Supply Chain in the Age of Industry 4.0: A Literature Review

Abstract

Background: The rapid digital transformation driven by Industry 4.0 technologies is reshaping manufacturing supply chains, yet comprehensive insights into how these technologies are integrated remain limited. Methods: This study addresses this research gap by conducting a systematic bibliometric analysis and literature review of integrating Industry 4.0 technologies in the manufacturing supply chain. We used different scientific databases, Scopus and Web of Science, to elaborate this study. Results: Using advanced bibliometric methods, this study examines the evolution of academic discourse, identifies key themes, and maps the intellectual structure of this transformative research field. By leveraging bibliometric tools, the study names the most prolific authors, countries, and journals contributing to this domain. The findings of the first phase reveal the growing focus on topics like supply chain resilience and real-time decision-making, while also finding gaps in the literature related to technology integration. In the second phase, the literature review identified the most used adoption models in empirical studies such as resource-based view, dynamic capabilities view, and technology acceptance model, we also categorized the adoption drivers into technological, organizational, and environmental. Conclusions: This review emphasizes that although research on Industry 4.0 has expanded significantly, the majority of studies predominantly concentrate on technology adoption and quantitative analysis, with little examination of integration, contextual factors, and longitudinal effects.

1. Introduction

The manufacturing supply chain is undergoing a profound transformation driven by the advent of Industry 4.0 technologies. Industry 4.0 embodies the integration of advanced digital technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), blockchain, big data analytics, and Cyber–Physical systems (CPS) into industrial processes. Industry 4.0 technologies enable industries and organizations to transform into intelligent environments [1]. This integration has been rising since the first appearance of the term Industry 4.0. Indeed, recent advances have made manufacturing companies more interested in integrating this new concept and being part of the fourth industrial revolution.

Over the past decade, the adoption of Industry 4.0 technologies has gained momentum, particularly within manufacturing supply chains that face increasing pressure to enhance efficiency, resilience, and sustainability. However, despite growing academic and industrial interest, there remains a lack of comprehensive synthesis on how these technologies are being integrated, adopted, and studied within the supply chain context. Existing reviews often focus on individual technologies or specific functional areas, leaving a fragmented understanding of the overall digital transformation of manufacturing supply chains. This highlights the need for a structured bibliometric analysis and literature review that captures the intellectual structure, thematic evolution, and collaborative landscape of research in this field.

Our bibliometric study thus investigates the following research questions:

• What are Industry 4.0 trends in the manufacturing supply chain?
• What are the journals, authors, and countries contributing to this domain?

Whereas our systematic literature review investigates these research questions:

• What are the models, theories, or frameworks for the adoption of advanced technologies?
• What are the adoption drivers and impact on the manufacturing companies?

The purpose of this study is to investigate systematically the landscape of Industry 4.0 technologies in the manufacturing supply chain through bibliometric analysis and a literature review. The research uses advanced bibliometric tools to identify and map major themes, key contributors, and collaborative networks in this evolving field. Technologies like IoT, AI, Big Data Analytics, and Blockchain have increasingly affected daily life; yet limited review papers address how these innovations shape academic outputs. By identifying patterns and highlighting trends, this study provides a comprehensive overview of their influence on the supply chain. The findings aim to inform academics and practitioners, enabling the adoption of innovative solutions that foster sustainable, efficient, and resilient supply chain practices.

Literature reviews help identify trends, gaps, and opportunities within a research field by analyzing a defined set of studies. Comparing them offers a deeper understanding, enabling a meta-analysis of their methodologies, findings, and conclusions. This comparison allows researchers to assess the consistency and reliability of results, highlight variations in scope and approach, and uncover areas for further investigation. In our corpus, we identified 13 literature reviews.

Based on the analysis of each literature review in our corpus, we could identify the authors’ main contributions. Five SLRs investigated the integration of only one technology in the supply chain [2]. One focused on the impact, benefits, and challenges of additive manufacturing in the supply chain [3]. Another focused mainly on the effects of additive manufacturing adoption, where the authors divided them into two categories: consolidated effects means that these effects were consistently positive or negative in the literature, and inconclusive effects are the effects that were contradictory by showing both positive and negative outcomes [4]. One identified the effect of additive manufacturing on manufacturing business models. Another SLR focused on the impact of blockchain [5]. One focused on blockchain’s application in the product lifecycle [6]. Authors in this SLR focused on the impact of Internet of Things (IoT) on supply chain visibility. They also identified key IoT technologies’ key benefits and challenges in its adoption. Other authors focused in their SLR on the opportunities and risks of digital technologies they identified [7]. This study focused mainly on 3D printing/Additive Manufacturing–Augmented Reality–Robotics–Cyber Security–Blockchain–IoT/Industrial IoT (IIoT)–Big Data–Cloud Computing-Radio Frequency Identification (RFID)-Simulation [8], while another paper focused on the implementation of all 89 technologies. They were divided into 11 categories of key technologies. The authors identified Artificial Intelligence (AI) as the most frequently associated technology, followed by Blockchain, IoT, and Digital Applications [9]. In this study, the authors proposed five dimensions of supply chain 4.0 adoption [10]. The authors focused in this study on implementing Industry 4.0 in supply chain management. They identified implementation challenges, divided their corpus into three areas using topic modeling, and classified the papers selected based on their methodology, whether it is an exploratory or confirmatory study, qualitative or quantitative approach, and management or Process/Technology level [11]. They conducted a literature review and focused on the impact, potential, shortfalls, and barriers of Industry 4.0 adoption. In addition, the authors listed some technologies and their business applications.

Different authors proposed a framework or a model based on the results of their literature review [12]. The authors in this study identified the enabling factors of digital manufacturing from the literature, divided them into five categories, and synthesized them into a maturity model [13]. A conceptual framework is proposed to integrate Industry 4.0 and supply chain integration, focusing on three dimensions: process and activity integration, technology and system integration, and organizational relationship linkages.

In another study, its authors conducted an SLR to identify barriers to Industry 4.0 adoption, lean management benefits, and moderating factors using a questionnaire. It examines the implementation of lean practices aimed at optimizing supply chain operations, workforce efficiency, and manufacturing workflows. It also investigates the readiness to integrate emerging technologies, alongside the financial planning and technical resources necessary for adopting Industry 4.0 [14]. Ninety responses from 16 companies in Sri Lanka were collected.

In the identified SLRs, the authors mainly focused on the impact of technologies on the supply chain. Our work primarily contributes to identifying technology adoption models that can facilitate the adoption process. We also focused on the challenges of Industry 4.0 technologies integration, while also mentioning their adoption drivers in the supply chain.

The remainder of this paper is structured as follows: Section 2 presents a background on the impact of Industry 4.0 in the supply chain. In Section 3, we move to our research methodology. In Section 4 and Section 5, we present the results of our bibliometric study and discuss the empirical studies located. We conclude this paper in Section 6.

2. Industry 4.0 in the Supply Chain

Industry 4.0, also referred to as the Fourth Industrial Revolution, represents a transformative shift towards the integration of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Cyber–Physical Systems in manufacturing and supply chains. This paradigm enables real-time data exchange and automation, fostering enhanced operational efficiency and responsiveness to market demands [15]. In manufacturing, Industry 4.0 facilitates smart factories where interconnected systems optimize processes, reduce waste, and improve product quality [16]. These advancements lead to unprecedented levels of customization and scalability, allowing manufacturers to address increasingly complex consumer demands [17]. Furthermore, Industry 4.0 technologies enhance sustainability by enabling energy-efficient operations and the minimization of resource consumption in production systems [18].

The supply chain refers to the set of interconnected operational processes and organizational units that coordinate the internal flow of materials, components, information, and financial resources from inbound procurement through production to outbound distribution, in order to deliver value to the end customer. This integrated system aims to enhance efficiency, reduce costs, and deliver value to the end customer [19]. This network spans procurement, production, inventory management, and distribution.

Within supply chains, these technologies enhance visibility, enable predictive analytics, and support agile decision-making, thereby addressing challenges such as demand fluctuations and supply disruptions [20]. Blockchain and IoT, for instance, create secure and transparent systems for tracking goods, improving traceability and trust across global supply networks [21]. As international markets become increasingly complex and competitive, adopting Industry 4.0 technologies is critical for achieving sustainable and resilient supply chain operations [22]. These technologies are not only reshaping operational paradigms but also driving innovation and collaboration across industries, making them indispensable for the future of manufacturing and supply chain management [15].

Collectively, these studies demonstrate that while the literature has advanced our understanding of the benefits of digital technologies in specific contexts, it remains fragmented across industries and technological domains. Few studies have systematically synthesized the intellectual structure and thematic evolution of Industry 4.0 technologies within the manufacturing supply chain. This gap underscores the need for a comprehensive bibliometric review that captures the interconnections between digital technologies, supply chain performance, and sustainability outcomes.

3. Materials and Methods

This study employs a two-phase research design combining bibliometric analysis and systematic literature review. The objective of this research is to explore recent research on the adoption of Industry 4.0 technologies within industrial organizations’ supply chains.

The bibliometric analysis focused on key factors, such as publication trends, where we identified annual publications, publication types, and journal counts. For the relational factor, we focused on collaborations between authors, and for the qualitative factor, we presented the impact factor of journals, publication citations, and total citations of an author.

The methodological workflow is illustrated in Figure 1 and Figure 2.

We started by defining our research objectives. The keywords used in searching for relevant studies are “Industry 4.0” AND “Adoption” AND “Supply Chain” AND “Manufacturing”. We used those keywords on the selected scientific databases, Scopus and Web of Science, to extract the papers indexed Scopus or Web of Science. We extracted the publications using Zotero’s extension and then gathered all the publications in Zotero. Scopus contained 280 publications, and Web of Science contained 294 publications. The Zotero database contained 557 publications. After that, we started the cleaning process by deleting duplicates. We ended up with 405 publications in Zotero. We extracted the RIS file from Zotero and imported it into Vosviewer, where we conducted our data analysis. Then, we visualized and interpreted the results. The bibliometric analysis was conducted on the 405 publications that we selected on the first phase.

Following the bibliometric phase, we conducted a systematic review to examine in-depth how Industry 4.0 technologies are adopted in manufacturing contexts. We deleted publications out of scope. From 405 publications, we deleted publications on sustainability, health care, security, and education. Studies addressing sustainability as the main outcome or focus were excluded, as the present review concentrates on operational and technological performance rather than environmental or social sustainability dimensions. We also deleted publications in the COVID-19 context. In addition, we excluded papers that do not address the manufacturing context. Papers with PDFs not available were also excluded. At this phase, we ended up with 75 papers. To study the adoption of I4.0 technologies from the company’s view, we focused only on empirical study; we could also localize the models used, the adoption drivers, and the enablers. We ended up with 28 publications for our systematic literature review. Figure 2 presents the PRISMA methodological process, also see checklist in Table S1.

This review focused exclusively on empirical research to ensure that the findings were grounded in observed evidence and real-world applications of Industry 4.0 technologies in manufacturing supply chains. Empirical studies provide data-driven insights into how these technologies are actually adopted, implemented, and how they influence operational performance. By focusing on empirical work, the review aimed to capture verifiable relationships and patterns, rather than conceptual discussions or theoretical propositions that may lack practical validation.

4. Bibliometric Analysis

4.1. Publication Trends

4.1.1. Total and Annual Publications

As presented in

Table 1. Number of publications per year.

Year	Number of Publications
2016	3
2018	8
2019	17
2020	29
2021	43
2022	82
2023	63
2024	84
2025	76

and Figure 3, we have 405 publications published in a period of nine years, from 2016 to 2025. As we can see in the figure below, the number of publications in this area has continuously evolved since 2019, with 17 publications in 2019, 29 publications in 2020, 43 publications in 2021, and 82 publications in 2022. In 2023, the number of publications decreased to 63, and in 2024, it increased to 84 publications.

4.1.2. Publications Type

In our corpus, we have four types of publications, as shown in

Table 2. Number of publications per type.

Publication Type	Number of Publications	%
Article	310	76.54
Conference paper	61	15.06
Book Chapter	29	7.1
Book	5	1.3

and Figure 4 below.

Articles represent almost 79% of the corpus, with 259 publications. Conference papers represent 13%, with 43 publications. We also have 21 book chapters, which represent almost 6% of our corpus, and 5 books, which represent 2%.

4.1.3. Journal

In our corpus, we identified 159 journals. In these journals, we will present the most influential journals in our field and their number of publications. We calculated the average of publications; the average is 4.5, so we focused on the journals with a minimum of 5 publications. We identified 12 journals with a minimum of 5 publications in our corpus.

We also focused on the impact factor of each journal found in our corpus. The impact factor serves as an indicator of the influence or visibility of a journal within its field. It is a measure that evaluates the average frequency with which articles published in a scientific journal are cited over a given period. In our case, we choose the H-index as an impact factor. It takes into account both the number of publications and citations per article.

The table below presents those journals, the number of publications in our corpus, and their h-index.

In

Table 3. Number of publications per journal and H-index.

Journal	Number of Publications	H-Index
Sustainability	17	169
International Journal of Production Economics	14	231
Operations Management Research	13	41
Technological Forecasting and Social Change	13	179
Business Strategy and the Environment	11	147
Journal of Manufacturing Technology Management	11	93
Benchmarking	10	81
Journal of Cleaner Production	7	309
Computers & Industrial Engineering	7	161
Computers in industry	6	129
Annals of operations research	6	125
International Journal of Productivity and Performance Management	5	77

, we can identify journals that are the most influential in our field based on the H-index, such as the Journal of Cleaner Production in the first place with an H-index of 309, followed by International Journal of Production Economics with an H-index of 231, and Technological Forecasting and Social Change with an H-index of 179 in third place.

4.1.4. Geographic Distribution

In this section, we present the distribution of publications worldwide. The publications were located in different countries all around the world, from Africa to Asia to Europe to Australia to North and South America. The investigation on the adoption of Industry 4.0 technologies is very large.

We excluded books because we can have different publications associated with different countries. Therefore, we only focused on articles, chapters, and conference papers.

In

Table 4. Number of articles per country.

Country	Number of Articles
INDIA	121
ITALY	32
UNITED KINGDOM	21
USA	17
CHINA	16
PAKISTAN	15
BRAZIL	14
MALAYSIA	11
MOROCCO	9
SOUTH AFRICA	9
BANGLADESH	8
SAUDI ARABIA	7
AUSTRALIA	6
SPAIN	6
GERMANY	5

, we present the number of articles from the top 15 countries. India is the most productive country in our corpus with 121 articles, followed by Italy with 32 publications, and the United Kingdom in the third place with 2118 publications. Morocco took the ninth place with 9 publications.

We also present the distribution of articles per country in Figure 5.

4.2. Keyword and Topic Analysis

Keyword Analysis

This section presents a keyword analysis based on their occurrences. Our data shows that there are 1716 keywords involved in our research. We focused on keywords that occurred at least in five publications of our corpus, so we ended up with 148 keywords.

Figure 6 presents a keyword network visualization based on occurrences. It shows the result of the analysis performed in Vosviewer. From this mapping, 151 items related to our Industry 4.0 and supply chain were found. All the clusters identified in our corpus are linked to each other. The term “Industry 4.0” is at the center of the network, which indicates that this is the primary focus of the dataset. The computational mapping results categorize the data into five distinct clusters, each representing the relationships between one term and another. Each cluster comprises articles closely related to the research topic [23]. The cluster identified the following:

• Cluster 1 has 42 items marked in red. The focus in this cluster is supply chain, big data, and circular economy. Keywords like “supply chain”, “resilience”, and “logistics” highlight research on adapting supply chain operations to Industry 4.0 technologies. The prominence of “big data” and “predictive analysis” suggests a focus on leveraging data for decision-making and optimization. Circular economy links sustainability practices with Industry 4.0 implementations. This cluster explores how data-driven insights and sustainability principles influence supply chains in Industry 4.0.
• Cluster 2 has 35 items marked in green. Its focus is Manufacturing Industries and IoT. Some of the items in this cluster are “IoT”, “Digital Twin”, “Productivity”, “Industries”, and “Security”. This cluster explores the integration of IoT and related technologies into manufacturing industries while addressing productivity and security concerns.
• Cluster 3 has 29 items marked in blue. The focus in this cluster is smart manufacturing and digital transformation. Keywords like “smart manufacturing”, “digital technologies”, and “augmented reality” indicate a focus on digital transformation in industrial processes. The keyword “blockchain” suggests the emerging role of blockchain in ensuring transparency and security in Industry 4.0. The link with “decision-making” highlights how technology helps strategic planning in manufacturing systems.
• Cluster 4 has 24 items marked in yellow. The main items are opportunities, optimization, management, quality management, management practices, models, system, design, framework, and determinants. Keywords like “models” and “design” highlight the role of systematic planning and innovative frameworks for Industry 4.0 implementation. This cluster focuses on management systems and their optimization using Industry 4.0 technologies.
• Cluster 5 has 21 items marked in purple. Keywords like “adoption”, “barriers”, “readiness”, “drivers”, “implementation”, “supply chain”, and “maturity model” dominate this cluster. This cluster addresses challenges in the adoption of Industry 4.0 technologies, especially in less technologically advanced regions or industries and testing their readiness.

The visualization provides insights into the thematic focus and interconnectedness of Industry 4.0 research. It highlights critical areas of investigation like supply chain management, manufacturing transformation, technological integration, and organizational readiness.

In summary, these clusters are not isolated topics but rather represent sequential and complementary stages in the evolution of Industry 4.0. Cluster 5 explains why and how firms initiate the adoption of Industry 4.0 technologies by examining readiness, drivers, and barriers to implementation. Cluster 4 builds on this foundation by illustrating how organizations manage and optimize the integration of these technologies through appropriate frameworks, models, and management practices. Clusters 2 and 3 then explain what technologies enable this transformation, focusing on the implementation of IoT, digital twins, blockchain, and other digital solutions that drive smart manufacturing and digital transformation. Finally, Cluster 1 demonstrates where these technologies create their most significant impact—across the supply chain—by promoting sustainability, resilience, and operational efficiency. Together, these clusters outline a coherent and progressive pathway from initial adoption to strategic and sustainable implementation of Industry 4.0.

Figure 7 presents a density visualization of the keywords in our corpus. The figure shows a concentration of studies in Industry 4.0, supply chain, and sustainability. As shown in Figure 5, Figure 6 and Figure 7, Industry 4.0 is the main focus.

Figure 8 presents an overlay visualization of keywords. This visualization is based on the number of occurrences, while colors reflect the average of the publication year. The keyword Industry 4.0 is in green; it means that the average of the publications year is 2022.

4.3. Co-Authorship and Collaboration Networks

4.3.1. Citation Analysis

This section presents the top 20 publications according to total citations. The first publication entitled Industry 4.0 technologies: Implementation patterns in manufacturing companies [24] is the most cited in our corpus with 3018 citations.

Table 5. Top 20 most cited publications.

References	Publication Type	Country	Total Citations
[24]	Journal Article	Brazil	3758
[25]	Journal Article	India	1226
[26]	Journal Article	Italy	971
[27]	Journal Article	South Africa	934
[28]	Journal Article	India	923
[29]	Journal Article	United Kingdom	901
[30]	Journal Article	Spain	853
[31]	Journal Article	India	750
[32]	Journal Article	South Africa	711
[33]	Journal Article	China	592
[34]	Journal Article	South Africa	577
[35]	Journal Article	South Africa	526
[36]	Journal Article	India	512
[37]	Journal Article	Spain	461
[38]	Journal Article	India	446
[39]	Journal Article	India	420
[40]	Journal Article	India	404
[41]	Journal Article	India	384
[42]	Journal Article	Italy	360
[43]	Journal Article	Italy	334

presents the total citations of the 20 most cited publications.

We also present the distribution of citations across countries.

Table 6. Total citations per country.

Country	Total Citations
India	9337
Brazil	3420
South Africa	2106
Italy	2306
United Kingdom	1415

presents the top five countries in citations. India has the most cited publications with 8553 citations. Brazil is second with 3414 citations, followed by South Africa with 2740 citations.

4.3.2. Co-Authorship

In this section, we present the collaboration between authors. Figure 5 presents the authors’ collaboration in our corpus. We focused on the authors with a minimum of three publications.

As the figure below shows, we can identify 10 clusters containing several items. Each cluster represents a group of authors who collaborate frequently. The first cluster in red contains nine authors; cluster 2 in green contains six authors; clusters 3 in blue and 4 in yellow have four authors; clusters 5 in purple, 6 in cyan, and 7 in orange have three authors, and clusters 8 in brown, 9 in pink, and 10 in light pink have one author.

Figure 9 shows interactions between the red, blue, and green clusters. These three clusters working together are likely contributing to a broader research theme or topic. Authors like Gunasekaran, A., and Bag, S. appear central to the network, acting as bridges between clusters. Their positioning suggests significant influence or collaboration across different research groups.

Many authors and clusters in our corpus are disconnected; this shows limited interactions between research groups. Authors such as Chowdhury, S., Nimawat, D., and Kumar, P. are relatively isolated, suggesting limited collaboration with the main network. Pairs like Gadekar, R., and Gadekar, A., or Haleem, A., within the cyan cluster show exclusive collaboration within small networks and indicate fewer collaborations or more niche topics.

4.3.3. Most Productive Authors

Table 7. Number of publications per author.

Author	Nbr of Publications	Country of Affiliation	Affiliation
Kumar, A.	9	India	Indian Institute of Management Rohtak
Bag, S.	8	South Africa	University of Johannesburg
Luthra, S.	7	India	Ch. Ranbir Singh State Institute of Engineering and Technology
Gupta, S.	7	India	Swarrnim Startup and Innovation University
Kumar, V.	7	United Kingdom	University of the West of England
Yadav, G.	6	India	Veermata Jijabai Technological Institute
Chowdhury, S.	4	France	TBS Business School
Khan, S.a.r.	4	China	Xuzhou University of Technology
Singh, Rk.	4	India	Management Development Institute

presents the most productive author in our corpus with their affiliations. The first author on our list with nine publications is Kumar, A., an author affiliated with India. Most of the productive authors are affiliated with India.

5. Discussion

5.1. Empirical Studies on Industry 4.0 Technologies Adoption in the Supply Chain

Understanding how organizations adopt new technologies is crucial in today’s rapidly evolving digital landscape. Over the years, researchers have developed various models to explain why and how businesses integrate innovations into their operations. These models help us grasp the factors influencing adoption, from organizational readiness to external pressures.

In this section, we explore the key adoption models that shape the way companies embrace technological change. By examining these frameworks, we gain valuable insights into the drivers that impact decision-making, ultimately shaping the success of technology adoption in different industries. Understanding these models allows businesses to develop strategies that enhance adoption rates and optimize the benefits of Industry 4.0 technologies within their supply chains. The main objective is to discover why companies should implement I4.0 technologies in their supply chain and to identify the factors contributing to a good implementation.

We identified the empirical studies in our corpus so we could address

Table 8. Overview of empirical studies.

Reference	Model	Sample Size	Data Collection
[44]	TOE-TE	931 respondents from manufacturing firms	Questionnaire
[45]	TOE	24 companies	Semi-structured interviews
[46]	INT-DCV	256 respondents from Indian manufacturing firms	Questionnaire
[47]	RBV	408 respondents from Chinese manufacturing firms	Questionnaire
[48]	RBV	510 managers of manufacturing firms	Questionnaire
[49]	PBV	80 respondents from Bangladeshi clothing factories	Questionnaire
[28]	TAM	271 respondents of United Kingdom SMEs	Questionnaire
[50]	TAM-INT-RBV	117 operation managers of UK manufacturing firms	Questionnaire
[29]	DCV	256 respondents from Indian manufacturing firms	Questionnaire
[51]	DCV	193 respondents from 28 Moroccan manufacturing companies	Questionnaire
[52]	BRT	215 respondents from manufacturing companies	Questionnaire
[53]	KBV-ROT	280 operation managers from Vietnamese SMEs	Questionnaire
[54]	-	154 respondents from Indian companies	Questionnaire
[41]	RBV	143 respondents from Indian manufacturing firms	Questionnaire
[55]	-	151 senior managers from Switzerland manufacturing firms	Questionnaire
[56]	RBV	110 respondents from Malaysian SMEs	Questionnaire
[57]	-	350 respondents from Indian manufacturing industries	Questionnaire
[58]	DCV	256 respondents from Spanish manufacturing firms	Questionnaire
[24]	-	92 respondents from manufacturing	Questionnaire
[59]	DOI–RBV–Socio-technical theory	502 respondents from Spanish and Dutch organizations	Questionnaire
[60]		188 respondents from Australian	Questionnaire
[61]	KBV	27 interviewees	Semi-structure Interviews
[26]	-	1331 respondents from Italian manufacturing firms	Questionnaire
[62]	-	163 respondents from manufacturing firms in Italy, Poland, Germany, Austria, and Hungary	Questionnaire
[63]	-	76 respondents from Norwegians manufacturing firms	Questionnaire
[64]	-	70 respondents from Indonesian manufacturing firms.	Questionnaire
[65]	-	240 respondents from Thailand SMEs	Questionnaire
[66]	RBV	16441 respondents from Chinese manufacturing firms	Questionnaire

, where we focused on the model used, the sample size, the methodology used to collect data, and the objective of the studies. The empirical studies that we analyzed used questionnaires for different objectives as addressed in the table.

We identified 28 empirical studies. Of these, 27 were quantitative studies using a questionnaire as a measurement instrument, and another study used semi-structured interviews. These studies investigated different aspects of adopting I4.0 technologies in the supply chain of manufacturing companies.

Studies above show that the questionnaire is the measurement instrument used. Some Authors prepared their questionnaire based on one or different models already present in the literature, others prepared their questionnaire using the literature. We also identified articles that investigated the adoption of technologies using semi-structured interviews. The articles considered in this section are qualitative and quantitative studies. From 28 publications, 18 used a known model to prepare their questionnaire. Authors of these publications prepared their questionnaire based on the models such as diffusion of innovation theory, institutional theory, resource-based theory, technology acceptance model, technology organization environment, and others.

We start by defining the models used in the empirical studies from the literature.

Diffusion of innovation (DOI) theory focuses primarily on the perceived characteristics of technologies and the innovativeness of the organizations that adopt them [67]. This theory defines five steps of the individual’s innovation–decision process: knowledge, persuasion, decision, implementation, and confirmation [68].

Institutional theory (INT) helps organizations engaged in various change programs understand the impact of internal and external influences, particularly those involving information technology-induced change [67].

The resource-based view (RBV) is a managerial framework for determining the strategic resources a company can use to achieve long-term competitive advantage [69]. Its central proposition is that to achieve a long-term competitive advantage, a company must acquire and control valuable, rare, unique, and inimitable resources and capabilities and have an organization in place that can absorb and apply them [70].

Dynamic capabilities view is a framework that can be conceptualized as an updated view of the RBV, and it refers to a firm’s ability to sense opportunities, seize them, and transform resources to maintain a competitive advantage in rapidly evolving markets [71].

Resource orchestration theory is a strategic management framework that builds upon the RBV and DCV. This theory focuses on the actions leaders take to enable the effective management of the firm’s resources [72].

The knowledge-based view builds on RBV but focuses more on intangible assets, particularly knowledge, as the key driver of competitive advantage. It posits that heterogeneous knowledge bases and capabilities among firms are the main determinants of sustained competitive advantage and superior corporate performance [73].

Technology organization environment (TOE) is a framework by Tornatzky & Fleische. This model concentrates on the impact of adopting the technology in the organizational, technological, and environmental context after its adoption. All three contexts influence technological innovation [74].

Davis developed the technology acceptance model (TAM) in 1989. This model suggests that the perceived ease of use and usefulness of technology are the predictors of user perception toward using the technology and resultant behavioral intentions, as well as actual usage [61]. This model focuses on users’ intentions.

Behavioral reasoning theory (BRT) is a theory that helps understand human psychology towards a particular subject, it analyses values, attitudes, intentions, and reasons for/against [52]. Authors used BRT to identify the behavioral patterns of the integration of Industry 4.0 technologies. They investigated whether the value and reason for/against of the actors of the supply chain in manufacturing companies influence the attitude and intention to integrate new technologies.

Socio-technical theory is a framework used to understand the interaction between social systems and technical systems within organizations. It considers organizations as consisting of two components: a technical system and a social system. The technical system encompasses equipment, tools, techniques, and processes, while the social system includes individuals and their relationships [75].

These models are often used in both research and practical applications to help organizations understand how users will interact with new technologies and what factors will drive their adoption.

Authors used these models for different objectives. They were either used to identify different factors, and barriers to the implementation of Industry 4.0 technologies, or to test the maturity level of companies in the context of technology implementation. Factors can help define the adoption drivers for the adoption of new technologies or the standard needed for a smooth adoption.

The most used model in our corpus is RBV with seven publications. The authors prepared their questionnaire using this framework; it means that they investigated the resources that a company can use to gain a competitive advantage. The studies considered those I4.0 technologies as resources to achieve resilience, operational performance, operational efficiency, and risk management of the supply chain. RBV emerged as the most frequently used model because it provides a solid theoretical basis for examining Industry 4.0 technologies as strategic resources that enhance firms’ competitiveness and operational performance. Its suitability for quantitative measurement also makes it a preferred framework for survey-based studies.

In second place, we find DCV, considered an updated version of RBV. Four publications used it in their questionnaire. This model is used to emphasize the ability of firms to adapt and reconfigure their resources dynamically to maintain competitive advantage. In third place we can find TAM, TOE, and INT with two publications each.

We can classify these models into two categories: technology adoption models and organizational and strategic models. TOE, TAM, DOI, INT, and BRT are considered technology adoption models. These models help define the factors that motivate the adoption of I4.0 technologies. DCV, KBV, and RBV are considered organizational and strategic models. They help discover the impact of the adoption of new technologies.

To summarize, we present

Table 9. Summary table of models.

Model	Category	Core Theoretical Focus	Application Context in I4.0 Studies
RBV	Organizational/Strategic	Emphasizes the strategic importance of firm resources in achieving sustained competitive advantage	Assess how I4.0 technologies are leveraged as internal resources to enhance supply chain resilience, operational performance, efficiency, and risk mitigation.
DCV	Organizational/Strategic	Builds on RBV by focusing on a firm’s capacity to reconfigure and adapt its resource base in response to dynamic environments	Examine how organizations reconfigure digital and operational resources during I4.0 adoption to sustain competitive advantage
TAM	Technology Adoption	Explains technology adoption based on perceived usefulness and perceived ease of use among users	Assess behavioral intention and attitude toward I4.0 technology uptake among decision-makers
TOE	Technology Adoption	Integrates contextual factors from technological, organizational, and environmental domains affecting technology adoption	Identify external and internal drivers influencing I4.0 integration within firms
INT	Technology Adoption	Investigates the influence of regulatory, normative, and mimetic pressures on organizational decision-making	Explore how institutional forces (e.g., government mandates, market norms) shape I4.0 adoption
DOI	Technology Adoption	Describes the process by which innovations are communicated over time among members of a social system	Evaluate the rate and pattern of I4.0 technology dissemination across supply chains
KBV	Organizational/Strategic	Highlights the role of knowledge as the most strategically significant organizational resource	Assess how knowledge acquisition, sharing, and utilization enable I4.0 readiness
BRT	Technology Adoption	Explores the underlying reasons for or against a particular behavioral decision within a given context	Uncover cognitive and affective reasoning influencing the adoption or rejection of I4.0 technologies

of models with definitions and application in the context of I4.0 studies.

Of the studies above, eleven did not use a specific model. Authors in those empirical studies prepared their questionnaires using either a focus group or interviews with actors in manufacturing companies.

In

Table 10. Quantitative studies.

Reference	Results	Analysis Methodology
[44]	All the hypotheses were supported in this study.	SEM
[28]	H1, H3, and H5 were supported. H2, H4, and H6 were rejected.	PLS SEM
[46]	H1, H2, H3a, H3b, and H4a were supported. H4b, H5a, and H5b were rejected.	PLS-SEM
[47]	H1, H3a, H3b, H4a, H4b, H5a, and H5b were supported. H4a and H4b were not supported.	SEM
[48]	All the hypotheses were supported in this study.	PLS-SEM
[49]	Both hypotheses were accepted.	SEM
[50]	Hypotheses were tested for each technology: Big data: Hypotheses H2, H3, H6, H8, H9, and H11 were not accepted. H1, H4, H5, H7, H10, H12, and H13 were accepted. Artificial Intelligence: Hypotheses H2, H6, and H7 were not accepted. H1, H3, H4, H5, H8, H9, H10, H11, H12, and H13 were accepted. Cloud computing: Hypotheses H2, H3, H4, H8, H9, H11, and H12 were not accepted. H1, H5, H6, H7, H10, and H13 were accepted. Blockchain: Hypotheses H2, H3, and H7 were not accepted. H1, H4, H5, H6, H8, H9, H10, H11, H12, and H13 were accepted.	SEM using SPSS 26
[29]	All the hypotheses were supported.	PLS technique using WarpPLS
[51]	H1 and H4 were not supported. H2, H3, H5, and H6 were supported.	PLS-SEM
[52]	H1, H5, H6, and H7 were not supported. H2, H3, H4, and H8 were supported.	SEM
[53]	H1, H2, H3, H4, H5, H5.1, H6, H7, H8, H9, H10, and H11 were accepted. H4.1 was rejected.	SEM
[54]	H1 was rejected and H2 was accepted.	Confirmatory factor analysis (CFA).
[41]	H1, H2, H3, H4, H5, H7, and H8 were supported. H6 was not supported.	SEM
[55]	External search depth is more important for the digitization of manufacturing than external search breadth. H3a, H3b, and H4 were rejected. H5 was supported.	Linear regression
[56]	H1, H2, H4, and H6 were supported. H3, H5, H7, and H8 were not supported.	Pls 3.0
[57]	All the Hypotheses were supported.	SEM-ANN using SPSS 26 tool
[58]	H1, H3, and H5 were supported. H2, H4, and H6 were not supported.	SEM
[24]	All hypotheses were supported.	Cluster Analysis-Pearson’s Chi-squared test-Fisher’s exact test
[59]	H1 and H3 were supported. H2 and H4 were not supported.	Regression Analysis
[26]	H1a, H2a, and H3 were supported. H1b, H2b, H4, and H5 were not supported.	Regression Analysis
[62]	H1, H2a and H2b, and H3 were only partially supported. H4a, H4b, H5a, and H5b were supported.	Confirmatory factor analysis using SPSS 26 Logistic Regression
[63]	H4 and H6 were supported. H1, H2, H3, and H5 were rejected.	Two-way ANOVA method
[64]	All hypotheses were supported.	Partial Least Square SEM
[65]	All hypotheses were supported.	SEM
[66]	All hypotheses were supported.	Logistic Regression

, we present the empirical studies that we identified, and the analysis methodology. The majority of the hypotheses were analyzed using structural equation modeling (SEM).

5.2. Adoption Drivers and Impact on Manufacturing Companies

Adoption drivers play a pivotal role in successfully implementing Industry 4.0 technologies within the manufacturing supply chain. These drivers push organizations to adopt digital transformation and new technologies like Internet of Things, Artificial Intelligence, robotics, blockchain, and big data analytics. Awareness of these drivers is critical since they signal the incentives and strategic objectives driving organizations toward adopting innovative practices.

Adoption drivers identified from the empirical studies are operational performance (increased productivity, efficiency, and effectiveness), supply chain performance, supply chain resilience, and flexibility.

To adopt new technologies, manufacturing companies need to define their ultimate goal. As seen in the precedent section, authors presented different hypotheses and supported or rejected them based on data gathered using questionnaires. Some of these empirical studies investigated adoption drivers. As presented in Table 10, the authors presented operational performance as an adoption driver of Industry 4.0 technologies [44,58]. Using digital technologies in production increases operational efficiency [59]. Another study focusing on BDA-AI supported hypotheses stating that BDA-AI impacts positively operational performance [29]. A study based in China supported that adopting intelligent manufacturing impacts positively companies’ labor productivity, which means that it impacts operational performance of the company [66]. In addition, the adoption of these technologies in manufacturing companies relates to higher flexibility [55]. Industry 4.0 technologies help achieve supply chain resilience [47,50], which could be considered as an adoption driver. In a more specific study, where the focus was on blockchain technology, authors concluded that blockchain affects supply chain resilience [51]. Industry 4.0 implementation is considered a strategic investment to achieve supply chain performance in manufacturing industries [48]. Implementing IT in the supply chain can improve its performance [64,65].

After identifying the adoption factor, it is necessary to identify factors helping in the adoption of I4.0 technologies; these factors facilitate the process of implementation and ensure a more pronounced impact. Workforce empowerment, system flexibility, operational accuracy, and stakeholder relations are critical factors in the adoption of I4.0 technologies in emerging economies [57]. Many authors agree that the technological context of an organization positively influences the adoption of I4.0 technologies. This includes technical capabilities and software infrastructure [44,57]. Resources alone cannot enable companies to achieve resilience, performance, and competitive advantage. Instead, it is the actions and initiatives such as leadership, organizational culture, and skills and competencies implemented by managers that foster a digital culture and drive innovation capabilities within organizations, especially SMEs [53]. Management support also moderates the impact that investments in advanced technologies have on adopting I4.0 technologies in SMEs [62]. In addition, Higher employee human capital quality, higher R&D intensity, and competitiveness are considered as factors that ensure a positive and more pronounced impact of this implementation [66].

Factors encouraging the adoption of I4.0 technologies can be categorized into three categories: technological, organizational, and environmental. Environmental includes competitiveness and R&D intensity, technological includes technical capabilities and software infrastructure, and organizational includes management support, organizational culture, and skills and competencies of employees.

6. Conclusions

Recognizing the significance of implementing machine learning techniques within the supply chain, organizations are integrating its methodologies into their supply chain operations [20]. A firm’s ability to profit from the ongoing transformation demands skilled adaptation from a range of angles. While many organizations are still confused about integrating Industry 4.0 technologies, this study aims to present different trends in this domain and the impact of its integration in the manufacturing industries.

This study highlights the significant impact of Industry 4.0 technologies such as IoT, AI, blockchain, and big data on enhancing supply chain performance, resilience, and flexibility. It reveals increasing academic interest since 2016, identifying India as the leading contributor and the International Journal of Production Economics as the most impactful publication source. Key research themes include supply chain optimization, smart manufacturing, digital transformation, and technology adoption models. Models like RBV, DCV, TOE, and TAM were most frequently utilized for understanding technology integration.

This study advances the theoretical understanding of Industry 4.0 in manufacturing supply chains by synthesizing empirical research and mapping dominant frameworks such as RBV, DCV, and technology adoption models. It organizes research into five interrelated thematic clusters, clarifying how adoption, management, technological implementation, and supply chain impact are conceptually linked. Additionally, it identifies gaps—such as limited qualitative research, cross-technology integration, and system-level analysis—providing a structured foundation for extending existing theories toward more integrated and practice-relevant models of Industry 4.0 adoption and performance.

Despite progress, the study identifies gaps in the literature concerning practical implementation challenges and technological readiness in less advanced contexts. Future research should emphasize overcoming adoption barriers, fostering collaboration among research groups, and aligning theoretical frameworks with practical applications for Industry 4.0-driven supply chain advancements. This systematic literature review provides valuable insights for academics and practitioners, guiding the strategic adoption of innovative solutions in supply chain management.

The near-absence of qualitative studies highlights a methodological gap in the literature. With 27 out of 28 studies relying on quantitative questionnaires, there is insufficient evidence to assess whether qualitative approaches might produce different or more nuanced conclusions. Future research should therefore adopt mixed or qualitative designs to deepen the understanding of how Industry 4.0 technologies influence operational practices and decision-making in real contexts.

This review highlights that while Industry 4.0 research has grown rapidly, most studies remain focused on technology adoption and quantitative analysis, with limited exploration of integration, contextual factors, and longitudinal impacts. Future research should move beyond identifying adoption barriers by developing empirical models that explain how organizational, technological, and human factors interact during digital transformation. Collaborative research between academia and industry should be encouraged to generate real-world case studies and cross-sector comparative analyses. Moreover, scholars should combine theoretical models such as RBV and DCV with practice-oriented frameworks to examine the tangible outcomes of Industry 4.0 implementation. Finally, future work should expand toward investigating cross-technology integration and its implications for supply chain performance, offering a more holistic and application-driven understanding of Industry 4.0 evolution.