The Boost 4.0 Experience

In the last few years, the potential impact of big data on the manufacturing industry has received enormous attention. This chapter details two large-scale trials that have been implemented in the context of the lighthouse project Boost 4.0. The chapter introduces the Boost 4.0 Reference Model, which adapts the more generic BDVA big data reference architectures to the needs of Industry 4.0. The Boost 4.0 reference model includes a reference architecture for the design and implementation of advanced big data pipelines and the digital factory service development reference architecture. The engineering and management of business network track and trace processes in high-end textile supply are explored with a focus on the assurance of Preferential Certification of Origin (PCO). Finally, the main findings from these two large-scale piloting activities in the area of service engineering are discussed.publishersversionpublishe

Integrated lifecycle requirements information management in construction

Effective management of information about client requirements in construction projects lifecycle can contribute to high construction productivity; within budget and schedule, and improve the quality of built facilities and service delivery. Traditionally, requirements management has been focused at the early stages of the construction lifecycle process where elicited client requirements information is used as the basis for design. Management of client requirements does not extend to the later phases. Client requirements often evolve and change dramatically over a facility’s life. Changing client requirements is one of the principal factors that contribute to delays and budget overruns of construction projects. This results in claims, disputes and client dissatisfaction. The problems of current requirements management process also include: lack of integrated and collaborative working with requirements; lack of integrated requirements information flow between the various heterogeneous systems used in the lifecycle processes, and between the multiple stakeholders; inefficient and ineffective coordination of changes within the lifecycle processes; manual checking of dependencies between changing requirements to facilitate assessment of cost and time impact of changes. The aim of the research is to specify a better approach to requirements information management to help construction organisations reduce operational cost and time in product development and service delivery; whilst increasing performance and productivity, and realising high quality of built facilities. In order to achieve the aim and the formulated objectives, firstly, a detailed review of literature on related work was conducted. Secondly, the research designed, developed and conducted three case studies to investigate the state-of-the-art of managing client requirements information. A combination of multiple data collection methods was applied which included observations, interviews, focus group and questionnaires. Following this, the data was analysed and problems were identified; the necessity for a lifecycle approach to managing the requirements information emerged. (Continues...)

Launching the Grand Challenges for Ocean Conservation

The ten most pressing Grand Challenges in Oceans Conservation were identified at the Oceans Big Think and described in a detailed working document:A Blue Revolution for Oceans: Reengineering Aquaculture for SustainabilityEnding and Recovering from Marine DebrisTransparency and Traceability from Sea to Shore:  Ending OverfishingProtecting Critical Ocean Habitats: New Tools for Marine ProtectionEngineering Ecological Resilience in Near Shore and Coastal AreasReducing the Ecological Footprint of Fishing through Smarter GearArresting the Alien Invasion: Combating Invasive SpeciesCombatting the Effects of Ocean AcidificationEnding Marine Wildlife TraffickingReviving Dead Zones: Combating Ocean Deoxygenation and Nutrient Runof

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Enabling Flexibility in Process-Aware Information Systems: Challenges, Methods, Technologies

In today’s dynamic business world, the success of a company increasingly depends on its ability to react to changes in its environment in a quick and flexible way. Companies have therefore identified process agility as a competitive advantage to address business trends like increasing product and service variability or faster time to market, and to ensure business IT alignment. Along this trend, a new generation of information systems has emerged—so-called process-aware information systems (PAIS), like workflow management systems, case handling tools, and service orchestration engines. With this book, Reichert and Weber address these flexibility needs and provide an overview of PAIS with a strong focus on methods and technologies fostering flexibility for all phases of the process lifecycle (i.e., modeling, configuration, execution and evolution). Their presentation is divided into six parts. Part I starts with an introduction of fundamental PAIS concepts and establishes the context of process flexibility in the light of practical scenarios. Part II focuses on flexibility support for pre-specified processes, the currently predominant paradigm in the field of business process management (BPM). Part III details flexibility support for loosely specified processes, which only partially specify the process model at build-time, while decisions regarding the exact specification of certain model parts are deferred to the run-time. Part IV deals with user- and data-driven processes, which aim at a tight integration of processes and data, and hence enable an increased flexibility compared to traditional PAIS. Part V introduces existing technologies and systems for the realization of a flexible PAIS. Finally, Part VI summarizes the main ideas of this book and gives an outlook on advanced flexibility issues. The attached pdf file gives a preview on Chapter 3 of the book which explains the book's overall structure

Transforming the Productivity of People in the Built Environment: Emergence of a Digital Competency Management Ecosystem

This chapter explores how we create and support a digitally enabled, agile, competent, and ultimately, productive workforce and determines the key research questions that need to be addressed if Digital Built Britain (DBB) is to provide return on investment and succeed as the catalyst for evolving the manner in which we conceive, plan, design, construct, operate, and interact with the built environment. The proposed vision is a digital competency management ecosystem where interdependent stakeholders are incentivised to work together in coopetition to create, capture, infer, interpret, specify, integrate, accredit, apply, use, monitor, and evolve competence as a working (data) asset. This needs to be in a consistent, objective, explicit, and scalable manner, with end2end transparency and traceability for all stakeholders that overcome the challenges of competency management. Moreover, a core element must be an ecosystem organised around digital infrastructure of competency frameworks and other knowledge sources of competence, so that competency frameworks are in digital operation and dynamic context