Abstract

     Today, industry 4.0 is becoming a major target for cybercriminals due to its hyper-connectivity. Fortunately, there are several advanced means of securing industrial systems such as Intrusion Detection Systems (IDS). However, one of the main limitations of industrial IDS is the high rate of false positives and how to distinguish a real attack from an industrial failure. This paper deals precisely with the two latter points and proposes a way to reduce the rate of false positives and to distinguish attacks from industrial failures. The proposed approach combines two kinds of IDS using Neural Network (NN) through a Decision Making System (DMS). It was tested on a real industrial environment. The performance results are promising with a high percentage of accuracy and a low false positive rate.

Introduction

     Nowadays, Industrial Control Systems (ICS) exist in many different industrial sectors such as meatpacking, chemistry, construction, automotive, electronics industry. But also in vital industrial sectors such as energy, health, military and food sectors. Therefore, the suspension or the stopping of these systems could be costly for industrialists and cause consequent damage. Today, securing such equipment becomes more than necessary. Over the past decade, industry has become the center of attackers’ focus and has been the victim of several attacks starting with Stuxnet, Black Energy, WannaCry. This wave of attacks has been succeeded by several ransomware attacks in 2020 during the coronavirus pandemic, especially with the increase in the number of remote workers and a lack of security in this new working model [1]. Kaspersky’s ICS CERT researchers forecast a list of attacks likely to target industries in 2023 [2]. Among these attacks are phishing pages and emails, Torjans, N-day vulnerabilities, attacks on cloud services, exploiting vulnerabilities in legitimate software, the spread of malware via removable media …

This cyber-criminality phenomenon is favored with the emergence of the industry 4.0. This 4th industrial revolution is characterized by the convergence of the worlds of Information Technology (IT) and Operation Technology (OT), the huge amount of generated data, the use of Cloud as new storage means. All these reasons increase the risk of cyber attacks in industry.

Fortunately, there are several solutions to secure the industry and its equipment. Among these mechanisms, we mention firewalls, anti-virus, auditing processes and IDS. Each of these securing mechanisms has a specific role such as detecting and removing malware, preventing unauthorized access or detecting intrusions by IDS. These latter give visibility to the system’s activities, which allows a timely detection and response to any suspicious events [3], [4].

Conclusions

     BIANO-IDS is a new intrusion detection approach combining two kinds of IDS: anomaly-based IDS and specification-based IDS. The approach has been tested on a real environment and the detection accuracy rate is high in addition to the different performance metrics. However, to further improve performance metrics, we intend to explore other traces such as system logs and apply reduction or selection features methods in the future to improve the computation and training time of neural networks.

Abstract

The current planetary crisis and the perceived urgency necessitates more sustainable production and consumption patterns. Businesses, particularly the multinationals, play a key role in transitioning to a circular economy, which provides a promising approach to achieving the UN Sustainable Development Goals. Industry 4.0 technologies can theoretically support this transition and enable circular economy through, for example, data-driven and smart business processes. However, merging these new technologies with a circular economy is not a straightforward and well-established process and it fundamentally changes the business value chain. Therefore, this paper provides a systematic overview to help better understand the transition process to circular business models and the enabling role of industry 4.0. Merging these concepts underlines that many factors are interrelated and should be investigated in a holistic way instead of siloes. The findings imply that changes transcend business boundaries, including new value chain characteristics and operation models and indicate the interconnected nature of certain factors along the product lifecycle, including service-based models, circular design, reverse flows, consumers and users, and collaborations. This paper proposes a conceptual transition framework to address how new circular business models can integrate digitally adaptive transformations and proposals for future research are reflected upon.

Introduction

Our world is currently facing numerous environmental, social and economic challenges and the consequences of these challenges can be seen in climate change, pollution, biodiversity loss and resource degradation (Okorie et al., 2018). We have reached a tipping point in what our Earth can sustain and the current living standards are not sustainable in the long-term. The problem can be tied back to the first industrial revolution and the logic our economy is built on. 91% of the world still follows this traditional, linear economic ‘take-make-use-dispose’ system, which is a driver of our production and consumption patterns (Nobre and Tavares, 2020). While innovations of the industrial revolutions have induced growth, with the growing population and the throwaway consumption patterns, products remain underutilized, are prematurely wasted, and their end-of-life is not generally considered (Rosa et al., 2020).

Therefore, it has become inevitable for production to transition to a more sustainable system and the search for solutions a priority for the social and political agenda globally (Ajwani-Ramchandani et al., 2021b; Bjørnbet et al., 2021). The United Nations Member States have summarized an action plan in the 2030 Agenda for Sustainable Development, articulating 17 transformative Sustainable Development Goals (SDGs) (United Nations, 2015). This initiative shows that there is an interest in finding a balance between human and nature to support the more efficient use of our resources and encourages us to do more and better with less (Tunn et al., 2019). Multinationals are particularly well-positioned to tackle this issue due to their overarching footprint (Ajwani-Ramchandani et al., 2021a).

Conclusion and limitations

This SLR has conceptualized the CE–I4.0 epistemology through current literature, synthesizing and providing an overview of the CE transition process through a business model lens. Our framework depicts which directions businesses should innovate to and it pinpoints specific I4.0 tools that enable the elements of service-based models, circular design, reverse flows, consumers and users, collaborations and product lifecycle management.

This framework contributes by bringing together relevant sciential and business factors into a single study. Although there has been an increasing number of studies in the field of CE–I4.0 (Bjørnbet et al., 2021), investigating the interconnected nature of elements along the business transition process to a CE has not yet been fully understood and conceptualized. The framework shows the transition to a CE as a paradigm shift (Campbell-Johnston et al., 2019), which transcends siloed solutions and requires a holistic, ‘embracing all’ approach and consideration of different stakeholder interest and expectations (Awan et al., 2021). With the proposed conceptual framework, we embrace a holistic approach, demonstrate which business elements influence each other and how within the same ‘circular system’, and we underline the role of specific I4.0 tools along this transition.

Abstract

     This paper investigates the transformation of information sharing and multi-tier supply chain management (MSCM) from the perspective of small and medium-sized enterprises (SMEs) in the context of Industry 4.0. SMEs are typically sharing less information digitally, are less active in conducting MSCM, but must be integrated in digital supplier networks to achieve supply chain transparency or traceability. For this purpose, this paper empirically analyzes SMEs from the manufacturing industry in Austria and Germany are investigated regarding information sharing and MSCM. The responses are analyzed to test three hypotheses, using 81 replies from manufacturing SMEs in Austria and Germany. The three hypotheses investigate the upstream and downstream MSCM practices of SMEs as well as which kind of information is shared with and by SMEs. The results help to understand and reveal potential barriers for information sharing besides technical possibilities to SMEs in order to be actively involved in MSCM.

Introduction and background

     Digital information sharing across supply networks represents a relevant topic since it enables data-based potentials such as in the concept of Industry 4.0 or for using Artificial Intelligence [1,2]. However, many companies do not recognize the importance of data availability and data quality yet. This is particularly true for small and medium-sized enterprises (SMEs), which are often impeded by missing trust, standardization, data exchange interfaces, or technological readiness [3-6] and do not recognize the relevance of digital technologies for their business [7]. Likewise, digital information sharing in multi-tier supply chain management (MSCM) is still in its early stages of development.

     This research investigates information sharing and subsequent data analysis in MSCM, focusing on SMEs. It aims at determining basic characteristics, e.g., size and vertical network position (up- or downstream), of early adopters of data-based MSCM. Raw material suppliers and upstream suppliers have a higher probability of being pressured by large buyer companies, which demand information sharing [2]. Hence, we argue that data based MSCM is more likely to be implemented in the upstream supply chain (H1). Moreover, five categories of information were evaluated if they are shared with SMEs or by SMEs (H1a and H1b): a) inventory level, b) state of delivery, c) expected delay, d) forecast data, and e) production plan

Discussion and Conclusion

Interpretation of Results

     From the interviews, we derived the motives of SMEs for carrying out MSCM and to share data. Mainly, SMEs aim to serve customer requests and to be able to fulfil their own production plan. This is in line with Arend and Wisner [14], who found that conducting supply chain management (SCM) is negatively related to SME performance since they do not implement SCM thoroughly enough or do it because they are forced by more powerful partners while not having benefits for themselves. Hence, before SMEs are able to conduct profitable MSCM, data exchange must be established with mutual benefits for supply chain partners [2]. In addition, we found that not all kinds of information may be relevant for partners in the supply network. For SMEs, binary notes would often suffice, e.g., whether the delivery is on time or not.

     In summary, it can be argued that SME must invest primarily in the area of data structures and availability in order to create the basis for data-based analyses. Particularly regarding network partners that are not directly connected to the company, there is a great need for data so that the entire structures can be analyzed and optimized. It is noticeable that there are differences between the companies in many areas of analysis, which result from the size of the company. Differences are particularly noticeable regarding data availability and the application of analyses in the supply chains. Larger companies are better equipped in terms of data availability and perform more analytics – presumably because they work more intensively with data and therefore also rate the potential higher than smaller ones. Likewise, larger companies have more power in the network and can therefore de facto secure better data availability from their network partners.

Abstract

Artificial Intelligence (AI) has opened up tremendous opportunities in the workplace through robotics innovation, which envelops both AI and the Internet of Things (IoT). Precision, Efficiency, and Flexibility are considered the potential benefits of Industry 4.0. The implementation of Industry 4.0 requires a lot of changes, including the Human Resource (HR) function. In Industry 4.0, the HR capability is more critical and gives an upper hand to the organization. The HR capability should be more cautious and adaptable to adjust to the difficulties and requirements. We study the contributions of AI in HR digitalization and practices in Industry 4.0. 271 HR experts working in Information Technology (IT), Manufacturing, and administration are selected to participate in this review focusing on five AI applications in HR capability and three elements of HR readiness. The information collected was examined utilizing the Statistical Package for Social Sciences (SPSS) tool and Analysis of Moment Structures (AMOS). The results uncovered that hierarchical organization examination is a fundamental part of acquiring sustainable development. Adaptability and human asset capability are upheld by each of the five components of AI application areas of HR. Well-being and Safety improvement were viewed as vital components under the AI application in HR.

Introduction

In the era of Industry 4.0, the Human Resource (HR) function plays a critical role in bridging the gap between technology and human resources. Although technology is taking over most of the tasks traditionally performed by human resources, there is still a growing need for flexible HR functions to address the challenges of managing people. To achieve this flexibility, technology can help bring agility to the HR process. Agility, which is the ability to move quickly and smoothly, is not a new concept and has been adopted by major companies such as Google, Apple, Facebook, Amazon, and Microsoft. In the context of HR, agility means the ability to adapt and develop individuals and processes in response to rapid and unpredictable changes, to support people, key strategies, and organizational adaptability [1,2]. As an HR or Learning and Development (LandD) professional, being agile means being able to drive employee engagement and retention in alignment with the company’s overall objectives. HR Agility is particularly suitable for volatile HR functions where standardization of functions is difficult [3,4].

Conclusion, limitations and scope for future study

Carrying out AI in HRM gives numerous benefits to the HR department and employees. However, those advantages accompany a few network safety dangers and lawful concerns. Assembling more worker information implies expanded protection concerns, and more gadgets accompany more noteworthy prospects of network safety assaults. Before executing AI for HR the board, organizations need to ensure that their workers’ information is not undermined. Associations likewise need to fabricate information-driven security to screen information itself and not simply organization to limit network protection dangers.

The study has addressed the application of AI concepts in various possible areas of HRM. These areas may not be there in the regular stream of activities. Still, it is trying to impose the importance of addressing the same—the dimensions measured under that showcase human aspects enhancement with the help of AI. The result revealed the way these aspects influence the agile capability of HRM. Digitization of HR and ONA is closed related technological implication in HR, enabling the iterative process of function. Both of the above aspects need an excellent organizational design to support the implementation and progress. Thus this study paved a new way by linking two elements prevailing today’s Industry 4.0 era.

Abstract

     A Smart Circular Supply Chain (SCSC) integrates both Industry 4.0 (I4.0) and Circular Economy (CE) concepts into supply chain in response to achieving sustainable goals/agenda. The purpose of this paper is to assess SCSC readiness and maturity level of SMEs considering different stakeholders from a multi-layered perspective. For this aim, a conceptual framework was proposed and accomplished through a case study of SMEs in Turkey’s textile industry. Such integrated approach to holistically assessing SCSC readiness and maturity makes a unique contribution to the field. The highlights of this study are summarized as follows: (1) approaching readiness and maturity in transitions by focusing on systems theory; (2) identifying the dimensions of readiness and maturity in transitions to I4.0 and CE; (3) assessment of readiness and maturity level of SMEs in transition to CE and (4) assessment of readiness and maturity level of SMEs in transition to I4.0 within the supply chain.

Introduction

     According to the United Nations, the global population has grown from 3 billion in 1960 to 7.7 billion in 2019 and it is expected to reach 8.5 billion in 2030 (Balestrucci, 2020, United Nations, 2019). Such rapid and substantial growth has significantly raised the demand for natural resources, which is increasing the pressure on natural ecosystems and can potentially lead to catastrophic environmental consequences (Balestrucci, 2020, Nykvist et al., 2009). One of the major organizational responses to this global challenge is the Circular Economy (CE). Unlike the linear economy, CE avoids generating waste and improves the reuse of resources by utilizing the cradle-to-cradle design (Kayikci et al., 2022b). More broadly, CE is an economic model aiming at the effective use of natural resources for waste minimisation, expansion of product life cycles, removal of primary resources, and closed-loops of goods, components of products, and materials in the context of environmental sustainability and socio-economic advantages (Morseletto, 2020). Such aims of CE can be obtained by various business models (Sehnem et al., 2022), such as shared consumption models, products-as-services, CE regenerative principles (repair, refurbishment, remanufacturing, replacement, regeneration, recovery, etc.), and industrial symbiosis.

Conclusion

     With the rising population, the value of CE in the utilization of scarce resources grows continually. Therefore, transition into a circular model of the economy has become a requirement. The purpose of this study is the evaluation of both readiness and maturity level for I4.0 and CE perspectives in SMEs. For a successful CE or I4.0 transition, reaching a certain level in the supply chain is not sufficient. Regardless of how ready or mature the firm is, it should also be at the same level as other stakeholders in the supply chain. Multi-dimensions encompassing the whole supply chain were included in the development of the proposed framework for determining the readiness and maturity levels of SMEs regarding I4.0 and CE in the supply chain as the systems theory suggests. The development of the readiness and maturity model for both I4.0 and CE can be considered as the major contribution of this study. This study examined the ecosystem participants of the supply chains (e.g., stakeholders) and their multi-tier perspectives to present a holistic perspective of the supply chain. This is the first study to assess both the readiness and maturity level of I4.0 and CE in the supply chain. The main limitation is that it was applied to only four companies in the same supply chain ecosystem, although these were specifically selected based on their different levels of readiness and maturity.

Abstract

     The paper evidence the link between two paradigms – Industry 4.0 and Green Supply Chain Management (GSCM) following an empirical study conducted in the automotive industry. 243 responses from the automotive supply chain professionals from Europe (including the UK) are used to test the developed hypotheses. An integrated, two-stage approach combining interpretive structural modelling and structural equation modelling develops a multi-level hierarchical structure for establishing the link between Industry 4.0 technologies, Green Supply Chain (GSC) practices and GSC performance. The study evidenced an indirect effect of Industry 4.0 technologies through GSC practices on GSC performance; and this link is found to be stronger than the direct effect of Industry 4.0 and GSC practices in the automotive supply chains. Future supply chains should focus on driving and linking technologies such as the Internet of Things (IoT), Cyber-Physical Systems (CPS) and Blockchain for effective implementation of GSC practices. GSC practices, mainly reverse logistics and green purchasing, are highly influenced by disruptive technologies and are critical for leading improvement in GSC performance. Identifying and linking key Industry 4.0 technologies with GSC practices will benefit organizations in making evidence-informed decisions for improved sustainability performance.

Introduction

     The main aim of Industry 4.0 is to make manufacturing operations/systems efficient, autonomous, and sustainable (Koh et al., 2019). Industry 4.0 is the latest industry transformation, attempting to build smart systems by integrating physical objects with digital technologies (Dalenogare et al., 2018, Fatorachian and Kazemi, 2021). Internet of Things (IoT), Cyber-Physical Systems (CPS), Big Data Analytics (BDA), Additive Manufacturing (AM) and Cloud Computing (CC) are some of the technologies of Industry 4.0. These Industry 4.0 capabilities provide higher productivity and flexibility, but they also help drive the organisations’ sustainability goals (Schroeder et al., 2019, Felsberger et al., 2020; Kamble et al., 2020).

     Regarding the economic dimension of sustainability, Industry 4.0 technologies can reduce set-up times, lead times, labour and material cost, increase production and design flexibility, and enhance productivity and customization (Wang et al., 2017). In case of the environmental dimension, the reduced energy and resource consumption leads to reduction of waste or Co2 emission across production and supply chain processes (Sarkis and Zhu, 2018). In case of the social dimension, smart factories and manufacturing support employee health and safety by improving working conditions, which results in higher employee satisfaction and motivation (Müller et al., 2018). These benefits highlight growing relationships between Industry 4.0 technologies and sustainability (Liu et al., 2020). However, except a few sporadic studies (such as Li et al., 2020, Bai et al., 2020), this relationship is not well investigated due to the lack of robust evidence (Kamble et al., 2018, de Sousa Jabbour et al., 2018).

Conclusion and implications

Discussion

     The paper aimed to assess and evidence the link between Industry 4.0 and GSC practices and how they influence GSC performance in the automotive supply chain following an empirical study. The scarce literature surrounding the relationship between two paradigms – Industry 4.0 and GSCM was the motivation for study. While attempting to address the defined research question, the study found that the implementation of Industry 4.0 technologies will positively impact the implementation of GSC practices in automotive supply chains. Furthermore, Industry 4.0 technologies will also positively improve GSC performance metrics and, therefore, provide evidence that the technologies of Industry 4.0 will assist organizations in transitioning towards sustainable development (Bonilla et al., 2018, Stock et al., 2018). Moreover, it is also apparent that the impact of Industry 4.0 on GSC practices will indirectly lead to an improvement in the green performance of automotive supply chains, which supports the findings made by Kamble et al. (2020).

     The study employed an integrated, two-stage approach by combining ISM and SEM methods to provide multiple findings. First, following the ISM method, a multi-level structural relationship among the key Industry 4.0 technologies and GSC practices for improving GSC performance was built. The ten-level hierarchical structure revealed that IoT and CPS are the most significant factors compared to other Industry 4.0 technologies influencing GSC practices. Later, MICMAC analysis supported in developing a driving and dependence power matrix. Within GSC practices, it was found that RL carried the highest driving power, strongly influencing GP and SCEC (Cluster II in Fig. 4). In linking factors, although CC, ME and BC carry the same level of dependence power on other high-level Industry 4.0 technologies, BC carries the highest driving power for linking Industry 4.0 technologies with GSC practices and other SC processes (Cluster III in Fig. 4).

Abstract

     The digitalisation of production driven by new paradigms such as Industry 4.0, factories of the future and smart manufacturing, create new challenges as to how manufacturers and other supply chain actors would discharge their corporate responsibility to respect human rights. These new paradigms enable novel approaches like distributed and collaborative manufacturing. Manufacturers increasingly leverage digital technologies, such as 3D printing, cloud manufacturing and artificial intelligence, to provide customised products. Digital technologies also improve predictive and preventive maintenance on the shop floor and across the supply chain, increasing the overall resilience of manufacturing industries in times of crisis. This article proposes a blueprint of a collaborative, decentralised approach to human rights due diligence in digital supply chains. It argues that the pooling of human rights due diligence efforts in manufacturing industries could have network-wide effects of incentivising value chain actors to also collaborate on providing collective remedy.

Introduction

     The manufacturing of a modern aircraft, like an Airbus A321, is the result of highly efficient cooperation across complex, tightly integrated global supply and manufacturing chains. The process starts with the design and engineering, through production and transport of aircraft sections, to final assembly and tests, certification and delivery to the customer. Growing demand in air travel in the past few decades has naturally increased the production needs. At the same time, fast-paced developments in the information and communications technology (ICT) industry have offered new ways of optimising manufacturing. As a result, the supply chain of modern aircraft production no longer consists of just raw materials, such as steel, aluminium, titanium and their alloys. The deployment of smart sensors on the shop floor and the utilization of automated systems means that supply chains now include also digital assets, such as datasets, computer simulations, digital twins and pre-trained machine learning models.

     New models of manufacturing are enabled by concepts like distributed, collaborative and additive manufacturing. At the same time, they also create distinct challenges as to how manufacturers and the various actors in their supply chains could discharge their corporate responsibility to respect human rights. It has become widely accepted that businesses do have a corporate responsibility to respect human rights. The United Nations Guiding Principles on Business and Human Rights (‘Guiding Principles’) promote this responsibility as a global standard of expected conduct from business enterprises, regardless of their size, sector, operational context, ownership or structure.1 Thus, from manufacturers to service-providing enterprises, all actors in the value chain are subject to the same responsibility.2 In this sense, the corporate responsibility to respect human rights applies equally to enterprises engaged in traditional manufacturing as well as those in smart manufacturing driven by the developments in so-called Industry 4.0.

Conclusion

     Collaborative smart manufacturing in Industry 4.0 represents a significant shift in the convention manufacturing paradigm. The vertical and horizontal integration of actors in the supply chain leads to the emergence of new business relationships entailing new potential adverse human rights impacts. It is beyond doubt that business entities have a corporate responsibility to respect human rights. A main tool to discharge this responsibility, human rights due diligence is premised on the idea that it is within the business enterprise’s control to prevent or mitigate human rights impacts at the beginning of a new business relationship or activity. This assumption, however, is challenged by technological and organisational processes within Industry 4.0 which are based on complex real-time network interactions and data-driven autonomous decision-making. In this new collaborative environment, the manufacturer becomes one of many actors in a network, along with the customer. This gives rise to new potential aggregate adverse human rights impacts at the level of the ecosystem which cannot be sufficiently addressed by any single actor alone.

1- Introduction

2- Related works

3- Ramp-up Decision Support System

4- Experimental procedure and results

5- Discussion

6- Conclusions

References

Abstract

Production ramp-up is a key phase during the introduction or changeover of a production system. Process calibration and tuning are inevitably required to make such a system fully operational and let it reach its maximum production yield. A complex decision-making process takes place in order to optimally tune the system and requires a long time for testing and experimenting that will determine the system behaviour. This work considers the sequential nature of ramp-up and proposes a Cyber-Physical Systems approach based on data capturing, learning mechanisms and knowledge extraction, leading to an Industry 4.0 compliant Decision Support System (DSS) for human operators. The proposed system is implemented as an online DSS and also supports offline learning using previously gathered knowledge. A number of experiments have been carried out on a micro scale assembly station, validating the expected benefits of the proposed DSS. Results show a reduction of over 40% in the number of ramp-up steps required when using the DSS.

Introduction

Nowadays, production systems (PSs) are subject to rapid technological changes, which led to very short product lifecycles and high variety of products, to meet the growing demand of product customization (Kalir and Rozen, 2018). In addition to that, PSs becomes more modular and able to change from one configuration to another (Colledani et al., 2018). The rapid introduction of new products, mostly complex, and changeover cause a prolonged and more frequent production ramp-up (Letmathe and Rößler, 2019). Ramp-up phase is very important for PSs because having high outputs with good quality and fast cycle time are the key factors for increasing profits, where at the beginning of product life-cycle product prices are the heights and competition is scarce (Kalir and Rozen, 2018). Production ramp-up is a decision-making process where human experts decide on the best actions to fine-tune the process. It is a highly complex parameter tuning process with many interrelated factors leading to a well-defined goal. Studies on ramp-up report long lead times of up to a few months from the initial sys-tem built to full production (Terwiesch and Bohn, 2001). Since the product, PS, and the supply chain are new, uncertainty is very high, making the ramp-up process very difficult to manage (Hansen and Grunow, 2015). Also, ramp-up process is unstable, expressed in the unpredictability of the system at this stage, which makes planning proactive steps to avoid some problems ineffective for many ramp-up problems (Schuh et al., 2015).

1- Introduction

2- Background

3- Related work

4- Proposed solution

5- Performance evaluation

6- Conclusion and future work

References

Abstract

Internet of Things (IoT) and cyberphysical system (CPS) technologies play huge roles in the context of Industry 4.0. These technologies introduce cognitive automation to implement the concept of intelligent production, leading to smart products and services. One of the technological challenges related to Industry 4.0 is to provide support to big data cloud based applications which demand QoS-enabled Internet connectivity for information gathering, exchange, and processing. In order to deal with this challenge, in this article, a QoS-aware cloud based solution is proposed by adapting a recently proposed seamless resources sharing architecture to the IoT scenario. The resulting solution aims at improving device to cloud communications considering the coexistence of different wireless networks technologies, particularly in the domain of Industry 4.0. Results are obtained via simulations of three QoS demanding industrial applications. The outcomes of the simulations show that both delay and jitter QoS metrics are kept below their specific thresholds in the context of VoIP applications used for distributed manipulators fine tuning control. In the case of video-based production control, the jitter was controlled to meet the application demands, and even the throughput for best-effort supervisory systems HTTP access is guaranteed.

Introduction

Industry 4.0 introduces several changes to the original approach of industrial automation. IoT and CPS technologies play huge roles in this context introducing cognitive automation and consequently implementing the concept of intelligent production, leading to smart products and services (Drath and Horch, 2014). Besides the aforementioned technologies, Industry 4.0 typically relies on big-data cloud based applications which demand QoS-enabled Internet connectivity for information gathering, exchange and processing. Flexibility and scalability are important requirements of Industry 4.0, since the physical environment of the factories is in constant change. Implementing these requirements demands dealing with the disposition of the sensors and actuator devices in the factories, and how easily they can be displaced, introduced, and reorganized (Freitas et al., 2013). This flexible environment can be attained by diminishing the needs for cabling to connect the devices, which is possible using wireless technologies. Implementing a QoS-aware cloud based solution is challenging in the context of wireless connections and demands three main issues to be tackled (Wollschlaeger et al., 2017): (I) the coexistence of different wireless protocols and systems, (II) interoperability between communication systems, and (III) seamless engineering to allow adaptive factory operation. In order to deal with these issues, in this article, a recently proposed seamless resources sharing architecture (Kunst et al., 2016a,b; Fernandez et al., 2017) is adapted to the IoT scenario aiming at improving the device to cloud communications considering the coexistence of different wireless networks technologies, particularly in the domain of Industry 4.0.

The Fourth Industrial Revolution poses significant challenges to manufacturing companies from the technological, organizational and management points of view. This paper aims to explore how top executives interpret the concept of Industry 4.0, the driving forces for introducing new technologies and the main barriers to Industry 4.0. The authors applied a qualitative case study design involving 26 semi-structured interviews with leading members of firms, including chief digital officers and chief executive officers. Company websites and annual reports were also examined to increase the reliability and validity of the results. The authors found that management desire to increase control and enable real-time performance measurement is a significant driving force behind Industry 4.0, alongside production factors. Organizational resistance at both employee and middle management levels can significantly hinder the introduction of Industry 4.0 technologies, though these technologies can also transform management functions. Multinational enterprises have higher driving forces and lower barriers to industry 4.0 than small and medium-sized companies, but these smaller companies have good opportunities, too.

Introduction

The Fourth Industrial Revolution, which is currently taking place, sets a number of challenges for manufacturing companies from the technological, organizational and management points of view. With the application of new technologies and the transformation of processes, significant changes are expected in the field of work, and future production systems demand new competencies from employees. Work organization is expected to become more flexible in time and space, with workflows becoming more transparent, decentralized, and less hierarchical (Münchner Kreis, 2013; Picot and Neuburger, 2014). The exact risk of digitization is difficult to forecast, but nowadays it is becoming clear that workers in some countries are more defenseless than others. For example, in some regions, more than 25% of jobs are at high risk of automation (Segal, 2018). In the world of future production systems, some processes are expected to be simplified, and others to become much more complex and embedded. This is likely to lead to an increase in the number of higher skilled jobs and a reduction in jobs requiring lower qualifications (Brühl, 2015; Spath et al., 2013). Industry 4.0 will therefore have a significant impact on both the labour market and society. According to Kovács (2017b), the success of Industry 4.0 will be a function of both technical feasibility and the social acceptability of the whole transformation process. Vacek (2017) emphasized that if technological changes are not accompanied by significant changes in socio-economic systems, social cohesion may weaken. Industry 4.0 is therefore both a technological and socio-economic phenomenon (Szabó et al., 2019).

Industry 4.0 is considered a new industrial stage in which vertical and horizontal manufacturing processes integration and product connectivity can help companies to achieve higher industrial performance. However, little is known about how industries see the potential contribution of the Industry 4.0 related technologies for industrial performance, especially in emerging countries. Based on the use of secondary data from a large-scale survey of 27 industrial sectors representing 2225 companies of the Brazilian industry, we studied how the adoption of different Industry 4.0 technologies is associated with expected benefits for product, operations and side-effects aspects. Using regression analysis, we show that some of the Industry 4.0 technologies are seen as promising for industrial performance while some of the emerging technologies are not, which contraries the conventional wisdom. We discuss the contextual conditions of the Brazilian industry that may require a partial implementation of the Industry 4.0 concepts created in developed countries. We summarize our findings in a framework, that shows the perception of Brazilian industries of Industry 4.0 technologies and their relations with the expected benefits. Thus, this work contributes by discussing the real expectations on the future performance of the industry when implementing new technologies, providing a background to advance in the research on real benefits of the Industry 4.0.

Introduction

Industry 4.0 is understood as a new industrial stage in which there is an integration between manufacturing operations systems and information and communication technologies (ICT) – especially the Internet of Things (IoT) – forming the so-called Cyber-Physical Systems (CPS) (Wang et al., 2015; Jeschke et al., 2017). This new industrial stage is affecting competition rules, the structure of industry and customers’ demands (Gilchrist, 2016; Bartodziej, 2017). It is changing competition rules because companies business models are being reframed by the adoption of IoT concepts and digitization of factories (Dregger et al., 2016; Lasi et al., 2014; Wang et al., 2015). From the market point of view, digital technologies allow companies to offer new digital solutions for customers, such as internet-based services embedded in products (Ayala et al., 2017; Coreynen et al., 2017). From the operational perspective, digital technologies, such as CPS, are proposed to reduce set-up times, labor and material costs and processing times, resulting in higher productivity of production processes (Brettel et al., 2014; Jeschke et al., 2017). Several countries have recently created local programs to enhance the development and adoption of Industry 4.0 technologies. In Germany – where this concept was born – this program was called “High-Tech Strategy 2020”, in the United States was established the “Advanced Manufacturing Partnership”, in China the “Made in China 2025” and in France the “La Nouvelle France Industrielle” (Kagermann et al., 2013; Rafael et al., 2014; Wahlster, 2013; Zhou, 2017; CNI, 2013; Liao et al., 2017). In Brazil, the program called “Towards Industry 4.0” (Rumo à Indústria 4.0) was created by the Brazilian Agency for Industrial Development (ABDI – Agência Brasileira de Desenvolvimento Industrial) together with other initiatives of the Ministry of Industry, Foreign Trade and Services (MDIC – Ministério da Indústria, Comércio Exterior e Serviços) (ABDI, 2017). All these programs, in both developed and emerging countries aim to disseminate the Industry 4.0 concepts and technologies in local firms.

Real-time communication, Big Data, human–machine cooperation, remote sensing, monitoring and process control, autonomous equipment and interconnectivity are becoming major assets in modern industry. As the fourth industrial revolution or Industry 4.0 becomes the predominant reality, it will bring new paradigm shifts, which will have an impact on the management of occupational health and safety (OHS). In the midst of this new and accelerating industrial trend, are we giving due consideration to changes in OHS imperatives? Are the OHS consequences of Industry 4.0 being evaluated properly? Do we stand to lose any of the gains made through proactive approaches? Are there rational grounds for major concerns? In this article, we examine these questions in order to raise consciousness with regard to the integration of OHS into Industry 4.0. It is clear that if the technologies driving Industry 4.0 develop in silos and manufacturers’ initiatives are isolated and fragmented, the dangers will multiply and the net impact on OHS will be negative. As major changes are implemented, previous gains in preventive management of workplace health and safety will be at risk. If we are to avoid putting technological progress and OHS on a collision course, researchers, field experts and industrialists will have to collaborate on a smooth transition towards Industry 4.0.

Introduction

Industrialisation has undergone remarkable transformations since its beginnings in the 18th century. Following the introduction of machinery powered by local generation of steam, which uncoupled production from the limitations of human manual effort (Industrial revolution 1.0), the next paradigm shift came in the 19th century with the introduction of electricity, which allowed the broad distribution of power from a central facility. Thanks to electricity, machinery became less bulky and ran faster (Industry 2.0). The 20th century brought powered assembly lines, and with the development of electronics, manufacturing became more and more automated (Industry 3.0) and focused on performance. With automation came opportunities to optimise manufacturing processes and improve productivity through the design of more flexible, ergonomic and safer machinery (MESI, 2016). In comparison, the term “Industry 4.0” was coined very recently. As might be expected, it refers to the convergence of manufacturing with the digital revolution, artificial intelligence, the Internet of things and with every device called “smart”. Its goal is to allow manufacturers to meet ever-changing demand more efficiently using adaptable and responsive machinery. This idea goes beyond the design of single machines and now encompasses a broadened vision that can best be described as a global revolution in manufacturing. Conceived in Germany, this vision has spread to several other industrialized countries, some of which have been investing heavily in order to catch up to the innovators (MacDougall, 2014; MESI, 2016). Real-time communication, Big Data, man–machine cooperation, remote sensing, monitoring and control, autonomous equipment and interconnectivity are all considered as non-negligible assets in industries that face fierce competition and seek to improve productivity and reduce costs. The fourth industrial revolution goes well beyond concepts such as interconnectivity and digital manufacturing. In Industry 4.0, businesses digitize their physical assets and integrate them into digital ecosystems throughout the value chain. Industry 4.0 promises increases in productivity through the integration of digital systems of production with analysis and communication of all data generated within an intelligent environment.