Exploring Relationships and Information Exchange in Grocery Supply Chains: a Case Study of the Enablers and Inhibitors.

The last decade has seen a growing interest from academics and practitioners in the development of collaborative supply chain relationships based on information exchange. Most of the evidence gathered within this management research area has been biased towards the role of the buyer/supplier dyadic exchange in the integration of the supply chain. The role of the other parties and the systemic nature of supply chain management have been relatively ignored. Previous research in this area has also been biased due to the narrow focus of investigation, with one problem being obtaining access to all parties involved in the supply chain. The purpose of this study was to overcome the aforementioned research biases and therefore, contribute to the understanding of the collaborative relationship development process from a broader supply chain perspective. Open access was gained to six organisations across three tiers of a coffee supply chain in the UK grocery sector. Within this context, a theory building approach was applied to the data collected in the case study. Through constant comparison and coding of data from multiple strategic, tactical, operational, inter- and intra-organisational exchanges within the same context, several findings were made. An interesting finding from the research is the evolving role of the supply chain integrator, whereby the manufacturer seeks to balance the needs of its retail customers with the sourcing and procurement of raw and packaging materials from its suppliers. In terms of the concepts of supply chain relationships and information exchange, there are a number of common enablers and inhibitors. The inter-relationship between the two concepts is however complex and requires further study. The other findings of the research are expressed as a tentative theoretical framework and a series of new emerging enablers and inhibitors to collaborative relationships and information exchange in the supply chain. Finally the enablers and inhibitors grounded from the case study provide a guide to the relational and often context specific factors that can influence the development of collaborative supply chain relationships based on information exchange

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

Towards a Relationship-Centered Approach in Higher Education: The Dynamic Student Development Metatheodel (DSDM)

College success is often too simply measured as ultimate graduation and overlooks students\u27 critical need for a perceived sense of affiliation and belonging to the collegiate community which develops self-identity as a college student and can result in a higher level of performance over the academic lifespan. This article presents the Dynamic Student Development Metatheodel (DSDM) which was developed from common factors identified in multiple theories and models of human development, student development, and learning. When intentionally employed, the DSDM can be expected to improve retention, persistence, and ultimate graduation, as well as improve students\u27 academic and co-curricular experience

Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy

PURPOSE: Minimally invasive treatments for renal carcinoma offer a low rate of complications and quick recovery. One drawback of the use of computed tomography (CT) for needle guidance is the use of iodinated contrast agents, which require an increased X-ray dose and can potentially cause adverse reactions. The purpose of this work is to generalise the problem of synthetic contrast enhancement to allow the generation of multiple phases on non-contrast CT data from a real-world, clinical dataset without training multiple convolutional neural networks. METHODS: A framework for switching between contrast phases by conditioning the network on the phase information is proposed and compared with separately trained networks. We then examine how the degree of supervision affects the generated contrast by evaluating three established architectures: U-Net (fully supervised), Pix2Pix (adversarial with supervision), and CycleGAN (fully adversarial). RESULTS: We demonstrate that there is no performance loss when testing the proposed method against separately trained networks. Of the training paradigms investigated, the fully adversarial CycleGAN performs the worst, while the fully supervised U-Net generates more realistic voxel intensities and performed better than Pix2Pix in generating contrast images for use in a downstream segmentation task. Lastly, two models are shown to generalise to intra-procedural data not seen during the training process, also enhancing features such as needles and ice balls relevant to interventional radiological procedures. CONCLUSION: The proposed contrast switching framework is a feasible option for generating multiple contrast phases without the overhead of training multiple neural networks, while also being robust towards unseen data and enhancing contrast in features relevant to clinical practice

Organizational Alignment and Supply Chain Governance Structure: Introduction and Construct Validation

Purpose – The purpose of this paper is to introduce and validate two new constructs with the potential to sharpen our understanding of how and why firms integrate their internal supply chains and assess the governance structure of their supply chains. The first construct, organizational alignment (OA), is a reflective scale measuring the extent to which upper management attempts to foster integration between internal supply chain functions. The second, supply chain governance structure (SCGS), is a formative index, and is a first attempt at developing a measurement instrument to assess SCGS along multiple dimensions. Design/methodology/approach – Following a literature review, measures of OA and SCGS are conceptualized. These instruments are used to collect data, after which they are refined and validated through parallel scale development (OA) and index construction (SCGS) processes. Findings – OA shows acceptable content and construct validity, and SCGS shows acceptable results for content and item specification, as well as multicollinearity. Practical implications – OA and SCGS may provide some insight into how to promote better internal supply chain integration within the firm, and may allow for an assessment of the governance structure of the firm\u27s supply chain. In different industries and at different times, this knowledge may prove useful in supply chain design and supply base optimization decisions. Originality/value – These scales have considerable applicability in logistics and supply chain management research. Together, they represent initial attempts to assess upper management influence on internal supply chain alignment (OA), and to assess the governance structure of a firm\u27s supply chain

Albuminuria in a remote South Australian Aboriginal community: results of a community based screening program for renal disease

Australi

Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol

INTRODUCTION: Chronic liver disease is a growing cause of morbidity and mortality in the UK. Acute presentation with advanced disease is common and prioritisation of resources to those at highest risk at earlier disease stages is essential to improving patient outcomes. Existing prognostic tools are of limited accuracy and to date no imaging-based tools are used in clinical practice, despite multiple anatomical imaging features that worsen with disease severity.In this paper, we outline our scoping review protocol that aims to provide an overview of existing prognostic factors and models that link anatomical imaging features with clinical endpoints in chronic liver disease. This will provide a summary of the number, type and methods used by existing imaging feature-based prognostic studies and indicate if there are sufficient studies to justify future systematic reviews. METHODS AND ANALYSIS: The protocol was developed in accordance with existing scoping review guidelines. Searches of MEDLINE and Embase will be conducted using titles, abstracts and Medical Subject Headings restricted to publications after 1980 to ensure imaging method relevance on OvidSP. Initial screening will be undertaken by two independent reviewers. Full-text data extraction will be undertaken by three pretrained reviewers who have participated in a group data extraction session to ensure reviewer consensus and reduce inter-rater variability. Where needed, data extraction queries will be resolved by reviewer team discussion. Reporting of results will be based on grouping of related factors and their cumulative frequencies. Prognostic anatomical imaging features and clinical endpoints will be reported using descriptive statistics to summarise the number of studies, study characteristics and the statistical methods used. ETHICS AND DISSEMINATION: Ethical approval is not required as this study is based on previously published work. Findings will be disseminated by peer-reviewed publication and/or conference presentations

Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation

Image quality assessment (IQA) in medical imaging can be used to ensure that downstream clinical tasks can be reliably performed. Quantifying the impact of an image on the specific target tasks, also named as task amenability, is needed. A task-specific IQA has recently been proposed to learn an image-amenability-predicting controller simultaneously with a target task predictor. This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e.g. segmentation and classification neural networks in modern clinical applications. In this work, we propose an extension to this task-specific IQA approach, by adding a task-agnostic IQA based on auto-encoding as the target task. Analysing the intersection between low-quality images, deemed by both the task-specific and task-agnostic IQA, may help to differentiate the underpinning factors that caused the poor target task performance. For example, common imaging artefacts may not adversely affect the target task, which would lead to a low task-agnostic quality and a high task-specific quality, whilst individual cases considered clinically challenging, which can not be improved by better imaging equipment or protocols, is likely to result in a high task-agnostic quality but a low task-specific quality. We first describe a flexible reward shaping strategy which allows for the adjustment of weighting between task-agnostic and task-specific quality scoring. Furthermore, we evaluate the proposed algorithm using a clinically challenging target task of prostate tumour segmentation on multiparametric magnetic resonance (mpMR) images, from 850 patients. The proposed reward shaping strategy, with appropriately weighted task-specific and task-agnostic qualities, successfully identified samples that need re-acquisition due to defected imaging process.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.or