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Interoperability of the future factory: an overview of concepts and research challenges
... Similarly, 3D virtual environments and discrete event simulation models are proposed for modeling, simulating, and evaluating manufacturing assets [31] in a virtual factory. In contrast, while a digital factory is likely to define its operational boundaries inside the company, a virtual factory extends the factory's capabilities across multiple organizations to provide a unified virtual environment to test, model, and simulate factory layouts and processes [32]. ...
The technological advancements promote the rise of the fourth industrial revolution, where key terms are efficiency, innovation, and enterprises’ digitalization. Market globalization, product mass customization, and more complex products need to reflect on changing the actual design methods and developing business processes and methodologies that have to be data-driven, AI-assisted, smart, and service-oriented. Therefore, there is a great interest in experimenting with emerging technologies and evaluating how they impact the actual business processes. This paper reports a comparison among the major trends in the digitalization of a Factory of the Future, in conjunction with the two major strategic programs of Industry 4.0 and China 2025. We have focused on these two programs because we have had experience with them in the context of the FIRST H2020 project. European industrialists identify the radical change in the traditional manufacturing production process as the rise of Industry 4.0. Conversely, China mainland launched its strategic plan in China 2025 to promote smart manufacturing to digitalize traditional manufacturing processes. The main contribution of this review paper is to report about a study, conducted and part of the aforementioned FIRST project, which aimed to investigate major trends in applying for both programs in terms of technologies and their applications for the factory’s digitalization. In particular, our analysis consists of the comparison between Digital Factory, Virtual Factory, Smart Manufacturing, and Cloud Manufacturing. We analyzed their essential characteristics, the operational boundaries, the employed technologies, and the interoperability offered at each factory level for each paradigm. Based on this analysis, we report the building blocks in terms of essential technologies required to develop the next generation of a factory of the future, as well as some of the interoperability challenges at a different scale, for enabling inter-factories communications between heterogeneous entities.
In Industry 4.0 manufacturing collaborative network, product design processes, manufacturing processes, maintenance processes should be integrated across different factories and enterprises. The collaborative manufacturing network 4.0 allows the amalgamation of manufacturing resources in multiple organizations to operate processes in a collaborative manner for reacting to the fast changes of markets or emergencies. In this paper, we propose a predictive maintenance service as a part of a virtual factory, a form of collaborative manufacturing network. Data-driven predictive maintenance service is built-in FIWARE, an industry 4.0 framework. To optimize predictive maintenance services based on different criteria within a virtual factor, such as geographical locations, similar types of machinery, or cost/time efficiency, etc., we provide our design and implementation to deal with providing better maintenance services and data exchanging across different collaborative partners with different requirements and modularizing of related functions.
The recent advent of novel concepts and technologies, such as the Internet of Things (IoT), Big Data, Augmented Reality, Cloud Computing, and Artificial intelligence is transforming industry and society as a whole. Today, the large amount of data generated requires the design and development of new schemes for extracting valuable information. At the same time, The COVID-19 epidemic is posing unprecedented challenges for businesses, governments and companies around the world. This article refers to a company in Southern Italy that has decided to repurpose its production line to manufacture surgical masks. The situation is completely new for the firm, which does not have historical data. Therefore, the aim is to propose a FIWARE-based IoT architecture for supporting real-time data acquisition and enabling the creation of a digital twin. Preliminary results show that the proposed solution effectively helps the company in starting the new business. Furthermore, the use of digital twin-based real-time dashboards enables continuous and agile improvement in the management of warehouse, production and maintenance activities.
The H2020 FIRST project addresses the virtual factories, which are digital abstractions of real factories. The exploitation of virtual factories enables interoperability between real components inside a factory as well as between different factories belonging to the same supply chain. Moreover, virtual factories can be exploited to manage and compose services inside a factory, defining dynamic adaptation of set of services depending on high-level goals. In this paper we sketch the project results and its current state.
Industry 4.0 has shifted the manufacturing related processes from conventional processes within one organization to collaborative processes across different organizations. For example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. This complex and competitive collaboration requires the underlying system architecture and platform to be flexible and extensible to support the demands of dynamic collaborations as well as advanced functionalities such as big data analytics. Both operation and condition of the production equipment are critical to the whole manufacturing process. Failures of any machine tools can easily have impact on the subsequent value-added processes of the collaboration. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. In this context, this paper explores how the FIWARE framework supports predictive maintenance. Specifically, it looks at applying a data driven approach to the Long Short-Term Memory Network (LSTM) model for machine condition and remaining useful life to support predictive maintenance using FIWARE framework in a modular fashion.
Nowadays, the introduction of digital technologies in the manufacturing industries paved the way to the Fourth Industrial Revolution, also known as Industry 4.0, fostering the evolution of traditional industrial systems to the concept of smart manufacturing. In last years, many reference models to describe the features of the smart factories have been proposed in literature. This paper aims at presenting an overview of these models in order to find the gaps in the research about the Industry 4.0 concepts. In particular, the business transformation required to evolve the traditional manufacturing systems into Industry 4.0-ready ones is discussed. Finally, future research topics are proposed.
The global industrial landscape has changed deeply in the last few years due to successive technological developments and innovations in manufacturing processes. The Industry 4.0 concept has emerged and the academic literature has paid an increased attention to this topic, which remains non-consensual or ill defined. In this research, a literature review is made to understand this concept in its technological dimension, and to comprehend its impacts. This new industrial paradigm brings together the digital and physical worlds through the Cyber-Physical Systems enhanced by Internet of Things and it is expected that this novel has consequences on industry, markets and economy, improving production processes and increasing productivity, affecting the whole product lifecycle, creating new business models, changing the work environment and restructuring the labor market. Therefore, this paper focuses on Industry 4.0 concept and contributes for its clarification and further understanding about the importance and implications of this complex technological system.
This paper proposes smart manufacturing systems for tailor-made rubber runninng shoes production. With the development of information and communication technology, manufacturing machines and products embedded with intelligent sensing devices can connect to each other, and acquire the current conditions and changes of each machine and product. Drastic changes in production fields based on the IoT (Internet of Things) technology have been occurred, and then several countries and companies have been tackling the development of smart manufacturing system such as Industrie 4.0 and Industrial Internet Consortium (IIC). This paper focuses a concept of value co-creation for improving users’ delights, and describes the constructed smart factory model system based on Cyber-Physical Systems (CPS). The current conditions and changes of the simulated production machines and products can be acquired using RF-ID systems. Our smart factory is implemented using a multi-agent system and managed in accordance with the mechanism of the autonomous negotiation such as a combinatorial auction mechanism.
The vision of smart factory is based on the notion of Industry 4.0 that denotes technologies and concepts related to cyber-physical systems and the Internet of Things (IoT). In smart factories cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, cyber-physical systems communicate and cooperate with each other in real time. This paper presents a smart factory architecture based on communication and computing layers that embed scheduling mechanisms within a mechanical shop floor. Every physical entity in the shop floor is seen as an autonomous intelligent agent that performs tasks guided by dynamic scheduling functions. A test bed has been set up to show how physical entities can be cooperative and autonomous units that can automatize the shop floor operation processes. The results verify the feasibility and efficiency of proposed method.
In recent years, Industry 4.0 has been introduced as a popular term to describe the trend towards digitisation and automation of the manufacturing environment. Despite its potential benefits in terms of improvements in productivity and quality, this concept has not gained much attention in the construction industry. This development is founded in the fact that the far-reaching implications of the increasingly digitised and automated manufacturing environment are still widely unknown. Against this backdrop, the primary objective of this paper is to explore the state of the art as well as the state of practice of Industry 4.0 relating technologies in the construction industry by pointing out the political, economic, social, technological, environmental and legal implications of its adoption. In this context, we present the results of our triangulation approach, which consists of a comprehensive systematic literature review and case study research, by illustrating a PESTEL framework and a value chain model. Additionally, we provide recommendations for further research within a research agenda.
With the application of Internet of Things and services to manufacturing, the fourth stage of industrialization, referred to as Industrie 4.0, is believed to be approaching. For Industrie 4.0 to come true, it is essential to implement the horizontal integration of inter-corporation value network, the end-to-end integration of engineering value chain, and the vertical integration of factory inside. In this paper, we focus on the vertical integration to implement flexible and reconfigurable smart factory. We first propose a brief framework that incorporates industrial wireless networks, cloud, and fixed or mobile terminals with smart artifacts such as machines, products, and conveyors. Then, we elaborate the operational mechanism from the perspective of control engineering, that is, the smart artifacts form a self-organized system which is assisted with the feedback and coordination blocks that are implemented on the cloud and based on the big data analytics. In addition, we outline the main technical features and beneficial outcomes and present a detailed design scheme. We conclude that the smart factory of Industrie 4.0 is achievable by extensively applying the existing enabling technologies while actively coping with the technical challenges.
Cloud manufacturing is a new concept extending and adopting the concept of Cloud computing for manufacturing. The aim is to transform manufacturing businesses to a new paradigm in that manufacturing capabilities and resources are componentized, integrated and optimized globally. This study presents an interoperable manufacturing perspective based on Cloud manufacturing. A literature search has been undertaken regarding Cloud architecture and technologies that can assist Cloud manufacturing. Manufacturing resources and capabilities are discussed in terms of Cloud service. A service-oriented, interoperable Cloud manufacturing system is proposed. Service methodologies are developed to support two types of Cloud users, i.e., customer user and enterprise user, along with standardized data models describing Cloud service and relevant features. Two case studies are undertaken to evaluate the proposed system. Cloud technology brings into manufacturing industry with a number of benefits such as openness, cost-efficiency, resource sharing and production scalability.
This paper demonstrates a new methodology for designing a virtual factory model and model execution on the basis of a real schedule plan. The main characteristic of the developed method is that the inputs are regarded as one of the main parameters of the production process, and the main objective is to create a low-cost production process model. The methodology is adjusted for use in SMEs (Small and Medium-Sized Enterprises) with individual or unique type of production. For such companies, the method represents an ability to optimize existing production processes through detecting and eliminating possible errors and disturbances before the real production process is executed at an acceptable cost. The applicability and suitability of the developed method for virtual production performance has been proven with the verification process, where the input data for the simulation was obtained from a real company. The simulation results have shown that the presented methodology is a useful tool for the optimization of the production process. (Received in June 2013, accepted in September 2013. This paper was with the authors 1 month for 1 revision.).
Structured methodologies and tools for the tailored design of factories are more and more adopted by suppliers of manufacturing systems but usually discontinued after the design phase. The use of an ontology-based virtual factory, continuously synchronized with the real plant, is proposed to guarantee digital continuity and enable in situ simulation during the operating phase of a factory. This digital counterpart of the system can be used for integrated shop-floor simulations to assess future impact of production and maintenance planning decisions. An industrial application is provided in the context of roll shops, i.e., systems devoted to the grinding of cylinders for rolling mills.
Industry 4.0 combines the strengths of traditional industries with cutting edge internet technologies. It embraces a set of technologies enabling smart products integrated into intertwined digital and physical processes. Therefore, many companies face the challenge to assess the diversity of developments and concepts summarized under the term industry 4.0. The paper presents the result of a study on the potential of industry 4.0. The use of current technologies like Big Data or cloud-computing are drivers for the individual potential of use of Industry 4.0. Furthermore mass customization as well as the use of idle data and production time improvement are strong influence factors to the potential of Industry 4.0. On the other hand business process complexity has a negative influence.
Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.
Information systems support organisations to achieve greater efficiency by automating their activities. Nowadays, in the actual competitive and global business context, the advent of enterprise networking has been challenging collaboration, coordination and continuous interactions among dissimilar information systems to adapt and improve them. Sustainability of interoperability among heterogeneous systems regarding sharing information and knowledge in a collaborative dynamic environment is hard to achieve and maintain. This paper proposes a service-based negotiation framework for advanced collaboration in enterprise networks, as a solution to improve the sustainability of interoperability within enterprise information systems. Validation in industrial scenario is presented and discussed.
One of the most significant directions in the development of computer science and information and communication technologies is represented by Cyber-Physical Systems (CPSs) which are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the internet. Cyber-Physical Production Systems (CPPSs), relying on the newest and foreseeable further developments of computer science, information and communication technologies on the one hand, and of manufacturing science and technology, on the other, may lead to the 4th Industrial Revolution, frequently noted as Industry 4.0. The key-note will underline that there are significant roots generally – and particularly in the CIRP community – which point towards CPPSs. Expectations and the related new R&D challenges will be outlined.
The world's energy consumption has doubled over the past 40 years and it is estimated that one-third comes from industry. Therefore, an increase of the efficiency in energy use in industries would greatly benefit the sustainability of the factories and consequently of the whole environment and society. A factory is a complex entity constituted by possibly networked plants which produce a set of products performing several processes requiring a set of production resources. All these aspects need to be considered as a whole especially is sustainability is of concern. However, this need implies the collaboration between several actors and tools having remarkably different competences and scopes. This paper presents a holistic framework, named Sustainable Factory Semantic Framework (SuFSeF), aiming at integrating digital models and tools to support the design and management of a sustainable factory thanks to its complete virtual representation. This framework extends the Virtual Factory Framework (VFF), outcome of a European research project, by characterizing the industrial building and considering energy and environmental sustainability of the factory during its lifecycle. Both commercial and prototypal software tools can be integrated in the framework. Specifically, the attention will be focused on tools to support the sustainability assessment during the factory design phase, 3D design tools, and the monitoring of the key energy-environmental indicators during the factory operating phase.
The majority of research and commercial efforts in computer-based methods for design and manufacture relate to applications in Computer Aided Design (CAD), whilst research in Computer-Automated Process Planning (CAPP) has substantially enriched the design knowledge domain with concepts that originated in the manufacturing domain. The development of isolated computer applications was recognized as a potential problem, leading to the definition of the Computer Integrated Manufacturing (CIM) concept, seeking to achieve the local integration of systems. The introduction and development of the internet have since provided a decisive advancement in the communications infrastructure. We can now capitalize on recent advances in computer graphic visualization and distributed information management systems, in order to positively impact product development and realization and to develop objective risk mitigation strategies on a global basis. In this context, Digital Enterprise Technology (DET) can be defined as 'the collection of systems and methods for the digital modelling of the global product development and realization process, in the context of lifecycle management'. This paper introduces the concept of DET and reviews the status of the design and manufacturing planning integration that is fundamental for the definition and development of DET methods and systems.
The recently posed challenge of developing an Enterprise Interoperability Science Foundation EISF prompted some academic agents to attempt a systematisation of the Interoperability Body of Knowledge IBoK. Still in their embryonic stages, these efforts ...
Design and operation of a manufacturing enterprise involve numerous types of decision-making at various levels and domains. A complex system has a large number of design variables and decision-making requires real-time data collected from machines, processes, and business environments. Enterprise systems (ESs) are used to support data acquisition, communication, and all decision-making activities. Therefore, information technology (IT) infrastructure for data acquisition and sharing affects the performance of an ES greatly. Our objective is to investigate the impact of emerging Internet of Things (IoT) on ESs in modern manufacturing. To achieve this objective, the evolution of manufacturing system paradigms is discussed to identify the requirements of decision support systems in dynamic and distributed environments; recent advances in IT are overviewed and associated with next-generation manufacturing paradigms; and the relation of IT infrastructure and ESs is explored to identify the technological gaps in adopting IoT as an IT infrastructure of ESs. The future research directions in this area are discussed.
The problem of factory sustainability is commonly addressed by focusing on specific aspects related to products, processes or production resources, while the impact of the building and facilities is usually neglected even though it counts for 40% of the total world’s energy consumption. This paper presents a holistic framework based on an integrated collaborative virtual environment that facilitates the sharing of the complete factory information and knowledge between various software tools, supporting the sustainable design and management of all the factory entities. In particular, the attention is focused on the Semantic Data Model that provides a semantic representation of the data and knowledge required for sustainability assessment.
The growing importance of manufacturing SMEs within the European economy, in terms of Gross Domestic Product and number of jobs, emphasizes the need of proper ICT tools to support their competitiveness. Major ICT players already offer one-does-all Product Lifecycle Management suites, however, these do show consistent shortcomings in terms of SME accessibility, degree of personalization and they often lack of an acceptable level of interoperability. These problems are being addressed by the development of a Virtual Factory Framework (VFF). The approach is based on four pillars: (1) Semantic Shared Data Model, (2) Virtual Factory Manager (VFM), (3) Decoupled Software Tools and (4) Integration of Knowledge. This paper will focus on the Virtual Factory Manager that acts as a server supporting the I/O communications within the framework and its stored knowledge for the Decoupled Software Tools needing to access its repository.
Competitive manufacturing companies have to effectively deal with the concurrent evolution of products, processes and production systems. This problem, known as Co-evolution, can be addressed only through the integrated use of different methodologies, provided that the digital tools implementing these methodologies can interoperate properly and effectively. This paper presents the concept of an integrated framework to support the interoperability between digital factory tools and shows how it can benefit the business processes along the whole factory life-cycle.
Modelling, simulation and evaluation of manufacturing systems are relevant activities that may strongly impact on the competitiveness of production enterprises both during the design and the operational phases. This paper addresses the application of a semantic data model for virtual factories to support the design and the performance evaluation of manufacturing systems, while exploiting the interoperability between various Digital Enterprise Technology tools. The paper shows how a shared ontology-based framework can be used to generate consistent 3D virtual environments and discrete event simulation models, demonstrating this way how the proposed solution can provide an interoperable backbone for heterogeneous software tools.
Unexpected disturbances and local decisions almost always deteriorate the execution of manufacturing plans. Digital enterprise technologies are hard to use, due to the complexity of production and the frequently changing circumstances. One of the main goals of the research described in the paper is the automatic model building of the discrete-event simulation system, based on intelligent analysis of the huge amount of information incorporated in the production database. The developed solution supports shop-floor dispatching and shop-floor managers in making control decisions.
One main characteristic of virtual enterprises are short-term collaborations between business partners to provide efficient and individualized services to customers. The MOVE project targets at a methodology and a software framework to support such flexible collaborations based on process oriented design and communication by Web services. MOVE'S framework supports the graphical design and verification of business processes, the execution and supervision of processes in transaction-oriented environment, and the dynamic composition and optimization of processes. A business process may be composed from a set of Web services, deployed itself as Web service and executed in the framework. The composition of processes from Web services is implemented with methods from AI-planning. We apply answer set programming (ASP) and map Web service descriptions and customer requests into the input language of the ASP software DL V. Composition goals and constraints guide a composition challenge. We show the performance of our program and give some implementation details. Finally we conclude with some insights.
In recent times, traditional manufacturing is upgrading and adopting Industry 4.0, which supports computerization of manufacturing by round-the-clock connection and communication of engineering objects. Consequently, Decisional DNA-based knowledge representation of manufacturing objects, processes, and system is achieved by virtual engineering objects (VEO), virtual engineering processes (VEP), and virtual engineering factories (VEF), respectively. In this study, assimilation of VEO-VEP-VEF concept in the Cyber-physical system-based Industry 4.0 is proposed. The planned concept is implemented on a case study. Also, Decisional DNA features such as similarity identification and phenotyping are explored for validation. It is concluded that this approach can support Industry 4.0 and can facilitate in real time critical, creative, and effective decision making.
Today, the manufacturing industry is aiming to improve competitiveness through the convergence with cutting-edge ICT technologies in order to secure a new growth engine. Smart Manufacturing, which is the fourth revolution in the manufacturing industry and is also considered as a new paradigm, is the collection of cutting-edge technologies that support effective and accurate engineering decision-making in real time through the introduction of various ICT technologies and the convergence with the existing manufacturing technologies. This paper surveyed and analyzed various articles related to Smart Manufacturing, identified the past and present levels, and predicted the future. For these purposes, 1) the major key technologies related to Smart Manufacturing were identified through the analysis of the policies and technology roadmaps of Germany, the U.S., and Korea that have government-driven leading movements for Smart Manufacturing, 2) the related articles on the overall Smart Manufacturing concept, the key system structure, or each key technology were investigated, and, finally, 3) the Smart Manufacturing-related trends were identified and the future was predicted by conducting various analyses on the application areas and technology development levels that have been addressed in each article.
Cloud manufacturing is a service-oriented, customer-centric and demand-driven process with well-established industrial automation. Even though, it does not necessarily mean the absence of human beings. Due to products and their corresponding manufacturing processes becoming increasingly complex, operators' daily working lives are also becoming more difficult. Enhanced human–machine interaction is one of the core areas for the success of the next generation of manufacturing. However, the current research only focuses on the automation and flexibility features of cloud manufacturing, the interaction between human and machine and the value co-creation among operators is missing. Therefore, a new method is needed for operators to support their work, with the objective of reducing the time and cost of machine control and maintenance. This paper describes a practical demonstration that uses the technologies of the Internet of things (IoT), wearable technologies, augmented reality, and cloud storage to support operators' activities and communication in discrete factories. This case study exhibits the capabilities and user experience of smart glasses in a cloud manufacturing environment, and shows that smart glasses help users stay productive and engaged.
Due to market pressures, manufacturing companies are forced to find new ways to remain competitive. Dynamic manufacturing networks (DMNs) could be a solution for this task. One of the main challenges during the process of building up and managing a DMN is the exchange of required data between the partners of such a DMN. However, the efficient exchange of relevant data between the partners within a DMN is a hard endeavour. Within this paper the authors show how a cloud-based integration platform for DMNs is able to effectively support the process of data exchange within a DMN. The major components of this platform and their functions are also discussed. Furthermore this paper presents how a cloud-based integration platform allows SMEs, even if they lack in proper production IT, to be part of a DMN that heavily relies on current production data.
With the process of globalisation and the development of management models and information technology, enterprise cooperation and collaboration has developed from intra-enterprise integration, outsourcing and inter-enterprise integration, and supply chain management, to virtual enterprises and enterprise networks. Some midfielder enterprises begin to serve for different supply chains. Therefore, they combine related supply chains into a complex enterprise network. The main challenges for enterprise network’s integration and collaboration are business process and data fragmentation beyond organisational boundaries. This paper reviews the requirements of enterprise network’s integration and collaboration, as well as the development of new information technologies. Based on service-oriented architecture (SOA), collaboration modelling and collaboration agents are introduced to solve problems of collaborative management for service convergence under the condition of process and data fragmentation. A model-driven methodology is developed to design and deploy the integrating framework. An industrial experiment is designed and implemented to illustrate the usage of developed technologies in this paper.
Smart Manufacturing is the dramatically intensified and pervasive application of networked information-based technologies throughout the manufacturing and supply chain enterprise. The defining technical threads are time, synchronization, integrated performance metrics and cyber-physical–workforce requirements. Smart Manufacturing responds and leads to a dramatic and fundamental business transformation to demand-dynamic economics keyed on customers, partners and the public; enterprise performance and variability management; real-time integrated computational materials engineering and rapid qualification, demand-driven supply chain services; and broad-based workforce involvement. IT-enabled Smart factories and supply networks can better respond to national interests and strategic imperatives and can revitalize the industrial sector by facilitating global competitiveness and exports, providing sustainable jobs, radically improving performance, and facilitating manufacturing innovation.
This paper deals with the enterprise integration in the area of manufacturing operations and management. It aims at presenting an overview of an international standardization initiative carried out by the joint working group JWG15 of IEC and ISO. It is concerned with the development of a multipart standard, named IEC 62264: Enterprise-Control system integration. After a brief introduction on the background, goal and approach, some hierarchy models are reviewed, which are used as a basis to define the scope of the standard. Then, a functional data-flow model, object models and associated data attributes are outlined. The activity models of manufacturing operations management are presented. The focus of the paper is on the methodological approach to develop the standard and its underlying concepts. Future works and conclusions are given at the end of the paper.
Rapid product/process realization and enterprise integration have been identified among the major imperatives for enabling the next generation manufacturing paradigm. This paper proposes a virtual factory modeling approach to support these imperatives. A virtual factory is defined as an integrated simulation model of major subsystems in a factory that considers the factory as a whole and provides an advanced decision support capability. It seeks to go beyond the typical modeling of one sub-system at a time, such as the manufacturing model, the business process model and/or the communication network model developed individually and in isolation. A basic virtual factory model of a semi-conductor backend factory has been developed for concept demonstration. Application examples are used to demonstrate the integration between business processes and manufacturing system performance. Future work will move further towards the development of the complete virtual factory and its industry applications.
The Stuttgart Model of adaptive, transformable and virtual factories, already implemented in German basic research performed
at the Universität Stuttgart has been extended with a new perspective, the so-called “Smart Factory”. The Smart Factory approach
is a new dimension of multi-scale manufacturing by using the state-of-the-art ubiquitous/pervasive computing technologies
and tools. The Smart Factory represents a context-sensitive manufacturing environment that can handle turbulences in real-time
production using decentralized information and communication structures for an optimum management of production processes.
This paper presents our research steps and future work in giving reality to the envisioned Smart Factory at the Universität
Stuttgart.
The comprehensive approach to the digital factory has become a subject of paramount importance to all major automotive companies, and the chances offered by it are numerous. However, extensive preliminary work is still necessary and thus requires a tremendous effort, especially for small and medium-sized enterprises (SME) which must be integrated as suppliers of components. The purposes of the present article are to describe, in a general way, how the vision of the digital factory can be implemented in reality and to outline the problems, which must still be expected in the further course of the endeavour. In addition, the status of research relating to the digital factory at IMAB, Anlagenprojektierung und Materialflusslogistik at the Technical University of Clausthal, and the fields of future developmental activity are illustrated through the use of an example.
The paper defines and clarifies basic concepts of enterprise architectures. Then an overview on architectures for enterprise integration developed since the middle of the 1980s is presented. The main part of the paper focuses on the recent developments on architectures for enterprise interoperability. The main initiatives and existing works are presented. Future trends and some research issues are discussed and conclusions are given at the end of the paper.
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