Industry 4.0 and the current status as well as future prospects on logistics

Industry 4.0, referred to as the “Fourth Industrial Revolution”, also known as “smart manufacturing”, “industrial internet” or “integrated industry”, is currently a much-discussed topic that supposedly has the potential to affect entire industries by transforming the way goods are designed, manufactured, delivered and payed. This paper seeks to discuss the opportunities of Industry 4.0 in the context of logistics management, since implications are expected in this field. The authors pursue the goal of shedding light on the young and mostly undiscovered topic of Industry 4.0 in the context of logistics management, thus following a conceptual research approach. At first, a logistics-oriented Industry 4.0 application model as well as the core components of Industry 4.0 are presented. Different logistics scenarios illustrate potential implications in a practice-oriented manner and are discussed with industrial experts. The studies reveal opportunities in terms of decentralisation, self-regulation and efficiency. Moreover, it becomes apparent that the concept of Industry 4.0 still lacks a clear understanding and is not fully established in practice yet. The investigations demonstrate potential Industry 4.0 implications in the context of Just-in-Time/Just-in-Sequence and cross-company Kanban systems in a precise manner. Practitioners could use the described scenarios as a reference to foster their own Industry 4.0 initiatives, with respect to logistics management.     

Analysis of fractal manufacturing systems framework towards industry 4.0

Currently, ways to approach the design of Cyber Physical Systems in Industry 4.0 are under development. Emerging concepts of Smart Factories require the development of specialized knowledge; new working methods are also needed to manage the transition from conventional industry to industry 4.0. To achieve this objective, fractal theory could provide the appropriate knowledge and tools. Fractal systems applied to manufacturing have been widely used over the last decades to design complex adaptive systems: it allows the introduction of resilience requirements (capacity to react to changes in a turbulent environment) and to reduce the complexity of its structure, operation and management. In order to know the potential and the possibility of applying fractal theory to the design of systems in Industry 4.0, this article reviews the publications that develop fractal systems for manufacturing engineering. The review includes contributions published between 1985 (approximate date of the first works on the theory applied to manufacturing engineering) and 2019. The objective is to gather those strategies, methodologies and successful case studies that can be useful for the approaches of Industry 4.0 and to define a set of future lines of work for the adaptation of the fractal theory to the new challenges posed by Industry 4.0.     

Cyber Security and Privacy Issues in Industrial Internet of Things

The emergence of industry 4.0 stems from research that has received a great deal of attention in the last few decades. Consequently, there has been a huge paradigm shift in the manufacturing and production sectors. However, this poses a challenge for cybersecurity and highlights the need to address the possible threats targeting (various pillars of) industry 4.0. However, before providing a concrete solution certain aspect need to be researched, for instance, cybersecurity threats and privacy issues in the industry. To fill this gap, this paper discusses potential solutions to cybersecurity targeting this industry and highlights the consequences of possible attacks and countermeasures (in detail). In particular, the focus of the paper is on investigating the possible cyber-attacks targeting 4 layers of IIoT that is one of the key pillars of Industry 4.0. Based on a detailed review of existing literature, in this study, we have identified possible cyber threats, their consequences, and countermeasures. Further, we have provided a comprehensive framework based on an analysis of cybersecurity and privacy challenges. The suggested framework provides for a deeper understanding of the current state of cybersecurity and sets out directions for future research and applications.     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

A survey on decision-making based on system reliability in the context of Industry 4.0

The business world is continually changing. Dynamic environments, full of uncertainties, complexities, and ambiguities, demand faster and more confident decisions. To compete in this environment, Industry 4.0 emerges as an essential alternative. In this context, the reliability of manufacturing is an essential aspect for companies to make successful decisions. In the literature, several technologies associated with Industry 4.0 have been applied to improve the availability of equipment, including the Internet of Things (IoT), Cyber-Physical Systems (CPS), blockchain, and data mining. Nevertheless, there is still no survey study that seeks to show how reliability has collaborated to support decision-making in organizations, in the context of Industry 4.0. In general, most applications still focus on the productivity and health of individual equipment. However, in today's volatile and complex businesses, local decisions are no longer sufficient; it is necessary to analyze the organization entirely. Thus, being aware of the impacts that a local failure can impose on the entire company has significant weight in the decision-making process. In this context, this article presents a survey to identify how researches on systems reliability has contributed to and supported the development of decision-making in Industry 4.0. The main contribution of this article is to highlight how reliability can be used to support different types of strategic decisions in the context of Industry 4.0. Finally, it highlights the need for research associating management decisions with the technologies of Industry 4.0.     

Challenges for the cyber-physical manufacturing enterprises of the future

This paper summarizes a vision of the challenges facing the so-called “Industry of the Future” as studied by the research community of the IFAC Coordinating Committee 5 on Manufacturing and Logistics Systems, which includes four Technical Committees (TC). Each TC brings its own vision and puts forward trends and issues important and relevant for future research. The analysis is performed on the enterprise-level topics with an interface too other relevant systems (e.g., supply chains). The vision developed might lead to the identification of new scientific control directions such as Industry 4.0 technology-enabled new production strategies that require highly customised supply network control, the creation of resilient enterprise to cope with risks, developments in management decision-support systems for the design, and scheduling and control of resilient and digital manufacturing networks, and collaborative control. Cobots, augmented reality and adaptable workstations are a few examples of how production and logistic systems are changing supporting the operator 4.0. Sustainable manufacturing techniques, such closed-loop supply chains, is another trend in this area. Due to increasing number of elements and systems, complex and heterogeneous enterprise systems need to be considered (e.g., for decision-making). These systems are heterogeneous and build by different stakeholders. To make use of these, an environment is needed that allows the integration of the systems forming a System-of-Systems (SoS). The changing environment requires models which adapt over time. Some of the adaptation is due to learning, other mechanisms include self-organisation by intelligent agents. In general, models and systems need to be modular and support modification and (self-)adaptation. An infrastructure is needed that supports loose coupling and evolving systems of systems. The vision of the overall contribution from the research community in manufacturing and logistics systems, over the next few years is to bring together researchers and practitioners presenting and discussing topics in modern manufacturing modelling, management and control in the emerging field of Industry 4.0-based resilient and innovative production SoS and supply networks.     

Ubiquitous knowledge empowers the Smart Factory: The impacts of a Service-oriented Digital Twin on enterprises' performance

While the Industry 4.0 is idolizing the potential of an artificial intelligence embedded into ``things", it is neglecting the role of the human component, which is still indispensable in different manufacturing activities, such as a machine setup or maintenance operations. The present research study first proposes an Industrial Internet pyramid as emergent human-centric manufacturing paradigm within Industry 4.0 in which central is the role of a Ubiquitous Knowledge about the manufacturing system intuitively accessed and used by the manufacturing employees. Second, the prototype of a Service-oriented Digital Twin, which leverage on a flexible ontology-oriented knowledge structure and on augmented reality combined to a vocal interaction system for an intuitive knowledge retrieval and fruition, has been designed and developed to deliver this manufacturing knowledge. Two test-beds, complimentary for the problems in practice (the former on the maintenance-production interface in a large enterprise, the latter majorly focused in production and setups in a small and medium enterprise), show the significant benefits in terms of time, costs and process quality, thus validating the approach proposed. This research shows that a human-centric and knowledge-driven approach can drive the performance of Industry 4.0 initiatives and lead a Smart Factory towards its full potential.     

A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM

Over recent years, the manufacturing industry has seen constant growth and change. From one side, it has been affected by the fourth industrial revolution (Industry 4.0). From the other side, it has had to enhance its ability to meet higher customer expectations, such as producing more customized products in a shorter time. In the contemporary competitive market of manufacturing, quality is a criterion of primary importance for winning market share. Quality improvement must be coupled with a concern for high performance. One of the most promising concepts for quality control and improvement is called zero defect manufacturing (ZDM), which utilizes the benefits of Industry 4.0 technologies. ZDM imposes the rule that any event in the production process should have a counter-action to mitigate it. In light of this, the current research developed a methodology the manufacturer can use to correctly select or design appropriate ZDM strategies and equipment to implement at each manufacturing stage. This methodology consists of several steps. The first step is to conduct several simulations using a dynamic scheduling tool with specific data sets to develop a digital twin (DT). The data sets are created using the Taguchi design of experiments methodology. The DT model is created for use in predicting the results of the developed scheduling tool without actually using said tool. Using the DT, multiple ZDM parameter-combination sets can be created and plugged into the model. This process generates ZDM performance maps that show the effect of each ZDM strategy at each manufacturing stage under different control parameters. These maps are intended to provide information for comparing different ZDM-oriented equipment to help manufacturers reach a final decision on correct and efficient ZDM implementation or to assist in the design phase of a ZDM strategy implementation.     

Moving from lean manufacturing to lean construction: Toward a common sociotechnological framework

Since the early 1990s, the construction industry has taken some interest in the application of lean production to its own case. A new body of research led by the International Group for Lean Construction (IGLC) has been exploring new techniques for the industry. In this article the techniques developed for lean construction are compared with those of lean manufacturing. Differences between the manufacturing and the construction cases showed why lean production does not fully suit the construction industry. Although some elements are in an embryonic state, lean construction has built a set of techniques that are transferable to any constructor. Lean manufacturing and lean construction share many common elements despite their different techniques. The need for a joint combination of technical and human elements shows that both approaches conform to a common sociotechnological design. © 2005 Wiley Periodicals, Inc. Hum Factors Man 15: 233–245, 2005.     

Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case

Industry 4.0 is considered to be the fourth industrial revolution introducing a new paradigm of digital, autonomous, and decentralized control for manufacturing systems. Two key objectives for Industry 4.0 applications are to guarantee maximum uptime throughout the production chain and to increase productivity while reducing production cost. As the data-driven economy evolves, enterprises have started to utilize big data techniques to achieve these objectives. Big data and IoT technologies are playing a pivotal role in building data-oriented applications such as predictive maintenance.In this paper, we use a systematic methodology to review the strengths and weaknesses of existing open-source technologies for big data and stream processing to establish their usage for Industry 4.0 use cases. We identified a set of requirements for the two selected use cases of predictive maintenance in the areas of rail transportation and wind energy. We conducted a breadth-first mapping of predictive maintenance use-case requirements to the capabilities of big data streaming technologies focusing on open-source tools. Based on our research, we propose some optimal combinations of open-source big data technologies for our selected use cases.     

A cost benefit analysis for industry 4.0 in a job shop environment using a mixed integer linear programming model

As the manufacturing industry is approaching implementation of the 4th industrial revolution, changes will be required in terms of scheduling, production planning and control as well as cost-accounting departments. Industry 4.0 promotes decentralized production and hence, cost models are required to capture costs of products and jobs within the production network considering the utilized manufacturing system paradigm A new mathematical cost model is proposed for assessing the cost-benefit analysis of introducing Industry 4.0 elements to the manufacturing facility, specifically, integrating and connecting external suppliers as strategic partners and establishing an infrastructure for communicating information between the manufacturing company and its strategic suppliers. The mathematical model takes into consideration the bi-directional relationship between hourly rates and total hours assigned to workcentres/activities in a certain production period. A case study, from a multinational machine builder, is developed and solved using the proposed model. Results suggest that though an additional cost is required to establish infrastructure to connect suppliers, the responsiveness and agility achieved resulting from uncertainty outweighs the additional cost.     

CPuS-IoT: A cyber-physical microservice and IoT-based framework for manufacturing assembly systems

Today's customers are characterized by individual requirements that lead the manufacturing industry to increased product variety and volume reduction. Manufacturing systems and more specifically assembly systems (ASs) should allow quick adaptation of manufacturing assets so as to respond to the evolving market requirements that lead to mass customization. Meanwhile, the manufacturing era is changing due to the fourth industrial revolution, i.e., Industry 4.0, that will change the traditional manufacturing environment to an IoT-based one. In this context, this paper introduces the concept of cyber-physical microservice in the Manufacturing and the ASs domain and presents the Cyber-Physical microservice and IoT-based (CPuS-IoT) framework. The CPuS-IoT framework exploits the benefits of the microservice architectural style and the IoT technologies, but also utilizes the existing in this domain huge investment based on traditional technologies, to support the life cycle of evolvable ASs in the age of Industry 4.0. It provides a solid basis to capture domain knowledge that is used by a model-driven engineering (MDE) approach to semi-automate the development, evolution and operation of ASs, as well as, to establish a common vocabulary for assembly system experts and IoT ones. The CPuS-IoT approach and framework effectively combines MDE with IoT and the microservice architectural paradigm. A case study for the assembly of an everyday life product is adopted to demonstrate the approach even to non-experts of this domain.     

基于装备制造业工业汉语平行语料库的构建及相关问题的研究

现阶段,平行语料库被广泛应用于社会各行业中,特别是在计算机语言学领域中蓬勃发展,平行语料库作为语料库的一种表达形式,不仅具备语料库的共性特征,也能够为实证研究、对比语言学与翻译理论等提供便利,还能够充分发挥其优势。因此,将装备制造业专业技术与应用语言学有机结合,有利于对装备制造业工业汉语语料库的架构、语料入库来源、语料分词模型、语料数据挖掘软件等技术等深入探索与研究,为构建装备制造业工业汉语语料库奠定坚实基础。以此实现装备制造业工业汉语语料库应用价值的有效发挥,拓展应用语言学研究领域。所以,在开展装备制造业工业时,应该借助科学技术与科技力量构建装备制造工业汉语平行语料库,并对其进行深入的探索与分析,从而保障装备制造工业的可持续发展。     

Digital Twin-driven smart manufacturing: Connotation,reference model,applications and research issues

This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.     

SDMSim: A manufacturing service supply–demand matching simulator under cloud environment

Nowadays, with the introduction and application of new information technologies in manufacturing, various advanced manufacturing modes and national strategies have been put forward and paid more and more attention, such as Industry 4.0, Industrial Internet, Cyber-Physical System or Cyber Manufacturing, Made in China 2025, Internet Plus Manufacturing, Cloud Manufacturing, etc. For these modes and strategies, how to realize the effective and intelligent supply–demand matching (SDM) of various manufacturing resources and capabilities (MR&C) in the form of service is one of the common issues and aims. In order to provide a uniformed research platform for related researchers both in academic and industry, the concept of manufacturing service SDM simulator (SDMSim) is proposed in this paper. A hypernetwork based architecture for the simulator is designed, as well as its seven key functions and subsystems, including manufacturing service management, manufacturing task management, manufacturing service SDM hypernetwork, manufacturing service SDM problem formulation and configuration, matching and scheduling algorithms/strategies selection and design, statistical analysis, and visualization. It illustrates that SDMSim has the potential to serve the users of manufacturing service provider, manufacturing service consumer, manufacturing service operator in the field of SoM, as well as the related researchers.     

A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0

This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Guobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study.