The findings arrive at a moment when supply chains have moved from the operational back office to the front pages. The pandemic, Brexit-related border friction, the Suez Canal blockage and ongoing geopolitical instability have exposed how fragile modern logistics networks can be, and vendors of AI-powered planning and visibility platforms have been quick to position their products as the remedy. The new research, however, suggests that the relationship between technology investment and supply chain performance is far more conditional than much of the marketing implies, and that organisations frequently underestimate the organisational work required before analytics can pay off.

To reach its conclusions, the team, drawn from the Royal Docks School of Business and Law at the University of East London and the University of Hertfordshire, conducted a large-scale systematic review of the academic literature. They examined more than 500 studies published over a decade, ultimately selecting 145 papers from the period between 2015 and 2026 for detailed analysis. The reviewed research spanned analytics, supply chain visibility, Industry 4.0, resilience, sustainability and governance, giving the authors an unusually broad evidence base from which to assess when and why big data analytics actually creates value in operations and supply chain management.

The central pattern that emerged from the evidence is that technology is a necessary but insufficient ingredient. Analytics platforms can crunch enormous volumes of transactional, sensor and logistics data to detect demand signals, flag supplier risk and shorten reaction times when disruption strikes. Yet the value of those outputs depends entirely on whether the information they produce is trusted, understood and acted upon by human decision-makers. A forecasting model that is statistically impressive but poorly explained, or a visibility dashboard that managers regard with suspicion, contributes almost nothing to operational performance, however advanced its underlying algorithms may be.

Dr Godfried Adaba, Lecturer in Supply Chain Management at the University of East London, summarised the message for business in blunt terms. ‘The message for businesses is that buying the latest AI or data technology is not enough. These tools can help businesses but only if the foundations are already in place,’ he said. He added that organisations need reliable data, people with the right skills, clear responsibility for decisions and managers who understand and trust the information they are being given. ‘If those things are missing, advanced technology can become an expensive system that delivers little real value,’ he warned.

The skills dimension is one of the most persistent constraints identified in the literature. Big data analytics in a supply chain context requires people who can combine statistical and computational competence with deep operational knowledge of procurement, logistics and production planning. Data scientists who do not understand how warehouses, transport networks or supplier relationships actually work can build models that are technically elegant but practically useless, while experienced operations managers who lack analytical literacy may dismiss or misinterpret valid analytical findings. The researchers point to the importance of hybrid teams that bring together technical and operational expertise, allowing analytical outputs to be shaped by practical realities and operational judgement to be informed by evidence.

Data quality and data governance form the second pillar of the argument. Machine learning models and AI systems are only as good as the data they are trained and run on, and supply chain data is notoriously messy, fragmented across enterprise systems, suppliers, carriers and customers, and often recorded to inconsistent standards. Poor master data, gaps in historical records and inconsistent definitions of key measures can silently corrupt even advanced models, producing forecasts and risk scores that look authoritative but are built on sand. Strong data governance, encompassing clear ownership, quality controls, standardised definitions and appropriate access rules, is therefore a precondition for analytics success rather than an optional add-on.

The third element is managerial: the ability to convert information into decisions. The review highlights that analytics creates value through a chain that runs from data to insight to decision to action, and the chain breaks wherever responsibility for decisions is unclear or where organisational incentives discourage acting on the evidence. Companies that treat an AI investment as an IT procurement exercise, delegating it to a technology function without changing decision rights, processes and performance metrics, tend to see sophisticated outputs ignored on the ground. By contrast, organisations that embed analytical findings into routine planning cycles, and whose managers understand and trust the tools, capture measurable improvements in forecasting accuracy, responsiveness and resilience.

On the basis of the evidence, the authors recommend that businesses strengthen their data governance, analytical skills and decision-making processes before expanding their use of advanced analytics. In practical terms, that means auditing data quality across the supply network, investing in training that bridges the technical and operational divide, clarifying who owns which decisions, and building the trust that allows front-line managers to act on what the systems tell them. Only then, the research suggests, can investments in AI, big data, cloud computing and the Internet of Things translate into faster, more resilient and more sustainable supply chains rather than shelf-ware.

The study, titled ‘Big data analytics value creation in operations and supply chain management: a review of capabilities, conditions and constraints’, is published in Benchmarking: An International Journal by Emerald Publishing. For British firms weighing further technology spending against continuing supply chain turbulence, the paper’s core lesson is deliberately unglamorous: artificial intelligence amplifies the quality of an organisation’s data, people and management rather than substituting for them, and firms that skip the foundational work should expect their AI ambitions to underperform the hype.

Subject of Research: The role of big data analytics capabilities in supply chain management and operations

Article Title: Why AI won’t fix Britain’s supply chains

Article References: Why AI won’t fix Britain’s supply chains. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, big data analytics, supply chains, supply chain resilience, data governance, Industry 4.0, Internet of Things, cloud computing, decision-making, operations management, University of East London, literature review

The core insight of the new study is deceptively simple. Different anions interact with surrounding materials in markedly different ways, and one of the most important determinants of that behaviour is how tightly each atom within the anion holds on to its electrons. An anion that readily shares electron density with a neighbouring electrode material will behave very differently in a battery than one that clings stubbornly to its charge. Until now, quantifying this so-called electron-donating tendency for a wide range of anions has required laborious, expensive laboratory experiments, one chemical at a time, which has slowed the pace of materials discovery considerably.

The University of East London team, working alongside their co-authors, attacked the problem with a two-pronged strategy combining experiment and computation. On the experimental side, they turned to X-ray photoelectron spectroscopy, or XPS, a technique that works by firing X-rays at a material and carefully measuring the energies of the electrons that are knocked loose in the process. Because each element and each chemical bonding environment produces a characteristic signature, XPS allows researchers to identify precisely which atoms are present in a sample and, crucially, how those atoms are bonded to their neighbours. By examining the photoelectron spectra of anions interacting with battery-relevant surfaces, the team could read off directly how strongly specific atoms within each anion were gripping their electrons.

That measurement alone would have been a useful incremental advance, but the researchers went a step further. They then built computer models capable of recreating those spectroscopic measurements virtually, calibrating the simulations against the experimental XPS data until the two agreed. Once the computational framework was validated, it could be used to predict the electron-donating behaviour of anions that had never been measured in the laboratory, effectively generating a fast, inexpensive screening tool. In practical terms, this means chemists could evaluate thousands of candidate anions on a computer and shortlist only the most promising ones for physical synthesis and testing, dramatically reducing the time and cost that typically separate a good idea from a working material.

The team describes the resulting quantities as element-specific donor numbers for anions, a metric that captures, atom by atom, the willingness of a negatively charged species to share electron density with its environment. Donor numbers have a long history in chemistry as a way of ranking how strongly solvents and other species bind to central atoms, but extending the concept to individual atoms within anions, and grounding it in hard spectroscopic data rather than indirect inference, is what distinguishes the new work. The approach gives researchers a common, quantitative language for comparing anions that previously had to be assessed through scattered, experiment-specific observations.

The immediate application is battery chemistry, where the choice of anion in an electrolyte influences everything from ionic conductivity to the stability of the electrode-electrolyte interface and, ultimately, how many charge and discharge cycles a cell can survive before degrading. Fluorinated anions, for example, are prized in lithium-ion and next-generation lithium metal batteries precisely because of the way their electron-withdrawing behaviour shapes protective interphases on electrode surfaces. With a validated predictive model in hand, researchers designing such electrolytes can now reason about candidate anions in terms of measured and modelled electron-donation properties rather than relying on trial-and-error, focusing their efforts on the materials that show the most promise.

Dr Richard Matthews, Senior Lecturer of Physical and Computational Chemistry at the University of East London and co-author of the study, emphasised the wider significance of the advance. We now have a much clearer picture of how anions interact with other materials, and that opens up some exciting possibilities, he said. By being able to predict these interactions, we can focus our efforts on the materials that show the most promise for better batteries and beyond. His framing captures the essential economic argument behind the work: in a field where every new candidate material must traditionally pass through synthesis, characterisation and testing, any method that filters out dead ends on a computer can save months of effort and substantial research funding.

The implications, however, stretch well beyond the electrochemistry laboratory. The researchers note that the measurement-and-modelling workflow could eventually be scaled up into a comprehensive database spanning many different elements and bonding environments. Such a dataset would be a valuable raw material for machine learning and artificial intelligence systems, which thrive on large volumes of high-quality, consistently defined measurements. Trainable models fed with element-specific donor numbers could surface unexpected patterns in chemical behaviour, propose novel anion structures for synthesis, and generally compress the discovery cycle for a wide range of chemical processes, not only those relevant to energy storage but also catalysis, corrosion science and medicinal chemistry, where anion interactions play equally decisive roles.

The study also illustrates a broader trend in modern materials research, in which the boundary between measurement and prediction is becoming increasingly porous. Spectroscopy provides the ground truth that anchors computational models to reality, while the models extend the reach of a limited number of experiments to a vast space of unmeasured chemistry. This virtuous loop, in which each new experimental data point improves the accuracy of predictions across an entire chemical family, is quickly becoming the standard playbook for labs hoping to compete in the race toward improved batteries, including the solid-state systems and sodium-based chemistries that many companies and national programmes are pursuing as alternatives to today’s lithium-ion technology.

For now, the University of East London team and their collaborators have laid the foundation, publishing a peer-reviewed method that others can adopt, replicate and extend. The paper, titled Element-Specific Donor Numbers for Anions, appeared in the Journal of the American Chemical Society under the DOI 10.1021/jacs.6c06567. If the framework spreads as widely as its authors hope, the humble anion, long treated as an afterthought in popular discussions of battery technology, may finally receive the systematic, quantitative attention it deserves, and consumers could one day reap the benefits in devices that hold their charge longer, degrade more slowly and cost less to develop.

Subject of Research: Element-specific donor numbers for anions as a predictive tool for battery material discovery

Article Title: Scientists discover method that could lead to longer-lasting batteries

Article References: Scientists discover method that could lead to longer-lasting batteries. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: batteries, anions, X-ray photoelectron spectroscopy, University of East London, electrolytes, computational chemistry, machine learning, energy storage, materials science, donor numbers, chemical discovery, electrochemistry

The relationship between the urban environment and public health is one of the most robustly established findings in modern epidemiology. The quality of housing, the design of transport infrastructure, the availability of green space and the walkability of neighbourhoods all measurably influence the risk that residents will develop life-threatening conditions. Poorly connected communities with limited access to parks and active travel options show elevated rates of obesity, cardiovascular disease, respiratory illness and several cancers. Yet despite decades of evidence, improving urban environments as a preventative public health measure remains extraordinarily complex, because it demands coordinated action from multiple actors with different mandates, budgets and priorities. Planning authorities, housing developers, transport agencies and health services each hold only part of the levers required to create healthier places, and no single organisation bears the full cost of the diseases that bad design can produce.

It is precisely this fragmentation that the researchers set out to address. To assess how economic evidence could support more integrated decision-making, the team conducted 167 qualitative interviews with key stakeholders drawn from across England’s urban development system, spanning national and local government, private industry and civil society. The interviews explored in depth how the HAUS tool could support collaboration and informed decision-making across sectors that rarely share a common evidence base or a common metric of success. What emerged was a striking consensus: stakeholders recognised the value of HAUS as a new way to support shared decision-making and to create incentives for organisations to work together toward better health outcomes, precisely because it assigns an economic value to impacts that have historically been invisible in development appraisals.

The technical core of HAUS is a valuation model that links specific features of the environment to health outcomes and their associated economic costs. The tool quantifies and monetises changes in disease occurrence and premature mortality linked with different urban development interventions and affecting local populations. In practical terms, a proposal to add walkable streets, expand green infrastructure or improve housing quality can be assessed not only in terms of construction costs and property values, but also in terms of the expected reduction in NHS treatment costs, productivity losses and the broader societal burden of illness. By translating health outcomes into economic terms, the tool helps decision-makers grasp the long-term value of healthier urban environments and supports more informed investment and planning decisions that might otherwise be dismissed on short-term financial grounds.

The tool did not appear from nowhere. It was initially developed during an earlier research pilot and was further refined throughout the project in collaboration with the UK’s Ministry of Housing, Communities and Local Government, giving it an unusually direct route from academic prototype to policy application. That collaboration has now borne institutional fruit: the ministry has adopted HAUS in its appraisal guides for local authorities, embedding health valuation into the standard machinery of English planning assessment. For a field in which public health arguments have long struggled to compete with housing delivery targets and economic growth imperatives, this represents a significant shift in what evidence counts when the future of a city is decided.

Dr Andrew Barnfield, Senior Lecturer in public health at the University of East London and co-author of the study, emphasised that the tool changes what decision-makers are able to see. ‘The HAUS tool enables decision makers to account for health costs that are linked with the urban environment,’ he said. ‘As our work shows, this is vital for making healthier and more equitable places.’ The equity dimension matters because the health burden of poor urban design falls disproportionately on disadvantaged communities, which are more likely to live near major roads, in lower-quality housing and with limited access to green space. By making these costs explicit, HAUS gives advocates for those communities a quantitative argument that resonates in boardrooms and town halls alike.

Professor Sarah Ayres, from the University of Bristol’s School for Policy Studies, framed the development as a genuine methodological milestone. ‘The collaboration between TRUUD and the Ministry of Housing, Communities and Local Government has created a version of the Health Appraisal of Urban Systems model which is to be used for housing, community and local government interventions,’ she said. ‘For the first time, there are now methods to quantify and value the potential health impacts of urban interventions for specific populations.’ The reference to TRUUD, the research consortium on urban health decarbonisation and decision-making of which the three universities are part, underlines the scale of the effort required to move such tools from theory into the working routines of government.

Dr Geoff Bates, from the University of Bath’s Institute for Policy Research, argued that the economic framing is not a concession to fiscal orthodoxy but a strategic necessity. ‘Making the economic case to pursue preventative policies is one way that can persuade policymakers to act to improve long-term health and wellbeing,’ he said. ‘Improving policymaker access to evidence and tools like HAUS that demonstrate the savings to the public purse from investing in healthier housing and urban environments is a step towards improving public health.’ The logic is familiar from other domains of preventative policy: the costs of disease are diffuse and delayed, while the costs of intervention are immediate and concentrated, and only tools that render future savings visible can rebalance that asymmetry in political decision-making.

The findings arrive at a moment when the pressures on urban systems are intensifying. Rising obesity rates, an ageing population, persistent air pollution and the health impacts of climate change all converge on the built environment, and health services across the developed world are increasingly looking upstream toward prevention. The researchers conclude that HAUS provides valuable evidence to encourage cross-sector collaboration and to embed preventative public health into urban development decisions, shifting the question from whether cities can afford to prioritise health to whether they can afford not to. If the tool’s adoption by national government spreads through local authority practice, the healthier city may cease to be an aspiration and become, at last, an audited line in the appraisal.

Subject of Research: Economic valuation of public health impacts in urban development decision-making

Article Title: Economic tool could help cities prioritize public health in urban development

Article References: Economic tool could help cities prioritize public health in urban development. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: urban development, public health, HAUS tool, economic valuation, planning policy, prevention, health equity, built environment, stakeholder interviews, England, University of East London, ministry adoption

The conceptualization of the South Asia Careers Hub aligns closely with the growing emphasis on bridging academic disciplines with practical skills tailored to regional economic demands. Initially, the Hub will hone its academic focus on Health and Behavioural Sciences, Engineering, Business Management, and Psychology. These fields were selected for their strategic resonance with Tamil Nadu’s economic and social developmental priorities, notably in sectors such as healthcare innovation, life sciences, and advanced manufacturing technologies. By aligning academic expertise with governmental and industrial objectives, the Hub aspires to drive inclusive growth through skill enhancement and entrepreneurial innovation.

Designed to operate at the intersection of academia and industry, the Hub’s model is emblematic of an emerging pedagogical paradigm—one where education is co-created with active participation from key stakeholders beyond the university, including government agencies and private-sector leaders. In partnership with the Tamil Nadu Industrial Development Corporation (TIDCO) and the Government of Tamil Nadu, UEL aims to forge a dynamic interface where theoretical knowledge is seamlessly translated into real-world applications. This synergy will empower students and professionals with hands-on experience integral to sectors undergoing rapid transformation, including digital innovation, health technologies, and the creative industries.

The announcement coincided with the India International Higher Education Summit (IGES), an event emblematic of the growing global focus on cross-border educational collaborations. Professor Amanda Broderick, Vice-Chancellor and President of UEL, led the University’s delegation and expounded on the university’s vision to embed its careers-centric, enterprise-led education model within the South Asian context. She emphasized that Chennai’s vibrant ecosystem of education, technology, and innovation serves as an ideal incubator for nurturing global talent tailored to regional demands.

This partnership foregrounds the growing importance of workforce development models that emphasize employability through industry immersion and applied research. The planned Hub will actively engage with sectors such as advanced manufacturing, health technologies, and digital innovation, addressing critical skill gaps that constrain India’s industrial competitiveness. By positioning students and professionals at the forefront of cutting-edge industry trends, the Hub intends not only to enhance individual career trajectories but also to catalyze broader socioeconomic advancements throughout the region.

Importantly, the Hub will foster lifelong learning and alumni engagement by cultivating enduring links between academia and industry. As professionals navigate evolving career landscapes, continuous education and knowledge exchange become paramount. The Hub’s initiatives will support this trajectory, facilitating a continuum of learning that adapts to emerging challenges and opportunities in the labor market. Furthermore, this engagement will invigorate opportunities for academic and research collaborations aligned with global innovation standards.

From a policy standpoint, the establishment of the Hub signals significant international confidence in Tamil Nadu’s governance framework, talent ecosystem, and policy stability. Formalized through a Memorandum of Understanding with TIDCO, the initiative resonates with the India–UK Vision 2035—an ambitious roadmap fostering bilateral cooperation in education, research, and workforce development. The Hub’s alignment with Tamil Nadu’s aspiration to be recognized as a global nexus for education, innovation, and talent mobility positions it as a critical node within this broader strategic context.

The collaboration embodies a forward-thinking approach to higher education that transcends national boundaries and disciplinary silos. It exemplifies a model where universities function as catalysts for regional development by responding dynamically to industrial needs and societal challenges. This model underscores the importance of interdisciplinary collaboration and knowledge co-creation at the confluence of health sciences, engineering, business, and psychology—fields increasingly interwoven in addressing complex societal issues.

With the Hub’s launch expected in 2026, there is a concerted emphasis on developing infrastructure and academic programs that are both globally competitive and regionally relevant. The incorporation of executive education and start-up support within the Hub’s offerings reflects an understanding that entrepreneurial agility and leadership development are critical to fostering a resilient workforce capable of adapting to technological disruptions and market volatility.

Moreover, the initiative highlights how universities can leverage their position as knowledge hubs to facilitate cross-sector innovation. By embedding applied research partnerships within the Hub, UEL aims to generate transformative knowledge that directly contributes to industrial advancements and policy innovation. This approach facilitates the translation of academic insights into scalable solutions that benefit society, from enhancing healthcare delivery systems to optimizing manufacturing processes.

The involvement of prominent leaders such as Professor Amanda Broderick further signifies the University of East London’s commitment to driving excellence through strategic international partnerships. Such leadership ensures that the Hub’s implementation remains aligned with global best practices while being sensitive to local cultural and economic contexts. It reflects a nuanced understanding that sustainable development necessitates educational models that are both adaptive and inclusive.

In sum, the establishment of the South Asia Careers Hub in Chennai represents a landmark convergence of academic ambition, industrial collaboration, and regional development strategy. By intentionally integrating education with practical experience and research, the Hub aspires to become a beacon of innovation and talent cultivation, driving inclusive economic growth in Tamil Nadu and beyond. This initiative not only reinforces Chennai’s stature as a premier destination for global education and advanced skills development but also sets a precedent for future international educational partnerships in emerging markets.

Subject of Research: Development of integrated academic-industry collaboration models focusing on workforce development, applied research, and innovation in Health and Behavioural Sciences, Engineering, Business Management, and Psychology within the South Asian context.

Article Title: University of East London to Launch South Asia Careers Hub in Chennai: Pioneering Academic-Industry Synergy for Regional Innovation and Workforce Development

News Publication Date: Not specified; announcement made during the India International Higher Education Summit (IGES) prior to Autumn 2026 opening.

Web References: https://mediasvc.eurekalert.org/Api/v1/Multimedia/65f00e08-40e8-4e36-ba40-a4fff88a641c/Rendition/low-res/Content/Public

Image Credits: University of East London

Keywords: South Asia Careers Hub, University of East London, Chennai, Industry-Academia Collaboration, Workforce Development, Applied Research, Health Sciences, Engineering, Business Management, Psychology, Tamil Nadu Industrial Development Corporation, Innovation, Higher Education, India-UK Vision 2035