Influencing Factors for the Adoption of Speech Assistance in Manufacturing

In recent years, there has been a steady increase in the use of speech assistants in the private environment. Although such assistance systems would also be beneficial for manufacturing (for example, because workers have their hands free), speech assistants have not yet been widely used in the industrial environment. Against this background, we develop in this article a model with factors that influence the decision of industrial firms whether to adopt speech assistance for manufacturing. In order to do so, we rely on the Technology-Organization-Environment (TOE) framework and conduct a multiple case study by interviewing 10 experts from firms that develop or use speech assistance solutions. Our model consists of 17 context factors that influence whether companies adopt speech assistance in manufacturing

Big Data Analytics and Its Applications in Supply Chain Management

In today’s competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Hence, explosive growth in volume and different types of data throughout the supply chain has created the need to develop technologies that can intelligently and rapidly analyze large volume of data. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. BDA provides a tool for extracting valuable patterns and information in large volume of data. So, the main purpose of this book chapter is to explore the application of BDA in supply chain management (SCM)

Strategies for the Development of IT Disaster Recovery Plans in the Manufacturing Industry

Information technology (IT) leaders have reported technology disruptions because of natural disasters, terror attacks, or adversarial threats. Information technology leaders are concerned with technology disruptions, as these disruptions are costing organizations as much as $22,000 per minute. Grounded in Zachman’s framework, the purpose of this qualitative multiple case study was to explore strategies IT managers in the manufacturing industry use to develop IT disaster recovery (DR) plans to support business operations. The participants included 3 manufacturing IT professionals, 2 Department of Defense manufacturing infrastructure specialists, and 1outsourcing contractor, each from firms located in the central United States who successfully developed IT DR plans to support business operations. Data collection comprised of interviews and documentation. I used Braun and Clarke’s (2006) six-step process for thematic analysis to identify 5 themes: contingency planning by priority, testing plans, levels of recovery, time requirements for recovery, and costs associations. The implications for positive social change include the potential for IT managers and leaders to contribute to strategic development of IT DR plans and prevent economic disruption for consumers, communities, and society during disaster events

Situation Management with Complex Event Processing

With the broader dissemination of digital technologies, visionary concepts like the Internet of Things also affect an increasing number of use cases with interfaces to humans, e.g. manufacturing environments with technical operators monitoring the processes. This leads to additional challenges, as besides the technical issues also human aspects have to be considered for a successful implementation of strategic initiatives like Industrie 4.0. From a technical perspective, complex event processing has proven itself in practice to be capable of integrating and analyzing huge amounts of heterogeneous data and establishing a basic level of situation awareness by detecting situations of interests. Whereas this reactive nature of complex event processing systems may be sufficient for machine-to-machine use cases, the new characteristic of application fields with humans remaining in the control loop leads to an increasing action distance and delayed reactions. Taking human aspects into consideration leads to new requirements, with transparency and comprehensibility of the processing of events being the most important ones. Improving the comprehensibility of complex event processing and extending its capabilities towards an effective support of human operators allows tackling technical and non-technical challenges at the same time. The main contribution of this thesis answers the question of how to evolve state-of-the-art complex event processing from its reactive nature towards a transparent and holistic situation management system. The goal is to improve the interaction among systems and humans in use cases with interfaces between both worlds. Realizing a holistic situation management requires three missing capabilities to be introduced by the contributions of this thesis: First, based on the achieved transparency, the retrospective analysis of situations is enabled by collecting information related to a situation\u27s occurrence and development. Therefore, CEP engine-specific situation descriptions are transformed into a common model, allowing the automatic decomposition of the underlying patterns to derive partial patterns describing the intermediate states of processing. Second, by introducing the psychological model of situation awareness into complex event processing, human aspects of information processing are taken into consideration and introduced into the complex event processing paradigm. Based on this model, an extended situation life-cycle and transition method are derived. The introduced concepts and methods allow the implementation of the controlling function of situation management and enable the effective acquisition and maintenance of situation awareness for human operators to purposefully direct their attention towards upcoming situations. Finally, completing the set of capabilities for situation management, an approach is presented to support the generation and integration of prediction models for predictive situation management. Therefore, methods are introduced to automatically label and extract relevant data for the generation of prediction models and to enable the embedding of the resulting models for an automatic evaluation and execution. The contributions are introduced, applied and evaluated along a scenario from the manufacturing domain

Role of openness in terms of switching costs for the Industrial Internet platform end-users

Industrial Internet is one of the novel paradigms in the recent years, it has many implications in both practical and literature reviews for almost all the industry sectors. Industrial Internet is a broad subject and many concepts are related to it such as Industry 4.0, CPS, smart manufacturing or IoT. One of the most talked topics related to Industrial Internet are platforms. They are a revolution in the way companies communicate, partner, share and create value. The purpose of the thesis is to develop a better understanding of the role of openness, which is a fundamental characteristic of the II-platforms in which the degree of participation of end-users, app developers and platform owners varies depending on the degree of openness. The impact of selecting a platform based on its openness is crucial for a company's long-term strategy. This thesis concentrates on the risks and downsides of openness. The thesis focuses on the risks given that there is already an extensive research in the benefits that platforms provide. These downsides are evaluated in terms of switching costs through two interviews that provide an overview of the role of openness and its effects on the end-user. The results show that openness plays an important role in the decision-making process. Companies are eager to know methods to evaluate its impacts and switching costs is an excellent tool well-known by companies. In addition, depending on the business openness influences positively or negatively or both, but it definitively has an impact in the long-term

New Trends in the Use of Artificial Intelligence for the Industry 4.0

Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0

Big Data Technology for Resilient Failure Management in Production Systems

Part 3: Knowledge Based Production ManagementInternational audienceDue to a growing complexity within value chains the susceptibility to failures in production processes increases. The research project BigPro explores the applicability of Big Data to realize a pro-active failure management in production systems. The BigPro-platform complements structured production data and unstructured human data to improve failure management. In a novel approach, the aggregated data is analyzed for reoccurring patterns that indicate possible failures of the production system, known from historic failure events. These patterns are linked to failures and respective countermeasures and documented in a catalog. The project results are validated in three industrial use cases