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.
With respect to Industry 4.0, there are many existing definitions and views published. An overview of selected perceptions of Industry 4.0 is presented in @cite_7 . Moreover, it also states that there is no generally accepted definition for the term Industry 4.0.
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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. 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. 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 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. 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. IT-driven innovation is an enormous factor in the worldwide economic leadership of the United States. It is larger than finance, construction, or transportation, and it employs nearly 6 of the US workforce. The top three companies, as measured by market capitalization, are IT companies - Apple, Google (now Alphabet), and Microsoft. Facebook, a relatively recent entry in the top 10 list by market capitalization has surpassed Walmart, the nation's largest retailer, and the largest employer in the world. The net income of just the top three exceeds $80 billion - roughly 100 times the total budget of the NSF CISE directorate which funds 87 of computing research. In short, the direct return on federal research investments in IT research has been enormously profitable to the nation. The IT industry ecosystem is also evolving. The time from conception to market of successful products has been cut from years to months. Product life cycles are increasingly a year or less. This change has pressured companies to focus industrial R&D on a pipeline or portfolio of technologies that bring immediate, or almost immediate, value to the companies. To defeat the competition and stay ahead of the pack, a company must devote resources to realizing gains that are shorter term, and must remain agile to respond quickly to market changes driven by new technologies, new startups, evolving user experience expectations, and the continuous consumer demand for new and exciting products. Amidst this landscape, the Computing Community Consortium convened a round-table of industry and academic participants to better understand the landscape of industry-academic interaction, and to discuss possible actions that might be taken to enhance those interactions. We close with some recommendations for actions that could expand the lively conversation we experienced at the round-table to a national scale. 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. 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. In conjunction with what is often called the industry 4.0, the new machine age, or the rise of the robots, the authors of this paper have each experienced the following phenomenon. At public events and roundtable discussions, among our circles of friends, or during interviews with the media, we are asked on a surprisingly regular basis: "How must humankind adapt to the imminent process of technological change? What do we have to learn in order to keep pace with the smart new machines? What new skills do we need to understand the robots?" Context: The global software industry and the software engineering (SE) academia are two large communities. However, unfortunately, the level of joint industry-academia collaborations in SE is still relatively very low, compared to the amount of activity in each of the two communities. It seems that the two 'camps' show only limited interest motivation to collaborate with one other. Many researchers and practitioners have written about the challenges, success patterns (what to do, i.e., how to collaborate) and anti-patterns (what not do do) for industry-academia collaborations.Objective: To identify (a) the challenges to avoid risks to the collaboration by being aware of the challenges, (b) the best practices to provide an inventory of practices (patterns) allowing for an informed choice of practices to use when planning and conducting collaborative projects.Method: A systematic review has been conducted. Synthesis has been done using grounded-theory based coding procedures.Results: Through thematic analysis we identified 10 challenge themes and 17 best practice themes. A key outcome was the inventory of best practices, the most common ones recommended in different contexts were to hold regular workshops and seminars with industry, assure continuous learning from industry and academic sides, ensure management engagement, the need for a champion, basing research on real-world problems, showing explicit benefits to the industry partner, be agile during the collaboration, and the co-location of the researcher on the industry side.Conclusion: Given the importance of industry-academia collaboration to conduct research of high practical relevance we provide a synthesis of challenges and best practices, which can be used by researchers and practitioners to make informed decisions on how to structure their collaborations. 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. (This article originally appeared in Management Science, January 1969, Volume 15, Number 5, pp. 215-227, published by The Institute of Management Sciences.) A growth model for the timing of initial purchase of new products is developed and tested empirically against data for eleven consumer durables. The basic assumption of the model is that the timing of a consumer's initial purchase is related to the number of previous buyers. A behavioral rationale for the model is offered in terms of innovative and imitative behavior. The model yields good predictions of the sales peak and the timing of the peak when applied to historical data. A long-range forecast is developed for the sales of color television sets. 0 Introduction.- I Themes and Problems.- 1.0 Introduction.- 1.1 The theory of finance capital.- 1.2 Interlocking directorates and economic power.- 1.3 Financial groups.- 1.4 Corporate elite and capitalist class.- 1.5 Summary.- II Between Market and Hierarchy.- 2.0 Introduction.- 2.1 The organization of firms and markets: a neo-classical explanation.- 2.2 Some definitions of firms.- 2.3 Power and control.- 2.4 Ownership and control.- 2.5 Competition, cooperation and control.- 2.5.1 Concentration and centralization.- 2.5.2 The level-of-analysis problem.- 2.6 Summary.- III Imperialism in the Seventies: two models.- 3.0 Introduction.- 3.1 Theories of imperialism.- 3.1.1 Their origins.- 3.1.2 After World War II.- 3.2 Two models.- 3.3 Research design.- 3.4 Summary.- IV The International Corporate Elite.- 4.0 Introduction.- 4.1 The organization of the supervising and executive function in different countries.- 4.2 Selection of the international corporate elite.- 4.3 Network characteristics of the international corporate elite.- 4.3.0 Introduction.- 4.3.1 The finance capitalists.- 4.3.2 The big linkers.- 4.4 Types of interlocking directorates.- 4.4.0 Introduction.- 4.4.1 Multiple interlocks.- 4.5 Summary.- V National Versus International Integration.- 5.0 Introduction to some graph-theoretical concepts.- 5.1 General patterns in the international network.- 5.1.1 Compactness of the international network.- 5.1.2 International integration of national networks.- 5.2 The international network of Western firms.- 5.2.0 Introduction.- 5.2.1 Local centrality.- 5.2.2 Global centrality.- 5.2.3 Overall centrality.- 5.3 Industrial concentration versus economic centralization.- 5.4 Summary.- VI Domination and Control.- 6.0 Introduction.- 6.1 Clusters of heavily interlocked firms.- 6.1.1 The network at multiplicity-level two.- 6.1.2 The network at multiplicity-level three.- 6.1.3 Conclusions.- 6.2 The network of officer-interlocks.- 6.2.0 Introduction.- 6.2.1 Domination and control.- 6.2.1.1 The network of all officer-interlocks.- 6.2.1.2 The network of control.- 6.2.2 Financial groups.- 6.2.3 Constellations of interests.- 6.3 Summary.- VII Competition and Cooperation: the role of banks.- 7.0 Introduction.- 7.1 Interlocks among banks.- 7.2 Overlapping spheres of interests.- 7.3 International bank consortia.- 7.4 The American banks.- 7.5 Summary.- VIII The Impact of World Crisis: changes in the network.- 8.0 Introduction.- 8.1 A new economic world order?.- 8.2 Selection of the 1976 sample.- 8.3 The international corporate elite.- 8.3.1 Finance capitalists.- 8.3.2 Big linkers.- 8.4 International versus national integration.- 8.5 Centrality in the nested networks (1976).- 8.6 Domination and control in the 1976 network.- 8.7 Summary.- IX Summary and Conclusions.- 9.1 Introduction.- 9.2 The structure of the international corporate elite.- 9.3 Conflict or cooperation.- 9.4 The meaning of interlocking directorates.- 9.5 The international corporate elite.- References.- Authors Index.- Firms Index.- Appendix A. Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) Network neighbors''--those consumers linked to a prior customer--adopt the service at a rate 3--5 times greater than baseline groups selected by the best practices of the firm's marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption. 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. In the highly competitive world, there has been a concomitant increase in the need for the research and planning methodology, which can perform an advanced assessment of technological opportunities and an early perception of threats and possibilities of the emerging technology according to the nation’s economic and social status. Abstract The authors examine behavioral outcomes following a customer-initiated contact (CIC) with a manufacturer and develop a framework to explain the impact of vendor performance during a CIC on a customer’s share of category requirements with a focal brand and word-of-mouth incidence following contact. The authors propose customer characteristics and context–specific factors that may relate to differences in the key characteristics of the underlying source model of share of category requirements and word of mouth. The authors then assess the overall importance of the explanatory variables in the source model and simultaneously test for systematic differences related to CIC-specific factors using survey data from more than 1700 CICs that involve more than 60 brands. A key assumption in much prior research that has examined customer-firm interactions is that CIC-specific factors, if they are included at all, create an automatic regularity that must be controlled for. The authors propose and find an addi... 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. 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. This book is open access under a CC BY 4.0 license. This book is intended primarily for practitioners who are facing the softwareisation of their business. It presents the Scaling Management Framework, a model based on collected experiences from companies that have already made the journey to give software a central role within the organization. The model is unique because it suggests a holistic method to analyze and plan your journey. It simply means that you cant just focus solely on your products or services. You also have to look closely at your processes and your organization, the way you make decisions and get things done. Inevitably, these will have to change. Software has changed the rules of the game. The world talks about the digitalization in industry and society how the focus has shifted from producing tangible things towards software and services. This trend started many years ago, but is now affecting every company, whether its a software company or not. There are many companies that have already made a digitalization journey and many are about to embark on this journey like you. How do you transform your organization when software is becoming a critical part of your business? This book comes with a map, a compass, and suggested journeys along with selected travel stories comprising best practices and lessons learned from past digitalization journeys. Use the map to find your way in the digitalization landscape, and use the compass to find the direction of your journey.