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Cross-docking is a supply chain distribution and logistics strategy for which less-than-truckload shipments are consolidated into full-truckload shipments. Goods are stored up to a maximum of 24 hours in a cross-docking terminal. In this chapter, we build on the literature review by Ladier and Alpan (2016), who reviewed cross-docking research and conducted interviews with cross-docking managers to find research gaps and provide recommendations for future research. We conduct a systematic literature review, following the framework by Ladier and Alpan (2016), on cross-docking literature from 2015 up to 2020. We focus on papers that consider the intersection of research and industry, e.g., case studies or studies presenting real-world data. We investigate whether the research has changed according to the recommendations of Ladier and Alpan (2016). Additionally, we examine the adoption of Industry 4.0 practices in cross-docking research, e.g., related to features of the physical internet, the Internet of Things and cyber-physical systems in cross-docking methodologies or case studies. We conclude that only small adaptations have been done based on the recommendations of Ladier and Alpan (2016), but we see growing attention for Industry 4.0 concepts in cross-docking, especially for physical internet hubs.

This chapter summarises the role of EU actions in supporting healthcare policies in the EU Member States, both looking at implemented actions and describing current priorities for the future. It argues that these coordinated actions can be beneficial for EU Member States by helping them to avoid duplication of effort and to attain economies of scale. Moreover, data sharing with proper safeguards can unleash vast amount of ‘learning what works’ both for medical treatments and for healthcare sustainability measures. The need for this common learning appears ever more urgent while facing the health and economic consequences of the present pandemic.

The Models of Child Health Appraised (MOCHA) project identified the different models of primary care that exist for children, examined the particular attributes that might be different from those directed at adults and considered how these models might be appraised. The project took the multiple and interrelated dimensions of primary care and simplified them into a conceptual framework for appraisal. A general description of the models in existence in all 30 countries of the EU and EEA countries, focusing on lead practitioner, financial and regulatory and service provision classifications, was created. We then used the WHO ‘building blocks’ for high-performing health systems as a starting point for identifying a good system for children. The building blocks encompass safe and good quality services from an educated and empowered workforce, providing good data systems, access to all necessary medical products, prevention and treatments, and a service that is adequately financed and well led. An extensive search of the literature failed to identify a suitable appraisal framework for MOCHA, because none of the frameworks focused on child primary care in its own right. This led the research team to devise an alternative conceptualisation, at the heart of which is the core theme of child centricity and ecology, and the need to focus on delivery to the child through the life course. The MOCHA model also focuses on the primary care team and the societal and environmental context of the primary care system.

The Models of Child Health Appraised (MOCHA) project recognises that child health policy is determined to a great extent by national culture; thus, exploring and understanding the cultural influences on national policies are essential to fully appraise the models of primary care. Cultures are created by the population who adopt national rituals, beliefs and code systems and are unique to each country. To understand the effects of culture on public policy, and the resulting primary care services, we explored the socio-cultural background of four components of policy-making: content, actors, contexts and processes. Responses from the MOCHA Country Agents about recent key national concerns and debates about child health and policy were analysed to identify the key factors as determinants of policy. These included awareness, contextual change, freedom, history, lifestyle, religion, societal activation and tolerance. To understand the influence of these factors on policy, we identified important internal and external structural determinants, which we grouped into those identified within the structure of health care policy (internal), and those which are only indirectly correlated with the policy environment (external). An important child-focused cultural determinant of policy is the national attitude to child abuse. We focused on the role of primary care in preventing and identifying abuse of children and young people, and treating its consequences, which can last a lifetime.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

A policy of surveillance which interferes with the fundamental right to a private life requires credible justification and a supportive evidence base. The authority for such interference should be clearly detailed in law, overseen by a transparent process and not left to the vagaries of administrative discretion. If a state surveils those it governs and claims the interference to be in the public interest, then the evidence base on which that claim stands and the operative conception of public interest should be subject to critical examination. Unfortunately, there is an inconsistency in the regulatory burden associated with access to confidential patient information for non-health-related surveillance purposes and access for health-related surveillance or research purposes. This inconsistency represents a systemic weakness to inform or challenge an evidence-based policy of non-health-related surveillance. This inconsistency is unjustified and undermines the qualities recognised to be necessary to maintain a trustworthy confidential public health service. Taking the withdrawn Memorandum of Understanding (MoU) between NHS Digital and the Home Office as a worked example, this chapter demonstrates how the capacity of the law to constrain the arbitrary or unwarranted exercise of power through judicial review is not sufficient to level the playing field. The authors recommend ‘levelling up’ in procedural oversight, and adopting independent mechanisms equivalent to those adopted for establishing the operative conceptions of public interest in the context of health research to non-health-related surveillance purposes.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

Given that the workforce constitutes a principal resource of primary care, appraisal of models of care requires thorough investigation of the health workforce in all Models of Child Health Appraised (MOCHA) countries. This chapter explores this in terms of workforce composition, remuneration, qualifications and training in relation to the needs of children and young people. We have focused on two principal disciplines of primary care; medicine and nursing, with a specific focus on training and skills to care for children in primary care, particularly those with complex care needs, adolescents and vulnerable groups. We found significant disparities in workforce provision and remuneration, in training curricula and in resultant skills of physicians and nurses in European Union and European Economic Area Countries. A lack of overarching standards and recognition of some of the specific needs of children reflected in training of physicians and nurses may lead to suboptimal care for children. There are, of course, many other professions that also contribute to primary care services for children, some of which are discussed in Chapter 15, but we have not had resources to study these to the same detail.