•Approach enables decision support for the formation of supply chain collaborations.•Tool provides market search and matchmaking functions for collaboration design.•Automated ontologies guide the ...multi-criteria team composition process.•Approach and tool validated for Automotive and Aerospace domains.•Solution can underpin the design of hyperconnected supply chains.
Industry 4.0 technologies, process digitalisation and automation can be applied to support the formation of supply chain collaborations in manufacturing. Underpinned by information and communication technologies, collaborations of independent companies can dynamically pool production capacities and capabilities to jointly react to new business opportunities. These collaborations may involve a wide range of enterprises with different sizes and scope that individually would not be able to tender for such new business opportunities. To form these collaborative teams, assistive processes and technologies can underpin the effort towards exploring the tender requirements, unbundling the tender into smaller tasks and finding a suitable supplier for each task. In this paper, we present an approach and a tool to support decision making concerning forming supply chain collaborations in Industry 4.0. The approach proposed is unique in integrating industry domain ontologies, assistive human-computer interaction tools and multi-criteria decision support techniques to form team compositions speeding-up the collaboration process whilst maximising the chances of forming a viable team to fulfil the tender requirements. We also show evaluation results involving stakeholders from the supply chain function pointing to the effectiveness of the proposed solution, available online as a demo11http://130.88.97.225:4200 (username: TDMS@uniman.eu; password: uniman)..
•An architectural design for constructing Industry 4.0 SCM platforms.•Semantic technologies used for application integration via the EDSOA model.•Open and extensible architecture supporting supply ...chain collaboration lifecycle.•Survey and comparative analysis assessing Industry 4.0 platform capabilities.
This paper presents the architectural design and implementation of DIGICOR — a collaborative Industry 4.0 (I4.0) platform aimed at enabling SMEs to dynamically form supply-chain collaborations so as to pool production capacities and capabilities and jointly address complex supply chain requests. The DIGICOR architecture builds on the event-driven service-oriented architecture (EDSOA) model to support the collaboration between SMEs, dynamic modelling of their systems and services, and their integration in the supply chains of large OEMs, enforcing digital platform governance rules for knowledge protection and security. In contrast to the extant platforms assessed through our systematic review, the proposed architecture supports the entire lifecycle of I4.0 collaborations, from creation of viable teams to deployment and operation. The architecture provides an open and extensible solution for (i) creating a marketplace for the collaboration partners, (ii) providing services for planning and controlling the collaborative production, logistics, and risk management, while supporting APIs for third parties to provide complementary services such as advanced analytics, simulation, and optimization; and (iii) seamless connectivity to automation solutions, smart objects and real-time data sources. We report on the design of the architecture and its innovative artefacts such as the component model description and the semantic model constructs created for meaningful event exchanges between architectural end-points. We also describe a running use case demonstrating implementation scenarios.
•Empirical study aimed at “scaling up” data quality (DQ) management applications.•Execution of data quality-aware queries over sensor-collected traffic data sets.•Exploration of Apache Spark and ...Pandas library to speed-up query execution.•Insights on choice of computational infrastructure to deploy DQ management tools.
Combining query processing techniques with data quality management approaches enables enforcement of quality constraints, such as timeliness, accuracy and completeness, as part of ad-hoc query specification and execution, improving the quality of query results. Despite the emergence of novel data quality processing tools, there is a dearth of studies assessing performance and scalability in the execution of data quality assessment tasks during query processing. This paper reports on an empirical study aiming to investigate the extent to which a big data computing framework (Spark) can offer significant gains in performance and scalability when executing data quality querying tasks over a range of computational platforms including a single commodity multi-core machine and a cluster-based platform for a wide range of workloads. Our results show that substantial performance and scalability gains can be obtained by using optimized data science libraries combined with the parallel and distributed capabilities of big data computing. We also provide guidelines on choosing the appropriate computational infrastructure for executing DQ-aware queries.
This thesis presents an investigation that identifies the factors that decision-makers consider when selecting business partners to form collaborative networks, conceptualises, designs and implements ...a computerised advisor supporting such decision-makers, and identifies the factors that influence users to utilise the computer-generated advice from that kind of computerised advisors. The research is undertaken in the context of inter-organisational collaboration and digitalisation in manufacturing industries, motivated by the fourth industrial revolutionâTMs push to transforming the team formation process for inter-organisational collaboration through information systems. The investigation utilises the methodology known as Action Design Research (ADR). ADR implies cooperation between researchers and practitioners to address a specific problem embedded in an organisation, and in the case of this research, the focus is on the business partner selection problem. The outcome of ADR is a composite artefact developed with the knowledge acquired through the researcher-practitioner collaboration; in this work, the artefact comprises a computerised advisor and a theory of computer advice utilisation applicable when the advice is provided by a computer in support of business partner selection in manufacturing industries. This research benefits from the involvement of practitioners from a number of organisations including a European aircraft manufacturer, a German aviation cluster with several members from small and medium-sized enterprises (SME), and a Welsh SME platform provider to automotive, aerospace and electronic manufacturing clusters. These organisations worked collaboratively in a European Project, to which this investigation had access to, called Decentralised Agile Coordination Across Supply Chains (DIGICOR, https://www.digicor-project.eu). This work extends the limits of knowledge regarding computerised business advisors, knowledge management, and virtual teams in four interwoven contributions. The two major contributions are (1) a working example of a computerised advisor supporting business partner selection, named âoeTender Decomposition and Matchmaking Systemâ (TDMS); and (2) a theory of computer advice utilisation identifying, describing and explaining the motivating factors and their relationships when it comes to end-users following computer-generated advice applicable in manufacturing industries. The two ancillary contributions are (3) a theoretical tool to assess the readiness of collaborative platforms and collaborative technologies toward inter-organisational collaboration in the digitalisation context; and (4) an example of the use of Action Design Research method (ADR) for theorising.
The work presented in this thesis comprises the design, implementation and evaluation of extensions made to the Data Quality Query System (DQ2S), a state-of-the-art data quality aware query ...processing framework and query language, towards testing and improving its scalability when working with increasing amounts of data. The purpose of the evaluation is to assess to what extent a big data framework, such as Apache Spark, can offer significant gains in performance, including runtime, required amount of memory, processing capacity, and resource utilisation, when running over different environments. DQ2S enables assessing and improving data quality within information management by facilitating profiling of the data in use, and leading to the support of data cleansing tasks, which represent an important step in the big data life-cycle. Despite this, DQ2S, as the majority of data quality management systems, is not designed to process very large amounts of data. This research describes the journey of how data quality extensions from an earlier implementation that processed two datasets with 50 000 rows each one in 397 seconds, were designed, implemented and tested to achieve a big data solution capable of processing 105 000 000 rows in 145 seconds. The research described in this thesis provides a detailed account of the experimental journey followed to extend DQ2S towards exploring the capabilities of a popular big data framework (Apache Spark), including the experiments used to measure the scalability and usefulness of the approach. The study also provides a roadmap for researchers interested in re-purposing and porting existing information management systems and tools to explore the capabilities provided by big data frameworks, particularly useful given that re-purposing and re-writing existing software to work with big data frameworks is a less costly and risky approach when compared to greenfield engineering of information management systems and tools.
In Industry 4.0 scenarios, the presence of formalized and enforced governance rules helps to clarify how core and external contributors should collaborate to co-design and deliver key subsystems of ...the finished product. This clarity diminishes the time for establishing collaborations and facilitates tasks execution. However, current governance rules are usually more implicit than explicit and often are subject to time-consuming negotiation before a new collaboration starts. The concept of Industry 4.0 demands higher levels of dynamism; therefore, we present the importance of governance rules in such digital marketplaces. In this short paper, we lay out the foundation for enabling the explicit definition and enforcement of governance rules by proposing a specialized Abstract Syntax Metamodel for specifying governance rules. With such a model, we aim to aid demand-driven production and logistics service collaborations between suppliers towards enabling a quicker and more predictable creation and execution of the tasks involved.
Manufacturing in supply chains commonly involves collaboration between multiple and diverse enterprises forming temporary coalitions. The matchmaking and assembly of multi-organizational resources to ...respond to a business opportunity is a key stage in production and supply chain management, particularly in the selection of suppliers taking into account enterprises of different sizes, capabilities and marketplace reputations. Industry 4.0 technologies can have a positive impact on collaborative manufacturing by digitalizing the team formation stage, speeding-up the collaboration process, minimizing supplier risk and maximizing coalition potential. This paper proposes a microservice for team formation to facilitate collaborative manufacturing in the context of Industry 4.0, guided by Design Science research practices and involving stakeholders' vision from the automotive and aviation industries. From a technical perspective, the microservice design is conceived to facilitate interoperability with Business-to-Business (B2B) platforms.
Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2 infection induces an exacerbated inflammation driven by innate immunity components. Dendritic cells (DCs) play a key role in the defense ...against viral infections, for instance plasmacytoid DCs (pDCs), have the capacity to produce vast amounts of interferon-alpha (IFN-α). In COVID-19 there is a deficit in DC numbers and IFN-α production, which has been associated with disease severity. In this work, we described that in addition to the DC deficiency, several DC activation and homing markers were altered in acute COVID-19 patients, which were associated with multiple inflammatory markers. Remarkably, previously hospitalized and nonhospitalized patients remained with decreased numbers of CD1c+ myeloid DCs and pDCs seven months after SARS-CoV-2 infection. Moreover, the expression of DC markers such as CD86 and CD4 were only restored in previously nonhospitalized patients, while no restoration of integrin β7 and indoleamine 2,3-dyoxigenase (IDO) levels were observed. These findings contribute to a better understanding of the immunological sequelae of COVID-19.
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