Práticas da Indústria 4.0 na Região Amazônica: uma Perspectiva da Gestão de Operações
|
Lindara Hage Dessimoni |
Bacharela engenharia de produção. Universidade
Federal do Amazonas (ICET/UFAM) – Brasil.
lindarahage@hotmail.com |
|
Nicole
Sales Libório |
Bacharela engenharia de produção. Universidade
Federal do Amazonas (ICET/UFAM) – Brasil.
nicoleufam@gmail.com |
|
Moisés Andrade Coelho |
Mestre engenharia de produção. Universidade
Federal do Amazonas (ICET/UFAM) – Brasil. moisescoelho@ufam.edu.br |
ABSTRACT
The adoption of
Industry 4.0 (I4.0) in peripheral regions, such as the Brazilian Amazon,
remains underexplored. This study analyzes I4.0 practices in Amazonian
manufacturing industries from an operations management perspective, assessing
their implementation, the adequacy of an assessment protocol, and the maturity
levels observed. A multiple-case study was conducted in two companies: a
thermal power plant with continuous production and 60 employees, and a plastic
injection molding company with batch production and 400 employees. A structured
protocol comprising eight constructs and 57 open-ended questions was applied,
combining document analysis, on-site observation (10 days per company), and 20
interviews totaling 30 hours. Data were analyzed using Bardin’s content
analysis framework. The results indicate that both companies exhibit maturity
levels between the computerization and connectivity stages (1–2), corresponding
to Industry 2.0 and Industry 3.0, with no advanced implementation of I4.0. The
thermal power plant exhibits greater sensor integration and horizontal
integration than the batch production company. Basic sensors and supervisory
control systems are the predominant technologies, whereas the Internet of
Things (IoT), big data, digital twins, and artificial intelligence are absent.
The findings indicate that the production system influences I4.0 maturity in
the companies analyzed. The protocol proved suitable for the qualitative
assessment of these practices, providing a replicable diagnostic tool. Social
implications include differences in employees’ understanding of I4.0 and
concerns about job displacement. This study contributes to operations
management by expanding knowledge of digital transformation in underexplored
industrial contexts.
Keywords: Industry 4.0. Operations management. Manufacturing
industries. Amazon
region. Maturity assessment.
RESUMO
A adoção da Indústria 4.0 (I4.0) em regiões periféricas, como a Amazônia
brasileira, permanece pouco explorada. Este estudo analisa práticas de I4.0 em
indústrias manufatureiras amazônicas sob a perspectiva da gestão de operações,
avaliando sua implementação, a adequação de um protocolo de avaliação e os
níveis de maturidade observados. Realizou-se um estudo de casos múltiplos em
duas empresas: uma usina termelétrica, com produção contínua e 60 funcionários,
e uma empresa de injeção plástica, com produção por lotes e 400 funcionários.
Aplicou-se um protocolo estruturado com oito construtos e 57 questões abertas,
combinando análise documental, observação in loco (10 dias por empresa) e 20
entrevistas, totalizando 30 horas. Os dados foram analisados por meio da
análise de conteúdo de Bardin. Os resultados indicam que ambas as empresas
apresentam níveis de maturidade entre os estágios de computadorização
e conectividade (1–2), correspondentes às Indústrias 2.0 e 3.0, sem
implementação avançada de I4.0. A usina termelétrica apresenta maior integração
de sensores e integração horizontal em comparação à empresa de produção por
lotes. Sensores básicos e sistemas supervisórios são as tecnologias
predominantes, enquanto Internet das Coisas (IoT), big data, gêmeos digitais e
inteligência artificial estão ausentes. Conclui-se que o sistema produtivo
influencia a maturidade em I4.0 nas empresas analisadas. O protocolo mostrou-se
adequado à avaliação qualitativa dessas práticas, oferecendo um instrumento
diagnóstico replicável. As implicações sociais incluem diferenças na
compreensão da I4.0 entre funcionários e preocupações com a substituição de
postos de trabalho. O estudo contribui para a gestão de operações ao ampliar o
conhecimento sobre a transformação digital em contextos industriais pouco
investigados.
Palavras-chave: Indústria 4.0. Gestão
de operações. Indústrias manufatureiras. Região Amazônia. Avaliação de
maturidade.
Recebido
em 17/07/2025. Aprovado em 17/03/2026.
Avaliado pelo sistema double blind peer review. Publicado conforme normas da APA.
https://doi.org/10.22279/navus.v18.2167
1 INTRODUCTION
Industrial production was transformed first by
steam power in the 19th century, then by electricity in the early 20th century,
and finally by automation in the 1970s. However, these waves of technological
advancement have not reduced overall employment. While the number of
manufacturing jobs has declined, new jobs have emerged and the demand for new
skills has grown. Today, another workforce transformation is on the horizon, as
manufacturing experiences a fourth wave of technological advancement: the emergence
of new digital industrial technologies that are collectively known as Industry
4.0 (Lorenz et al., 2015).
The Fourth Industrial Revolution is
characterized by the increasing digitization and interconnection of products,
value chains, and business models. It is characterized by the fusion of
technologies that blur the lines between the physical, digital, and biological
spheres. There are three reasons these transformations are not just an
extension of the third industrial revolution: (1) speed, (2) scope, and (3)
systems impact. The speed of current disruptions, which is without historical
precedent when compared to previous revolutions, means the Fourth Revolution
involves an exponential rather than a linear pace. It influences almost all
industries and countries, signifying that the breadth and depth of these
changes herald the transformation of entire systems of production, management,
and governance (Koch et al., 2014; Schwab, 2015).
The term Industry 4.0 (I4.0) has been used
since 2011 to describe the widespread integration of information and
communication technology into industrial manufacturing. The term
"Industrie 4.0" was introduced in 2011 by the Communication Promoters
Group of the Industry Science Research Alliance to describe the widespread
integration of information and communication technology into industrial
production. The origins of the idea can be found in the German government's
2006 High Tech Strategy. Some of the features of I4.0 were identified in
Germany's industrial policy in 2010 and in 2012 the government made I4.0 one of
the 10 future projects part of its High Technology Strategy (Schuh et al.,
2020; Smith et al., 2016).
I4.0 represents a paradigm shift from
“centralized” to “decentralized” production, that is, made possible by
technological advances that constitute a reversal of the logic of the
conventional production process. Thus, this means that the industrial production
machinery no longer simply "processes" the product, but that the
product communicates with the machinery to inform it exactly. I4.0 involves the
development of independent systems capable of making their own decisions using
machine learning. Therefore, this industry allows for the exploration of
pillars such as the Internet of Things (IoT), Big Data and data analytics,
collaborative robots, additive manufacturing, cloud computing, artificial
intelligence, 5G networks, and others (Bunse et al., 2014; Ortiz et al., 2020).
The research literature on I4.0 has grown
considerably in recent years. This growth in the subject has been documented in
research reviews conducted on various topics, such as the capacity of I4.0
technologies (Justus et al., 2018); evaluation model for SMEs (Essakly et al., 2019; Kolla et al., 2019; Rauch et al.,
2020); macroeconomic assessment of I4.0 (Roland Berger, 2014); skills and
competencies for I4.0 (Cotet et al., 2017; Pacaux-Lemoine et al., 2021; Perini et al., 2017); and
interface between lean manufacturing and I4.0 (Kolla et al., 2019).
In conclusion, the Manaus Free Trade Zone (ZFM)
emerged in 1967 as a development model with the objective of fostering the
economic development of the region, integrating the states of the Northern
Region and promoting the occupation of this region. Currently, there are
approximately 500 companies installed in the Industrial Pole of Manaus (PIM)
and more than 120 thousand direct jobs. The PIM recorded revenue of R$ 204.39
billion in 2024, which is equivalent to a nominal growth of 16.24% over the
value obtained in 2023 (R$ 175.83 billion). Computer goods (9.93%), electronics
(11.71%) and two-wheelers (18.32%) were the PIM segments with the largest
participation (Superintendência da Zona Franca de
Manaus [SUFRAMA], 2025b).
Therefore, the gap addressed by this study lies
in conducting qualitative research to understand managers' and employees'
perceptions of Industry 4.0 practices within the production environment,
specifically involving operations management. Therefore, the following research
questions were developed for investigation:
RQ1: Do companies implement Industry 4.0
practices in their production environment?
RQ2: Does the survey framework provide an
adequate assessment of practices related to Industry 4.0?
RQ3: What is the observed Industry 4.0 maturity
level in these companies, according to the literature?
Thus, this study aims to understand practices
related to Industry 4.0 in the manufacturing industries in the Amazon region. This
research employed a multiple-case study as an epistemological approach, based
on a protocol for understanding Industry 4.0 practices to support organizations
in their pursuit of a new productive and technological trajectory. The second
section deals with concepts related to Industry 4.0, its dimensions, and the
research reviews existing in the literature in recent years. Section three
discusses the study's methodology and research structure; section four deals
with the presentation of the case study companies; section five presents the
results and the discussion carried out throughout the study. Finally, the last
section presents the conclusion with the final position of the study.
2 BACKGROUND
2.1 Concepts of Industry 4.0 and its dimensions
The term Industry 4.0 has been used since 2011
to describe the widespread integration of information and communication
technology into industrial manufacturing. Consequently, the importance of
Industry 4.0 lies in the pivotal role of information processing in enabling
rapid organizational adaptation. The faster an organization can adapt to an
event that alters its circumstances, the greater the benefits of adaptation
(Schuh et al., 2020).
The Fourth Revolution represents a new level of
organization and control across the entire value chain throughout product life
cycles. This cycle focuses on increasingly personalised
customer demands, encompassing everything from conception to order processing,
development, production, and delivery to the end customer and ultimately,
recycling including all associated services. Its specific potential lies in
highly flexible production, high productivity, and low resource consumption,
enabling the manufacture of highly individualised
products under the economic conditions of mass production. Engineering,
production, logistics, services, and marketing are ultimately interconnected
within dynamic, inter-company networks, optimised in
real time to generate added value (Cologne Institute for Economic Research – IW,
2015).
While Industry 3.0 focuses on automating individual machines and processes, I4.0 focuses on end-to-end digitization of all physical assets and integration into digital ecosystems with value chain partners. Data generation, analysis, and communication underpin the gains promised by I4.0, which networks a wide range of new technologies to create value. Therefore, Industry 4.0 is driven by (Reinhard et al., 2016):
1. Digitization and integration of
vertical and horizontal value chains;
2. Digitization of product and service
offers;
3. Digital Business Models and Customer
Access.
The essence of the I4.0 vision is the
ubiquitous connection of people, things, and machines. This connection aims to
produce a variety of new goods and services. Products, means of transport, or
tools are expected to “negotiate” within a virtual market about which
production elements could best perform the next production step (Koch et al.,
2014).
Industry 4.0 encompasses six design principles
in its framework: decentralization, virtualization, interoperability,
modularity, real-time capability, and service orientation. These principles are
called “design principles” because they contribute to the design process or
transition from a common industry (3.0), to Industry 4.0 (Hermann et al.,
2015).
This network within an “internet of things,
services, data, and people” will transform the future of manufacturing. Several
authors use the term “Industry 4.0” to refer to the fourth industrial
revolution with four main characteristics (Deloitte, 2015, 2016):
1. The vertical network of intelligent
production systems, such as factories and intelligent products, a network of
intelligent logistics, production, marketing, and intelligent services, with a
strong need-oriented, individualized, and customer-specific production
operation;
2. Horizontal integration through a new
generation of global value creation networks, including integration of business
partners and customers, and new business models and cooperation across
countries and continents;
3. Through engineering across the
entire value chain, encompassing not just the production process but also the
final product, i.e., the entire product life cycle;
4. Acceleration through exponential
technologies that, while not new in terms of their development history, are
only now capable of mass-market application as their cost and size have
decreased (e.g., sensor technology) and their computing power has increased
enormously.
Several disruptive technologies are related to
I4.0, enabling the digitization of an organization. These technologies involve
(1) data, computational power, and connectivity; (2) analytics and
intelligence; (3) human-machine interaction; and (4) physical-to-digital
conversion. The I4.0 allows us to explore certain pillars, such as the Internet
of Things (IoT); Big Data and data analysis; augmented reality; cybersecurity;
collaborative robots; additive production; cloud computing; artificial
intelligence; and 5G networks (Oztemel & Gursev,
2020; Wee et al., 2015).
Therefore, I4.0 describes the organization of
production processes based on technology and devices that communicate
autonomously along the value chain in virtual computer models. It involves a
series of disruptive innovations in production and leaps in industrial
processes, resulting in significantly higher productivity. The value of the Industry 4.0 concept can be enhanced by clarifying the role
played by technologies that facilitate the physical manipulation of objects.
Among the various technologies, the Internet of Things (IoT) stands out; IoT is
a crucial element of Industry 4.0. This concept has gained momentum in recent
years as the importance of connectivity, both in the creation of products and
services and in increasing customer and consumer satisfaction, has become
better understood (Smith et al., 2016; Sniderman et al., 2016).
The uses of I4.0 often require the use of
investments in new technologies that do not pay for themselves in the same
year. However, these investments can be minimized by applying the minimum
viable product (MVP) approach, that is, thinking about the smallest possible
implementation, but delivering relevant commercial value in a given area. In
Industry 4.0 transformations, easy-to-implement pilots are often at the fringes
of the operation’s key value creation processes. Adopting a
“technology-forward” approach instead of an “impact-backward” selection of
Industry 4.0 solutions can lead to pilot projects that demonstrate unclear
commercial value (Tschiesner et al., 2019).
In conclusion, the use of Industry 4.0
technologies in manufacturing can boost productivity and mitigate the
environmental impact of industrialisation,
potentially creating employment rather than displacing it – thereby fostering
inclusive development. However, these technologies may fail to redistribute
wage advantages in manufacturing from emerging economies to industrialised
nations. To counter this, tailored science, technology and innovation (STI)
policies must be implemented, commensurate with each country's developmental
stage. Such policies should prepare both businesses and workforces for an era
of rapid transformation, while establishing a robust and diversified industrial
base (UNCTAD, 2022).
2.2 Assessment in Industry 4.0
Concerning the assessment in I4.0, the
consultancy Roland Berger (2014) mapped the extent of progress with the
implementation of I4.0 in different EU Member States in terms of two criteria:
“industrial excellence” and “value networks”; the article by Schumacher et al.
(2016) proposed a new empirically grounded model and its implementation to
assess Industry 4.0 maturity in industrial enterprises within the domain of
discrete manufacturing. The primary objective was to broaden the dominant
technological focus of recently developed models by incorporating organizational
aspects. The dimensions "Products", "Customers",
"Operations", and "Technology" were established to evaluate
fundamental enablers. Furthermore, the dimensions "Strategy",
"Leadership", "Governance", "Culture", and
"People" allow for the inclusion of organizational aspects in the
assessment.
Cotet et al. (2017) identified and assessed the core of the various complex
skills required in the I4.0 environment using a psychological instrument to
assess transversal capabilities; and Perini et al. (2017) present a Training
Data Evaluation Tool (TDET), which is based on the integration of a Training
Evaluation Ontology (TEO) with a Training Analytical Model (TAM) for defining
workers' skill levels.
Vaidya et al. (2018) provided an overview of
Industry 4.0, along with an understanding of its nine pillars. The nine pillars
of Industry 4.0 are explained with examples to illustrate the application of
Industry 4.0, as well as to identify the challenges and issues associated with
its implementation; Justus
et al. (2018) stand out. They evaluate the capacity of Industry 4.0
technologies against the functional requirements, specifically focusing on the
Proof of Concept (PoC) for Predictive Maintenance in the automotive industry
for the construction of a Referential Architecture; and Colli et al. (2018)
provided a new digital maturity assessment approach (360 Digital Maturity
Assessment) based on problem-based learning (PBL).
Colli et al. (2019) indicate the need to
support companies in this transformation is currently covered by several
maturity models, usually operationalized through standardized questionnaires;
and Essakly et al. (2019) developed a performance
metrics framework for the holistic assessment of the impact of I4.0 solutions
on SMEs.
Additionally, Sharpe et al. (2019) analyzed the
evaluation of RAMI4.0, a Research Architecture (RA) designed for I4.0, through
the representation of the core functionality of an existing Cybernetic-Physical
System (CPS). Lin et al. (2019) release the Smart Industry Readiness Index
(SIRI). SIRI is one of the I4.0 maturity models that help industrial companies
identify opportunities moving towards I4.0. Kolla et al. (2019) provided state
of the art literature on existing assessment models and mapped lean and I4.0
components to the specific characteristics of manufacturing SMEs.
Rauch et al. (2020) developed an assessment
model for SMEs that provides a clear overview of existing Industry 4.0 concepts;
Yüksel (2020) investigated the applications of I4.0 in companies in Turkey, a
developing country; Dikhanbayeva et al. (2020) identified
research gaps concerning Industry 4.0 design, based on their analysis of 12
maturity models derived from eight basic design principles; Liebrecht et al.
(2021) evaluated available I4.0 methods to select the most suitable for an
individual company; and Pacaux-Lemoine et al. (2021)
evaluated the design methodology called Cognitive Work Analysis (CWA) and its
applicability when designing an assistance system to support humans in
controlling the Intelligent Manufacturing System in I4.0.
El Baz et al. (2022) investigated how
sustainability drivers can be taken into account when implementing Industry 4.0
technologies. The authors propose addressing sustainability-oriented Industry
4.0 implementation through three main stages: approach, deployment, and
outcomes (externalities). Anshari and Hamdan (2022)
identified critical new skills and capabilities required among organizational
personnel to maintain competitiveness, innovation and relevance regarding KM's
role in developing new skill sets or capabilities within the Fourth Industrial
Revolution context. Belhadi et al. (2022) developed a self-assessment model to
evaluate the systemic level of circular economy and Industry 4.0 integration
within organizations using a dynamic capabilities perspective.
Govindan and Arampatzis (2023)
propose a framework to assist industries in adopting Industry 4.0 through two
phases. In the initial phase, the company's readiness level is assessed. In the
second phase, existing barriers to Industry 4.0 implementation (based on the company's
readiness evaluated in the first phase) are analysed.
Hajoary et al. (2023), with further developments in Hajoary et al. (2024), developed a novel multidimensional
analytical framework and indicators to assess Industry 4.0 maturity and
readiness across six dimensions and 32 indicators. Sajjad et al. (2024)
developed and validated a comprehensive, compatible modified lean manufacturing
maturity model for Industry 4.0 (LM4I4.0).
In
conclusion, Rakiman et al. (2024) determined the readiness level for Industry
4.0 in the manufacturing sector and identified Industry 4.0 implementation
barriers within manufacturing industries. Sulistyo et
al. (2025) developed an integrated Lean 4.0 readiness assessment tool to bridge
the gap between Lean readiness and Industry 4.0 readiness. McDermott et al.
(2024) investigated Industry 4.0 readiness and comprehension within the
pharmaceutical industry, while McDermott et al. (2025) subsequently assessed
the Industry 4.0 readiness status in their medical
device manufacturer case study.
2.3 Manaus Industrial Park (PIM) and Amazonas
Economic Development
The Manaus Free Trade Zone (Zona
Franca de Manaus – ZFM) was established with the objective of creating a
free-trade area for the storage, processing, and export of products originating
from abroad. On February 28, 1967, ten years later, the government expanded
this legislation, and President Castello Branco signed Decree-Law No. 288,
which reformulated the model and established tax incentives for 30 years to
implement a development hub in the Amazon region. This decree-law defined the
ZFM as an industrial, commercial, and agricultural center.
Over its more than 50 years of
existence, the Manaus Free Trade Zone model has undergone at least five phases
concerning regional development theories. The first phase (1967–1975) was
characterized by the stimulation of import substitution of final goods and the
formation of the internal market. The second phase (1975–1990) was marked by
measures to foster the national input industry. The third phase (1991–1996) had
as its main characteristics the opening of the country's economy, the reduction
of import taxes, and an emphasis on quality and productivity. The fourth phase
(1996–2002) was defined by the adaptation of companies and the model to the
globalized economy, as well as adjustments arising from the Plano Real
(Brazil's economic stabilization plan). Finally, the fifth phase (2003 to the
present) has seen the model seek greater productive efficiency, valuing
companies' innovation capacity, and fostering export expansion (Silva et al.,
2019; SUFRAMA, 2025a).
Botelho (2006) indicates that, even
considering the relative success of the ZFM project from the perspective of the
industrialization model based on import substitution — which drove the economic
growth of the city of Manaus — economic development was not achieved. The ZFM
populated the city of Manaus, and migrants from the countryside and riverine
communities moved to Manaus, forming a contingent of cheap labor, leading to
upward population growth, an increase in the number of slums, land invasions,
and degradation of water bodies. Neighborhoods emerged with urbanization devoid
of citizenship, marked by social discrimination and inefficient public services
(Gonçalves, 2012; Nascimento, 2013).
One of the strong appeals for the
creation of the ZFM — which generated intense migratory flows to the capital —
was the objective of bringing development through job creation for the
population and, consequently, better quality of life in the urban space. This
motivated migration because it opened a possibility of hope in the city with
promising proposals, among them the possibility of achieving homeownership (Brianezi,
2018; Giatti et al., 2015; Silva & Scudeller, 2022).
Cities in the interior did not
possess the economic power to retain this workforce, which remained abandoned
due to the lack of investment in those locations. These populations became
attracted to the transformation process concentrated in Manaus as a "possible
dream" — the possibility of having better access to health, education,
housing, and, above all, employment with better living conditions. This
disparity between the capital and the interior resulted in high migratory
pressure and a tendency for the city to continue growing demographically,
through unplanned growth and the worsening of social injustices in the
interior, beyond its capacity to provide living conditions for the population.
In other words, industrialization energized the capital but emptied the
interior and its capacity to generate economic activity (Brianezi, 2018; Costa,
2021; Giatti et al., 2015).
3 DATA AND METHOD
3.1 Research design
In this study, a case study design was chosen
to explore the phenomenon and establish a strong foundation for the
epistemological approach. Multiple case studies provide a comparison of data
collection through different websites, which can be useful to improve our
understanding of the phenomenon under study (Eisenhardt & Graebner, 2007).
Furthermore, the purpose of this research was to conduct exploratory case
studies (McCutcheon & Meredith, 1993) in which the conclusions obtained
from the analysis of the data will be based on empirical evidence. Cases were
selected based on theoretically convenient sampling criteria.
The case study is suitable for analyzing
longitudinal change processes (Eisenhardt, 1989) and for gaining a holistic as
opposed to a reductionist perspective of a phenomenon (Gummesson, 1991). The
case study offers a research methodology that needs to be taken into account by
a science whose objective is to satisfy the most pressing needs for a greater
understanding of the society it serves (Villarreal, 2017; Villarreal &
Calvo, 2015; Villarreal & Landeta, 2010). The process of conducting the
research case studies followed the steps of Voss et al. (2002): (1) rationale
for case research; (2) developing the research framework; (3) case selection;
(4) developing the research protocol; (5) conducting the research; (6) data
documentation and coding; and (7) data analysis, hypothesis development, and testing.
3.2 Epistemological approach
The study used a protocol for understanding
practices related to I4.0 considering the characteristics presented in Deloitte
(2015, 2016), Koch et al. (2014), Smith et al. (2016), Lee et al. (2017), Schuh
et al. (2020), Lorenz et al. (2015), Bunse et al. (2014), Wee et al. (2015),
Cologne Institute for Economic Research – IW (2015), Reinhard et al. (2016), Roland
Berger (2014), and Oztemel and Gursev (2020). The
Protocol aims to understand the current status of the companies' production
systems regarding Industry 4.0.
In total, there are eight constructs, with the
analyzed criteria detailed in Table 1, comprising 57 open questions designed to
provide an adequate evaluation of the productive system. In the end, a radar
chart was prepared with the scores of the two companies (scores from 1 to 4),
where this score reflects the stage of implementation of the practices observed
in the application of the protocol:
1. Practice not considered, rarely
occurring, or not applicable to the company's reality;
2. Practice where there is some
awareness of its use, yet it occurs in a disorderly or occasional manner (e.g.,
through informal systems);
3. Practice that occurs consciously,
based on appropriate formal systems, however, can still be improved; and
4. Practice is based on effective and
highly developed systems, with autonomous capacity for improvement and
development.
Table 1 Relationship between constructs and source
|
Constructs |
Source |
|
Cyber-physical production systems (A) |
Deloitte
(2015); Koch et al. (2014); Smith et al. (2016);
Lee et al. (2017) |
|
Smart factory (B) |
Schuh et al. (2020); Cologne Institute for Economic
Research – IW (2015); Smith et al. (2016). |
|
People (C) |
Lorenz et al.
(2015); Cologne Institute for Economic Research – IW (2015). |
|
Vertical network (D) |
Koch et al. (2014); Deloitte (2015); Cologne
Institute for Economic Research – IW (2015); Oztemel and Gursev
(2020). |
|
Horizontal integration (E) |
Bunse et al.
(2014); Koch et al. (2014); Deloitte (2015); Cologne Institute for Economic
Research – IW (2015); Oztemel and Gursev (2020). |
|
Through-Engineering (F) |
Deloitte
(2015, 2016). |
|
Exponential technologies (G) |
Lorenz et al.
(2015); Wee et al. (2015); Deloitte (2015); Oztemel and Gursev
(2020). |
|
Industry 4.0 environment (H) |
Reinhard et al. (2016); Roland Berger (2014); Cologne
Institute for Economic Research – IW (2015); Oztemel and Gursev
(2020). |
Source: Authors.
3.3 Companies
Case 1. Thermal power plant
The first case represents a thermal power
plant, operating since 2002. The company has an installed capacity of 9 MW,
with a continuous production process, and with a high level of automation.
Currently, the plant located in the Amazon has approximately 60 employees.
Case 2. Plastic injection molding company
The second case represents a plastic injection
molding company, being an important national player in the segment of plastics
for the electronics, home appliances, automobile, and electrical products
industries. Currently, the plant located at the PIM has approximately 400
employees.
3.4 Data collection
The organizational intervention was conducted
from 2018 to 2024 as part of a large-scale research project on Industry 4.0
practices in the Amazon region. Data collection focused on two manufacturing
companies located in the Manaus Industrial Pole: a thermal power plant
(continuous production, 60 employees) and a plastic injection molding company
(batch production, 400 employees).
A structured protocol was employed, based on
Deloitte (2015, 2016), Koch et al. (2014), Smith et al. (2016), Lee et al.
(2017), Schuh et al. (2020), Lorenz et al. (2015), Bunse et al. (2014), Wee et
al. (2015), Cologne Institute for Economic Research – IW (2015), Reinhard et
al. (2016), Roland Berger (2014), and Oztemel and Gursev
(2020). The protocol comprised eight constructs and 57 open-ended questions
designed to assess I4.0 practices across the companies' productive systems.
Three complementary research techniques were
used, following Marconi and Lakatos (2002) and Vergara (2009):
1. Indirect documentation: documentary
and bibliographic research was conducted, including the analysis of standard
operating procedures (SOPs), production plans, check sheets, PQCDSME indicators
(Productivity, Quality, Cost, Delivery, Safety, Morale, Environment), and PDCA
records. These documents provided insights into formalized practices and
continuous improvement routines.
2. Intensive direct observation:
on-site observation was carried out over 10 days per company, covering both
productive and administrative areas. This allowed the research team to capture
informal practices, human-machine interactions, and the actual implementation
of I4.0-related technologies.
3. Open structured interviews: a total
of 20 interviews were conducted, with an average duration of 40 to 90 minutes
each, totaling approximately 30 hours of interviews. The respondents were
selected to represent different strategic and operational levels, including: 2
operations managers, 2 industrial managers, 4 engineers, 2 industrial analysts,
3 production leaders, and 7 machine operators. This diversity ensured that the
assessment captured both strategic policy execution and shop-floor perceptions.
Each of the 57 questions was scored on a
4-point Likert-type scale, as follows: (1) Score 1: practice not considered,
rarely occurring, or not applicable to the company's reality; (2) Score 2:
practice where there is some awareness of its use, yet it occurs in a
disorderly or occasional manner (e.g., through informal systems); (3) Score 3:
practice that occurs consciously, based on appropriate formal systems, however,
can still be improved; (4) Score 4: practice based on effective and highly
developed systems, with autonomous capacity for improvement and development.
Scores were first assigned per question by the
research team based on triangulation of documentary evidence, observational
notes, and interview responses. Subsequently, the perceptions and strategic
importance attributed by the respondents were incorporated into the final
scoring, ensuring that the assessment reflected not only technical
implementation but also the actors' understanding of I4.0 phenomena. Scores
were then aggregated by construct (eight constructs in total), and an
arithmetic mean was calculated for each construct per company. Finally, a radar
chart was generated for each company to visualize the maturity profile across
all eight I4.0 dimensions. A summary of the methodological procedures used is
presented in Table 2.
Table 2 Summary
of the methodological procedures
|
Stage |
Method |
Source |
Comments |
|
Approach
to the problem |
Qualitative |
Gummesson (1991); Miles and Huberman (1994); Maxwell
(1998) |
Interpretation of the opinion of the interviewees |
|
Type of Research |
Exploratory case study (Multiple cases) |
Villarreal (2017); Villarreal and Calvo (2015);
Villarreal and Landeta (2010); Voss et al. (2002) |
Thermal power plant and Plastic injection molding company located in the Amazon |
|
Procedure |
Indirect documentation |
Guérin
et al., 2001; Marconi & Lakatos, 2002; Vergara, 2009 |
External and internal reports and procedures |
|
Data
gathering |
Multiple case study |
Guérin
et al., 2001; Marconi & Lakatos, 2002; Vergara, 2009 |
Quality department Operations management department Industrial engineering department |
|
Analysis of data |
Analysis of content |
Bardin,
1977 |
Understanding and perception of respondents about
the phenomenon studied from the following steps: (1) pre-analysis, (2) data
exploration, and (3) treatment of results, inference, and interpretation. |
Source: Authors.
Data triangulation (Eisenhardt,
1989; Yin, 2018) was achieved by cross-referencing the three sources:
documents, observations, and interviews. For qualitative data, content analysis
was performed following Bardin's (1977) framework, which comprises three steps:
(1) pre-analysis (systematization and establishment of interpretation
indicators), (2) data exploration (coding, classification, and categorization),
and (3) treatment of results, inference, and interpretation. This combination
of quantitative scoring and qualitative discourse analysis enabled a holistic
and in-depth understanding of I4.0 practices in the Amazonian context.
4 RESULTS
The main results and implications observed from
the analysis of the case studies regarding the I4.0 criteria are discussed in
this section and summarized in Table 3. The score of each case is also
presented for observation of the level of implementation of practices related
to I4.0 based on the understanding of the actors involved and the adopted
protocol.
Table 3 Case study results
|
Criteria |
Thermal power plant |
Plastic injection molding company |
|
Cyber-physical production systems |
It uses a supervisory system designed to capture and
store information about the production process in a database. The information
comes from sensors that capture the process variables of the industrial
plant. |
It has
performance, production, and problem control based on operator reports, which
immediately send the collected data to the production control telemetry
system. There is an integrated sensing system that notifies the needs of the
asset in real time, sending the information to an easily accessible panel
that can be viewed at various points of the production line by machine
operators. |
|
Smart factory |
Approximately 15% of
the process still involves human-machine interaction, such as in the
operation of wood chippers and supervisory tasks, where small human
interventions are necessary. The company lacks
sensors aimed at achieving zero defects and predictability, thus hindering
the improvement of maintenance management, including a predictive approach. |
The factory has some automated processes, but when
faced with all existing sub-processes, there is still a small level of
automation. It has a well-installed preventive and corrective maintenance
system, the machines already undergo constant sensorial integration to the
quality and control process, accompanied by the company's engineering team,
however, it is not possible to monitor and control the entire process from a
single point in time. Digitalized in real-time. |
|
People |
Human resources need to be better valued by the
organization, in the sense of qualification, training, and staff engagement.
The impact of automation on the workforce was observed due to the reduction
in the number of employees. |
The teams work
autonomously to create and implement continuous improvement methods within
their sectors to improve their information and problem-resolution chains. |
|
Vertical network |
There is a
deficiency in the integration of information systems. There is no integration
of networked resources and products, as well as materials and components of
the process, making it difficult to locate them anywhere and at any time in
the production process. The collection, analysis, and management of data are
done only for the production system and not for the entire company. |
The process has efficient data collection from the
machines that are integrated into the entire plant system, providing diverse
information on maintenance and productivity, with certain points in the
process being able to be monitored through cell phones carried by authorized
employees. |
|
Horizontal integration |
There is not a constant process of optimizing the
production process and the business model to respond quickly to problems and
failures, as there is not enough productive flexibility to respond to
changes, problems, and failures. |
The company has an internal program for optimizing
the production process in response to periodic problems and failures.
However, this improvement process does not extend to the company's business
model, which has remained constant in recent years. |
|
Through-Engineering |
There is no
monitoring of the entire product/service life cycle by engineering, it is
concentrated in certain sectors that require a more technical opinion. |
The process is customized by the client and sent
ready-made, or to be subject to adaptation by the company's engineering team,
which sometimes works to solve problems in the most efficient way possible,
within the technical limitations of its staff, and integrates the project
into its production capacity. |
|
Exponential technologies |
Most exponential technologies do not fit the needs
of the current context of the company. From the exponential technologies to
the only one used by the company are the sensors, which act in the conversion
of the physical phenomena of the industrial plant, heat, movement, vibration,
and sounds into electrical signals. |
Robots are
already utilized in the manufacturing cells for product removal from
injectors. However, sensing is limited to the control of these production
robots, which helps reduce failures and increase productivity. |
|
Industry 4.0 environment |
The concepts of a
smart building, social web, business web, smart logistics, products and/or
services connected to the concept of smart homes, product digitalization
process, and service portfolio, are also outside the analyzed organizational
reality. |
The concepts of a
smart building, social web, business web, smart logistics, products and/or
services connected to the concept of smart homes, product digitalization
process, and service portfolio, are also outside the analyzed organizational
reality. |
|
Score |
|
|
Source: Authors.
The thermal power plant achieved the following
scores: cyber-physical production systems (2.5), smart factory (1.8), people
(1.5), vertical network (1.5), horizontal integration (2.0),
through-engineering (1.0), exponential technologies (1.5), and Industry 4.0
environment (1.0). Its overall average score was 1.60.
The plastic injection molding company achieved
the following scores: cyber-physical production systems (2.0), smart factory
(2.2), people (2.5), vertical network (2.0), horizontal integration (2.5),
through-engineering (2.0), exponential technologies (2.0), and Industry 4.0
environment (1.5). Its overall average score was 2.09.
Scores range from 1 (practice not considered,
rarely occurring, or not applicable to the company's reality) to 4 (practice
based on effective and highly developed systems, with autonomous capacity for
improvement and development).
5 DISCUSSION
The main results and implications observed in
the case studies regarding the considered dimensions are presented in Table 3.
The scores reflect the level of understanding of the organizational actors
added to the other research techniques adopted in the protocol.
The radar scores reveal a clear maturity gap
between the two companies. The thermal power plant achieved an overall average
of 1.60, placing it between "practice not considered" (score 1) and
"occasional/disorderly awareness" (score 2). Its highest score (2.5)
was in cyber-physical systems, due to the presence of supervisory control and
data acquisition (SCADA) — a legacy of its continuous production system. Its lowest
scores (1.0) were observed in through-engineering and I4.0 environment,
indicating that product lifecycle management and digital ecosystems are
entirely absent.
The plastic injection molding company achieved
an overall average of 2.09, approaching "conscious use with formal
systems, still improvable" (score 3). Its highest score (2.5) was in the
"People" dimension, reflecting autonomous work teams and a culture of
continuous improvement. Its lowest score (1.5) was in I4.0 environment, similar
to the thermal power plant, suggesting that external digital ecosystems (e.g.,
smart logistics, connected products) remain beyond the current reality of
Amazonian manufacturers.
The radar visualization (Table 3) allows for
quick comparison: the injection molding company outperforms the thermal power
plant in six out of eight dimensions, with the largest gaps in
"People" (+1.0) and "Exponential technologies" (+0.5). This
suggests that production system characteristics (continuous vs. batch)
influence not only automation levels but also organizational culture and
workforce readiness.
RQ1: Do companies
implement Industry 4.0 practices in their production environment?
Regarding the 'cyber-physical production
systems' dimension, both companies are in the initial stage of connecting
information generated within their production processes. The thermal power
plant, by adopting a continuous production system, the thermal power plant
achieves better monitoring of its production process, as it has sensors located
at various stages. The plastic injection molding company, however, still relies
on employees to manually collect data and feed it into the telemetry system.
The panel adopted by the company only provides information on products in
process, that is, various information from the production process is either
manually informed or does not yet have real-time monitoring. Consequently,
companies exhibit an early-stage understanding and perception of the concepts
presented by Deloitte (2015, 2016); Koch et al. (2014); Smith et al. (2016);
and Lee et al. (2017).
As for the “smart factory” dimension, both
companies have a significant part of the production process without the use of
sensors or intelligent machines to monitor the performance of machines and
products in the process. While maintenance management in both companies shows
advancements in preventive and predictive maintenance, it is not yet possible
to plan machine maintenance remotely or digitally. Companies are in the initial
stages (stage one – computerization and stage two – connectivity) dialoguing
with Schuh et al. (2020) and Smith et al. (2016).
In the “people” dimension, the thermal power
plant needs to go through a workforce update process aimed at adopting the
skills required by I4.0. The plastic injection molding company, while fostering
a culture that enables work teams' autonomy for process improvement, also needs
to enhance workforce training to acquire the necessary skills for Industry 4.0 (Lorenz
et al., 2015). In the thermal power plant, the score reflected a workforce not
yet prepared for I4.0 requirements. Operators reported fear of
automation-driven layoffs, and no formal upskilling program was in place. In
contrast, the plastic injection molding company scored higher due to autonomous
work teams that continuously propose improvements. However, even in this
company, training on digital skills (e.g., data interpretation, basic
programming) remained absent. The main difference between the two companies
lies in organizational culture: the injection molding company has a longer
tradition of team-based problem-solving, which facilitates future I4.0
adoption.
The “vertical network” dimension indicates both
companies need to digitize and connect more functions along the value chain.
This means greater integration from order processing and product development to
integrated customer service. It is observed that in both companies, integration
has only advanced internally. Both upstream and downstream processes in
organizations are not integrated within the value chain (Deloitte, 2015; Koch
et al., 2014).
As for the “horizontal integration” dimension,
the thermal power plant, by using a continuous production system, achieves
greater horizontal integration (Bunse et al., 2014; Deloitte, 2015; Koch et
al., 2014), while the plastic injection molding company can only integrate a
few stages of the main production process. In both cases, it is necessary to
seek greater integration between the departments to achieve greater production
efficiency. This is one of the dimensions with the best performance in the
evaluation.
Concerning the “through-engineering” dimension,
the thermal power plant needs to improve the follow-up of the product's life
cycle; currently, this monitoring only occurs at the most technical levels. The
plastic injection molding company has better monitoring of the product life cycle, however, it does not have autonomy for
decision-making in aspects related to the product development process that is
under the responsibility of its customers. A closer relationship between the
company and the customer is necessary to better monitor the life cycle of
products, according to Deloitte (2015).
The “exponential technologies” dimension
indicates the need for both companies to adopt more technologies related to
I4.0 in their production processes. It is observed that companies are still in
a stage of automation of the production process, needing to adopt several
technologies, such as digital twin, the Internet of things, and big data, among
others to improve the efficiency of internal processes and the entire chain of
value (Deloitte, 2015; Lorenz et al., 2015; Wee et al., 2015). The thermal
power plant uses only basic sensors for process variables (temperature,
pressure, vibration). No IoT, cloud computing, or digital twin was identified.
The plastic injection molding company employs collaborative robots for product
removal from injectors, representing a more advanced — albeit still initial —
stage. However, sensing is limited to production robots only, and data is not
integrated across the production line. Neither company uses big data analytics
or artificial intelligence.
Finally, the “Industry 4.0 environment”
dimension demonstrates that several concepts that are related to the I4.0
environment (Reinhard et al., 2016; Roland Berger, 2014) are outside the
reality of the companies studied. This may be a reflection of the current
technological process they are in. Also added to this scenario is the local
reality of the economic and social aspects of the Amazon region.
RQ2: Does the survey
framework provide an adequate assessment of practices related to Industry 4.0?
The research framework enabled an understanding
of Industry 4.0 practices in two manufacturing industries in the Amazon region.
While most studies on Industry 4.0 evaluation emphasize quantitative aspects,
this study employed qualitative research techniques to understand perceptions
of Industry 4.0 practices, making it an original contribution to operations
management.
The use of varied qualitative research
techniques (open structured interview, structured observation, content
analysis, and documentary research) provided a broader and deeper understanding
of the phenomenon within the companies studied, as pointed out in Voss et al.
(2002), Miguel (2012), Creswell (2009), and McCutcheon and Meredith (1993).
The application of open structured interviews
together with structured observation and documentary research enabled an
in-depth case that contributes to the literature by deepening the real
situations in which the various aspects of I4.0 are used.
The internal validity of the case study is
confirmed by a systematic comparison of the literature about the research
framework. The reliability of the study is justified by the systematization of
the database which was organized, integrated, and synthesized from the
information obtained from different sources of evidence resulting from the
various research techniques employed (Villarreal, 2017; Villarreal & Calvo,
2015; Villarreal & Landeta, 2010).
RQ3: What is the
observed Industry 4.0 maturity level in these companies, according to the
literature?
Regarding the current maturity level of these
companies, the case studies reveal that they are in a stage between
computerization and connectivity, as indicated by Schuh et al. (2020). In the
case of the thermal power plant, the continuous production system facilitates
the adoption of sensors and supervisory systems throughout the production
process, which results in greater connectivity. The plastic injection molding
company, as it is part of a more dynamic activity that involves the use of
cutting-edge technologies in its production process, is also suitable for a
stage between computerization and connectivity. The study also enables
comparison of results with the Industry 4.0 factory
typology presented in Tschiesner et al. (2019), where
based on key value drivers - (1) number of product variants manufactured in a
factory and (2) average batch size - it was observed that the thermal power
plant is characterised as high-volume production,
while the plastic injection molding company represents mass-customised
production.
The results indicate that companies are
positioned between Industry 2.0 and Industry 3.0, in a similar fashion to the
work of Hajoary et al. (2024). This study aligns with
the research by McDermott et al. (2024, 2025) through its adoption of
qualitative approaches to assess Industry 4.0 practices. That is to say,
evaluation methods employing qualitative techniques remain limited despite the growing
body of work addressing Industry 4.0 assessment across diverse industrial
sectors.
Table 3 presents the radar scores per construct
for both companies. The thermal power plant achieved the following scores:
cyber-physical production systems (2.5), smart factory (1.8), people (1.5),
vertical network (1.5), horizontal integration (2.0), through-engineering
(1.0), exponential technologies (1.5), and Industry 4.0 environment (1.0). Its
overall average score was 1.60.
The plastic injection molding company achieved
the following scores: cyber-physical production systems (2.0), smart factory
(2.2), people (2.5), vertical network (2.0), horizontal integration (2.5),
through-engineering (2.0), exponential technologies (2.0), and Industry 4.0
environment (1.5). Its overall average score was 2.09.
Scores range from 1 (practice not considered,
rarely occurring, or not applicable to the company's reality) to 4 (practice
based on effective and highly developed systems, with autonomous capacity for
improvement and development).
6 CONCLUSION
This study aimed to understand
practices related to Industry 4.0 in two manufacturing industries in the Amazon
region. A research framework was developed, considering that most studies on
Industry 4.0 evaluation emphasize quantitative aspects. This research utilized
qualitative techniques to understand the perception and adoption of Industry
4.0 practices, thereby making an original contribution to operations
management.
6.1 Detailed
findings by dimension
The radar scores revealed distinct
patterns across the eight I4.0 dimensions. Regarding “cyber-physical production
systems”, the thermal power plant outperformed the injection molding company
(2.5 vs. 2.0), due to its continuous production process enabling broader sensor
deployment. In the “smart factory” dimension, both companies scored below 2.5,
indicating that predictive maintenance and real-time centralized monitoring
remain incipient. The “people” dimension showed the largest gap between
companies (1.5 vs. 2.5), reflecting that organizational culture and workforce
readiness vary significantly even within the same region.
Both companies scored lowest in “through-engineering”
(1.0 and 2.0) and “Industry 4.0 environment” (1.0 and 1.5), indicating that
product lifecycle management, digital ecosystems, smart logistics, and
connected products are entirely absent from their current reality. Exponential
Technologies” scored at most 2.0 in both cases, with only basic sensors and
collaborative robots identified; no IoT, big data, digital twin, or artificial
intelligence was observed. “Vertical and horizontal integration” scored between
1.5 and 2.5, suggesting that internal data connectivity exists but does not
extend to upstream or downstream value chain partners.
These results demonstrate that Amazonian
manufacturers are positioned between Industry 2.0 and 3.0, with maturity
concentrated in basic automation and isolated connectivity, while advanced I4.0
pillars remain unreached. The production system (continuous vs. batch)
influences cyber-physical and integration dimensions, but does not affect
weaknesses in through-engineering and I4.0 environment, which appear structural
to the regional context.
6.2 Theoretical
contribution
The theoretical contribution lies in expanding
the limited body of knowledge concerning Industry 4.0, particularly regarding
operations management and the understanding of Industry 4.0 practices by
managers and employees in a productive environment. The eight constructs
applied in the protocol present in the epistemological approach make it
possible to visualize practices related to I4.0 based on the understanding of
the actors involved in the process (managers and employees). The protocol made
it possible to understand the current state of the companies concerning the
subject, supporting the development of research in the Amazonian context.
6.3 Managerial
contribution
As for the managerial contribution, the study
could serve for the implementation or improvement of I4.0 in other
manufacturing industries, in addition to the possibility of adaptation to the
reality of micro and small manufacturing companies. The results indicate that
an organization's adopted production system can directly influence its Industry
4.0 maturity level.
6.4 Social
implication
As for the social implication, the application
protocol, especially the open structured interviews, provided an interpretation
of the I4.0 phenomenon based on people's perceptions. Unlike other operations
management approaches (e.g., quality management and lean manufacturing), which
often show a consistent understanding across different organizational and
educational levels, this study observed a lack of uniformity in understanding
of Industry 4.0, even among individuals at the same organizational and
educational levels. This result may have broad sociological and organizational
behavior implications for individuals regarding the advantages (training,
higher wages) or disadvantages (layoffs, unemployment) of adopting Industry 4.0
and the man-machine relationship in the face of the elimination or
precariousness of work (Compan et al., 2021; Biencourt et al., 2021; Gamkrelidze et al., 2021; Zouinar, 2020).
6.5 Limitations
and future research
The research limitation is associated with the
sample size (limited). However, the study was carried out in the form of an
in-depth case, which allowed a greater depth of the organization's reality. It
was also not possible to carry out a longitudinal analysis that would help in a
greater understanding of the organizational reality over time. For future
studies, it is recommended to conduct similar research in a specific sector, so
it will be feasible to analyze the peculiarities of the I4.0 implementation in
a specific industrial sector.
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