0.357
0.767
0.813
Eigenvalue
2.986
1.919
1.135
% of variance explained (cumulative)
37.32%
61.31%
75.49%
Cronbach’s alpha
0.807
0.750
0.720
(a) High factorial loadings (>0.5) are represented in bold and underlined
The final version of is paper has been published at the International Journal of Production Economics, DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
Table 5 Correlation matrix and descriptive analysis
MEAN
(%)
S.D.
Skweness
Kurtosis
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
PRODUCT
0.24
0.06
0.402
0.596
--
2
OPERATIONAL
0.36
0.05
-0.639
-0.112
0.000
--
3
SIDE-EFFECTS
0.07
0.02
-0.284
-0.328
0.000
0.000
--
4
CAD_CAM
0.27
0.17
0.542
-0.771
0.648**
0.402*
0.080
--
5
ENG_SYS
0.14
0.08
0.352
-0.683
0.597**
0.446*
0.215
0.749**
--
6
SENSORING
0.20
0.09
-0.080
-0.400
-0.191
0.335
0.281
-0.157
0.229
--
7
FLEXIBLE
0.06
0.04
0.343
-0.327
0.191
0.207
0.062
-0.069
0.303
0.699**
--
8
MES-SCADA
0.05
0.03
0.347
-0.747
-0.305
0.297
0.234
-0.113
0.300
0.714**
0.505**
--
9
BIG_DATA
0.07
0.04
0.795
0.970
-0.279
0.487*
0.187
0.045
0.246
0.256
0.109
0.516**
--
10
DIGITAL_SERV
0.03
0.02
1.054
2.517
0.381
0.306
-0.104
0.226
0.363
-0.020
0.256
0.073
0.353
--
11
ADDITIVE
0.04
0.04
1.415
1.308
0.625**
0.124
0.356
0.522**
0.669**
0.107
0.314
0.139
0.323
0.463*
--
12
CLOUD
0.06
0.03
0.043
0.069
0.064
-0.020
0.050
-0.191
-0.207
0.409*
0.418*
0.115
-0.078
0.041
0.042
--
13
Control_tech_low
0.48
0.51
0.079
-2.160
0.014
-0.395*
-0.228
-0.348
-0.298
-0.465*
-0.239
-0.244
-0.345
-0.229
-0.264
-0.114
--
14
Control_tech_high
0.26
0.45
1.164
-0.702
0.279
0.415*
0.045
0.496**
0.444*
0.173
0.283
0.049
0.087
0.491**
0.356
0.225
-0.570**
--
** p< 0.01; * p<0.05.
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
4 Results
We used an ordinary least square (OLS) regression
5
to understand the association of Industry
4.0 related-technologies to three types of expected benefits: Product expected benefits
[PRODUCT], Operational expected benefits [OPERATIONAL] and Side-effects expected benefits
[SIDE-EFFECTS]. OLS regression should be used only if some standard requirements of the
database are achieved, such as normality, linearity, and homoscedasticity (Hair et al., 2009). The
skewness and kurtosis values reported in Table 5 suggest that the variables can be assumed as
normal distributed, since they are below the threshold of 2.58 (α=0.01) (Hair et al., 2009). We
also assessed data normality graphically by means of an examination of the residuals. We
analyzed collinearity by plotting the partial regressions for the independent variables while
homoscedasticity was visually examined in plots of standardized residuals against predicted
value. All these requirements were met in our dataset. Moreover, multicollinearity could be also
a problem for OLS regression (Hair et al., 2009). Therefore, we tested the variance inflation
factor (VIF) among the independent variables, resulting in VIF<3.5 for the independent variables
and control variables, excepting for CAD/CAM, ENG_SYS and SENSORING which resulted in
VIF<8.14. As all these values were below the threshold VIF=10.0, multicollinearity may not be a
concern in our regression model (Hair et al., 2009).
We performed three independent regression models, one for each of the expected benefits (i.e.
PRODUCT, OPERATIONAL and SIDE-EFFECTS). The results of the regression models for the three
industrial expected benefits metrics are shown in Table 6. Two of the three models were
significant at p<0.05 and one did not show statistical significance. The first regression model (F=
14.245, p<0.001) explained 84.9% of the variance of PRODUCT; while the second model
(F=3.042, p = 0.024) explained 46.3% of the OPERATIONAL variance. Lastly, we identified that
SIDE-EFFECTS was not significant (F= 0.751, p = 0.679).
Regarding the association of the specific Industry 4.0 related technologies with the expecting
PRODUCT, the following technologies presented positive and significant effects: integrated
engineering systems for product development and manufacturing [ENG_SYS] = 0.438, p =
0.063); incorporation of digital services into products [DIGITAL_SERV] (β = 0.286, p = 0.022);
additive manufacturing [ADDITIVE] = 0.261, p = 0.050); and Cloud Services [CLOUD] (β = 0.255,
p = 0.043). In addition, one technology is negatively associated to the expected outcome of this
expected benefits metric: big data analysis [BIG_DATA] (β = -0.388, p = 0.004).
5
OLS regression was performed using IBM SPSS Statistics ® version 20.
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
In the second expected benefits metric, OPERATIONAL, the technologies with positive and
significant association were: Computer-Aided Design with Computer-Aided Manufacturing
[CAD/CAM] = 0.774, p = 0.046); digital automation with sensors for process control
[SENSORING] (β = 0.778, p = 0.064) and Big Data [BIG_DATA] = 0.658, p = 0.008). On the other
hand, additive manufacturing [ADDITIVE] had a negative association = -0.529, p = 0.036) to
this expected benefits metric. ADDITIVE also showed a positive association to SIDE-EFFECTS (β =
0.622, p = 0.081), although the complete model for SIDE-EFFECTS was not statistical significant.
Table 6 Results of the regression analysis(a)
Expected benefits for
PRODUCT
OPERATIONAL
SIDE-Effects
CAD_CAM
0.310
0.774**
-0.306
ENG_SYS
0.438*
-0.129
0.118
SENSORING
-0.189
0.778*
0.303
FLEXIBLE
0.212
0.062
-0.409
MES-SCADA
-0.246
-0.345
0.078
BIGDATA
-0.388***
0.658***
-0.040
DIGITAL_SERV
0.286**
0.192
-0.308
ADDITIVE
0.261**
-0.529**
0.622*
CLOUD
0.255**
-0.149
0.009
Control_tech_low
0.257
0.379
-0.300
Control_tech_high
0.426*
0.241
-0.126
F-value
14.245***
3.042**
0.751
R2
0.913
0.690
0.355
Adjusted R2
0.849
0.463
-0.118
(a) Significant effects are represented in bold and underlined; *p<0.1; **p<0.05;
***p<0.01.
Furthermore, we performed a statistical power analysis of our two significant models (PRODUCT
and OPERATION) based on (Cohen et al., 2003). We first estimated the population effect size of
R2 using Cohen’s f2 estimation
6
. For the PRODUCT model we obtained a f2 = 10.45, which
represents a statistical power of > 0.99 at α = 0.01, while for the OPERATION regression model
the f2 was 2.23, which represents a statistical power of ≈ 0.93 at α = 0.01. We also considered
the statistical power of the partial coefficients using Cohen’s f2 estimation for the predictors
7
and the range of effects suggested by them: 0.02 small effect, 0.15 medium effect, and 0.35
large effect (Cohen et al., 2003, p. 95). Considering the statistical significant independent
6
According to Cohen et al. (2003, p. 92):

7
According to Cohen et al. (2003, p. 94): 
; where sr2 represents the squared semipartial
correlation coefficient for the predictor of interest.
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
variables in the PRODUCT model, two of them showed large effects: BIGDATA (0.78) and
DIGITAL_SERV (0.44), while all the others showed medium effect (≥0.27). For the significant
regressors in the OPERATION model, two technologies indicate large effect: BIGDATA (0.63) and
ADDITIVE (0.35), while the other two CAD_CAM and SENSORING presented medium effects
(0.32 and 0.27 respectively). Therefore, we can conclude that the significant effects have also
satisfactory statistical power in our sample.
5 Discussions
We summarized our findings in Figure 1, aiming to illustrate the connections between the
different Industry 4.0 related technologies and the expected benefits. We use this framework
(Figure 1) to guide the discussion of our findings and to clarify how these Industry 4.0
technologies can be understood in the Brazilian context. Firstly, we divided our framework
(Figure 1) in two set of technologies as our findings showed in Table 6 The first set is related to
(i) Product Development Technologies of the Industry 4.0 while the second set is related to (ii)
Manufacturing Technologies of the Industry 4.0. We divided technologies in these two groups
because, as we shown in our results, the industrial sectors have different expectations for them.
According to our findings of Table 6, technologies that are expected to contribute for Product
Performance (i.e. Product Development Technologies) are ENG_SYS, DIGITAL_SERV, ADDITIVE
and CLOUD, while the technologies expected to bring benefits for operational performance (i.e.
Manufacturing Technologies) are CAD_CAM, SENSORING and BIGDATA. Two technologies,
integrated engineering systems [ENG_SYS] and Computer-Aided Design and Manufacturing
[CAD/CAM] are considered integration systems in the interface between product and
operational processes, as shown in Figure 1 (Tao et al., 2018a). Next, we discuss in detail the
configuration of this framework based on our findings and on prior evidences from the
literature.
Firstly, regarding the Product Development Technologies (Figure 1), additive manufacturing
[ADDITIVE], which in product development is represented by 3D-printing, is associated with the
expected benefits for new product development. This expectation is aligned with the literature,
which highlights that the use of additive technology brings several advantages since products
can be digitally modified before their physical production, reducing the processing times,
resources and tools needed. This technology accelerates product innovation and assists co-
design activities, promoting more customized products (Yin et al., 2017). While additive
manufacturing (3D-printing) promotes customization of the products, our findings (Table 6)
show that the industry also expects digital services in products [DIGITAL_SERV] and Cloud
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
Services [CLOUD] to increase the value perceived by the customers (Figure 1). According to
Porter and Heppelmann (2014) digital services connected in the cloud are a global trend in
companies, allowing them to launch smart products with embedded sensors, processors,
software and connected via internet, which enables new functions and capabilities related to
their monitoring, control, optimization and autonomy. With the Internet of Things (IoT),
products can communicate with other products and systems of products, optimizing overall
results and enabling after-sales service solutions. These technologies should improve the
performance of extant products and the development of new products, and its utilization shows
some degree of differentiation strategies expected by Brazilian industrial sectors. However, as
the (CNI, 2016) report state, there are still few industrial sectors that incorporate digital services
in their products with cloud systems and that use additive manufacturing, also shown in Table
6.
On the other hand, the use of Big Data collection and analysis [BIG_DATA] showed a negative
association to the benefits expected for product performance. This is a surprising result for us,
since the literature describes this technology as of great potential to leverage innovation,
competition and productivity in business processes (Wamba et al., 2015). While the industry is
expecting positive outcomes for integrating data in the cloud (i.e. CLOUD was positive), they do
not present an optimistic perspective for the latter technology. In other words, IoT technologies
are perceived as useful for real-time processing but not for data storage and analysis. This may
suggest that the Brazilian industry still lags in the implementation of one of the most promising
tools in the Industry 4.0 for product improvement and innovation (Wamba et al., 2015).
Therefore, even though these technologies have been widely diffused in developed countries,
their diffusion and adoption in Brazil is still behind the competitive level expected. Such problem
can be corroborated with a recent industrial survey conducted by PwC consulting (PWC, 2016)
that indicates that around 63% of Brazilian companies considered themselves in a weak maturity
level for Big data analytics, 30% in a middle maturity level and those that represented the
remaining 7% outsourced data analytics competencies. As most industrial sectors do not have
the capacity to properly analyze the large amount of data they generate, we conclude that this
lack of knowledge might impair the perception of usefulness for the development of new
products, which represents a diffusion-adoption gap for the Industry 4.0 in Brazil.
Regarding the interface between the (i) Product Development Technologies and (ii)
Manufacturing Technologies, our findings (Table 6) showed that there are two complementary
integration technologies: ENG_SYS, which is positively associated to PRODUCT expected
benefits, and CAD/CAM, which is positively associated to OPERATIONAL expected benefits
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
(Figure 1). We argue that based on the findings and on the fact that ENG_SYS work with the
integration of the whole product lifecycle data, from the product conception to its production
and commercialization (Abramovici, 2007; Stark, 2011; Bruun et al., 2015). This technology can
aid different industrial sectors to overcome the well-known communication and coordination
barriers they face when involving suppliers in a collaborative NPD for complex products (Langner
and Seidel, 2009; Peng et al., 2014). Moreover, as horizontal integration is one of the main
Industry 4.0 characteristics, integrated engineering systems also have an important role for
connecting people, objects and systems through digital platforms, what clearly simplify the
orchestration of services and applications in industrial activities (Kagermann et al., 2013). On
the other hand, CAD/CAM can help the operational aspects for vertical integration, since it can
help to translate the product lifecycle data from end-to-end engineering into product design
specifications, enhancing the visibility of manufacturing processes still in the design phase
(Jeschke et al., 2017).
Following the Manufacturing Technologies dimension, surprisingly neither MES/SCADA nor
flexible manufacturing lines [FLEXIBLE] were significantly associated to the OPERATIONAL
expected benefits. Based on the extant literature, we were expecting a positive association of
them, jointly with the integration systems (ENG_SYS and CAD/CAM) and the digital automation
with sensors [SENSORING], as a set of standard technologies for the Industry 4.0 manufacturing
system. While ENG_SYS and CAD/CAM integrate product development data with manufacturing
processes (Miranda et al., 2017), SENSORING enables data collection in the manufacturing
process (Konyha and Bányai, 2017), which could be used by the flexible manufacturing lines
[FLEXIBLE] to reconfigure or adapt the processing sequence, schedule, etc. (Wang et al., 2015)
with MES/SCADA support (Jeschke et al., 2017). In other words, these technologies should form
a system that enables both, horizontal and vertical integration (Zhou et al., 2015). ENG_SYS
contributes for information sharing among functional areas in the factory, both internally and
externally, which in the latter constitutes the horizontal integration. FLEXIBLE and MES/SCADA
contribute to the integration among process stages in the hierarchical areas. The first aims to
build reconfigurable lines with sensor technology, in order to ease the change the product types
in the production lines (Brettel et al. 2014; Steimer et al., 2016), while MES/SCADA generate
daily production orders from the ERP, considering several restrictions from machine data
(Jeschke et al., 2017), SENSORING acts at the most basic levels of the equipment operation
(Gerber et al., 2013). One reason because MES/SCADA and FLEXIBLE might be not statistically
associated to the OPERATIONAL expected benefits is because they are in very early stage of
adoption in the Brazilian industry, since only around 8% of the industry has adopted these
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
technologies for operational processes, according to the CNI report (CNI, 2016). Thus, several
industrial sectors may not be aware of their contribution for operational benefits.
Digital automation with sensors for process control [SENSORING] showed a significant
association to the OPERATIONAL expected benefits, being one of the most implemented
technologies (around 27%) in the industries of the survey (CNI, 2016). Even though this is one of
the less advanced technologies in the Industry 4.0 concept (Yu et al., 2015), it provides the basis
for production cells control and data collection of manufacturing flow and cells demand, aiming
to provide inputs for the flexible lines and the MES/SCADA, as shown in Figure 1. SENSORING
also allows to create operational big data [BIG_DATA] also positively significant in our findings
for further analysis aiming for predicting maintenance, machine-learning (self-adapting),
scheduling for the Manufacturing Execution System (MES) and to provide information for new
design and manufacturing in the CAD/CAM system (Tao et al., 2018b), as we show in the
framework of Figure 1. On the other hand, it is worth noticing that cloud services [CLOUD] did
not show significant association to the OPERATIONAL expected benefits while BIG_DATA did, as
we explained before. Based on prior studies (e.g. Gilchrist, 2015; Jeschke et al., 2017) we
expected a joint contribution of these technologies. One possible reason is that CLOUD is
associated with external data warehousing and this is still a concern in the industry due to data
security, which represent a barrier for its implementation (Wang et al., 2015).
The last Industry 4.0 technology at the operational level is additive manufacturing [ADDITIVE]
which we represented in Figure 1 as overlapped with different manufacturing operations. This
means that ADDITIVE could be used in different operation stages and for different production
purposes. However, our findings showed a negative association of this technology with
OPERATIONAL expected benefits. According to Weller et al. (2015), additive manufacturing still
has several restrictions for its application in manufacturing processes, such as the availability of
materials and lack of defined quality standards. Moreover, although this technology can improve
product development, this equipment has still low production throughput speed, when
compared to conventional manufacturing, which may affect larger-scale production levels with
cost efficiency, as suggested by our results.
Finally, regarding the SIDE-EFFECTS expected benefits, Figure 1 represents it as a possible
secondary perceived benefit from the Industry 4.0. Our results indicated a positive association
with additive manufacturing. However, the complete model for SIDE-EFFECTS was not
statistically significant even when ADDITIVE has a positive association to this output
suggesting that this performance is not expected with the use of most of the Industry 4.0-related
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
technologies. This is an unexpected finding, since the improvement of resource consumption
efficiency is one of the main areas of Industry 4.0 (Kagermann et al., 2013), and the technologies
analysed in this paper are suggested to contribute to sustainability (e.g. Kiel et al., 2016; Stock
and Seliger, 2016; Man and Strandhagen, 2017; De Sousa Jabbour et al., 2018), and indirectly
for labor claim reduction, by automatizing the production process which reduces the need for
manpower (e.g. Hozdić, 2015). The concern with Industry 4.0 as a way to deal with these side-
effects aspects has been addressed in studies of developed economies. However, when
considering emerging economies such as Brazil, other aspects may be priority in the industry’s
concern. As acknowledged by the CNI report (CNI, 2016), the main efforts of Brazilian industries
with digital technologies has been to increase productivity, while the side-effects benefits are
not yet a clear objective of the industry when investing in Industry 4.0 technologies. Therefore,
they could be a secondary benefit only perceived after the achievement of product and
operational benefits. This is also in line with the general literature about sustainability in
industry, which evidences differences in such concern between developed and emerging
countries (Hansen et al., 2018; Viotti, 2002).
The final version of is paper has been published at the International Journal of Production Economics, DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
Figure 1 Framework summarizing the findings and discussions of the paper
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
6 Conclusions
In this paper we analyzed the perception of the Brazilian Industry about the benefits of Industry
4.0 related-technologies for three industrial performance metrics: product, operational and
side-effects. Our results showed that some of these technologies are positively associated to the
expected industrial benefits while others are still at a very early stage of adoption and, thus,
without clear expected benefits. We discussed reasons for the lack of expectation of benefits
for some of the promising technologies of the Industry 4.0 in this specific emerging industry.
Our main contribution to the state-of-the-art is that we show how these technologies are used
and seen in an emerging economy, since most of the studies on this matter have been conducted
in developed countries. In this sense, we showed how different set of technologies are
associated with different expected benefits. We showed that the Brazilian industry has not yet
taken advantage from some promising technologies such as product big data analysis, cloud
services for manufacturing, among other technologies for the digitalization of the factory and
for the analysis of the product performance. A further contribution is that we could not find any
relation between the Industry 4.0 and the expected benefits for sustainability and labor claims
[SIDE-EFFECTS], which represents a different pattern when comparing to developed economies.
Based on prior evidences from developed countries, we argued that since side-effects tend to
be at the second level of priority in the industries, after achieving operational and product
performance benefits, the Brazilian industry is still not focused on this aspect, but this deserves
future investigation.
Practical implications
Our results can be useful for both, operations managers and industrial policy-makers. For
operations management, our results showed which are expected to be the most powerful
technologies to enhance product and operational performance in the Brazilian context,
according to the industry perception. Companies that want to initiate their digitalization journey
towards the Industry 4.0 should first think, before implementing any technology, what are their
strategic goals. Thus, companies with a focus on differentiation should prioritize the
implementation of those technologies pointed as significantly associated to the Product
Development Technologies dimension (Figure 1), according to what is expected by the industry
and the literature; while companies with a focus on low cost, productivity or operational
flexibility should prioritize those Industry 4.0 technologies that have significant contribution for
the Manufacturing Technologies dimension. On the other hand, industrial policy-makers in
emerging countries can use our findings as a guideline about what technologies still need to be
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
developed for the industry to achieve the competitiveness standards of developed countries.
For instance, big data, cloud services and additive manufacturing (e.g. 3D printing) are strong
industrial trends in developed countries that should be considered for the future of the
emerging countries. However, this field needs further debates regarding the industrial policy
approaches to foster the national competitiveness of the country.
Limitations and future research
The use of a secondary dataset for our analysis allowed us to obtain a broad overview of a still
little explored emerging industry. However, some limitations are present due to this kind of
research. Firstly, our results have limitations on the statistical inferences since we considered
expecting benefits from the industry 4.0 technologies and not current benefits obtained from
them. This is because the implementation of many of these technologies are recent and the
benefits are not feasible to be obtained in the short-term. Future works can use our findings to
advance in the study of real improvements, which could be done only in the middle or long-term
of this new industrial trend. Experimental studies can provide quicker answers to these aspects
when compared with survey studies. However, it is well known that experimental studies have
also limitations regarding the generalization of the results.
Furthermore, we used aggregated-level data analysis and thus we studied the industrial sector
behavior. In this sense, we call the attention to the risk of ecological fallacy, when macro-level
analysis using aggregate data is used in micro-level conclusions (firm-level) (Clark and Avery,
1975). In this sense, our results are only valid at the industry-level behavior. Other future studies
could, therefore, deepen our research by conducting company-level surveys. We also studied a
cross-sectional sample, thus future longitudinal studies on the effect of the Industry 4.0
technologies could evidence patterns and maturity levels of the adoption of such technologies.
We know that future research is called to address the endogeneity problems that can be present
in large-scale survey studies (Bascle, 2008), especially because the adoption of technologies
might depend not only on internal decisions but on the access to public funds and other kind of
governmental incentives (Frank et al., 2016). There are other inherent aspects regarding
endogeneity in operations management that we did not addressed in this work and are part of
an emerging discussion in this field (Ketokivi and McIntosh, 2017). We were aware about these
limitations, but due to the limitation of information in our dataset we cannot include
instrumental variables that may be helpful to test alternative models to the OLS models used in
this paper. Finally, we mentioned in our work that, from a sociotechnical perspective,
organizational and human factors are very relevant to the implementation of technologies. Since
The final version of is paper has been published at the International Journal of Production Economics,
DOI: http://dx.doi.org/10.1016/j.ijpe.2018.08.019
we delimited our research only to technological factors in a specific environment, future studies
could expand to these other two factors, in order to consider how they facilitate or not the
implementation of the technologies addressed in our work.
Acknowledgments
The authors thank the Brazilian National Council for Scientific and Technological Development
(CNPq Conselho Nacional de Desenvolvimento Científico e Tecnológico) (Process n.
305844/2015-6), the Research Council of the State of Rio Grande do Sul (FAPERGS, Fundação de
Amparo à Pesquisa do Estado do Rio Grande do Sul) (Process n. 17/2551-0001) and the Research
Coordination of the Brazilian Ministry of Education (CAPES), for the financial support received
to conduct this research.
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Industry 4.0 initiatives can influence whole business system via transforming the means the products are designed, produced, delivered and discarded. Industry 4.0 is relatively novel to developing nations, especially in India and needs a clear definition for proper understanding and practice in business. This paper aims to recognize key challenges to Industry 4.0 initiatives and analyze the identified key challenges to prioritize them for effective Industry 4.0 concepts for supply chain sustainability in emerging economies by taking Indian manufacturing industry perspective. Industry 4.0 initiatives can help industries to incorporate environmental protection and control initiatives as well as process safety measures in supply chains towards sustainable supply chains. However, adoption of Industry 4.0 initiatives is not so easy due to existence of many challenges. Therefore, the present research identifies 18 key challenges to Industry 4.0 initiatives for developing supply chain sustainability using an extensive literature review. These challenges were analyzed through 96 responses received from Indian manufacturing sector using a questionnaire based survey. Explanatory Factor Analysis results classified identified challenges into four key dimensions of challenges. Analytical Hierarchy Process further ranks the identified dimensions of challenges and related challenges. Findings of the study revealed that Organizational challenges holds the highest importance followed by Technological challenges, Strategic challenges, and Legal and ethical issues. This work is very useful for practitioners, policy makers, regulatory bodies and managers to develop an in-depth understanding of Industry 4.0 initiatives and eradicate the potential challenges in adopting Industry 4.0 initiatives for supply chain sustainability.
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We are witnessing an increasing adoption of digital technologies in manufacturing industries around the globe. This trend is often debated under the label Industry 4.0. A key claim put forward in these debates is that Industry 4.0 represents a revolution that will reshape manufacturing industries akin to previous industrial revolutions. Despite the popularity of this claim, it provides little help to clarify the identity of Industry 4.0. Such a clarification is however much needed given the worldwide proliferation of digital technologies in manufacturing industries. I address this gap by arguing to view Industry 4.0 as policy-driven innovation discourse in manufacturing industries that aims to institutionalize innovation systems that encompass business, academia, and politics. This clarification of the identity of Industry 4.0 adds to a better understanding of the relationship between manufacturing and politics as well as technological change in manufacturing.
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