Industry 4.0 is becoming more and more important for manufacturers as the developments in the area of Internet of Things advance. Another technology gaining more attention is data stream processing systems. Although such streaming frameworks seem to be a natural fit for Industry 4.0 scenarios, their application in this context is still low. The contributions in this paper are threefold. Firstly, we present industry findings that we derived from site inspections with a focus on Industry 4.0. Moreover, our view on Industry 4.0 and important related aspects is elaborated. As a third contribution, we illustrate our opinion on why data stream processing technologies could act as an enabler for Industry 4.0 and point out possible obstacles on this way.
A recent work developed a framework called Production Assessment 4.0, which aims to support enterprises developing Industry 4.0 use cases. For doing so, they made use of the design thinking approach. After elaborating on the framework and its processes, a section about its evaluation is presented. Production Assessment 4.0 was evaluated in several consulting projects with enterprises. However, no details about, e.g., their data characteristics or their state of Industry 4.0 adoption progress are given @cite_17 .
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Facing a wide range of new technologies and best practices within Industry 4.0, companies are seeking a systematic approach to identify potential application scenarios in their production. Best practices are often customized use cases – generally too specific to apply in a different manufacturing setting right away. The Production Assessment 4.0 presents a pragmatic approach to support companies to develop Industry 4.0 use cases in their factories. The approach follows the Design Thinking method and focus especially on the human role as a key aspect in the use cases designing process. The increasing integration of the Internet of Everything into the industrial value chain has built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted understanding of the term does not exist. As a result, discussing the topic on an academic level is difficult, and so is implementing Industrie 4.0 scenarios. Based on a quantitative text analysis and a qualitative literature review, the paper identifies design principles of Industrie 4.0. Taking into account these principles, academics may be enabled to further investigate on the topic, while practitioners may find assistance in identifying appropriate scenarios. A case study illustrates how the identified design principles support practitioners in identifying Industrie 4.0 scenarios. Abstract Lean Production is widely recognized and accepted in the industrial setting. It concerns the strict integration of humans in the manufacturing process, a continuous improvement and focus on value-adding activities by avoiding waste. However, a new paradigm called Industry 4.0 or the fourth industrial revolution has recently emerged in the manufacturing sector. It allows creating a smart network of machines, products, components, properties, individuals and ICT systems in the entire value chain to have an intelligent factory. So, now a question arises if, and how these two approaches can coexist and support each other. Under the context of Industrie 4.0 (I4.0), future production systems provide balanced operations between manufacturing flexibility and efficiency, realized in an autonomous, horizontal, and decentralized item-level production control framework. Structured interoperability via precise formulations on an appropriate degree is crucial to achieve software engineering efficiency in the system life cycle. However, selecting the degree of formalization can be challenging, as it crucially depends on the desired common understanding (semantic degree) between multiple parties. In this paper, we categorize different semantic degrees and map a set of technologies in industrial automation to their associated degrees. Furthermore, we created guidelines to assist engineers selecting appropriate semantic degrees in their design. We applied these guidelines on publicly available scenarios to examine the validity of the approach, and identified semantic elements over internally developed use cases concerning plug-and-produce. Under the context of Industrie 4.0 (I4.0), future production systems provide balanced operations between manufacturing flexibility and efficiency, realized in an autonomous, horizontal, and decentralized item-level production control framework. Structured interoperability via precise formulations on an appropriate degree is crucial to achieve engineering efficiency in the system life cycle. However, selecting the degree of formalization can be challenging, as it crucially depends on the desired common understanding (semantic degree) between multiple parties. In this paper, we categorize different semantic degrees and map a set of technologies in industrial automation to their associated degrees. Furthermore, we created guidelines to assist engineers selecting appropriate semantic degrees in their design. We applied these guidelines on publically available scenarios to examine the validity of the approach, and identified semantic elements over internally developed use cases targeting semantically-enabled plug-and-produce. Modeling engineering knowledge explicitly and representing it by means of standardized modeling languages and in machine-understandable form enables advanced engineering processes in industrial and factory automation. This affects positively both process and product quality. In this paper we explore how the AutomationML format, an emerging data exchange standard, that supports the Industry 4.0 vision, can be represented by means of two established modeling approaches - Model-Driven Engineering (MDE) and Semantic Web. We report observed differences w.r.t. resulting model features and model creation process and, additionally, present the application possibilities of the developed models for engineering process improvement in a production system engineering context. Recent advances in ICT enable the evolution of the manufacturing industry to meet the new requirements of the society. Cyber-physical systems, Internet-of-Things (IoT), and Cloud computing, play a key role in the fourth industrial revolution known as Industry 4.0. The microservice architecture has evolved as an alternative to SOA and promises to address many of the challenges in software development. In this paper, we adopt the concept of microservice and describe a framework for manufacturing systems that has the cyber-physical microservice as the key construct. The manufacturing plant processes are defined as compositions of primitive cyber-physical microservices adopting either the orchestration or the choreography pattern. IoT technologies are used for system integration and model-driven engineering is utilized to semi-automate the development process for the industrial engineer, who is not familiar with microservices and IoT. Two case studies demonstrate the feasibility of the proposed approach. This paper presents our architectural approach to building a collaborative Industry 4.0 platform that enables IoT-based real-time monitoring, optimization and negotiation in manufacturing supply chains. The platform is called NIMBLE and is currently being developed in a European research project. The presented approach utilizes microservice technology, and implements the core business functionality of the platform through a composition of decentralized, scalable services. The communication among services, and with platform users, manufacturers, suppliers, sensors and Web resources, is supported through simple protocols and lightweight mechanisms. Core business services of the implemented architecture are released as open source software, enabling multiple prospective platform-providers to establish B2B marketplaces for collaboration within their own industrial sector or region. To demonstrate microservices in practice, we present two scenarios, both related to manufacturing of wooden home buildings: one is IoT-based data sharing in a supply chain, and the other deals with product driven logistics planning. The further development of the platform will be driven by the requirements of at least four different use cases throughout Europe, and by incorporating advanced business models to support the growth of powerful network effects of the platform. Software process improvement (SPI) is challenging, particularly for small and medium sized enterprises. Most existing SPI frameworks are either too expensive to deploy, or do not take an organizations' specific needs into consideration. There is a need for light weight SPI frameworks that enable practitioners to base improvement efforts on the issues that are the most critical for the specific organization. This paper presents a step-by-step guide to process assessment and improvement planning using improvement framework utilizing light weight assessment and improvement planning (iFLAP), aimed at practitioners undertaking SPI initiatives. In addition to the guide itself the industrial application of iFLAP is shown through two industrial cases. iFLAP is a packaged improvement framework, containing both assessment and improvement planning capabilities, explicitly developed to be light weight in nature. Assessment is performed by eliciting improvements issues based on the organization's experience and knowledge. The findings are validated through triangulation utilizing multiple data sources. iFLAP actively involves practitioners in prioritizing improvement issues and identifying dependencies between them in order to package improvements, and thus establish a, for the organization, realistic improvement plan. The two cases of iFLAP application in industry are presented together with lessons learned in order to exemplify actual use of the framework as well as challenges encountered. Background: The Expanded Disability Status Scale (EDSS) is widely used to rate multiple sclerosis (MS) disability, but lack of disease duration information limits utility in assessing severity. EDSS ranking at specific disease durations was used to devise the MS Severity Score, which is gaining popularity for predicting outcomes. As this requires validation in longitudinal cohorts, we aimed to assess the utility of EDSS ranking as a predictor of 5-year outcome in the MSBase Registry. Methods: Rank stability of EDSS over time was examined in the MSBase Registry, a large multicentre MS cohort. Scores were ranked for 5-year intervals, and correlation of rank across intervals was assessed using Spearman's rank correlation. EDSS progression outcomes at 10 years were disaggregated by 5-year EDSS scores. Results: Correlation coefficients for EDSS rank over 5-year intervals increased with MS duration: years 1-6=0.55, years 4-9=0.74, years 7-12=0.80 and years 10-15=0.83. EDSS progression risk at 10 years after onset was highly dependent on EDSS at 5 years; one-point progression risk was greater for EDSS score of >2 than ≤2. Two-point progression was uncommon for EDSS score of <2 and more common at EDSS score of 4. Conclusions: EDSS rank stability increases with disease duration, probably due to reduced relapses and less random variation in later disease. After 4 years duration, EDSS rank was highly predictive of EDSS rank 5 years later. Risk of progression by 10 years was highly dependent on EDSS score at 5 years duration. We confirm the utility of EDSS ranking to predict 5-year outcome in individuals 4 years after disease onset. We discuss the key role of semantic technologies for the mass customization of smart products, smart data, and smart services. It is shown that new semantic representation languages for the description of services and product memories like USDL and OMM provide the glue for integrating the Internet of Things, the Internet of Data, and the Internet of Services. Semantic service matchmaking in cyber-physical production systems is presented as a key enabler of the disruptive change in the production logic for Industrie 4.0. Finally, we discuss the platform stack for mass customization and show how a customized smart product can serve as a platform for personalized smart services that are based on smart data provided by the connected product. Small and medium enterprises are a very important cog in the gears of the world economy. The software industry in most countries is composed of an industrial scheme that is made up mainly of small and medium software enterprises--SMEs. To strengthen these types of organizations, efficient Software Engineering practices are needed--practices which have been adapted to their size and type of business. Over the last two decades, the Software Engineering community has expressed special interest in software process improvement (SPI) in an effort to increase software product quality, as well as the productivity of software development. However, there is a widespread tendency to make a point of stressing that the success of SPI is only possible for large companies. In this article, a systematic review of published case studies on the SPI efforts carried out in SMEs is presented. Its objective is to analyse the existing approaches towards SPI which focus on SMEs and which report a case study carried out in industry. A further objective is that of discussing the significant issues related to this area of knowledge, and to provide an up-to-date state of the art, from which innovative research activities can be thought of and planned. Cyber-physical systems and the Internet-of-Things are getting more and more traction in different application areas. Boosted by initiatives such as Industrie 4.0 in Germany or the Industrial Internet Consortium in the US, they are enablers for innovation in industrial automation. To provide the advanced flexibility in production envisioned for future automation systems, Programmable Logic Controllers (PLCs), as one of their main building blocks, also need to become more flexible. However, the conservative nature of this domain prohibits changes in the controller architecture impacting the installed base. Currently there exist various approaches that evolve control architectures to the next level, but none of them address flexible function deployment at the same time with legacy support. In this paper, we present a an architecture for a multi-purpose controller that is inspired by the virtualization trend in cloud systems which moves from heavyweight virtual machines to lightweight containers solutions such as LXC or Docker. Our solution includes the support for multiple PLC execution engines and adds support for the emulation of legacy engines as well. We evaluate this architecture by executing performance measurements that analyze the impact of container technologies to the real-time aspects of PLC engines. Abstract Cyber-physical systems and the Internet-of-Things are getting more and more traction in different application areas. Boosted by initiatives such as Industrie 4.0 in Germany or the Industrial Internet Consortium in the US, they are enablers for innovation in industrial automation. To provide the advanced flexibility in production envisioned for future automation systems, Programmable Logic Controllers (PLCs), as one of their main building blocks, also need to become more flexible. However, the conservative nature of this domain prohibits changes in the controller architecture impacting the installed base. Currently there exist various approaches that evolve control architectures to the next level, but none of them address flexible function deployment at the same time with legacy support. In this paper, we present an architecture for a multi-purpose controller that is inspired by the virtualization trend in cloud systems which moves from heavyweight virtual machines to lightweight containers solutions such as LXC or Docker. Our solution includes the support for multiple PLC execution engines and adds support for the emulation of legacy engines as well. We evaluate this architecture by executing performance measurements that analyze the impact of container technologies to the real-time aspects of PLC engines. State-of-the-art statistical machine translation (MT) systems have made significant progress towards producing user-acceptable translation output. However, there is still no efficient way for MT systems to inform users which words are likely translated correctly and how confident it is about the whole sentence. We propose a novel framework to predict word-level and sentence-level MT errors with a large number of novel features. Experimental results show that the MT error prediction accuracy is increased from 69.1 to 72.2 in F-score. The Pearson correlation between the proposed confidence measure and the human-targeted translation edit rate (HTER) is 0.6. Improvements between 0.4 and 0.9 TER reduction are obtained with the n-best list reranking task using the proposed confidence measure. Also, we present a visualization prototype of MT errors at the word and sentence levels with the objective to improve post-editor productivity. Online software upgrades are often plagued by runtime behaviors that are poorly understood and difficult to ascertain. For example, the interactions among multiple versions of the software expose the system to race conditions that can introduce latent errors or data corruption. Moreover, industry trends suggest that online upgrades are currently needed in large-scale enterprise systems, which often span multiple administrative domains (e.g., Web 2.0 applications that rely on AJAX client-side code or systems that lease cloud-computing resources). In such systems, the enterprise does not control all the tiers of the system and cannot coordinate the upgrade process, making existing techniques inadequate to prevent mixed-version races. In this paper, we present an analytical framework for impact assessment, which allows system administrators to directly compare the risk of following an online-upgrade plan with the risk of delaying or canceling the upgrade. We also describe an executable model that implements our formal impact assessment and enables a systematic approach for deciding whether an online upgrade is appropriate. Our model provides a method of last resort for avoiding undesirable program behaviors, in situations where mixed-version races cannot be avoided through other technical means. Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines and assembly lines generate large amounts of data and effective visual exploration techniques are needed for troubleshooting, process optimization, and decision making. However, developing effective visual analytics solutions for this application domain is a challenging task due to the sheer volume and the complexity of the data collected in the manufacturing processes. We report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of assembly line performance and historical data exploration to identify inefficiencies, locate anomalies, and form hypotheses about their causes and effects. The system is designed based on a set of requirements gathered through discussions with the managers and operators from manufacturing sites. It features interlinked views displaying data at different levels of detail. In particular, we apply and extend the Marey's graph by introducing a time-aware outlier-preserving visual aggregation technique to support effective troubleshooting in manufacturing processes. We also introduce two novel interaction techniques, namely the quantiles brush and samples brush, for the users to interactively steer the outlier detection algorithms. We evaluate the system with example use cases and an in-depth user interview, both conducted together with the managers and operators from manufacturing plants. The result demonstrates its effectiveness and reports a successful pilot application of visual analytics for manufacturing in smart factories. This work is licensed under a Creative Commons Attribution 4.0 International Licence. Page numbers and proceedings footer are added by the organisers. Licence details: http: creativecommons.org licenses by 4.0 Abstract Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture is proposed as a guideline for implementation of CPS. Recently, mesh networking and blockchain are two of the hottest technologies in the telecommunications industry. Combining both can reformulate Internet access. While mesh networking makes connecting to the Internet easy and affordable, blockchain on top of mesh networks makes Internet access profitable by enabling bandwidth-sharing for crypto-tokens. Hyperledger Fabric (HLF) is a blockchain framework implementation and one of the Hyperledger projects hosted by The Linux Foundation. We evaluate HLF in a real production mesh network and in the laboratory. We quantify the performance, bottlenecks and limitations of the current implementation v1.0. We identify the opportunities for improvement to serve the needs of wireless mesh access networks. To the best of our knowledge, this is the first HLF deployment made in a production wireless mesh network.