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Impact of Industry 4.0 on Sustainability—Bibliometric Literature Review

Faculty of Production Engineering, Warsaw University of Technology, 02-524 Warsaw, Poland
Author to whom correspondence should be addressed.
Sustainability 2020, 12(14), 5650;
Submission received: 20 June 2020 / Revised: 4 July 2020 / Accepted: 11 July 2020 / Published: 14 July 2020


Nowadays, sustainability and Industry 4.0 (I4.0) are trending concepts used in the literature on industrial processes. Industry 4.0 has been mainly addressed by the current literature from a technological perspective, overlooking sustainability challenges regarding this recent paradigm. The objective of this paper is to evaluate the state of the art of relations between sustainability and I4.0. The goal will be met by (1) mapping and summarizing existing research efforts, (2) identifying research agendas, (3) examining gaps and opportunities for further research. Web of Science, Scopus, and a set of specific keywords were used to select peer-reviewed papers presenting evidence on the relationship between sustainability and I4.0. To achieve this goal, it was decided to use a dynamic methodology called “systematic literature network analysis”. This methodology combines a systematic literature review approach with the analysis of bibliographic networks. Selected papers were used to build a reference framework formed by I4.0 technologies and sustainability issues. The paper contributes to the Sustainable Industry 4.0 reference framework with application procedures. It aims to show how I4.0 can support ideas of sustainability. The results showed that apart from a huge contribution to both concepts, many papers do not provide an insight into realization of initiatives to introduce Sustainable Industry 4.0.

1. Introduction

Industry 4.0 (I4.0) as a term and strategic initiative of the German government was introduced in 2011 [1,2]. Many similar transforming actions towards Industry 4.0 were taken in other developed countries, e.g., US Advanced Manufacturing Partnership, Chinese Made in China, British Smart Factory and others [3]. I4.0 and smart manufacturing/factory (which are synonymous) are the natural consequence of the historical developments of computer integrated manufacturing and flexible manufacturing systems [4] from the past decades. However, nowadays, through advanced technologies those ideas of CIM (Computer-integrated manufacturing) and FMS (Flexible manufacturing system) could be further developed and implemented at lower costs. Speaking of Industry 4.0, it should be considered as the applying of flexible automation, cyber-physical systems, (industrial) Internet of Things, sensors, collaborative and cognitive robotics, cloud computing, big data, computer modelling and simulations, additive manufacturing (3D printing) [5]. This picture clearly shows the technology-driven nature of I4.0 on the one hand. On the other hand, businesses are expecting great benefits:
  • economic, e.g., savings through more accurate planning, shorter lead times, increase of energy efficiency;
  • environmental, e.g., increase of energy efficiency, decrease of manufacturing scrap waste, etc.;
  • social, e.g., increase of safety, more comfortable working environment, etc.
Those benefits are foreseen through emerging possibilities for innovative execution of business processes leading to the creation of a strategic advantage.
On the other hand, technology-driven nature combined with the relatively early phases of I4.0 technologies lifecycle imply and raise several concerns:
  • economic, especially the cost-intensive nature and difficulties with estimation of full financial benefits and economic effectiveness (what could be approached with one of I4.0 technologies: computer simulation modelling);
  • environmental, e.g., increase of electro-waste, increase of energy consumption;
  • social, e.g., human-robot interaction issues, unemployment threats, privacy issues.
It is clear that I4.0 does not only deliver benefits, but also requires careful consideration of these concerns. Both benefits and concerns could be clearly pictured in the triple bottom line categories: economic, environmental and social. Therefore, the general question arises of what the real impact of I4.0 is on sustainability, both in terms of pros and cons.
Many authors researched phenomena of I4.0, its impacts on economy, society, its barriers and limitations [6,7,8,9,10]. Still growing interests in Industry 4.0 as it marries advanced manufacturing and technology seems to be changing the way businesses function. Those changes are deployed to improve the existing production systems by new technologies, offering a better way to organize and manage all processes within manufacturing and services with their logistics and supply chains. It represents the ways in which all emerging capabilities such as blockchain technology, artificial intelligence (AI), robotics and cognitive technologies, augmented and virtual reality might connect with different organizational process assets into unified sustainable production systems [5].
Some systems and solutions combine recently emerging new technologies, e.g., cyber-physical systems, but none of technology could solve a problem without the integration with others. All these technologies put together might give opportunities to [5]:
  • realize the unique challenges of Industry 4.0;
  • create a sustainable industrial environment;
  • enhance its impact in the literature and business area.
Industry 4.0 as a contributor to the Sustainable Development Goals (SDGs) [11] builds connectivity between the industry and sustainability by finding a significant relation between their components. The main efforts are, therefore, related to the tools and methods used for the comprehensive analysis of these terms. Much research or state-of-the-art reviews have been already done by researchers, separately for sustainability and Industry 4.0 phenomena [12,13]. A systematic literature review of Industry 4.0 resulting in modelling relationships between sustainability functions and Industry 4.0 was presented in [14], while [15] proposed a new concept of Sustainable Industry 4.0. As intended by the United Nations Sustainable Development Goals [11] for 2030, technological progress is driving the challenge of transition from traditional technology into intelligent machines without limiting the sustainability of the industrial economy. The combination of AI, robotics and other advanced technologies applied across many sectors of economy, e.g., the supply chain, distribution channels, manufacturing, provides a significant impact on the natural environment leading to reduction of pollution, decrease in greenhouse gases emission, decrease in energy consumption and increase in profits, simultaneously. The emergence of Industry 4.0 opens the opportunity of connectivity of technology with resources and skills in terms of sustainability benefits (zero impact—lower cost—social equity). Industry 4.0 can reduce the environmental impact of a product, a process, or a service based on footprint data availability and traceable analysis [16]. Additionally, it helps to leverage a greater efficiency of functions e.g., reduction of resource consumption. Therefore, Industry 4.0 might contribute sustainability to develop digital sustainable operations allowing to meet SDGs goals. Furthermore, increasing development of smart technologies is envisaged as affecting sustainability. The potential of Industry 4.0 is still existing with its unknown impact on other areas like socio-environmental sustainability [14] or making opportunities for realizing Industry 4.0 through intelligent systems.
There is a growing number of reviews about the impact on and/or connection of Industry 4.0 with sustainability over the last years. For comparison of fourteen identified reviews, see Table 1. This paper significantly differs from other identified reviews as those are narrowed and focused on specific processes (e.g., maintenance), technologies (e.g., big data), industries (e.g., pharma), aspects (e.g., environmental only). There are no reviews available that tackle comprehensively all triple bottom line perspectives. This research delivers novelty in the developed framework to support decision makers seeking I4.0 impacts on sustainability. The framework is developed from a bibliometric literature review. Therefore, it is comprehensive and covering possibly the widest range of problems and relations. The framework is a one-of-its-kind roadmap. This map enables making sustainability-concerned decisions on I4.0 initiatives. It identifies, refers to, and indicates papers containing research on links, pros, cons, convergences and discrepancies of the I4.0 toolset and different sustainability-oriented concepts.
In this paper, a literature-based analysis will be performed to find a relationship between Industry 4.0 and sustainability across many industries. In particular, a research challenge on Sustainable Industry 4.0 is seen for industrial application. This relation which is still quite fuzzy lies in a comprehensive qualitative assessment based on a literature review. To address this concern, this scientific paper is focused on systematic literature network analysis in order to find the link between Industry 4.0 and sustainability. Beside the extensive research on the Fourth Industrial Revolution direction which was conducted, a systematic review of literature aims to investigate the bidirectional relation of sustainability with Industry 4.0. Therefore, this paper can provide motivation for understanding the scale of interest and implications, while on the other hand systematizing the existing knowledge about separate concepts (Industry 4.0 and sustainability) and extending this knowledge about research towards the integration of the mentioned concepts, making a valuable theoretical contribution to the body of scientific literature in this research field. The consequence of the studies is to develop directions for further research. It would allow scholars and other interested parties to conduct more complex research on the development of quantitative assessment methods combining sustainability and Industry 4.0, which is rarely available [29].
The advantage of the authors’ paper over another scientific articles lies in a comprehensive systematic literature review on sustainability and Industry 4.0 by:
  • rationalizing and systemizing the state-of-the-art knowledge on the considered topics using the dynamic systematic literature network analysis;
  • presenting the literature analysis in terms of its three dimensions: (1) systematic review, (2) type and application of reviewed study, and (3) bibliographic networks of literature review;
  • making an attempt to deliver answers addressing research questions in the current published studies on these topics;
  • contributing to the existing body of research literature focused on combining the concepts.
The paper is structured as follows. Section 2 presents the research methodology and adopted bibliometric software. Then research questions are formulated, and results of systematic literature network analysis are described in Section 3. Section 3 also contains SLNA-based Sustainable Industry 4.0 reference framework along with its application procedure. Section 4 discusses the results presenting answers to research questions. Directions for future research are also described in Section 4. Conclusions are outlined in Section 5.

2. Materials and Methods

Two databases were used for literature analysis: Web of Science Core Collection (WoSCC) and Scopus, because those are the most common databases for conducting literature searches [30]. These databases are also considered to be the two most important multidisciplinary bibliometric databases [31] that are used for field delineation [17]. WoSCC and Scopus are also leading databases with their significant scientific impacts characterized by a high level of quality of reported documents [32]. The content of those databases is of high quality due to restrictive indexing procedures.
Systematic Literature Network Analysis (SLNA) [17,33] is the methodology chosen for the selection and analysis of papers (Figure 1).
The SLNA methodology (Figure 1) consists of two main steps. Systematic literature review (SLR) is the first step. It allows to determine the scope of research and generate studies for use as input in the next step (namely, bibliographic network analysis and visualization). The SLR approach [34,35] provides identification and selection of the most appropriate papers that will be included in further analyses of secondary data. SLR differs from other literature review methods due to its principles of transparency, inclusivity, explanatory and heuristic nature which allow for a more objective overview of search results, and elimination of any bias and errors [36]. To formulate research questions and define a scope of the analysis, Denyer and Tranfield [36] proposed the use of the Context, Intervention, Mechanism and Outcome approach (CIMO). The second step is bibliographic network analysis and visualization (BNAV) that allows to determine the development of major existing topics and emerging research trends using network analyses, e.g., citation network analysis (CNA), co-occurrence of keywords and burst detection analysis. CNA is one of the main areas of bibliometric research that uses various methods of citation analysis to establish relationships between authors or their studies. It assumes that the citation network is a system of channels that transform scientific knowledge or information. It is possible because scientists from one organization tend to cite each other to place their study in the field of previous or existing knowledge [37]. Typically, a network analysis is performed to map the range and structure of the discipline when discovering key research clusters [18].
The article adopts the methodology that is consistent with the prominent review papers from the field [34,38,39]. The systematic review process is based on manual filtering which allows to minimize the bias of the results of the literature review. It also enables the identification, assessment and synthesis of all relevant studies using a transparent and replicable process. This approach is also suitable for obtaining more information and a thorough understanding of both quantitative and qualitative issues, not automatic filtering enough just for quantitative considerations. The approach is the right way to set selection criteria and ensure more rigorous methodological control. It also allows to provide a thorough understanding of qualitative aspects, which perfectly complement the bibliometric analysis [40]. Centobelli et al. [41] examined not only abstracts and keywords, but also full-text academic papers. The approach adopted is also consistent with the content analysis, which is a method enabling the development of reliable literature reviews [42,43]. Berelson [44] defines content analysis as “a research technique for the objective, systematic and quantitative description of the manifest content of communication”. The use of content analysis should minimize research bias and improve the reliability and repeatability of given constructs [45].
In order to conduct analyses on selected papers, it was decided to use two software tools, i.e., VOSviewer, version 1.6.15 [46] and CiteSpace, version 5.6.R5 [47]. The VOSviewer tool [48] provides easy access to bibliometric mapping using data extracted from Web of Science and Scopus. The software is based on the Visualization of Similarities (VOS) algorithm introduced by van Eck and Waltman [49]. It allows to visualize a relationship between entities in such a way that both direct and indirect connections between them place these units closer together on the map [49]. Visualization can have one of three forms: network, overlay or density. The size and clarity of the label of a given element suggests the frequency of its occurrence in the analyzed set. In turn, the proximity of the location of the elements indicates, more commonly than in the case of distant ones, co-occurrence in specific sets. Moreover, if a given element appears in the center of the map (the strongest cluster, color-coded in a properly prepared visualization view), it can be concluded that it is in relation to a larger and more diverse group of other elements [50]. The software, after selecting the type of analysis (e.g., citation, co-occurrence), unit of analysis (e.g., documents, keywords), type of counting (full, fractional), as well as providing threshold values, e.g., number documents, citations, keywords, allows to create a co-existence network. Hence, VOSviewer was used in the paper to create a citation network and a co-occurrence network of authors’ keywords.
CiteSpace is a Java application for detecting, visualizing and analyzing emerging trends and transient patterns in scientific literature [51]. It was created as a software tool for progressive visualization of domain of knowledge [52]. CiteSpace focuses on searching for critical points for the development of a given discipline or field of knowledge, more precisely pivotal and intellectual turning points [51,53]. The tool is useful in the case of temporal and structural analyses of various networks (e.g., authors’ keywords) based on scientific studies. CiteSpace is equipped with a burst detection algorithm that allows to detect a series of keywords. The result of the algorithm execution is a list of popular keywords over time (topic bursts). CiteSpace was used to perform a burst detection analysis.
The primary source of input for both applications were files generated in WoSCC and/or Scopus. Basic quantitative information about the papers selected for the analysis and Global Citation Score Analysis results were obtained from WoSCC and Scopus bibliometric tools. The adopted research methodology and selected software tools were also used to create a literature review on the concept of Lean Industry 4.0 [54].

3. Results

3.1. Systematic Literature Review

3.1.1. Research Question Formulation

Based on the available literature reviews on the relationship between “sustainability” and “Industry 4.0” [12,15,55,56,57], the following three research questions (RQs) were formulated, which have not yet been fully answered on scientific grounds:
  • RQ1. How applications of Industry 4.0 can contribute to sustainable development?
  • RQ2. How Industry 4.0 technologies and tools can be integrated into sustainability practices on a theoretical and practical basis?
  • RQ3. What are the main approaches/methodologies/frameworks/tools that should be considered for integrating Industry 4.0 with sustainable development?
In order to answer these questions and fill emerging gaps in the literature on this topic, the SLNA methodology was applied (Figure 1). Based on the results obtained, it was possible to organize knowledge in the above-mentioned research area and develop a reference framework to understand how Industry 4.0 can support the triple bottom line [58], represented by the three dimensions (economic, social and environmental) of sustainable development. The developed framework also includes application procedures which will facilitate its use in future research and industrial practice.

3.1.2. Locating Studies and Inclusion/Exclusion Criteria

The exploration of existing literature on the links between sustainability and Industry 4.0 was based on the identification of available research based on specific keywords. The preliminary or trial selection of relevant papers for bibliographic research was focused on the construction of a search query including various terms, synonyms and abbreviations related to the words “sustainability” and “Industry 4.0”.
To identify all effective terms, synonyms and abbreviations for the above-mentioned words, the authors studied the most frequently cited literature reviews on sustainability [20,59,60,61] and Industry 4.0 [15,21,22] in WoSCC and Scopus. In this case, to avoid duplication of papers, a query expression (Equation (1)) reflecting the keyword “sustainability” was initially explored: “(sustainab* OR triple bottom line OR TPL OR (…)”. This was aimed to shed light on a scale of sustainability research activities to understand how these studies support the Sustainable Development Goals. The focus was around determining challenges in implementing SDGs in industrial practice.
The keyword “Industry 4.0” is mainly used in Europe, while in both the Americas and Asia this paradigm is most often called smart manufacturing, smart production or smart factory [17]. The reason for using the different names lies in national strategies that have been developed addressing the need of national economies to increase their competitiveness in the manufacturing area. Strategies such as: “Industry 4.0” in Germany [2], “Smart Manufacturing Plan” [62] and “Advances Manufacturing Partnership (APM)” in the United States [21], “Made in China 2025” [63], are crucial due to creating employment opportunities, improving innovation and advancing sustainability [64]. These initiatives based on the foundations of the so-called Fourth Industrial Revolution also provide great potential for predicting the future of manufacturing. The core of this revolution is smart manufacturing which was defined by the National Institute of Standards and Technology (NIST) as a fully integrated, collaborative manufacturing system that responds in real time to meet changing requirements and conditions in the factory, in the supply chain, and the needs of customers [65]. As other names and abbreviations for the term “Industry 4.0”are being used, the authors assumed: “4.0 Industry“, “Industrie 4.0”, “I4.0”, “I4”, ”Fourth Industrial Revolution”, “4th Industrial Revolution” [54]. In order to avoid mistakes, e.g., plural forms, wildcards are used in the following keywords “sustainab*”, “green*”, “clean*”, “industr*”, “4*”, “manufactur*”, “factor*”, “enterpris*” [19]. Based on the keyword considerations above, the searching query was formulated as follows (Equation (1)):
{   sustainab *   OR   triple   bottom   line   OR   TPL     OR   [ ( green *   OR   clean * )   AND   production ]   }   AND { Industr *   4 *   OR   4 *   Industr *   OR   I 4.0   OR   I 4   OR [ ( Fourth   OR   4 * )   AND   Indsustrial   Revolution ]   OR [ smart   AND   ( manufactur *   OR   factor *   OR   enterpris * ) ] OR   enterprise *   4 * }
In the created query (Equation (1)) the most common scientific terms/synonyms/abbreviations for Industry 4.0 were used based on earlier studies. There are also other, less popular terms/synonyms of Industry 4.0 such as: Industrial Internet of Things (IIoT) or Future Manufacturing. There are papers in which IIoT is actually treated as a synonym for Industry 4.0 [66], but most researchers state that IIoT is one of the main technologies of Industry 4.0 (next to CPS and big data). For this reason, it was not included in the query. It is also worth noting that in the vast majority of IIoT articles, the term Industry 4.0 also appears in the title/abstract/keywords.
The identified query was introduced in WoSCC and Scopus including search: title, abstract and keywords at the beginning of April 2020 (06.04.2020). The search was limited to scientific research works published after 2011, because in this year the term “Industry 4.0” was used for the first time and the basic assumptions of the Fourth Industrial Revolution were defined [2]. Only articles in English from reviewed journals (including ones in press) were considered for further analysis. There were 312 papers in WoSCC and 406 in Scopus. Only 25% of searched articles indexed in WoSCC were not indexed in Scopus. It should also be noted that the first 20 papers with the highest number of citations and the highest average citations per year indexed in WoSCC are also found in Scopus. Therefore, the search results from the Scopus database were chosen for further analysis. The objective was to select studies which concerned the relationship between sustainability and Industry 4.0. Paper selection was limited to the following fields: engineering, environmental science, energy, business, management and accounting, social sciences, computer science, decision sciences, chemical engineering, economics, econometrics and finance, materials science.
162 relevant publications were selected after screening titles, abstracts and keywords of the identified documents. Then, the full version of text was read if there was any doubt whether the paper concerned the relationship between sustainability and Industry 4.0 and was helpful in finding answers to the formulated research questions.

3.1.3. Papers Selected for the Analysis

Figure 2 shows that the interest in the topic was not significant in the period 2012–2017. In 2018, there was a visible increase in interest in combining both concepts (36 papers). In 2019, another essential increase was recorded—over 100% compared to 2017 (76). Such a dynamic growth in interest in this topic underlines its current importance and relevance. In the first 3 months of 2020, 34 articles were identified, so if this trend continues, it will mean another increase in the number of papers compared to the previous year.
As shown in Figure 3, most of the papers were created in Asia (India—15%, China—9%), Europe (Italy—11%, United Kingdom—11%, Germany—8%, Spain—7%, France—6%) and North and South America (United States—12%, Brazil—7%). Other articles constitute a small percentage of the total papers. A dispersion of studies between continents and countries may show that the discussed topic is very important and has a global significance. An additional argument is the fact that many countries (e.g., USA, Germany, China, UK) are pursuing national strategies for the development of the Fourth Industrial Revolution, of which sustainability is a very important aspect.
Subject areas to which selected papers relate are presented in Figure 4.
The selected papers are dominated by one subject area: engineering (80 articles). It proves that in most articles the authors focused on aspects related to manufacturing in which the integration of Industry 4.0 and sustainability concepts enables the creation of new engineering solutions to achieve more sustainable and green production. The next most numerous subject areas regarding the analyzed topic are environmental science (59) and energy (53). This may indicate that these papers concentrated on using Industry 4.0 technologies and tools to protect the natural environment, increase energy efficiency and achieve sustainable development goals. Next up are articles related to business, management and accounting (46), social sciences (46) and computer science (37). These articles examine the impact of Industry 4.0 on business management issues based on the triple bottom line (TBL) framework. The impact can be measured using IT tools/algorithms based on Industry 4.0 technologies. Other subject matters are less numerous in terms of articles and cover many different fields (although all of them are related to engineering and management), which may indicate the interdisciplinary nature of the issue of sustainability and I4.0.
The largest number of articles have been published in Sustainability (Figure 5) by far. It is a consequence of the thematic scope of the journal, one of the main themes of which is to improve sustainability through Industry 4.0. The number of papers in other journals is smaller, but all of them focus on the issues of cleaner/green production and manufacturing, production and operations management considering environmental and sustainability research and practice. The number of selected papers was classified in terms of applications and types (Table 2).
The results depicted that according to the paper types, conceptual studies dominated (84) over literature reviews (25) and surveys (19). Slightly less represented than the abovementioned paper types are empirical research (18) and case studies (16). This indicates a visible advantage of theoretical papers over practical ones. When it comes to applications, most of the articles are academic and industrial (86). These papers develop scientific issues and contain references to their applications in industry. It can also be seen that academic applications (64) significantly outweigh industrial ones (12). This confirms the advantage of theoretical works over practical ones.

3.2. Bibliographic Network Analysis

3.2.1. Citation Network Analysis

Citation network analysis (CNA) is a method of network analysis in which papers are presented in the form of nodes and citations are depicted as links between them. Thanks to the CNA, it is possible to track the citation network which allows for a better understanding of the impact of previous studies on subsequent works and observation of knowledge flow. Papers that are not cited are excluded from the analysis. The consequence of the exclusion is the isolation of clusters (smaller networks) that includes documents in which everyone must have at least one connection with another within the cluster. It allows, among others, for easier definition of the thematic cluster scope (Table 3).
Figure 6 outlines a citations network of selected papers (overlay view). As a result, it became possible to determine which publications have the largest number of citations in others (weights) in the entire created network. The total number of citations in the Scopus database was presented using a color scale.
The network built in this way consists of 88 nodes and 166 links (Figure 6). It is assumed that the CNA gives the best results when clusters consist of many nodes because the amount of information that can be obtained from them is much larger than information from small clusters [67]. Based on this assumption, it presented the 7 (out of 17) largest clusters created by VOSviewer with the most important information associated with them (Table 3).
To identify the main research topics of clusters, the main references were analyzed, i.e., the papers with the largest number of citations (Table 3). It should be considered that it is not always necessary for all papers assigned to a given cluster to be closely related to its main topic, because the authors sometimes cite works that practically do not relate to the main topic discussed in the article. This may also be the case for review papers.
The CNA shows that research related to the integration of sustainability and Industry 4.0 is fragmented and multidisciplinary. The identified research topics were initiated in countries that run programs related to the Fourth Industrial Revolution: the USA, China, Germany, the United Kingdom, emphasizing their leading role in creating the basis for research on the integration of the concepts of sustainability and Industry 4.0. As a result of the CNA, seven clusters were outlined.
Cluster 1 presents how smart technologies can help manage a sustainable product life cycle [75]. An example of using Big Data Analytics (BDA) in this process is given in [23]. The term sustainable smart manufacturing (SSM) is also defined and a conceptual framework of BDA in SSM is proposed from the lifecycle BDA perspective [23]. The rationale for making decisions based on big data sets in sustainable smart manufacturing is sketched in [24]. This paper also includes state-of-the-art sustainable and smart manufacturing. The studies [68,76,77] discussed the issue of performance of smart manufacturing systems and proposed possible assessment approaches. In both articles the importance of sustainability is emphasized as one of the elements of smart manufacturing assessment. The requirements, platform architecture, functionalities, challenges and opportunities of smart manufacturing are presented in [69].
Cluster 2 raises the issue of critical factors for the implementation of Industry 4.0 and emphasizes the role of IoT in achieving sustainability. An integrative framework for understanding the synergies between I4.0 and environmentally sustainable manufacturing through critical success factors is presented in [70]. The analysis of factors that enables the realization of sustainable development by creating knowledge through digitalization is described in [78]. In the paper [56], factors of the influence of I4.0 on sustainability are included and a model of the structural equation with six hypotheses is proposed. This model is used to quantitatively measure the effects of Lean Manufacturing and Industry 4.0 in the field of sustainable development. The article [71] states that Smart Production Systems (SPS) will integrate the virtual and physical world on IoT platforms to ensure flexibility and resource efficiency. It also presents sustainability impacts on SPS. Three main potential benefits (transparency, resource efficiency, sustainable energy) for the use of Industrial IoT (IIoT) supporting sustainable development goals are discussed and the results of empirical verification are presented in the article [79].
Cluster 3 focuses on the issue of smart manufacturing. The general concept of a smart manufacturing enterprise, pillars of smart manufacturing and its possible integration with the supply and distribution chain are presented in [65]. Key drivers of I4.0 and their impact on sustainable supply chain are described in [80]. The possibility of reducing manufacturing conversion costs using real-time data digitization is outlined in [81]. The importance of modern ICT technologies and digitization of entire value chains in Industry 4.0 is highlighted in [82,83]. Business strategy based on the lean-digitized manufacturing system in I4.0 era is characterized in [83].
Cluster 4 explains the relationship between Industry 4.0 and circular economy (CE). Paper [72] contains a pioneering roadmap to enhance the application of CE principles in organizations due to I4.0 approaches. Possibilities of CE support through digital technologies are presented in [25]. The article [84] presents an eco-project related to the use of Internet of Things (IoT) and I4.0 technologies as an effective methodological approach for developing products compatible with the principles of the CE. The opportunities of I4.0 support for remanufacturing and CE acceleration are described in [85]. The cluster also discussed the issues of production planning and control based on Industry 4.0 technologies. A mathematical programming model [86] with activity-based costing (ABC) and theory of constraints (TOC) maximizing profit and reducing carbon dioxide emissions, energy recycling and reuse of waste simultaneously was developed. This model using I4.0 techniques was empirically verified in the paper industry [87] and textile industry [88].
Cluster 5 concerns the assessment of the potential of Industry 4.0 to achieve the goals of sustainable development and to be compliant with the TBL. The results of the qualitative assessment of the potential I4.0 in the ecological and social perspective are included in the paper [29]. In turn, the paper [89] contains quantitative results of the I4.0 assessment in the context of improving quality management due to using the Supply Chain Operations Reference (SCOR) model. Another article [55] presents the risk framework in the context of Industry 4.0 implementation that is related to TBL. The issue of smart and sustainable eMaintenance and the associated TBL-based benefits are discussed in [90]. The cluster also includes papers on the topic of the circular economy which emphasize the importance of this topic (it was also mentioned in Cluster 4). Hence, the conceptual framework for the transition to a circular economy for Sustainable Supply Chain 4.0 for the healthcare industry is reported in [91]. The procedure for introducing the principles of sustainable development in a production environment by designing a new circular business model (CBM) is described in [92]. This procedure was tested and approved in an Italian company producing ceramic tiles using the digitization of production processes in the Industry 4.0 environment.
Cluster 6 addresses issues related to the use of Industry 4.0 to achieve a sustainable supply chain (SSC). In paper [73] 18 challenges for I4.0 initiatives regarding SSC were identified and ranked by the AHP method according to their priority. An empirical analysis was made in emerging economies on the Indian market. The paper [93] continues the previous research [73] identifying 28 challenges related to sustainable supply chain management (SSCM) and 22 measures for Industry 4.0 and the circular economy. This case study was also carried out in the Indian automotive industry. The combination of Industry 4.0 and cyber-physical holon [94] enabled researchers to develop the concept of adaptive and integrated sustainable supply chain management (AISSCM) in emerging economies. The holonic framework allows for integrating smart social metabolism into the natural environment in order to enable co-evolution of the supply chain in the environment. In the article of [95] the term “Supply Chain 4.0” is defined for the application of Industry 4.0 technologies in the supply chain aimed at planning with greater efficiency and better meeting demand. To achieve supply chain 4.0, autonomous vehicles and equipment were proposed. In the paper [15] which is based on a systematic literature review concerning Industry 4.0, sustainability was indicated as one of the five main research categories in the Sustainable Industry 4.0 framework. The impact of I4.0 on sustainable organizational performance in Indian manufacturing companies through lean manufacturing practices is described in [96].
Cluster 7 refers to the development of new business models and organizational structures using I4.0 technologies. In [19,74] new organizational approaches to sustainable development were highlighted. An example of a new organizational model is the comprehensive structure “Customer-Product-Process-Resource (CPPR) 4.0” represented by the cycle of creating value propositions for the I4.0 environment [97]. The cluster also includes examples of using I4.0 to achieve a sustainable supply chain in the pharmaceutical sector (so-called “Pharma Industry 4.0” or “Pharma 4.0”) [26,98]. The pharmaceutical industry is crucial due to potential benefits of I4.0 identified in the empirical research of Thai companies [99]. For this sector, an interesting model, a cyber-physical-based Process Analytical Technology framework (called “CPbPAT”) for implementing smart manufacturing systems in the pharmaceutical industry was developed [98].

3.2.2. Global Citation Score Analysis

Global Citation Score (GCS) analysis can be used to detect groundbreaking publications. The GCS indicator informs about the total number of citations obtained in the entire database (e.g., Scopus). Studies with a high GCS value are considered groundbreaking or have a significant impact on the associated field [100]. In other words, GCS allows to identify papers that form the basis of a given knowledge area and are often used by other authors to develop their studies. It should be noticed that a high GCS value does not always indicate that the paper has a significant impact and promising scientific contribution to a given field [54].
The normalized GCS was used to deepen the analyses and detect promising, perspective papers in the field. Its value was determined in two ways: (1) the ratio of cumulative citations in Scopus until 2019 to the total number of years since they were published [101]; (2) the ratio of the latest yearly GCS (i.e., for 2019) to the total number of years since it was published [17]. In fact, this process “weighs” cumulative citations or citations received in a given year based on the “lifespan” of papers. By normalizing the GCS, it is possible to identify groundbreaking publications that could have a potentially large impact and promising scientific input in the analyzed topic. Groundbreaking papers are not always characterized by high GCS (i.e., the total number of citations in Scopus), although they are currently attractive to the scientific community. Table 4 contains the twelve most frequently cited papers ranked by normalized GCS.
Based on Table 4, it can be concluded that 8 out of 12 papers with the highest normalized GCS belong to the seven largest clusters identified during the analysis of the co-occurrence of citations (Table 3). This proves that groundbreaking studies do not always have to be included in the main citation coexistence networks. But it is worth noting that the normalized GCS rankings calculated in two different ways would differ very slightly. However, there are papers with relatively low GCS (e.g., [55]) and high value of normalized GCS which confirm the correctness of the adopted approach.
Particularly noteworthy are the papers [23,55] which despite their publication in 2019 have relatively large number of citations. In addition, the first article belongs to the largest citation cluster. Usually, papers get more citations in the years immediately following their publication. In this case, it may mean that these papers are breakthrough studies setting out further research directions. The paper [23] is an ordering of existing knowledge related to the use of BDA in sustainable smart manufacturing (SSM). The article contains a conceptual framework of BDA in SSM from the perspective of product lifecycle. The paper also discusses the potential applications and key benefits of BDA in SSM and sets challenges and future research directions for this area. The paper [55] is also treated as a conceptual article which proposes the framework of risks for implementing Industry 4.0 in the context of the triple bottom line. Based on a literature review and interviews with experts, a list of risks in five categories (ecological, social, environmental, technical/IT, legal/political) was developed and discussed.
Most of the studies in Table 4 are reviews or conceptual papers. They were described in the CNA analysis. Kusiak [65] pointed to sustainability as one of the pillars of smart manufacturing. Paper [13] presented 4 scenarios regarding the challenges of Industry 4.0 as well as their impact on sustainability. One of the scenarios concerned the integration and compliance of Industry 4.0 with the goals of sustainable development.
Table 4 also contains papers in which a literature review or conceptual framework was supported by expert interviews, case studies or empirical analysis. The work [29] provided a qualitative assessment of the potential for sustainable value creation in Industry 4.0 on a macro and micro scale based on a literature review and expert interviews. The paper [103] presented a case study of a company implementing an individualized business model based on the IoT, big data, and analytics. Eight specific functionalities, which enable use these technologies regarding CE and sustainability, were identified. The article [104] contains a comprehensive and structured picture of Industrial IoT related to TBL benefits and challenges based on semi-structured expert interviews driven from German manufacturing companies. The work [102] contains a research model on the opportunities and challenges of Industry 4.0 as a driver of its implementation in the context of sustainability. To validate this model, a partial least square structural equation modeling was used for a sample of German manufacturing companies.

3.2.3. Co-Occurrence Network of Authors’ Keywords

Authors’ keywords analysis can be helpful in detecting research trends covering information contained in all articles [105]. The following research covers the authors’ keywords of the entire set of selected papers. This will complement the results obtained from the CNA analysis. Analysis of the citation network is important because it allows to identify articles and group them into clusters based on citations. However, the main disadvantage of CNA is that it does not include important papers that are not associated with any citation (this may be the case especially for new studies). Therefore, there is a need to support CNA analysis using the authors’ keyword network analysis.
To analyze the authors’ keywords, a co-occurrence (or co-word) network was built [106]. In this network, the nodes correspond to the authors’ keywords, and the link weights provide information on how many times the words appear together in the same articles. The basic assumption of the co-occurrence analysis is that the authors’ keywords present an appropriate description of the content or relations contained in the paper. The existence of many co-occurrences of the same word or pair of words may correspond to the research topic and reveal patterns and trends in a discipline [105].
Conducting an analysis of co-occurrence in VOSviewer is possible thanks to three steps: (1) extracting the authors’ keywords from 162 papers selected after screening abstracts in the Scopus database, (2) “cleaning” data, i.e., merging synonyms, merging abbreviations with full terms, and correcting spelling differences, (3) the cleaned data is a batch file used by VOSviewer to generate a map presenting the authors’ keywords network. The network was created in a way that keyword has appear in at least 5 papers to be included in the analysis. The impact of this parameter value on the network size and number of clusters is described in [54]. Five as the minimum number of occurrences of the keyword was proposed in [17,101].
VOSviewer generated 15 nodes which were grouped into 5 clusters (Figure 7). There were 47 links in the co-occurrence network, and the total link strength was 176. The total link strength was expressed by a positive value that indicates the number of documents where the keywords appear together. The higher this value, the more often a given keyword coexists with others and is more relevant to the network. The nodes were grouped into clusters that do not overlap (i.e., a given keyword can belong to only one cluster). The larger and more transparent a node, the higher the frequency of the keyword in the analyzed set. In turn, the proximity of the location of nodes indicates more frequent than in the case of distant coexistence in specific sets.
Detailed information on keywords and clusters is provided in Table 5. The extracted clusters reflect 5 different research topics. Then, research topics are ordered based on the co-occurrence of keywords (i.e., the frequency of keywords appearing in the data set).
Cluster 1 focuses on research related to the application of major I4.0 technologies to achieve sustainable manufacturing. The paper [107] presents a mathematical model of municipal waste collection routing using I4.0 technologies (big data, CPS, cloud computing, RFID) which allows optimization of the waste collection process. The article [108] presents how industries can use a smart management system (SMgS) that is based on IoT and big data and will allow achieving lean and sustainable systems with less effort than traditional approaches. The study [109] contains a smart factory framework in which CPS and IoT are indicated as the key elements of I4.0. The developed model was validated in a cement plant and the key environmental performance indicators were improved. The work [110] depicts a case study of the sensing system of gripping forces which is an IoT-based robotic application and allows considering numerous environmental and social factors. The work [111] describes the development and specification of industrial cyber-physical systems (ICPS). It presents also a service-oriented ICPS model that enables to provide sustainable industrial system and more environmentally friendly businesses. The reference framework for the sensing, smart and sustainable product development (S3-Product) based on the Integrated Product, Process, and Manufacturing System Development Reference Model (IPPMD) is presented in [112]. Using the proposed reference framework, Cyber-Physical Production Systems (CPPSs) can be developed which, in turn it allow sustainability to be improved. The use of one of the concepts of I4.0—Digital Twin to improve leather cutting efficiency and leather sustainability in the automotive industry is presented in [113]. The article [114] presents the smart end-of-life (EOL) management framework together with the major procedures of smart recovery decision-making. The model considering economic and environmental sustainability has been validated on a belt lifter case study. Another paper [115] deals with smart manufacturing in the semiconductor industry, in which sustainability is one of the most common topics.
Cluster 2 presents how Industry 4.0 and related issues (digital transformation, IIoT) allow for achieving sustainable environment. In the study [116], collaboration networks were presented as a pillar of I4.0 and digital transformation that enable sustainable solutions and entire business ecosystems. In [27] it was found that digital transformation of manufacturing is synonymous with the concepts of I4.0 or smart manufacturing and it was pointed out that one of the advantages of digital manufacturing is the sustainability improvement. An analysis of intergovernmental organizations documents contained in [117] shows that I4.0 is strongly linked to energy efficiency potentials that could lead to climate change mitigation and more sustainable energy consumption in the industrial sector. In the paper [118] it was stated that the I4.0 paradigm is aimed at integrating digital technologies with business processes to increase the level of efficiency and develop new business models. The paper discusses the use of digital technologies in the field of precision agriculture. Research conducted on German and Chinese SMEs confirms that IIoT provides benefits in the three dimensions of TBL [119]. In [120] a cyber physical energy system was developed increasing the energy efficiency of the dyeing process by collecting and analyzing manufacturing big data through IIoT devices. The work [121] presents the human-machine interfaces platform, which due to the use of IIoT reduces energy consumption and the entire factory can be more efficient and sustainable in accordance with the I4.0 paradigm. The study [28] presents the concept of SSC Eco System with I4.0 in which digital connection plays a very important role because of IoT. In addition, the presented SSCM assessment framework for I4.0 included a sustainable development perspective. The issues of achieving the SDGs through I4.0 functions, mainly carbon footprint (CO2) reduction, were described in [122,123].
Cluster 3 raises issues related to sustainability as one of the main elements of a smart factory and emphasizes the importance of digitization in implementing sustainable development policy. The results of surveys among enterprises from Germany and China [124] indicate that the digitalization of industry creates an opportunity for an ecological dimension of sustainable development. The respondents are also of the opinion that due to digitalization the assumed improvement of resource efficiency can be achieved, and it may contribute to the increased use of renewable energy. A literature review and the results of surveys confirming the important role of industrial digitalization in sustainability can be found in [79]. This research states that better transparency provided by digitalization with IIoT enabled throughout the entire supply chain can lead to better environmental management of companies. For this reason, digitalization is the core of the concept for eMaintenance enabling an increase in the environmental sustainability and reduction of natural resources consumption [90]. In turn, in the paper [125] it was stated in that sustainability is one of the drivers of I4.0 that transforms traditional factories into smart factories through digitalization. The study also includes a discussion on the impact of I4.0 on TBL and the interactions between the TBL dimensions. The study [24] found that during designing and implementing smart factories, sustainability and energy efficiency should be important goals. Based on the literature review, the issue of energy efficiency and enhancing sustainability in smart factories was investigated. In [126], it was stated that a smart factory is usually associated with sustainability and presents an integrated architecture for implementing extended producer responsibility programs using I4.0. A new Enterprise 4.0 framework was presented in paper [19] in which sustainability plays an emerging feature in business models.
Cluster 4 discusses the issue of using Industry 4.0 in the concepts of CE and SSC. In the paper [127] some barriers of Industry 4.0, which hinder the achievement of CE, are discussed. In turn, the paper [128] presents the theoretical framework for the integration of Industry 4.0 and CE. This study includes an analysis of how integration of I4.0 with CE business models will allow to be compliant with CE principles. The work [129] emphasizes the potential of Industry 4.0 for the sharing economy, which is one of CE’s business models. It was found that I4.0 can play an important role in unlocking the sharing economy. Research findings in [91] prove that the relationships between TBL, I4.0 and corporate social responsibility allow to make the transition from a linear model to a circular model and can improve the healthcare sustainable supply chain 4.0. The paper [94] contains a conceptual framework presenting the relationship between social metabolism (flows of materials and energy that occur between nature and society, between different societies, and within societies), SSC, CE, and enablers from I4.0 and the holonic paradigm. The significance of the proposed conceptual framework lies in the integration of SSC with I4.0, CE and the sustainability pillars. The study [93] presents a framework to overcome the challenges of SSCM through I4.0 and solutions based on the CE which was validated in the automotive organization.
Cluster 5 addresses the issue of I4.0 impact on the green supply chain. Paper [130] presents the framework for green supply chain management in existence of I4.0, which was verified in 2 companies. The article [131] outlines a conceptual model of the supply chain for the shipbuilding industry based on the supply chain paradigms (Lean, Agile, Resilience, Green—LARG) and I4.0. Since the shipbuilding industry has a special interest in adapting to the changes proposed by I4.0, the term “Shipbuilding 4.0” appeared in [132]. A special index based on LARG paradigm was developed in [132] which allows to adapt quicker and more efficiently the supply chain to Industry 4.0.

3.2.4. Burst Detection Analysis

A burst detection is based on Kleinberg’s Burst Detection algorithm [133]. The algorithm enables to detect the evolution of literature in the field. It may be seen as a series of appearing topics that are developed for a certain span of time, and then disappear [133]. This algorithm thus ensures time ordering by identifying the increasing and decreasing visibility of individual research topics. More precisely, Kleinberg’s approach for detecting series is based on modeling the stream of generated keywords that reflect research topics. The appearance of a given keyword in the paper stream is signaled by the “burst of activity”. When the term becomes common, it is no longer considered to be bursting [134]. This is the key difference between the series detection measure and the simpler measure of the frequency of keywords over time.
Burst detection gives an insight into the popularity of authors’ keywords over time which is an extension of the co-occurrence network of authors’ keywords. This makes it easier to determine future research directions. Burst detection was implemented to normalized authors’ keywords that appeared in the selected papers. Normalization included adapting all word tokens to lowercase letters, not including stop words, removing plurals and deleted dots from initialisms or acronyms. The CiteSpace software was used and the results of the algorithm application are shown in Figure 8.
CiteSpace detected 9 keywords characterized by bursts of activity (Figure 8). The bursts of flow control and manufacture in 2015 and their strength confirm the great interest of research in manufacturing, industrial engineering and logistics. It is worth noting that in research done between 2015 and 2017 regarding the relation between Industry 4.0 and sustainability, some keywords were very popular: agility of manufacturing systems [76,77], economics [69,104,112,135,136], decision making [69,135,137,138].
The interest in specific industrial research and themes of industrial production emerged later, in 2017. According to [29,125] the increase in the appearance of the keyword industrial research in the years 2016–2019 confirms researchers’ interest in overcoming the lack of empirical research, case studies and industrial applications. Referring to the latest research, there is a great focus in Industry 4.0 and digitalization [78,80,103,124,139,140] paradigms. The main burst is related to Industry 4.0 (2017–2019, strength: 1.5285). This means that the research interest in this topic has increased in recent years. The results are consistent with the results obtained from GCS analyses and the main research topics identified by VOS clustering. In fact, Industry 4.0 allows the use of many modern technologies that help achieve the SDGs meeting the TBL.
Burst detection confirmed (e.g., highlighting manufacturing and industrial production as the main areas of research, paying attention to the lack of empirical research, case studies and industrial applications, economics as an important reference point) and complemented earlier analyses (e.g., indication of relevant studies related to agility and decision making in the context of Industry 4.0 and sustainability). So, burst analysis also helped to more easily identify the differences between CNA, GCS and co-occurrence network of authors’ keywords. For example, only co-occurrence network of authors’ keywords indicated digitalization as an emerging topic. In summary, the results of the burst detection analysis deepen further and consolidate the findings arising from the previously used bibliometric tools.
It was also decided to use burst detection algorithm for the top-cited articles among the selected papers. The results are presented in Figure 9.
Two articles are characterized by burst of activity: “Smart manufacturing” [69] and “Sustainability in manufacturing, and factories of the future” [141]. Both papers raise issues related to the development of manufacturing. The work [69] states that smart manufacturing will meet the expectations related to environmental sustainability. The paper [141] presents the holistic understanding of a factory of the future, one of whose essential elements is sustainability in manufacturing. It was not included in selected papers and, therefore, was not the subject of previous analyses. This confirms the advantages of using a burst detection algorithm, as well as its complementarity with other bibliometric tools.

3.3. Reference Framework “Sustainable Industry 4.0”

Grounding on cluster analysis depicted in previous chapters, the reference framework was constructed (Figure 10).
The goal of this framework is to serve as a kind of manual and guideline for Industry 4.0 applications supporting sustainability principles. The use of framework is based on selection of which sustainability issues are planned to be addressed or defined as critical by an organization. Then, search term is to be found in the framework and next, one may find which I4.0 topics and their relations with the selected sustainability issue were discussed in the literature. There is reference given to the clusters where more details, case studies, best practices could be found. The reverse procedure is applied if one is seeking for possible impacts of planned I4.0 applications on sustainability (Figure 11). After finding the cluster with the topics of interest, one may use GCS values to seek for the most prominent papers in the cluster and start own research from this point. In Figure 10, papers with the highest GCS are listed in bold.
The implication of the framework may result from the industrial use of emerging technologies distributing the sustainable value propositions in an Industry 4.0 environment. The Sustainable Industry 4.0 reference framework is divided into two separate application procedures which depict a logical order of activities of:
  • how I4.0 implements selected ideas of sustainability;
  • how sustainability principles are supported by planned I4.0 application.
The first procedure starts with finding out sustainability issues of interest and I4.0 related topics in the framework. Using the search, a network composed of linked clusters and articles can be received. In the second procedure a similar sequence of activities was applied. A group of related papers and related clusters that explore how principles are supported by planned I4.0 application may be found using browse.
The application of the sustainable Industry 4.0 reference framework needs to support the development of real-time data-based functions or systems addressing the specific industrial domain sectors. The interrelated elements of the framework provide synergy where processes, Industry 4.0 principles, digital platforms must be interfaced.
The presented framework is of a general nature, therefore it is applicable in a wide variety of organizations. Its strengths are simplicity, clarity and ease of use. However, for specific cases one may need to extend the framework and include details on a lower level, such as guidelines for particular I4.0 technologies or sustainability issues.

4. Discussion and Future Research Directions

When analyzing the framework (Figure 10), one may notice that relations are unidirectional, i.e., I4.0 supports implementation of sustainability concepts. There are no papers reporting research on reverse relations, i.e., how selected sustainability concepts could support implementation of selected I4.0 technologies.
The research discovered a gap in the literature. There was lack of research approaching issues of sustainability and Industry 4.0 in a comprehensive way. There are frameworks existing and findings on barriers, success factors, the state of the art of I4.0 implementation in selected economies, industries, research directions presented by researchers [142,143]. However, this research did not aim to extend existing frameworks or detail them. It was aimed to cover the above-mentioned gap by mapping I4.0 technologies and sustainability concepts through description of their relations evidenced from literature review. Findings from the literature review presented in this paper lead to a recommendation to put more focus on sustainability issues when implementing I4.0 as these problems have not gained proper attention. Even though some research papers tackling I4.0 and sustainability together were found, they were not complex, i.e., focused on specific sustainability concept, dimension or specific I4.0 technology. This shortcoming of the existing literature made impossible a holistic approach to the assessment of I4.0 applications on all triple bottom line dimensions of sustainability. The framework developed in this paper covers this gap as it forms signposts for decision makers, where they can find evidence on possible impacts of I4.0 on sustainability.
To develop the roadmap to Industry 4.0 the bibliometric analysis of the selected papers was used instead of the citation method (the most popular in systematic reviews), aiming to minimize any mistakes regarding the accuracy of the information. It prevented the exclusion of essential papers, information and strengthen the results due to use of wide scientific research databases. In the future research, the authors will validate this target sample using the citation method to select relevant papers by searching using query keywords. The advantage of the cross-search tool is to retrieve any important papers cited in the body of literature compared to the previous method relying on the use of the selected academic databases and search terms. The decision of how the method will be applied in the literature research is a hot spot of arguments. The application of large research databases allows to do the exhaustive analysis, on one hand, but on the other hand may lead to misinterpretation of findings.
The bibliometric analysis showed that the topic is very vivid and motivates future research in an investigation of how the technology portfolio of Industry 4.0 could allow to achieve the 17 major goals of sustainable development. Among many challenges and opportunities which are already being examined are understanding of the SDG goals and translating them into unification of indicators/targets across industries in terms of specific functions. It requires to set normative values and then threshold levels for material, energy consumption, etc. It seems that Industry 4.0 has been developing in many industries considering holistic paradigm. Therefore, more focus on separate research on the connectivity of sustainability and Industry 4.0 with benefits from their integration in the fields of interest is needed. In addition, application of the current sustainability assessment tools such as LCA, LCC, SLCA embodied in an advanced IT framework might provide added value.
On the other hand, the development of smart technologies is still growing and leading to dramatic environmental burdens. First, traditional methods for measuring harmful impact are unable to estimate such impact. Second, assessment of the contribution of I4.0 in supporting sustainability is still missing [29]. That is why a research gap appears between the sustainability assessment field and technologies represented by a specific sector. A new potential for strengthening this research direction is seen in modelling decision-making mechanisms.
The benefits of combining sustainability with I4.0 could be found. In particular, the paper sketches challenges for Industry 4.0 and its potential in the context of sustainability. By combining sustainability to findings of research on I4.0 which have not been done yet, this current study stream has been complemented. It leads to the development of a new concept of sustainable Industry 4.0 leveraging a greater efficiency of functions or actions through using IT-based technologies (connectivity of technologies with resources, organizational structures, skills, employee empowerment, etc.) A mutual collaboration between efforts of I4.0 and sustainability issues can achieve advantages at the same time: bringing knowledge, economies of scale, fair distribution of cost, experts’ experiences, industry-specific data exchange and storage. This high coordination of efforts allows for creating a sustainability-driven business model in a (eco)system. The advantages of this interrelation might be strengthened if the emerging technologies could be compared and assessed considering sustainability dimensions.
For further study, another point could be highlighted to incorporate expert knowledge, experience, and sustainability assessment methods and map them into the Sustainable Industry 4.0 reference framework to provide a synergistic effect.
Based on the research done by the authors answers to each research question were provided:
  • RQ1. How applications of Industry 4.0 can contribute to sustainable development?
It enables achieving circular economy paradigms (see clusters: 4 and 5 co-citation, 4 co-occurrence of keywords).
It enables sustainable supply chains and value chains (see clusters: 3 and 6 co-citation, 4 and 5 co-occurrence of keywords).
It enables new sustainable business models (see Cluster 7 co-citation).
It enables monitoring of the full product life cycle (see Cluster 1 co-citation).
Answering this question, synergy exists between I4.0 and sustainability due to digital technology. By using I4.0-technology affecting sustainability through the responsible, effective use of resources, circular economy can be reached. The concept lying on decentralization of manufacturing which embodied in an IT technology framework was the response to the pressure on changing conventional business models in order to develop new sustainable business models (circular business model). An indispensable way to achieve CE is based on technologies which are often successful when combined with IoT. Industry 4.0 can act as a driver of redesign of traditional supply chains aiming at resource efficiency and circularity.
The I4.0 technologies, e.g., sensors deployed in many machines enable the tracking of production performance and product data over the full product life cycle. In consequence, an analysis of collected data results in productivity improvements.
  • RQ2. How Industry 4.0 technologies and tools can be integrated into sustainability practices on a theoretical and practical basis?
Mainly IoT, digitization, sensors and big data could be employed to monitor sustainability.
The study confirms that IIoT is an important element of Industry 4.0 and has an impact on sustainability. In future studies, the authors will undoubtedly pay more attention to the importance of IIoT in the context of Sustainable Industry 4.0.
Smart technologies of Big Data Analytics, sensors, etc. displaced conventional computer-aided manufacturing industry to deliver tremendous business values or outcomes. On one hand, it provides socio-economic values, on the other hand, it creates challenges for doing scientific research on the real-time speed of manufacturing data and data storage.
The application of Industry 4.0 technologies and tools in an efficient way should give opportunities to manage big data (acquisition, extraction, transmission, storage).
The I4.0 technologies can help to reduce both machine operational time and waste thanks to more effective machine and resources utilization consequently ensuring cost-effective operation. Sensors used in production allow to gather a machine’s status data to analyze load of machines, reducing downtime and protecting products against unexpected failures which have a great impact on product quality.
  • RQ3. What are the main approaches/methodologies/frameworks/tools that should be considered for integrating Industry 4.0 with sustainable development?
Critical success factors were discussed in the literature (see Cluster 2—co-citation). Regarding the third question, coalesced with the second one, the paper addressed some of the critical success factors for the use of IoT, e.g., transparency, resource efficiency, creating knowledge through digitalization that impacts the achievement of sustainability. The identified factors, which interact with IoT system, sensors, etc., contribute to SDGs. It would also support Sustainable Industry 4.0 reference framework implementation in manufacturing. On the other hand, some factors might hinder the readiness to implement 4.0 technology. By incorporation of digitalization as a binder of the factors, sustainability performance in the production environment could be measured and monitored, tracking data in real time. In this sense, it leads to an increase in resource efficiency, reducing inefficient work, revealing costs treated as inefficient when using the latest technology. Thanks to the capturing of massive data, production can be now deeply analyzed and reliable information about production efficiency is still available and up-to-date.
Effective use of critical success factors and approaches pertaining to Industry 4.0 allow the fostering of sustainability in smart data-driven manufacturing.

5. Conclusions

The depicted research introduced contributions in literature review based on SLNA methodology. This bibliometric analysis and the reference framework constitute the novelty delivered in this article. The combination of SLR and BNAV allowed to place an emphasis on the relation between sustainability and Industry 4.0 which presents new challenges in many sectors. This study was clustered based on relevant journals, keywords or specific thematic fields. The results demonstrated the relationship between sustainability and Industry 4.0 across different sectors or specific functions. It may help researchers and practitioners to understand the scale of interest and implications, while on the other hand contributing theoretically to the development of the topic.
The article was aimed at rationalizing and systematizing the existing body of scientific literature focused on combining the concepts of sustainability and Industry 4.0. To achieve the aim, various quantitative bibliometric analyses were conducted based on algorithms and software tools which resulted in a dynamic presentation of research progress on the interaction of both concepts over time. In this way, a broad picture of the theoretical and practical combination of sustainability and Industry 4.0 was presented, and the main research trends were identified, and future research directions were proposed.
In terms of theoretical implications, this study is an extension of knowledge about the integration of the concepts of sustainability and Industry 4.0. It makes a valuable contribution to current knowledge in this area by analyzing the development of this issue, presenting trends and emerging topics that have not been sufficiently developed and require further research. The application of SLNA methodology to a relatively new research area should be considered an additional theoretical implication that may facilitate the use of SLNA in other fields. The adopted methodology allowed to obtain a full picture of the field due to the use of a citation network and an authors’ keywords network. The separated clusters allowed to identify and discuss the most important research topics, as well as to identify key publications. Moreover, the presented methodology could be tailored to specific industry-based technology showing an impact on environmental, economic and social sustainability simultaneously.
The obtained results were confirmed and enriched using GCS and a burst detection algorithm, which complemented the analyses in the adopted methodology. The article may form the basis for further development of knowledge in the considered area by identifying key issues and problems, emerging trends and trajectories of evolution.
The article also has implications for practitioners and academia. It is the first comprehensive study on the integration of the concept of sustainability and Industry 4.0. The comprehensive study presents information on the current state of knowledge and sets out future research directions. As stated at the beginning of the paper, investigation regarding opportunities for the integration of the concepts of sustainability and Industry 4.0 was missing. The use of bibliometric analyses to identify the possibilities of supporting the implementation of SDGs and TBL through Industry 4.0 should be considered as a valuable investigation tool for assessing the current state of knowledge. Thanks to the presented research results, information was also provided on what the scientific and industrial communities should focus on to enable the effective interaction of sustainability and Industry 4.0. The results would mean an input to realize sustainability in Industry 4.0 for these companies, which have not yet implemented any form of intervention measure. In a future perspective this action might allow to gather data to benchmark and convert information into smart data which really needed to be fully digitalized.
The bibliometric analysis provides a visualization of scientific research output in finding links between Industry 4.0 and sustainability to draw a roadmap to Sustainable Industry 4.0. It was aimed at helping to explicate and describe the reference framework through determination of these links, promoting the kind of patterns of an IT-based business model and impact of sustainable Industry 4.0.
It is not the intention of the article to look for deep convergences from previous studies in Industry 4.0, but rather to cover new ground by examining the recent literature in this field to discover relationships between Industry 4.0 and sustainability, thus promoting sustainable I4.0 that can be undertaken by policy makers. In this context, the convergence or inclusion of the emerging I4.0 technologies with sustainable goals requires supportive measures and specific policies to ensure the competitiveness of local actors suggesting that the development of technological capabilities that can contribute to operational competitiveness, firm reputation and the sustainable performance of firms. In the area of practical implication, the proposed integrated framework is expected to drive opportunities to act proactively by decision makers through embodying the I4.0 technologies on IT platforms to ensure the sustainability of the 4P (performance, product, process, people). The engagement of policy makers at the beginning stage of the inclusion of sophisticated IT-based production systems in sustainability could help them to anticipate the nature of technology impacts, and, as a consequence, to reshape Industry 4.0. The results outlined that beside a huge theoretical contribution to Industry 4.0 and sustainability, many papers do not provide an insight into practical implications to introduce Sustainable Industry 4.0.
The analyses carried out also have some limitations. Literature reviews using bibliometric tools may ignore important details of studies despite providing a clear summary and ordering of existing papers. The main disadvantage is the inclusion of citations or keywords that do not always fully reflect the real contribution of the articles for a given area of knowledge. Researchers very often cite works that already have many citations (the so-called “Matthew effect”). This is mainly because such articles are considered reliable sources of information due to their reputation or popularity. Keywords do not always accurately reflect the content of the article, and some keywords may be omitted when building the network, because they do not meet the conditions of coexistence. Another disadvantage is the application for bibliometric analyses of data collected from one database only—Scopus, which, although comprehensive and prestigious, contains only a small fraction of all scientific publications. The research was limited only to articles from peer-reviewed journals, and thus omitted, e.g., conference papers or book chapters, which may also be a valuable source of information.

Author Contributions

K.E. worked on the table of content, research design and methodology, the assessment of data using bibliometric software, wrote Materials and Methods, Results, Conclusions and prepared the first draft. B.G. supported Materials and Methods (especially with illustration of the methodology), conducted burst detection analysis, worked on Reference Framework, Discussion and Conclusions, was responsible for preparing figures and revised the final draft. A.K. worked on the Introduction, Discussion, Conclusions and revised the final draft. All authors have read and agreed to the published version of the manuscript.


This research is partially funded by a grant for employees of the Warsaw University of Technology, “The synergy and anergy effects of Industry 4.0, sustainable development and lean management”.


We would like to thank the anonymous reviewers for their comments that allowed to further enhance the outcome of this research.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Research methodology based on SLNA.
Figure 1. Research methodology based on SLNA.
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Figure 2. Number of selected papers in Scopus.
Figure 2. Number of selected papers in Scopus.
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Figure 3. Countries of selected papers.
Figure 3. Countries of selected papers.
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Figure 4. Research subjects of the selected papers according to Scopus.
Figure 4. Research subjects of the selected papers according to Scopus.
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Figure 5. Journals of selected papers.
Figure 5. Journals of selected papers.
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Figure 6. Citation network.
Figure 6. Citation network.
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Figure 7. Co-occurrence network of authors’ keywords.
Figure 7. Co-occurrence network of authors’ keywords.
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Figure 8. Burst detection results for normalized authors’ keywords from 2012–2019.
Figure 8. Burst detection results for normalized authors’ keywords from 2012–2019.
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Figure 9. Burst detection results for cited journals from 2012–2019.
Figure 9. Burst detection results for cited journals from 2012–2019.
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Figure 10. Sustainable Industry 4.0 reference framework based on SLNA from 2012–2019.
Figure 10. Sustainable Industry 4.0 reference framework based on SLNA from 2012–2019.
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Figure 11. Sustainable Industry 4.0 reference framework—application procedures.
Figure 11. Sustainable Industry 4.0 reference framework—application procedures.
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Table 1. Reviews about the impact on and/or connection of Industry 4.0 on sustainability.
Table 1. Reviews about the impact on and/or connection of Industry 4.0 on sustainability.
PaperDescription of the Content
[12]Focused and narrowed to the specific area of maintenance activities supported by I4.0 and its impact on sustainability
[15]Review presents I4.0 and sustainability technologies; the Sustainable Industry 4.0 framework is a combination of Industry 4.0 technologies, process integration and sustainable outcomes
[17]General review on smart factory with no specific focus on sustainability issues
[18]General review on green supply chain management with no focus on manufacturing and no focus on specific issues related to I4.0 impacts on green concepts, narrowed to environmental aspects with no focus on economic and social concerns
[19]Review focused on social concerns with no focus on economic and environmental ones
[20]Review on sustainable supply chains with no focus on manufacturing and no focus on I4.0 support for sustainable supply chains
[21]Review with no specific focus on sustainability of I4.0, focused on the development of a research agenda for I4.0
[22]Review with no specific focus on sustainability of I4.0, focused on the development of a research agenda for I4.0
[23]Review focused on specific technology of big data applications
[24]Review focused on energy efficiency
[25]Review focused on specific issues of digitization
[26]Narrowed to specificity of pharma industry
[27]Narrowed to digitization
[28]Narrowed to IoT and focused on supply chains with no focus on manufacturing
Table 2. Number of according due to their application and type.
Table 2. Number of according due to their application and type.
ApplicationAcademicIndustrialAcademic & Industrial
Literature review15010
Case study538
Empirical research3213
Table 3. Research topics based on the largest clusters in citation network.
Table 3. Research topics based on the largest clusters in citation network.
ClusterNodesLinksMain Research TopicsMost Cited Papers in Scopus (Minimum 20 Citations)Time RangeSize in the Citation Network (i.e., 88 Connected Nodes) (100%)
1910product lifecycle management, performance of smart manufacturing systems, sustainable smart manufacturing[23,68,69]2012–202010%
2810critical factors of implementing I4.0, (Industrial) IoT supporting sustainability[70,71]2017–20209%
376smart manufacturing[65]2018–20208%
476circular economy, production planning and control[72]2018–20198%
566circular economy, assessment of I4.0 based on TBL[29,55]2018–20197%
667sustainable supply chain, sustainable I4.0[15,73]2018–20207%
765business models for I4.0, I4.0 in the pharmaceutical sector[74]2015–20197%
Table 4. Ranking of top 12 cited papers in Scopus according to normalized GCS.
Table 4. Ranking of top 12 cited papers in Scopus according to normalized GCS.
RankPaperYearGCSAppear in the Seven Biggest Citation ClustersCumulative GCS up to 2019/years Since PublicationGCS in 2019/years Since Publication
Table 5. Research topics in clusters based on authors’ keywords.
Table 5. Research topics in clusters based on authors’ keywords.
ClusterAuthors’ KeywordsTotal Link StrengthOccurrencesMain Research Topics
1smart manufacturing
sustainable manufacturing
internet of things
big data
cyber-physical systems
application of I4.0 technologies (IoT, big data, CPS) to enhance smart and sustainable manufacturing
2industry 4.0
industrial internet of things
sustainable development
digital transformation
digital transformation and IIoT as the core for sustainable solutions for industrial systems, I4.0 as enabler for a sustainable development
smart factory
impact of digitalization on sustainability, sustainability as a pillar of smart factory
4circular economy
sustainable supply chain
integrating I4.0 with CE and SSC
5supply chain55green supply chain, shipbuilding 4.0 supply chain

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Ejsmont, K.; Gladysz, B.; Kluczek, A. Impact of Industry 4.0 on Sustainability—Bibliometric Literature Review. Sustainability 2020, 12, 5650.

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Ejsmont K, Gladysz B, Kluczek A. Impact of Industry 4.0 on Sustainability—Bibliometric Literature Review. Sustainability. 2020; 12(14):5650.

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Ejsmont, Krzysztof, Bartlomiej Gladysz, and Aldona Kluczek. 2020. "Impact of Industry 4.0 on Sustainability—Bibliometric Literature Review" Sustainability 12, no. 14: 5650.

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