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Marketing / Consumer Marketing
1,987 words
MindBand: A Creative Marketing Plan for a Mental Wellbeing Wearable in the UK Smart Device Market
This postgraduate marketing report develops a creative marketing plan for MindBand, a proposed smart wearable designed to support mental wellbeing and emotional regulation in the UK wearable-device market. The assessment responds to a brief requiring students to identify an unmet product need, create an original product concept and apply strategic marketing principles to establish how the innovation could attract consumers in a highly competitive smart-device industry. Reassessment-Individual Assignm… The analysis identifies a potential gap between mainstream fitness-focused wearables and consumers seeking discreet, everyday support for stress, emotional wellbeing and cognitive fatigue. MindBand is positioned as a minimalist wrist-worn device that uses biometric indicators such as heart-rate variability, skin conductance and temperature to identify stress-related patterns and provide context-sensitive interventions. Unlike conventional wearables that primarily display performance data, the concept emphasises behavioural support, simplicity and low-effort interaction. Creative Marketing Plan for a P… Creative Marketing Plan for a P… The marketing plan targets UK professionals aged approximately 30–55, particularly individuals experiencing high cognitive workloads, digital fatigue and work-life pressures. The proposed value proposition focuses on personalised emotional support, discretion and ease of use rather than extensive fitness functionality. This positioning is reinforced through a calm, trust-oriented brand identity intended to distinguish MindBand from performance-led smartwatch and fitness-tracker brands. Creative Marketing Plan for a P… Creative Marketing Plan for a P… The communications strategy adopts a digital-first approach, using educational content, podcasts, professional experts, thought leadership, paid media and customer testimonials to build credibility and awareness. Distribution is primarily direct-to-consumer through e-commerce, supplemented by partnerships with corporate wellbeing programmes and healthcare providers. A premium-value pricing strategy and optional subscription-based services are proposed to support recurring revenue and continued product development. Creative Marketing Plan for a P… The report also considers performance measurement, brand equity, customer retention, privacy, informed consent and responsible use of biometric data. Overall, the work integrates product innovation, consumer behaviour, segmentation, positioning, communications, pricing, distribution and ethical marketing into a coherent smart-wearable marketing proposal.
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Supply Chain Operations and Logistics Management
3,000 words
Critical Review of Global Logistics: Supply Chain Strategy, Resilience and Sustainability
This postgraduate logistics assessment requires students to conduct a critical review of the global logistics operations and strategies of a selected multinational manufacturing organisation. The chosen company must operate internationally and have sufficient publicly available information to support detailed academic and industry research. The report examines how global manufacturers structure, manage and improve their upstream and downstream logistics channels in increasingly complex and uncertain supply-chain environments. Component_2_NBS_7054A_25_Report… The first part analyses the organisation’s upstream and downstream logistics channels and evaluates the logistics strategies it has adopted. Particular attention is given to how the organisation responded to disruption caused by the COVID-19 pandemic. Students examine how supply-chain networks were reconfigured and how logistics strategies were adapted to maintain operations, while explicitly relating these responses to relevant performance objectives and supply-chain dimensions. Component_2_NBS_7054A_25_Report… The second part focuses on value creation within global logistics networks. Students evaluate how supply-chain intermediaries contribute to value co-creation while balancing operational costs and reducing waste or Muda. The analysis also considers how communication and information sharing support the objectives of the organisation’s marketing channel network and identifies lessons learned from pandemic-related disruption. Component_2_NBS_7054A_25_Report… The final section examines how the organisation can strengthen the sustainability of its logistics operations. Students assess the feasibility of aligning circular economy principles and Industry 4.0 technologies with existing inbound and outbound logistics activities. Relevant issues include reduced material use, reuse, repair, refurbishment, alternative transport modes, carbon-footprint reduction and environmental efficiency. Students must also recommend operational decisions that apply innovation-led lean approaches to sustainability initiatives. Component_2_NBS_7054A_25_Report… The marking criteria place 25% of the assessment on logistics theories and strategies, 35% on value co-creation, communication and resilient logistics networks, and 40% on circular economy, Industry 4.0 and sustainability recommendations. Marking_Criteria_NBS_7054A_25_R… Overall, the assessment integrates global logistics strategy, resilience, lean operations, digitalisation, sustainability and evidence-based supply-chain decision-making within a real organisational case study. Overview word count: approximately 340 words.
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Integrated Communications
2,000 words
CW2 Communications Engagement Report – Client Stream
This assignment requires students to produce an individual 2,000-word Communications Engagement Report in the form of an engagement communications campaign for Stream. Stream is a collaboration between 16 UK water companies, supported by industry and civil society partners including the Open Data Institute, with the aim of unlocking the potential of water-sector data to benefit customers, society and the environment. The organisation has established data-sharing infrastructure designed to enable data to be used to generate insights, support innovation, improve decision-making and contribute to greater transparency across the water sector. The central focus of the assignment is to develop a communications campaign that attracts and engages the research and academic community. Stream has identified postgraduate and postdoctoral students, together with relevant university faculty leads, as an important priority audience because of their potential to transform published water-sector data into valuable research insights, innovative products and services. Relevant academic departments may include computer science, engineering, mathematics, environmental science, social science, town planning and business development. Students are expected to consider how this audience can be reached, segmented and prioritised according to its interest and willingness to engage with Stream. The proposed campaign should build awareness of Stream, communicate the value of accessing and using water-sector data, and encourage longer-term engagement between Stream, universities and students. Existing communication channels include LinkedIn, the Stream website and communications distributed through delivery partners such as the Open Data Institute and Aiimi, as well as Ofwat. Key campaign opportunities include Open Data Day in March and Open October in mid-October. These events should be considered as important campaign moments while also being integrated into a longer-term communications programme. The report should include an introduction and background to Stream, including brand history, market position, competitive analysis, the need for an engagement communications campaign and relevant market research. It should establish short-, medium- and long-term communication objectives with clear timeframes, define the target audience, and apply an appropriate theoretical framework to develop the communication strategy. Students must provide four recommended communication pieces supported by detailed rationale, together with a Gantt chart showing campaign timings and a clear campaign measurement approach. The report should conclude with a summary of the overall proposal. The assessment requires critical application of communication theories and concepts, research evidence and relevant literature. Students must demonstrate analysis, evaluation and justification rather than simply describing communication activities. A minimum of 15 high-quality references is required, including at least eight journal articles, using Cite Them Right Harvard referencing. The report is assessed on presentation and structure, intellectual curiosity and referencing, content and application, campaign integration, discussion, conclusion, recommendations, campaign timeframes and measurement.
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2,000 words
Covid 19 and the Automotive Industry
This individual report examines the impact of COVID-19 on the automotive industry and the management of automotive supply chains during the disruption. The assignment requires students to select a company operating in the automotive sector and focus the report on a particular product manufactured by that company. The analysis must consider how the COVID-19 pandemic disrupted the supply chain and how the resulting social impacts and government-imposed restrictions affected automotive supply chain activities. The report requires consideration of both upstream and downstream activities within the selected automotive supply chain. Students are expected to analyse the effects of COVID-19 on the chosen country, organisation or product and examine how entities within the supply chain responded to the disruptive events and government restrictions. The analysis should consider the actions taken by supply chain participants and evaluate the successes and failures associated with their approaches to managing the disruption. A central requirement of the assignment is to examine supply chain management and adaptation during the pandemic. Students should explain how the selected automotive company and relevant supply chain entities responded to disruption and restrictions, considering the challenges affecting the movement and management of supply chain activities. The report should then provide independent analysis of the effectiveness of the approaches used rather than simply describing the events or responses. The assignment also requires students to develop recommendations for sustainably managing an automotive supply chain in the event of similar disruptions in the future. These recommendations should follow from the analysis of the selected company, product and supply chain and should address how supply chain management could be improved to respond more effectively to disruptive events. The suggested report structure consists of an introduction, background, supply chain impacts, supply chain management, analysis, recommendations and conclusions, followed by references. The background section should discuss the COVID-19 disruption and describe the relevant automotive supply chain, while the supply chain impacts section should examine the effects on the selected country, organisation or product. The supply chain management section should discuss how the supply chain adapted to the disruption, followed by an independent analysis of the successes and failures of those management responses. The report must be supported by ample and suitable academic and other appropriate references. The brief specifically requires Harvard referencing and states that students should cite core books, journal articles and non-journal articles. Blogs and Wikipedia should be avoided. The completed report has a maximum length of 2,000 words and the stated submission deadline is 5:00 pm on 10 December 2025.
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Sustainable Development / Resource Management
6,000 words
Resource-Related Challenges in a Selected Country (STREAM) — Term Paper: India's Energy Sector, Coal Dependence and the Transition to Solar and Storage
This first-semester term paper for the STREAM programme takes a single country and a single resource sector and examines the challenges that arise where the two meet. India was selected for its energy sector — a case where scale, growth rate and an entrenched coal base make the tension between development need and environmental limit unusually sharp. The paper is written as an academic scientific text of roughly twenty pages, structured across the four sections the brief specifies and supported throughout by peer-reviewed literature, institutional reports and official statistical sources. The opening section establishes the country context: geographical position and climatic variation, population size and distribution, the shape and growth trajectory of the economy, development status against recognised indicators, and why the energy sector in particular is decisive for the country's near-term development path. The second section analyses the current status of the resource itself. Domestic coal reserves and their geographical concentration are set against renewable potential, particularly solar irradiance across the western and southern states. Production and consumption figures are traced over recent years, the import dependency for crude oil, gas and coking coal is quantified, and the major pressures are identified — demand growth outpacing capacity addition, the financial condition of distribution utilities, grid integration limits for variable generation, and the storage gap that constrains how far solar can displace baseload. The third section covers infrastructure and value chains. It describes the generation fleet, transmission and distribution network, and the logistics moving coal from pithead to plant, then follows the value chains attached to the resource — mining, power generation, equipment manufacturing and the growing domestic solar module and cell industry. Key stakeholders are mapped across central and state government, regulators, public and private generators, distribution companies, industrial consumers and the mining workforce whose livelihoods a transition directly affects. The final section addresses environmental problems and their connections to resource use. Ambient air quality and its public health burden, the water demand of thermal generation in already water-stressed basins, land degradation and displacement around mining regions, ash management and greenhouse gas emissions are each examined as consequences of the existing energy system rather than as separate issues. The section closes on future developments and risks: the plausible trajectories for renewable capacity and storage deployment, the stranded asset question for recently built thermal plants, and what a socially just transition would require of policy.
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Data Visualisation / Business Intelligence
2,500 words
Data Visualisation (BS666) — Business Analyst Client Report: Dashboard Development, Tool Evaluation and Accessibility in Power BI and Tableau
This Level 7 resit assessment takes the form of a single client-facing report written from the position of a qualified business analyst. Rather than assembling semester activities, it asks for one sustained piece of analytical writing that carries a business case from raw open data through to a defended set of visualisations and the decisions they support. The report opens by identifying a data source drawn from an approved open repository and setting out the business problem the client faces. Provenance, structure, granularity and known limitations of the dataset are examined honestly at this stage, since every downstream claim rests on them, and all data is referenced in full — including data appearing inside charts, as the brief specifically requires. The main body works through three connected strands of critical evaluation. The first traces the development sequence of the visualisations themselves: how the data was prepared and cleaned, why particular chart types were selected over the alternatives available, how layout and interactivity were arranged for the intended audience, and how the design changed across iterations once weaknesses became visible. At least three completed visualisations are then evaluated individually and critically — what each one reveals, where each falls short, and what a reader could reasonably conclude from it. The second strand compares Power BI and Tableau as working environments for this specific dataset rather than in the abstract. Data connectivity, transformation and calculation capability, visual flexibility, publishing and sharing, licensing and governance are all weighed against what the client actually needs, with the practical friction encountered during the build reported rather than smoothed over. The third strand addresses accessibility and cognitive processing. It examines how each platform handles colour contrast and colour-vision deficiency, text alternatives, keyboard navigation and screen reader support, and then moves into the perceptual side — pre-attentive attributes, data-ink economy, chart junk, working memory limits and how visual encoding choices either reduce or inflate the effort a reader must spend to extract meaning. The argument connects this directly to decision quality in organisations with diverse analytical literacy. Findings are reported at length and translated into concrete business implications, with a conclusion that states what the client should do and on what evidence. The submission follows the prescribed structure throughout: title page, executive summary, contents, introduction, business problem, main evaluative section, findings, conclusion, Harvard reference list and appendices, presented as a single file for Turnitin.
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Data Science / Deep Learning
3,000 words
Advanced Research Topics (7PAM2016) — Building GANs from Scratch and Applying Them to Medical Imaging, Network Traffic and Sketch Generation
This Masters-level assessment asks for a complete generative adversarial network study, delivered as an annotated code submission carrying sixty per cent of the marks and a six-to-eight page technical report carrying the remaining forty. The work spans four separate GAN implementations, moving from a controlled synthetic setting into three contrasting real-world application domains. Part one builds a GAN from scratch in PyTorch on synthetic two-dimensional data. The tutorial sine-wave generator is reproduced first as a baseline, then a new distribution is modelled — a noisy parametric curve of the form y = sin(2x) + 0.3cos(5x) with an additive noise term — before the architecture itself is varied. Activation functions and layer depth are altered systematically and the resulting sample distributions plotted against the originals, so the effect of each architectural choice on convergence and sample fidelity can be seen rather than asserted. Part two applies the same principles at scale across three domains. The medical strand trains a DCGAN on the OCTMNIST subset of MedMNIST, generating synthetic optical coherence tomography retinal images, tracking generator and discriminator losses across training, and evaluating output both visually and quantitatively using Fréchet Inception Distance. A conditional GAN extension conditions the generator on class label so that images for a chosen retinal pathology can be produced on demand. The cybersecurity strand shifts from images to feature vectors, using preprocessed CICIDS 2017 network intrusion data. Benign and DoS traffic is combined and explored for class balance, a GAN is built to synthesise tabular feature vectors rather than pixels, and real against generated distributions are compared through PCA and t-SNE projections, with a discussion of how well the model generalises across attack types. The creative strand trains a DCGAN on the QuickDraw 'birthday cake' sketch category, tracking visual outputs epoch by epoch and benchmarking generated sketches against real ones, with an extension covering additional categories of differing sketch complexity. The accompanying report explains the analysis steps and the reasoning behind each architectural decision rather than restating textbook definitions of the method. It gives brief descriptions of the models used, presents generated samples and loss curves as figures, interprets the evaluation metrics, and reflects honestly on failure modes — training instability, mode collapse, and the visible flaws in synthetic output that determine whether such data is fit for downstream use. The code is written as reusable functions, commented for a reader other than its author, and reproduces every figure and numerical value quoted in the report.
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Artificial Intelligence
2,000 words
End-to-End Applied AI Development — Comparative Machine Learning and Neural Network Modelling on a Public Dataset
This assessment runs a complete applied AI development cycle end to end: problem definition, dataset selection, preprocessing, model building, optimisation, evaluation and critical reflection. Students identify a real-world problem themselves, formulate a research question from it, and source a suitable dataset from a recognised public repository such as UCI, Kaggle, Data.gov or OpenML. Dataset choice carries more weight than students expect. It must be genuinely suitable for supervised learning, complex enough to make preprocessing and feature engineering meaningful, and — critically — structured so that a traditional machine learning approach and a deep learning approach can be sensibly compared on it. A dataset too small or too clean makes the neural network component pointless; one too large or too noisy makes the whole pipeline unfinishable within the page limit. The source must be referenced and the choice explicitly justified against the research problem. The modelling requirement is fixed: at least two supervised machine learning models, plus one artificial neural network built in a mainstream deep learning framework, all trained and tested. The comparison between them is the analytical core of the work. Reporting that the neural network scored higher is not an answer; explaining why, in terms of the data's structure and each model's inductive assumptions, is. Marks are distributed across problem framing, the traditional models, the deep learning model, evaluation and critical analysis including responsible AI considerations, and academic communication. That responsible AI component is easy to overlook and is not decorative — it asks what the model's limitations mean for anyone who might rely on it. Presentation requirements are specific. The report is page-limited rather than purely word-limited, and every plot must be described in the text while also being legible enough to communicate on its own — a common failure is dense default library output pasted in without axis labels or scale. The implementation is documented in a notebook combining markdown and code cells so the development process is visible, not just the final result, and submissions typically include the cleaned dataset alongside the code. The strongest submissions treat the notebook and the report as one argument. Weaker ones produce a working notebook and then write a report that describes it, rather than a report that uses it as evidence. Our support on assessments of this type is guidance-based. Typical areas of help include: advising on whether a candidate dataset can actually support the required model comparison, explaining how to justify preprocessing decisions, clarifying which evaluation metrics suit which problem type and why accuracy alone is often misleading, showing how to structure a critical limitations and responsible AI discussion, checking Harvard referencing, and reviewing a student's own draft against the published marking criteria.
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