8,893 research outputs found

    An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains

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    This research aimed to develop an empirical understanding of the relationships between integration, dynamic capabilities and performance in the supply chain domain, based on which, two conceptual frameworks were constructed to advance the field. The core motivation for the research was that, at the stage of writing the thesis, the combined relationship between the three concepts had not yet been examined, although their interrelationships have been studied individually. To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative study, which was undertaken via multiple case studies to investigate lines of enquiry that would address the research questions formulated. This is consistent with the author’s philosophical adoption of the ontology of relativism and the epistemology of constructionism, which was considered appropriate to address the research questions. Empirical data and evidence were collected, and various triangulation techniques were employed to ensure their credibility. Some key features of grounded theory coding techniques were drawn upon for data coding and analysis, generating two levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in improving performance, the performance also informed the former. This reflects a cyclical and iterative approach rather than one purely based on linearity. Adopting a holistic approach towards the relationship was key in producing complementary strategies that can deliver sustainable supply chain performance. The research makes theoretical, methodological and practical contributions to the field of supply chain management. The theoretical contribution includes the development of two emerging conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed insight into their correlations. The latter gives a holistic view of their relationships and how they are connected, reflecting a middle-range theory that bridges theory and practice. The methodological contribution lies in presenting models that address gaps associated with the inconsistent use of terminologies in philosophical assumptions, and lack of rigor in deploying case study research methods. In terms of its practical contribution, this research offers insights that practitioners could adopt to enhance their performance. They can do so without necessarily having to forgo certain desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology

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    There are approximately 88 million tonnes of food waste generated annually in the EU alone. Food spoilage during distribution accounts for some of this waste. To minimise this spoilage, it is of utmost importance to maintain the cold chain during the transportation of perishable foods such as meats, fruits, and vegetables. However, these products are often unfortunately wasted in large quantities when unpredictable failures occur in the refrigeration units of transport vehicles. This work proposes a real-time IoT anomaly detection system to detect equipment failures and provide decision support options to warehouse staff and delivery drivers, thus reducing potential food wastage. We developed a bespoke Internet of Things (IoT) solution for real-time product monitoring and alerting during cold chain transportation, which is based on the Digital Matter Eagle cellular data logger and two temperature probes. A visual dashboard was developed to allow logistics staff to perform monitoring, and business-defined temperature thresholds were used to develop a text and email decision support system, notifying relevant staff members if anomalies were detected. The IoT anomaly detection system was deployed with Musgrave Marketplace, Ireland’s largest grocery distributor, in three of their delivery vans operating in the greater Belfast area. Results show that the LTE-M cellular IoT system is power efficient and avoids sending false alerts due to the novel alerting system which was developed based on trip detection

    Healthcare when the bets are off : symptoms, trajectories and treatment of individuals with gambling disorder

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    Gambling Disorder is a debilitating condition associated with several adverse outcomes. Despite available treatment, most of those suffering never seek help and those that do often present a complex clinical picture with a long duration of gambling problems and additional psychiatric disorders. Currently, there are several gaps in knowledge about this patient population, the care provided within addiction services, and the long-term consequences for these individuals in terms of functionality and work life. The current thesis, therefore, aims to address some of these gaps, with the goal of increasing knowledge about the patient group and improving treatments. In addition, when disseminating scientifically evaluated treatment to clinical practice, the problem gambling counselor plays an important role. What treatments are actually offered, by whom, and the role of the counselor has not previously been explored. Moreover, factors contributing to the development and maintenance of GD have been proposed and deserve further investigation; gambling cravings and difficulties in emotion regulation. Therefore, the overall aim of this thesis is to map the harms of GD, the treatment available and the characteristics of those delivering it, and track changes in gambling during the Covid-19 pandemic. Furthermore, the thesis aims to explore individual factors contributing to maintaining the disorder, i.e., craving and difficulties in emotion regulation. Study I is a case-cohort study using merged national registries to investigate GD’s association with work disability and trajectory groups in a longitudinal design over a sixyear period. The results showed that GD is associated with an increased risk of work disability over a four-year period, starting the year before diagnosis and peaking at the time of diagnosis (AOR = 1.89, 95% CIs = 1.67-2.13). The risk was unevenly distributed: females, those with psychiatric comorbidities or having medicated for psychiatric symptoms, and older individuals were at higher risk of work disability. The results add knowledge to what social and financial harms are associated with GD. Study II maps available treatment in a cross-sectional study surveying practicing PG counselors. This study found that CBT and MI are the most frequently offered treatments and that treating more clients monthly is associated with higher adequacy (OR = 1.49, 95% CIs = 1.12 -1.95), legitimacy (OR = 1.38, 95% CIs = 1.08 – 1.75), and willingness (OR = 1.95, 95% CIs = 1.49-2.61 in their role as PG counselors. Standard CBT techniques and addressing motivation were rated as the most important to include in treatment. Notable, most PG counselors (>70%) saw fewer than two clients monthly with PG. Study III is a qualitative study applying thematic analysis to interviews of individuals with GD or alcohol use disorder, all with recent experiences of craving, and exploring content and modes of thought when craving. Participants described their cravings as dominated by mental imagery often involving positive content of anticipation, carrying out the addictive behavior or expected outcomes. Craving for alcohol was more related to seeking relief and craving for gambling to gain financial assets. Study IV evaluated the feasibility of a transdiagnostic intervention, emotion regulation-enhanced CBT, among treatment seekers in addiction care using a mixed methods design. Participants improved on gambling outcomes: a decrease in symptoms of GD from a pretreatment mean of 7.0 to 2.1 at 12 months follow-up and a reduction in gambling expenditure and comorbid symptoms of depression and anxiety. In addition, participants rated the treatment high in acceptance and credibility, and interviews indicated that individual tailoring and prolongation as potential improvements. Adding emotion regulation to the treatment of GD is feasible and deserves further investigation. Study V investigated changes in gambling behavior during the Covid-19 pandemic in a cohort recruited from social media and a gambling helpline. We did not find associations between pandemic restrictions and increased problem gambling, worries about mental health due to the pandemic (OR = 2.85, p < 0.001), and initiating high-risk gambling formats (OR = 7.44, p < 0.001), such as online casino, were associated with PG. We did not observe any significant migrations between gambling formats despite the change in availability during the initial phase of the pandemic. In conclusion, the findings demonstrate that GD is associated with an elevated risk of work disability, CBT and MI are treatments available, but PG counselors seem to lack important clinical practice; cravings are often experienced as mental imagery of anticipation, and emotion regulation could be added to the clinical toolbox in the treatment for GD and deserves further investigation. In addition, Swedish pandemic restrictions did not seem to have been linked to increased gambling, but those initiating a high-risk gambling format or being worried about their mental health during the pandemic were more likely to develop PG

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Bibliographic Control in the Digital Ecosystem

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    With the contributions of international experts, the book aims to explore the new boundaries of universal bibliographic control. Bibliographic control is radically changing because the bibliographic universe is radically changing: resources, agents, technologies, standards and practices. Among the main topics addressed: library cooperation networks; legal deposit; national bibliographies; new tools and standards (IFLA LRM, RDA, BIBFRAME); authority control and new alliances (Wikidata, Wikibase, Identifiers); new ways of indexing resources (artificial intelligence); institutional repositories; new book supply chain; “discoverability” in the IIIF digital ecosystem; role of thesauri and ontologies in the digital ecosystem; bibliographic control and search engines

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
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