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. 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. 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. 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. The importance of software product evaluations will grow with the awareness of the need for better software quality. The process to conduct such evaluations is crucial to get evaluation results that can be applied and meet customers' expectations. This paper reviews a well-known evaluation process: the ISO 14598 standard. The review focuses on the difficulties in selecting and evaluating the appropriate evaluation techniques. The review shows that the standard has problems in applying evaluation processes in practice due to insufficient attention to goal definition and to relationships between activities being implicit. Also, the standard ignores the trade-off between goals and resources and pays insufficient attention to feedback. To address these deficiencies, the W-process is proposed. It extends the standard through an improved process structure and additional guidelines. Abstract Context For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, they still do not allow for empirical generalisations. Objective To lay an empirical and externally valid foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. Method We designed a globally distributed family of surveys in joint collaborations with different researchers and completed the first run in Germany. The instrument is based on a theory in the form of a set of hypotheses inferred from our experiences and available studies. We test each hypothesis in our theory and identify further candidates to extend the theory by correlation and Grounded Theory analysis. Results In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from Germany with participants from 58 companies. The results reveal, for example, a tendency to improve RE via internally defined qualitative methods rather than relying on normative approaches like CMMI. We also discovered various RE problems that are statistically significant in practice. For instance, we could corroborate communication flaws or moving targets as problems in practice. Our results are not yet fully representative but already give first insights into current practices and problems in RE, and they allow us to draw lessons learnt for future replications. Conclusion Our results obtained from this first run in Germany make us confident that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice. While Model-Driven Development (MDD) is an increasingly popular software development approach, its impact on the development process in large-scale, industrial practice is not yet clear. For this study the application of MDD in a large-scale industrial software development project is analyzed over a period of two years. Applying a grounded theory approach we identified 14 factors which impact the architectural process. We found that scope creep is more likely to occur, late changes can imply more extensive rework and that business engineers need to be more aware of the technical impact of their decisions. In addition, the introduced Domain-Specific Language (DSL) provides a new common idiom that can be used by more team members and will ease communication among team members and with clients. Also, modelers need to be much more explicit and complete in their descriptions. Parallel development of a code generator and defining a proper meta-model require additional time investments. Lastly, the more central role of software architecture design documentation requires more structured, detailed and complete architectural information and consequently, more frequent reviews. We present an assessment of the state and historic development of evaluation practices as reported in papers published at the IEEE Visualization conference. Our goal is to reflect on a meta-level about evaluation in our community through a systematic understanding of the characteristics and goals of presented evaluations. For this purpose we conducted a systematic review of ten years of evaluations in the published papers using and extending a coding scheme previously established by [2012]. The results of our review include an overview of the most common evaluation goals in the community, how they evolved over time, and how they contrast or align to those of the IEEE Information Visualization conference. In particular, we found that evaluations specific to assessing resulting images and algorithm performance are the most prevalent (with consistently 80-90 of all papers since 1997). However, especially over the last six years there is a steady increase in evaluation methods that include participants, either by evaluating their performances and subjective feedback or by evaluating their work practices and their improved analysis and reasoning capabilities using visual tools. Up to 2010, this trend in the IEEE Visualization conference was much more pronounced than in the IEEE Information Visualization conference which only showed an increasing percentage of evaluation through user performance and experience testing. Since 2011, however, also papers in IEEE Information Visualization show such an increase of evaluations of work practices and analysis as well as reasoning using visual tools. Further, we found that generally the studies reporting requirements analyses and domain-specific work practices are too informally reported which hinders cross-comparison and lowers external validity. The Industry 4.0 is a vision that includes connecting more intensively physical systems with their virtual counterparts in computers. This computerization of manufacturing will bring many advantages, including allowing data gathering, integration and analysis in the scale not seen earlier. In this paper we describe our Semantic Big Data Historian that is intended to handle large volumes of heterogeneous data gathered from distributed data sources. We describe the approach and implementation with a special focus on using Semantic Web technologies for integrating the data. Abstract In today's manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision-makers to prioritize green manufacturing. The Internet of Things paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from manufacturing processes can be collected easily, and then analyzed, to improve energy-aware decision-making. Relying on a comprehensive literature review and on experts' insight, this paper contributes to the understanding of energy-efficient production management practices that are enhanced and enabled by the Internet of Things technology. In addition, it discusses the benefits that can be obtained thanks to adopting such management practices. Eventually, a framework is presented to support the integration of gathered energy data into a company's information technology tools and platforms. This is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage on such data in order to improve energy efficiency, and therefore competitiveness, of manufacturing companies. With the outcomes of this paper, energy managers can approach the Internet of Things adoption in a benefit-driven manner, addressing those energy management practices that are more aligned with company maturity, measurable data and available information systems and tools. In this paper, we propose a new framework for the organization of the electric power industry, based on extensive use of information technology (IT) and on interactive decision making, where consumers and distributed producers join the traditional actors, utilities in particular, in making decisions. While many ideas considered in this paper have been put forward in recent years, such as the need to manage intermittency of renewable resources by means of proactive forecasting, and coordination with responsive demand and storage, we introduce a possible systematic IT-enabled mechanism necessary for the actual implementation of these technologies. We point out that in order to achieve a long-term sustainable energy utilization, it is essential to provide on-line information to internalize the value of just-in-time, just-in-place, and just-in-context distributed adaptation across the entire supply chain, ranging from the smallest consumers and energy providers, through their aggregators and system coordinators. We illustrate using our model-based novel simulator, how a carefully designed multidirectional and multitemporal information exchange could enable sustainable decision making while accounting for unique needs and capabilities of various resources and users. At the same time, information incentivizes the resources and users to contribute to system-wide sustainability objectives at value. We illustrate the dependence of such decisions-driven industry evolution on the industry rules (choice of performance objectives), as well as on the operating and planning practices for implementing the industry rules (temporal and spatial factors). Our model-based simulator could be used as a means of designing novel regulation defining rules, rights, and responsibilities regarding the type and rate of information to be exchanged in support of sustainable industry evolution. Some current object-oriented analysis methods provide use cases. Scenarios or similar concepts to describe functional requirements for software systems. The authors introduced the use case point method to measure the size of large-scale software systems based on such requirements specifications. With the measured size and the measured effort the real productivity can be calculated in terms of delivered functionality. In the status report they summarize the experiences made with size metrics and productivity rates at a major Swiss banking institute. They analyzed the quality of requirements documents and the measured use case points in order to test and calibrate the use case point method. Experiences are based on empirical data of a productivity benchmark of 23 measured projects (quantitative analysis), 64 evaluated questionnaires of project members and 11 post benchmark interviews held with selected project managers (qualitative analysis). 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. 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. Practices in seven key areas from requirements engineering (RE) good practice guide are examined via in-depth interviews with ten Australian software development organisations. Our objective is to provide a better understanding of the relative perceived value of the RE practices investigated and to provide an initial assessment of the appropriateness of Sommerville and Sawyer's three classification levels. We used in-depth interviews as our main approach to collecting data. We assessed practices as either standardised use, normal use, used at the discretion of project manager or never used. We found that the single most standardised, or valuable, practice for 1) documentation, was making a business case of a project, for 2) elicitation, it was assessing system feasibility, 3) for analysis and negotiation, it was defining system boundaries, 4) for requirements description, it was to specify requirements quantitatively and to define standard templates for requirements, 5) for system modelling, it was to use a data dictionary, 6) for validation, it was to propose test cases, and 7) for management, it was to define a change management process. We suggest Sommerville and Sawyer's classification of basic, intermediate and advanced practices needs some reconsideration to bring his list into alignment with current industry practices. A system documentation process maturity model and assessment procedure were developed and used to assess 91 projects at 41 different companies over a seven year period. During this time the original version evolved into a total of four versions based on feedback from industry and the experience gained from the assessments. This paper reports the overall results obtained from the assessments which strongly suggest that the practice of documentation is not getting a passing grade in the software industry. The results show a clear maturity gap between documentation practices concerned with defining policy and practices concerned with adherence to those policies. The results further illustrate the need to recognize the importance of improving the documentation process, and to transform the good intentions into explicit policies and actions. A first step toward process improvement is identifying the strengths and weaknesses of an organization's software processes to determine effective improvement actions. An assessment can help an organization examine its processes against a reference model to determine the processes' capability or the organization's maturity, to meet quality, cost, and schedule goals, but small companies have difficulty running them. MARES, a set of guidelines for conducting 15504-conformant software process assessment focuses on small companies [Background:] Nowadays, industries are facing the problem that the Requirements Engineering (RE) process is highly volatile, since it depends on project influences from the customer's domain or from process models used. Artefact-based approaches promise to provide guidance in the creation of consistent artefacts in volatile project environments, because these approaches concentrate on the artefacts and their dependencies, instead of prescribing processes. Yet missing, however, is empirical evidence on the advantages of applying artefact-based RE approaches in real projects. [Aim:] We developed a customisable artefact-based RE approach for the domain of business information systems. Our goal is to investigate the advantages and limitations of applying this customisable approach in an industrial context. [Method:] We conduct a case study with our artefact-based RE approach and its customisation procedure. For this, we apply it at a software development project at Siemens following the steps of the customisation procedure. We assess our approach in direct comparison with the previously used RE approach considering possible improvements in the process and in the quality of the produced artefacts. [Results:] We show that our approach is flexible enough to respond to the individual needs in the analysed project environment. Although the approach is not rated to be more productive, we find an improvement in the syntactic and the semantic quality of the created artefacts. [Conclusions:] We close a gap in the RE literature by giving empirical evidence on the advantages of artefact orientation in RE in an industrial setting. Many organizations maintain textual process descriptions alongside graphical process models. The purpose is to make process information accessible to various stakeholders, including those who are not familiar with reading and interpreting the complex execution logic of process models. Despite this merit, there is a clear risk that model and text become misaligned when changes are not applied to both descriptions consistently. For organizations with hundreds of different processes, the effort required to identify and clear up such conflicts is considerable. To support organizations in keeping their process descriptions consistent, we present an approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach detects cases where the two process representations describe activities in different orders and detect process model activities not contained in the textual description. A quantitative evaluation with 53 real-life model-text pairs demonstrates that our approach accurately identifies inconsistencies between model and text. HighlightsWe propose an approach to detect conflicts between textual and model-based process descriptions.The approach is fully automatic based on tailored natural language processing techniques.Quantitative evaluation demonstrates the applicability of the approach on real-life data.