Mini Special Issue on Smart Manufacturing

Cyber-physical systems (CPS) have gained an increasing attention recently for their immense potential towards the next generation smart systems that integrate cyber technology into the physical processes. However, CPS did not initiate either smart factories or smart manufacturing, and vice versa. Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0. Also emerging are other related models such as cloud manufacturing, social manufacturing and proactive manufacturing with the introduction of cloud computing (broadly, the Internet of Services, IoS), social networking (broadly, the Internet of People, IoP) and big data (broadly, the Internet of Content and Knowledge, IoCK), respectively. At present, there is a lack of a systemic and comprehensive study on the linkages and relations between these terms. Therefore, this study first presents a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS. Then, the paper addresses CPS-based smart manufacturing as an eight tuple of CPS,IoT, IoS and IoCK as elements. Further, the paper extends the eight-tuple CPS-based manufacturing to social-CPS (SCPS)-based manufacturing, termed wisdom manufacturing, which forms a nine tuple with the addition of one more element, the IoP and which is based on the SCPS instead of CPS. Both architectures and characteristics for smart and wisdom manufacturing are addressed. As such, these terms’ linkages are established and relations are clarified with a special discussion. This study thus contributes as a theoretical basis and as a comprehensive framework for emerging manufacturing integration.

This paper focuses on Blockchain Technologies in Supply Chain Management (SCM). It is based on the qualitative study “Potentials of Blockchain Technologies in Supply Chain Management—A Conceptual Model” by Härting et al. (Science Direct. 24th International conference on knowledge-based and intelligent information & engineering systems. Elsevier B.V., pp 1–9, 2020 [1]). The paper aims to investigate the potentials of Blockchain Technology in Supply Chain Management based on the conceptual model with six influencing factors and four moderators. A quantitative study is used to gain further insights and to examine relevant factors influencing the potentials of Blockchain Technologies in SCM. The results of the quantitative research approach show that the factors efficiency and scalability positively and significantly influence the potentials of Blockchain Technologies in SCM.KeywordsBlockchain technologyBlockchainBlockchain technology in supply chain managementSCMSupply chainQuantitative study

When cyber-physical systems (CPS) are connected to perform smart manufacturing, we have a cyber-physical production system (CPPS). In such systems, CPS collaborate in an intelligent way to obtain and maintain the optimum of the manufacturing process, handle disturbances and adapt to changing conditions. This work, based on a systematic literature review, shows current trends in cyber-physical production systems with a special focus on the role of connectivity and control systems in production. We are looking for the enablers of CPPS focusing on the current evolution of control systems in the smart factory of the future. As a result, a conceptual framework as well as a CPPS control system architecture for the introduction and organisation of enablers for CPPS is presented to support practitioners in the realisation of smart manufacturing control concepts.

The role of the Internet of Things (IoT) in Smart Manufacturing, aimed to illuminate its transformative impact on operational efficiency, responsiveness, and environmental sustainability. The aim of the investigation was to explore IoT's pivotal significance in reshaping manufacturing processes towards heightened efficiency, responsiveness, and environmental consciousness. The study presents results from performance metric assessments, visualizations, and data simulations. It contains information about way IoT data is shown in manufacturing environments, the clear relationship between temperature and pressure, the distribution of security risks and related safety measures, and the dynamic behaviour of important IoT components. The importance of IoT in real-time environmental management, process optimization, and security enhancement within paradigms of smart manufacturing are the key points of observation. The comparative analysis of Conventional Analytical Models and Composite Models highlights the choice between stability and adaptability, providing crucial insights for modeling approaches tailored to distinct manufacturing requirements. IoT's transformative potential within Smart Manufacturing, emphasizing data integrity, security, sensor dynamics, analytics, and sustainability.

As the needs of the modern supply chain industry grows bigger, the technology to deal the complexities demands a simple but an resilient solutions that meets both profitability and yet environmental needs for the organizations, Kirchner (Five ways to weather supply-chain disruptions). Supply Chain Industry have already started their digital transformation journey by adopting futuristic technologies like Machine Learning to sense a specific event across the supply chain and solve problems such as delivery delays and weather-related disruptions in unexpected ways (Kirchner in Five ways to weather supply-chain disruptions). In addition to AI and Machine learning, supply chain industry also warrants new technology like Internet of Things (IoT) and Blockchain to improve the operations excellence by bringing more automation in product monitoring and control along with the application of Smart contract to establish the statement of trust between supply chain stakeholders. This work has been designed to provide readers an comprehensive and yet with critical insights in the context of current advancements and the future vision around the technologies like Internet of Things (IoT), Artificial Intelligence/Machine Learning, Blockchain and Quantum Computing within supply chain domain.KeywordsAIMachine learningIoTValue chainRFIDEPCQR codeTensorflowKerasInboundOutboundBlockchainSmart contractIndustry 4.0

In concert with advances in information and communication technology and their application to manufacturing environments, physical entities in factories are acquiring more intelligence via integration with cyber systems. This integration brings about Cyber-Physical Production Systems and leads to smart manufacturing, the next generation manufacturing paradigm. In the new paradigm, high levels of agility, flexibility, and real-time control make it possible to keep the system running efficiently and self-organized. At the same time, however, it becomes difficult in a self-organized and decentralized system to capture the system's status, evaluate the system's performance, and predict the system's future events. In this article, we suggest improvements to smart manufacturing systems where the intelligence from smart entities could be fully utilized without losing system control. To achieve this goal, a solution for integrating schedule-driven production (push systems) and event-driven production (pull systems) is proposed to optimize both material flow and information flow for manufacturing operations. For each entity in a smart manufacturing system, details of decision making are encapsulated and its status is exposed. The status-based decisions filter out unimportant information and make smart manufacturing systems loosely-coupled and predictable. A simulation case study based on Devices Profile for Web Services [1] is used to illustrate the effectiveness of such an approach. The case study suggests that status-based decisions could be applied to smart manufacturing and that they can be part of an approach that balances the self-organized control with overall performance. Therefore, we can make full use of intelligent entities in lower levels of a factory while keeping the entire system under control.

Cyber-Physical Production Systems (CPPS) and Smart Products are considered key features in the development of the fourth industrial revolution. To create a connected environment in manufacturing based on CPPS, components must be able to store and exchange data with machines, and with other components and assemblies along the entire production system. At the same time, Smart Product features require that products and their components be able to store and exchange data throughout their entire lifecycle. Therefore, the aim of this paper is to present a preliminary integrated component data model based on Unified Modeling Language (UML) for the implementation of CPPS and Smart Product features. The development of the data model is based on requirements gathered both from the literature review and from corporate interviews with potential users. The results are still preliminary since the research results are part of a bigger research effort under an international collaboration network.

The article presents an analysis of the world experience in the use of digital technologies, including in the implementation of the "Industry-4.0" project, and highlights the key areas of use of "smart technologies" in the coal industry. The considered innovative scenario for the development of the digital transformation of the coal industry corresponds to the state’s intention to make the transition from a resource-based economy to an intellectual innovation. Digitalization, cyber-physical production systems and the "Internet of things" systems will be key technologies in the coal industry’s transformation. It was noted that it is necessary to create robotic mines to expand the area of coal production and production in a continuous mode, and coal processing based on the use of production cyber-physical systems, as well as the creation of an industrial Internet network in the coal industry. "Internet of things", automation and robotics of production processes will form the basis for the emergence of new production units - "Intelligent mine (section)", "Intelligent factory" and "Intelligent transport". The central element in the basic processes of mining production will be cyber-physical production systems in the extraction, preparation of reserves, processing and transportation of coal. It is emphasized that a full-fledged joint work of services and specialists in a single information space is necessary, which has difficulties in connection with finding the industry at the first level of "digital maturity". The research highlights the main promising technologies and areas of implementation of the "Industry-4.0" project in the coal industry.

The Internet of Things (IoT) paradigm is revolutionising the world of manufacturing into what is known as Smart Manufacturing or Industry 4.0. The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to automate the prediction of faults, thus cutting maintenance time and cost and improving the product quality. However, faults in real industries are overwhelmingly outweighed by instances of good performance (faultless samples); this bias is reflected in the data captured by IoT devices. Imbalanced data limits the success of ML in predicting faults, thus presents a significant hindrance in the progress of smart manufacturing. Although various techniques have been proposed to tackle this challenge in general, this work is the first to present a framework for evaluating the effectiveness of these remedies in the context of manufacturing. We present a comprehensive comparative analysis in which we apply our proposed framework to benchmark the performance of different combinations of algorithm components using a real-world manufacturing dataset. We draw key insights into the effectiveness of each component and inter-relatedness between the dataset, the application context, and the design of the ML algorithm.

The necessity for decreasing the negative impact of the manufacturing industry has recently increased. This is getting recognized as a global challenge due to the rapid increase in life quality standards, demand, and the decrease in available resources. Thus, manufacturing, as a core of the product provision system and a fundamental pillar of civilized existence, is significantly influenced by sustainability issues. Furthermore, current manufacturing modeling and assessment criteria require intensive revisions and upgrades to keep up with these new challenges. Nearly all current manufacturing models are based on the old paradigm, which was proven to be inadequate. Therefore, manufacturing technology, along with culture and economy, are held responsible for providing new tools and opportunities for building novel resolutions towards a sustainable manufacturing concept. One of such tools is sustainability assessment measures. Revising and updating such tools is a core responsibility of the manufacturing sector to efficiently evaluate and enhance sustainable manufacturing performance. These measures should be adequate to respond to the growing sustainability concerns in pursuit of an integrated sustainability concept. The triple bottom line (TBL) that includes environment, economic, and social dimensions has usually been used to evaluate sustainability. However, there is a lack of standard sets of sustainable manufacturing performance measures. In addition to the sustainability concept, a new concept of smart manufacturing is emerging. The smart manufacturing concept takes advantage of the recent technological leap in Artificial Intelligent (AI), Cloud Computing (CC), and the Internet of Things (IoT). Although this concept offers an important step to boost the current production capabilities to meet the growing need, it is still not clear whether the two concepts of smart manufacturing and sustainability will constructively or destructively interact. Therefore, the current study aims to integrate the sustainable smart manufacturing performance by incorporating sustainable manufacturing measures and discussing current and future challenges that are faced by the manufacturing sector. In addition, the opportunities for future research incorporating sustainable smart manufacturing are also presented.

In the manufacturing industry, technologies of The Fourth Industrial Revolution have been integrated into Smart Factory and is evolving into an intelligent factory. In other words, smart factories create an optimal economic production environment by exchanging information through the Internet of Things and by providing real-time monitoring, analysis of all processes. The recent decline in birthrate, aging population and rising labor costs require automated production plants. At the same time, the more diversi- fied demands of consumers become, the shorter the life cycle of products becomes and the capability of customized mass production has emerged as the core of competitiveness. Therefore, smart factories are an essential product of innovation to adapt to these socioeconomic changes. In addition, smart factories provide breakthrough results such as high quality, reduced defect rate and improved productivity. In this study, we will first explore the cases of the operations of German automated factories, the place of origin of the Fourth Industrial Revolution and the world’s strongest manufacturer, to derive elements that can be benchmarked to Korean small and medium-sized enterprises (SME’s). Based on this, the ultimate purpose of this study is to suggest implications for the governmental projects supporting compa- nies that want to build, maintain and spread smart factories in Korea. In this study, prior literary studies on domestic and foreign papers and research reports and empirical case studies through corporate visits were used as research methodologies. While the existing research is an indirect study through domestic and international literature or information on the Internet, this study is differentiated in that it is an empirical case study of visiting demonstration. The implication is that in terms of corporate strategy, smart factories should first commence with manufacturing processes that require automation and then spread them sequentially in line with the corporate management environment (financial, market competition structure, and growth potential). Furthermore, various information and new technologies in the smart manufacturing process should be actually integrated to create optimal synergy, and various internal and external management resources such as human resources and environmental energy should be planned and implemented in an integrated manner. In terms of policy support, the focus should be on customized modular support that companies need. At the same time, it is also necessary to standardize the process of smart manufacturing through joint cooperative support for smart factories among the same companies and induce mutual utilization of generated data, minimization of use costs and maximum synergy efficiency. In particular, it is necessary to seek ways to utilize idle smart factories when ordering small or excessive amounts of orders. At the same time, expansion of smart factory supporting platforms and managemental aid are also required through a consortium between professional companies that supply new technologies to smart factories (e.g. Robots, Sense, AI, Total solution SW, VR etc.). Environment-friendly smart manu- facturing and optimal utilization of energy and materials should also be considered comprehensively. To this end, it is necessary to have a smart management organization, or a research advisory group, that studies human and environment-friendly smart manufacturing within the “Smart Manufacturing Innovation Promotion Group” to induce social innovation. It will be necessary to minimize the waste of policy support by thoroughly managing and supervising the implementation of the beneficiary entities. Finally, support for the introduction and application of smart factories for SME’s and Midsize Business should be strengthened to promote the smart manufacturing of SME’s to increase their competitiveness.

Currently, sustainable development has become one of the most fundamental priorities for decision-makers and policy formulators to build a healthier environment, an advanced social setting, and a more robust economy. As one of the sectors with the most significant and complex production system pressures worldwide to achieve sustainable development goals, the construction industry has an essential contribution in the context of economic growth and improves living conditions without damaging future generations' opportunities or depleting natural resources. Because of the complexity and wide range of activities in the supply chain in this industry, it is necessary to employ proper methods and approaches to optimize performance, decrease costs, and use resources more efficiently. Thus, adopting new technologies and understanding their attributes offer promising solutions. This research proposes a model for incorporating Integrated Project Delivery and Internet of Things technologies in supply chain management for construction projects in Iran, focusing on sustainable development. The study comprises three phases: (I) Criteria Collection and Validation: Identifying effective parameters from integrating IPD and IoT on supply chain performance with a sustainable development approach; (II) Importance Weight Calculation: Using the fuzzy SWARA method to determine the significance of each criterion and sub-criterion; (III) Objective Prioritization and Solution Proposals: Employing the fuzzy ARAS method to prioritize objectives and propose solutions. The findings indicate that project savings (cost and time) and operational efficiency are top priorities from the perspectives of project management, technical factors, economic factors, and industry growth. Operational efficiency and project savings, with desirability scores of 78.77% and 78.36%, rank first and second, respectively. Supply chain visibility, remote operations, and augmented reality, with desirability scores of 70.77%, 70.66%, and 69.71%, follow in priority for integrating Internet of Things and Integrated Project Delivery in supply chain management performance within the construction industry, focusing on sustainable development.

This paper systematically reviews literature related with digital supply chains (DSC) and investigates the application status and development trend of different digital technologies in supply chain management. The review is conducted from the perspective of seven key digital supply chain technologies, i.e. Internet of Things (IoT) & Radio Frequency Identification (RFID), 5th Generation Mobile Communication Technology (5G), 3D Printing, Big data (BD), Blockchain, Digital Twins (DT), and Intelligent autonomous vehicles (IAVs). It highlights the main limitations and opportunities of the various DSC technologies, provides an overview of prior studies, and identifies knowledge gaps by outlining the advantages, weaknesses and restrictions of individual technology. The paper also aims at providing a development framework as a roadmap for the match of different digital technologies with different strategic goals.

Chapter 6 - Polymer and composites additive manufacturing: material extrusion processes

The Concept behind industry 4.0 is to create common platform to establish the communication between machines in the production/ Manufacturing plant. This can be fulfilled by interconnecting all machines with internet well known as Internet of Things and interconnecting the same with people is known as Internet of people. As the machines communicating with each other is known as cyber-physical production system. This will help the manufacturing industries to make decision in improvements on their product by data collected in past. And, it will help the quality team to analyze the failure of the product in field by back tracking the assembly and testing process. All data will be preserved in centralized server of the manufacturing plant. The key area of Industry 4.0in physics is introduction of 3D printing which gives preview of how a production may look like. Additive manufacturing involves production of functional components with predefined mechanical and thermal properties.The Concept behind industry 4.0 is to create common platform to establish the communication between machines in the production/ Manufacturing plant. This can be fulfilled by interconnecting all machines with internet well known as Internet of Things and interconnecting the same with people is known as Internet of people. As the machines communicating with each other is known as cyber-physical production system. This will help the manufacturing industries to make decision in improvements on their product by data collected in past. And, it will help the quality team to analyze the failure of the product in field by back tracking the assembly and testing process. All data will be preserved in centralized server of the manufacturing plant. The key area of Industry 4.0in physics is introduction of 3D printing which gives preview of how a production may look like. Additive manufacturing involves production of functional components with predefined mechanical and thermal properties.