Automated Creation and Provisioning of Decision Information Packages for the Smart Factory

In recent years, Industry 4.0 emerges as a new trend, enabling the integration of data-intensive cyber physical systems, Internet of Things, and mobile applications, into production environments. Even though Industry 4.0 concentrates on automated engineering and manufacturing processing, the human actor is still important for decision making in the product lifecycle process. To support correct and efficient decision making, human actors have to be provided with relevant data depending on the current context. This data needs to be retrieved from distributed sources like bill of material systems, product data management and manufacturing execution systems, holding product model and factory model. In this article, we address this issue by introducing the concept of decision information packages, which enable to compose relevant engineering data for a specific context from distributed data sources. To determine relevant data, we specify a context-aware engineering data model and corresponding operators. To realize our approach, we provide an architecture and a prototypical implementation based on requirements of a real case scenario. This article is a revised and selected version of the previous work

IAMM: A maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility

Towards Self-Protective Multi-Cloud Applications: MUSA – a Holistic Framework to Support the Security-Intelligent Lifecycle Management of Multi-Cloud Applications

The most challenging applications in heterogeneous cloud ecosystems are those that are able to maximise the benefits of the combination of the cloud resources in use: multi-cloud applications. They have to deal with the security of the individual components as well as with the overall application security including the communications and the data flow between the components. In this paper we present a novel approach currently in progress, the MUSA framework. The MUSA framework aims to support the security-intelligent lifecycle management of distributed applications over heterogeneous cloud resources. The framework includes security-by-design mechanisms to allow application self-protection at runtime, as well as methods and tools for the integrated security assurance in both the engineering and operation of multi-cloud applications. The MUSA framework leverages security-by-design, agile and DevOps approaches to enable the security-aware development and operation of multi-cloud applications.European Commission's H202

Intent-based network slicing for SDN vertical services with assurance: Context, design and preliminary experiments

Network slicing is announced to be one of the key features for 5G infrastructures enabling network operators to provide network services with the flexibility and dynamicity necessary for the vertical services, while relying on Network Function Virtualization (NFV) and Software-defined Networking (SDN). On the other hand, vertical industries are attracted by flexibility and customization offered by operators through network slicing, especially if slices come with in-built SDN capabilities to programmatically connect their application components and if they are relieved of dealing with detailed technicalities of the underlying (virtual) infrastructure. In this paper, we present an Intent-based deployment of a NFV orchestration stack that allows for the setup of Qos-aware and SDN-enabled network slices toward effective service chaining in the vertical domain. The main aim of the work is to simplify and automate the deployment of tenant-managed SDN-enabled network slices through a declarative approach while abstracting the underlying implementation details and unburdening verticals to deal with technology-specific low-level networking directives. In our approach, the intent-based framework we propose is based on an ETSI NFV MANO platform and is assessed through a set of experimental results demonstrating its feasibility and effectiveness

The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well

The role of big data analytics in industrial internet of things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions. © 2019 Elsevier B.V

Decentralized manufacturing of cell and gene therapies: Overcoming challenges and identifying opportunities

Decentralized or “redistributed” manufacturing has the potential to revolutionize the manufacturing approach for cell and gene therapies (CGTs), moving away from the “Fordist” paradigm, delivering health care locally, customized to the end user and, by its very nature, overcoming many of the challenges associated with manufacturing and distribution of high volume goods. In departing from the traditional centralized model of manufacturing, decentralized manufacturing divides production across sites or geographic regions. This paradigm shift imposes significant structural and organisational changes on a business presenting both hidden challenges that must be addressed and opportunities to be embraced. By profoundly adapting business practices, significant advantages can be realized through a democratized value chain, creation of professional-level jobs without geographic restriction to the central hub and a flexibility in response to external pressures and demands. To realize these potential opportunities, however, advances in manufacturing technology and support systems are required, as well as significant changes in the way CGTs are regulated to facilitate multi-site manufacturing. Decentralized manufacturing is likely to be the manufacturing platform of choice for advanced health care therapies—in particular, those with a high degree of personalization. The future success of these promising products will be enhanced by adopting sound business strategies early in development. To realize the benefits that decentralized manufacturing of CGTs has to offer, it is important to examine both the risks and the substantial opportunities present. In this research, we examine both the challenges and the opportunities this shift in business strategy represents in an effort to maximize the success of adoption

Signing and security of Hue software

Developing software for the Hue devices poses plenty of challenges among the engineers at Philips Lighting. These challenges arise at each stage of the Software Development Life-Cycle (SDLC). Improvement of it is of immense importance to the Philips Lighting. This report describes a project which focus was to automate the SDLC, as well as to improve the security in it. The end result solves many challenges. It delivers a complete release management tool dedicated to the engineers at the Home Systems department. First, it visualizes release workflows in a simple user interface. Second, the core activities of the SDLC, such as the software signing, are fully automated. What is more important is that the signing is executed in a highly secure environment. This is very important for Philips Lighting not only because this automation saves a lot of time, but also because it reduces the risk of a human error. The same benefits are gained through an automation of other activities, such as approvals, distribution of the software to the factories, and deploying the software to the device cloud. Third, the system provides a traceability about each step executed in the process. Finally, the system is highly configurable, which makes it easy to be extended and adjusted to support different device types with different release workflows