Exploring the reciprocal influences of Industry 4.0 technologies and Sustainability

Inhaltsverzeichnis

Abstract

Acknowledgement

1 Introduction

2 Literature Review
2.1 Sustainability
2.2 Industry 4.0
2.3 Sustainable industrial value creation
2.4 Need for research

3 Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data analysis

4 Results & discussion
4.1 Descriptive statistics
4.2 Quantitative analyses
4.3 Qualitative analyses

5 Limitations, Future Research and Conclusion
5.1 Limitations
5.2 Future Research
5.3 Conclusion

6 References

7 Full Bibliography of 120 included papers

8 Appendices

Abstract

This research explores the reciprocal influences of Industry 4.0 technologies and sustainability based on a systematic literature review to gain knowledge on how to manage the transition towards a more sustainable industrial value creation efficiently and effectively. Quantitative and qualitative analyses based on 120 investigated papers were performed, showing increasing research activity within the field, with a slight tendency towards empirical research and manufacturing focus. Sustainability issues are main drivers for both scientific investigation and the increasing demand for industrial changes, while Industry 4.0 technologies are considered to be suitable solutions to tackle emerging sustainability challenges on each sustainability dimension.

Keywords: Industry 4.0; Sustainable Industrial Value Creation; Sustainability; Systematic Literature Review

Acknowledgement

The author would like to thank Professor P. F. for his dedicated supervision and continuous reflected feedback while consistently allowing the author to create his own work. Without his valuable, directed comments, this project would not have been possible.

This work used infrastructure and resources funded by Fundação para a Ciência e a Tecnologia (UID/ECO/00124/2013, UID/ECO/00124/2019 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209).

1 Introduction

With 10 years to achieve the United Nation’s Agenda 2030 and its 17 Sustainable Development Goals (SDGs), pressure is rising for political, societal, and corporate actors to implement sustainability (SUS) strategies addressing pressing global environmental, social, and economic challenges. While non-binding, these goals have emerged as a roadmap for a more sustainable future, guiding individual and collective action. In recent years, corporations have been challenged by multinational institutions, regulators, customers, and future employees alike to take on responsibility and leverage their power to drive progress and positive change. They are solicited more than ever to incorporate SUS as a whole (People, Planet, and Profit) into their decision-making processes (Elkington 2002; Camilleri 2017). The negative environmental and social externalities of business activities, particularly industrial ones, need to be minimized or, best case, turned into attractive opportunities to create positive and shared value for all stakeholders (Elkington 2002). Indeed, with 27.81% of global value added and 22.0% contribution to global greenhouse gas emissions (World Bank 2018), the industrial sector represents a key lever in shaping a more sustainable and responsible future.

In parallel to this urgent need to reconsider business’ role as a societal actor, the industrial sector is currently undergoing a fourth industrial revolution, commonly known as Industry 4.0 (I4.0) (Lasi et al. 2014). Broadly speaking, I4.0 aims to fully integrate machinery and processes both horizontally and vertically across the whole value creation network, using advanced information and communication technologies (ICT) for enhanced real-time data collection, data exchange, and data analysis. Based on the increased granularity and availability of real-time data, smart algorithms can predict future behaviours and take optimal countermeasures with none or minor human intervention, enhancing operational performance and resource efficiency among others (Abubakr et al. 2020). Leveraging on both advanced ICT and top-edge manufacturing techniques, emerging technologies show promising potential to boost the transition to a more sustainable industrial value creation (Nara et al. 2021; de Sousa Jabbour et al. 2018), counteracting negative contributions and generating solutions. However, empirical studies conducted in recent years show that performance measurement within the production landscape remains largely focused on economic indicators, with environmental and social metrics appearing to be used solely for legal compliance (Sartal et al. 2020). Whilst literature hints to the potential contribution of I4.0 technologies (I4.0Ts) to the SDGs (Bai et al. 2020; Ivascu 2020), a significant lack of knowledge of the actual relationship between I4.0Ts and SUS remains, e.g., whether environmental benefits outweigh the increased material demand due to the huge increase of integrated technology (Kamble, Gunasekaran, and Gawankar 2018; Bai et al. 2020). Thus, an integrative and interdisciplinary investigation is needed to derive a holistic understanding of the current research status (Müller 2020).

This systematic literature review (SLR) aims to contribute to the field with a comprehensive investigation of I4.0Ts’s and SUS’s reciprocal influence, providing an integrated overview of the status quo in existing research, elaborating on existing insights, ongoing research directions, and research gaps as future research proposals. To do so, following research questions (RQ) have been set:

RQ1: What is the current status of research investigating the relation between Industry 4.0 and sustainability?

RQ2: To what extent do Industry 4.0 technologies and sustainability dimensions influence each other?

Furthermore, according sub-research questions (SRQ) were formulated (shown in Section 3) to guide this systematic review. In doing so, this research provides a systematic and comprehensive overview of the research field’s current status, its relevant articles, and existing derived insights both academics and practitioners can benefit from by using the provided findings for future research and scientific-based decision making, respectively.

The rest of the research paper is structured as follows. Section 2 provides a brief discussion on this research’s main domains, viz., Sustainability, Industry 4.0, and Sustainable Industrial Value Creation, followed by a description of the methodology employed for achieving this research’s goals and the respective results in Section 3. Section 4 shows and discusses these results as respective findings. Section 5 provides the paper’s limitations and derives successive future research directions before concluding this research paper.

2 Literature Review

2.1 Sustainability

Sustainable development, as it was coined at the turn of the 20th century, emerged as a paradigm seeking to balance social, economic and environmental considerations to ensure a viable, desirable future for the planet and our society (Brundtland 1987). Whilst often perceived to be a matter of political decision-making and institutional powers, harmony between people, planet and profits (Elkington 2002) cannot be achieved without the buy in and proactive involvement of the private sector (Braccini and Margherita 2018). Indeed, economic activities, whilst undeniably serving to generate value to improve the prosperity of societies (Gajdzik et al. 2020), have followed an unbridled growth trajectory, overextending the use of resources and generating of waste beyond planetary boundaries (Jackson 2017). The unbounded negative externalities on the environment and people’s basic rights and well-being are a high price being paid for the expansion of economic capital, especially considering the inequality in the distribution of wealth on one hand, and the discriminatory effects of environmental consequence on the other (Ghobakhloo 2020). In recent years, global frameworks like the SDGs have emerged to structure collective progress for a more sustainable future, and been used as a reference point for the private sector to work towards.

The industrial sector, a historic culprit in the linear, often pollution-intensive use of resources, has also been increasingly researching and implementing solutions to better manage the unwanted externalities of their activities (Tiwari and Khan 2020). Driven by concerns for eco-efficiency, stakeholder scrutiny or truly seeing the competitive advantage they can offer, corpo-rations across the globe are investing into solutions to monitor and reduce their negative exter-nalities and contribute to the development of solutions (Jabbour et al. 2020; Golini, Longoni, and Cagliano 2014). Emerging technologies coined under the umbrella of I4.0 are beginning to prove their potential in supporting the transition of the entire sector towards more environ-mentally and socially responsible practices, whilst ensuring sustainable economic growth.

2.2 Industry 4.0

The industrial sector, often referred to as “industry”, is the part of the economy producing material goods in a thoroughly planned manner with well described, mostly mechanized and automated processes (Lasi et al. 2014). Incremental and fundamental technological innovations have shaped the industry continuously over the past centuries, with four technological leaps being particularly profound, entailing industrial revolutions: The invention of the steam engine, enabling mechanisation of production (Industry 1.0); the electrification of production, enabling mass production based on assembly lines and the division of labour (Industry 2.0); the widespread introduction of computer and industrial robot technology, enabling high degrees of automatization and digitalization (Industry 3.0) (Xu, Xu, and Li 2018; Lasi et al. 2014). The end-to-end integration of smart objects and systems across the whole value chain into an integrated industrial ecosystem based on highly advanced ICT are the main characteristics of the new paradigm shift, namely Industry 4.0 (Lasi et al. 2014; Xu, Xu, and Li 2018). Main I4.0Ts are thereby Cyber-Physical-Systems, Internet of Things, Cloud Computing, Big Data Analytics – including Artificial Intelligence – and Smart Objects like Smart Sensors and Advanced Robotics (Nara et al. 2021; Hermann, Pentek, and Otto 2016; Lasi et al. 2014; Xu, Xu, and Li 2018). Based on these enabling technologies, further I4.0Ts can be employed, such as Additive Manufacturing, Advanced Simulations, and Extended Reality (i.e. Augmented Reality and Virtual Reality) (Nara et al. 2021; Bai et al. 2020). Unlike technology trends driving previous industrial revolutions, those underlying the fourth industrial revolution rely on their complementary characteristics, since none of these systems operates independently (Gilchrist 2016). Instead, the smart systems involved communicate and interact with each other in order to make or propose decisions with little to no human intervention (Ghobakhloo 2020).

Based on the performed literature review, following I4.0Ts and principles are further investigated within this SLR’s scope, and further explained in Appendix 1: Cyber-Physical-Systems (CPS); Internet of Things (IoT); Cloud Computing (CC); Big Data Analytics (BD); Automation and Robotics (ROB); Horizontal and Vertical System-Integration (HVI); Extended Reality (XR); Additive Manufacturing (AM); Advanced Simulation (SIM); Cyber Security (CS); Blockchain (BC).

2.3 Sustainable industrial value creation

The potential represented by the interplay of these two increasingly present, “hot” opportunities of I4.0 and SUS has engaged academics and practitioners to study the concept of sustainable industrial value creation, in which emerging technologies are leveraged to generate positive value among the social dimension (SO), economic dimension (EC) and environmental dimension (EN) of SUS (Felsberger and Reiner 2020). Integrated, automated and connected technologies, for example, have a crucial role to play in underpinning more resource-efficient production patterns to support corporations’ development of sustainable operations. By supporting the optimization of production systems from order to dispatch, not only energy and waste streams, but also health and safety risks can be reduced, generating better environmental (emissions, by-products and waste) and social (working conditions) footprints (Lee et al. 2018; Braccini and Margherita 2018; Margherita and Braccini 2020; R. Kumar, Singh, and Dwivedi 2020). Furthermore, I4.0Ts facilitate the monitoring and accountable reporting of SUS indicators, such as environmental footprint or human rights (Felsberger and Reiner 2020). Even though social concerns surrounding the digitalization and automation of production facilities arise, Felsberger and Reiner (2020) highlight a growing body of research demonstrating that, whilst I4.0 will replace tasks and robotize processes, it is creating jobs across increasingly relevant functions, re-skilling the workforce of the future.

2.4 Need for research

Even though Bai et al. (2020) provide an overview on how latest I4.0Ts may tackle each SDG, there is still a significant lack of scientific knowledge regarding the relationship between I4.0Ts and SUS (Kamble, Gunasekaran, and Gawankar 2018). Whilst most studies focus on I4.0Ts (Felsberger and Reiner 2020) and the correlated positive impacts, such as Margherita and Braccini (2020), Felsberger et al. (2020), and Strandhagen et al. (2020), only few studies focus on risks associated with the introduction of latest I4.0Ts, i.e. Birkel et al. (2019), or the actual intentions of I4.0 implementation efforts, as Brozzi et al. (2020) do. Furthermore, a sharp rise in articles published containing the terms “Sustainability” and “Industry 4.0” in abstract, title or keywords can be observed, with 258 articles published since 2019 compared to 93 articles published before 2019 on Scopus¹. In view of this sharp rise in research activities relating to the interplay between I4.0 and SUS, an integrative and interdisciplinary investigation is needed to build a comprehensive understanding of ongoing research directions, existing research gaps and respective future research proposals within this field (Müller 2020).

3 Methodology

3.1 Research Design

In accordance to this research’s objectives, namely (1) examining the current status of research investigating the relation between I4.0 and SUS, and (2) examining to what extent I4.0Ts and sustainability dimensions (SDs) influence each other, a systematic bibliometric research method was considered as highly suitable for achieving these objectives. In particular, a systematic literature review (SLR), recognized as a transparent, replicable, and scientific method (Mian et al. 2005), was chosen to scrutinize the vast amount of publications in both fields while minimizing potential biases caused by the researcher. Furthermore, the SLR methodology enables a systematic overview of relevant literature and a subsequent content analysis, highlighting scientific consensus and potential divergence and thus proposes viable future research directions (Krippendorff 2018; Duriau, Reger, and Pfarrer 2007). Two main RQs, eleven SRQs, respective RQ aims, as well as needed data and needed analysis to answer these questions were elaborated in order to guide this SLR, fully shown in Appendix 2. Table 1 and Table 2 provide an overview of the two RQs and the respective SRQ.

Table 1 – RQ1 and according SRQs 1.1 – 1.7

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Table 2 – RQ2 and according SRQs 2.1 – 2.4

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3.2 Data Collection

In order to maximize neutrality during the data collection and analysis process, the steps to be performed for the SLR were clearly predefined before execution. The widely acknowledged PRISMA method (Moher et al. 2009) provided appropriate planning guidelines to further increase generalizability and to enhance the selection’s eligibility for statistical and qualitative data descriptions (Bryman 2006; Curry, Nembhard, and Bradley 2009). With the aim to derive the reciprocal influence of I4.0 and SUS, search results ought contain both "Industry 4.0" AND "Sustainability" or one of their respective synonyms in an industrial context, in either the abstract, title or keywords to obtain a comprehensive set of papers. The respective synonyms are based on the literature review previously performed, and shown in Table 3.

Table 3 – Synonyms used for search string

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Relevant academic papers are also subject to (1) be published online and to (2) be published in scientific journals. In order not to exclude any potentially relevant articles, no date constraint was set. Inclusion criteria (IC) and exclusion criteria (EC), as shown in Table 4, were defined to minimize exposure to subjective opinions, ensuring a consistent assessment of search results.

Table 4 – Overview of applied IC and EC during screening process

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Once the search terms, the search constraints, and the IC and EC were set, the actual search was conducted on three electronic, scientific databases, namely (1) Scopus, (2) Web of Science, and (3) ScienceDirect, all being considered as valid, appropriate databases for rigorous evidence identification for systematic review purposes (Gusenbauer and Haddaway 2020; Harzing and Alakangas 2016), resulting in 351, 231, and 267 articles, respectively. Screening and full-text eligibility analysis, both times applying the defined IC and EC shown in Table 4, resulted in 120 relevant papers. The respective PRISMA flow chart indicating the different stages and numbers of articles included and excluded per stage, is fully shown in Appendix 3.

After collecting these 120 eligible papers for this research’s scope, the actual data collection was performed. Next to the paper’s title, author(s), abstract, keywords, year of publication, and publishing journal, different type of data was added to the database by the author, derived from the respective SRQs, as described in following paragraphs.

SRQ1.4 aims to explore which research approach is used most/least often, thus the applied research approach of the respective paper was extracted and added to the database. Based on common practice within systematic research, such as Kamble et al. (2018) and Felsberger and Rainer (2020), two main research approaches were considered for categorization, namely (1) theoretical research and (2) empirical research, further broken down into (1) conceptual research and SLR, and (2) case study, interview, survey, and simulation. In this context, theoretical research focusses on theories, ideas, potential opportunities and challenges, and frameworks among others without raising primary data. Empirical research, however, focuses on measurable activities and processes, raising primary data.

SRQ1.5 aims to explore which I4.0 domain is explored most/least often, thus the paper’s I4.0 domain focus was extracted and added to the database. In this context, three domains of I4.0 were considered, based on previous literature research: Manufacturing (MF), focusing on the actual production processes within a (smart) factory; Supply Chain (SC), focusing on SC processes across factory boundaries; Circular Economy (CE), focusing on the nexus of both MF and SC with the explicit goal to achieve a CE.

SRQ1.6 aims to explore which I4.0Ts are talked about most/least often, thus I4.0Ts investigated in the context of SUS were extracted and added to the database to gain insights about which I4.0Ts are driving the sustainable development. As mentioned in Section 2.2, following I4.0Ts were considered: CPS; IoT; CC; BD; ROB; HVI; XR; AM; SIM; CS; BC; SS

SRQ1.7 aims to explore which SDs are talked about most/least often, thus SDs investigated in the context of I4.0 were extracted and added to the database to gain insights about research’s SD focus (EN, EC, SO).

Furthermore, qualitative data was added to answer RQ1 and RQ2 qualitatively, in particular data about (1) What was done? (2) What are the main findings of the author(s)? (3) What is the relation of I4.0 and SUS? The full database is shown in Appendix 11.

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¹ referring to articles in English language