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Managing the Global Supply Chain and Sustainability
2,500 words
Managing the Global Supply Chain and Sustainability – Individual Company Case Analysis
This assessment is an individual company case analysis for the Level 7 module Managing the Global Supply Chain and Sustainability. Students are required to select a real organisation of any size, type, industry or country and produce a comprehensive analysis of one operations performance objective. The available performance objectives are cost, dependability, flexibility, speed and quality. The report should identify current issues faced by the chosen organisation in relation to the selected objective and critically analyse the factors affecting its performance. Students are expected to apply relevant theories, concepts and tools covered during lectures and seminars to the selected company case. Rather than providing only descriptive explanations of theoretical concepts, the assessment requires students to demonstrate how these concepts can be applied to understand the organisation's operational or supply chain challenges. The analysis should consider how relevant theories, models or tools could contribute to improving the company's performance. The report should also provide a critical discussion of appropriate performance-improvement practices. These may include the use of digital technologies, sustainability practices, supply chain strategies, operational approaches or other relevant methods. Students should relate these practices directly to the findings from their chosen company case and consider their practical implications. Academic evidence and real-world examples should be used to support the analysis, with appropriate attention given to supply chain and operations management literature. The assessment is designed to develop students' ability to understand supply chain strategies, assess their contribution to agility, viability and sustainability, propose practical solutions to modern logistics and supply chain challenges, and evaluate how digital technologies and sustainability practices can improve supply chain performance. The report should therefore demonstrate analysis, application of theory, critical evaluation and evidence-based recommendations rather than simply describing the organisation or its supply chain. The individual report has a maximum length of 2,500 words, excluding references, tables, figures and appendices. It must include a contents page, be spell-checked and follow the specified formatting requirements. The report should use Arial 12-point font, 1.5 line spacing, A4 page size and 2.54 cm margins. Academic sources such as journal articles and books should be used, and all sources cited within the report must appear in a Harvard-style reference list. The report is submitted through Moodle Turnitin as a Word document.
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Databases and Business Intelligence
Databases and Business Intelligence – KAP Speciality Chocolates Database Coursewor
This individual coursework for the Databases and Business Intelligence module provides practical experience of database systems through the realistic KAP Speciality Chocolates case study. Students are required to design and implement a relational database solution that supports the management of products, stock, suppliers and purchase orders for KAP Speciality Chocolates Ltd. The case study describes a growing chocolate business with a shop and a storeroom that requires a database system to reduce operational errors and improve stock control. The first task requires students to analyse the case study and identify appropriate entities, attributes, relationships, primary keys, foreign keys and dependencies. Students must produce an extended Entity Relationship Diagram showing how the identified elements should be related. The diagram must include participation and cardinality constraints, and students must clearly document the assumptions made and the notation used. A range of modelling notations and software tools may be used, provided that the required database elements are clearly represented. The second task requires students to translate the ER design into a relational database implementation by writing SQL Data Definition Language statements. The SQL must define suitable tables, attributes, data types, primary and foreign keys and relevant constraints. The chosen data types should be appropriate and foreign-key data types must correspond correctly to their related primary keys. The third task focuses on SQL Data Manipulation Language and requires students to create queries addressing a range of business information needs. These include producing a product price list, identifying purchase orders due for delivery on a specified day, generating shop re-stocking lists, changing product cost and selling prices, identifying products with outstanding purchase-order deliveries, producing a re-order list for products below their minimum warehouse stock level, and identifying suppliers that provide multiple cartons. Students must populate their tables with sufficient suitable data to demonstrate that the SQL works and provide evidence of testing through screen captures of SQL statements and results. The fourth task requires students to demonstrate that the database design is normalised to Third Normal Form (3NF). The normalisation process must begin with unnormalised data and show the stages through First Normal Form (1NF), Second Normal Form (2NF) and Third Normal Form (3NF), explaining the changes and reasons at each stage. The final normalised solution must remain consistent with the ER diagram, assumptions and entity and attribute names used in the database implementation. The final submission must combine all tasks into one report written in English, using Arial 12-point font and single spacing. The report must contain the extended ER diagram and assumptions, SQL table definitions, SQL queries with explanations and testing evidence, and a written explanation of the normalisation process. The completed report must be submitted as a single PDF through SurreyLearn by the specified deadline.
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Global Strategic Management
3,500 words
Global Strategic Management – Linear to Circular Business Model Transformation
This assessment for the Global Strategic Management module examines how organisations can transform from traditional linear business models towards circular business models. Students must select one of four approved organisational cases and use the same case consistently across both summative assessments. The approved cases are SKF, focusing on servitisation and remanufacturing transformation; Sandvik, focusing on closed-loop materials and tool recovery; Volvo Construction Equipment, focusing on design for remanufacturing and lifecycle optimisation; and Husqvarna, focusing on access-based and circular consumer models. The assessment requires students to apply strategic management, sustainability and circular economy concepts to evaluate the transformation of the selected organisation. Assessment A is a Strategic Poster worth 30% of the overall module mark. The poster should visually and analytically map the transformation of the selected organisation from a linear to a circular business model. It contains three main components. First, students must identify and analyse external drivers using PESTEL, explaining the factors creating pressure or opportunities for circular transformation. Second, students must explicitly reconfigure the organisation's value chain, distinguishing between primary activities such as inbound logistics, operations, outbound logistics, marketing and sales, and service or lifecycle activities, and supporting activities such as firm infrastructure, human resource management, technology development and procurement. Third, students must identify one core structural strategic tension created by the transition. This tension should represent a built-in trade-off in which improving one strategic objective may constrain or challenge another. Assessment B is a Strategic Evaluation Report worth 70% of the overall module mark. The report has a word limit of 3,500 words with a 10% tolerance, excluding the table of contents, reference list and appendices. The report requires a comprehensive strategic evaluation of the selected organisation's transition from a linear to a circular business model and should integrate concepts and frameworks covered throughout the module. The first component of the report requires a Business Model Reconfiguration Analysis using the Business Model Canvas. Students should critically analyse how the circular transformation changes the organisation's value proposition, customer segments, customer relationships, channels, key activities, key resources, key partnerships, revenue streams and cost structure. The analysis should explain how value is created, delivered and captured differently under the circular model. The second component evaluates sustainability and legitimacy using an integrated analysis of the Triple Bottom Line, SAFe framework and stakeholder power and interest analysis. Students should assess economic, environmental and social sustainability, evaluate the suitability, acceptability and feasibility of the transformation, and identify stakeholders who can enable or constrain implementation. The third component requires students to analyse the same strategic tension identified in the poster. The tension should be evaluated in greater depth using appropriate strategic, sustainability and stakeholder frameworks, followed by a theoretically grounded and operationally feasible recommendation. The final component requires students to integrate four of their strongest reflective blog posts from the twelve seminar weeks. The reflection should demonstrate intellectual development, critical thinking, engagement with theory, seminar participation and responsiveness to tutor feedback rather than simply describing the content of the seminars. The assessment is expected to demonstrate Level 7 academic standards through integrated framework application, critical evaluation, strategic judgement and advanced reflective insight. Academic sources must be cited using Harvard referencing, with appropriate in-text citations and a complete reference list.
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Big Data and Cloud Computing
2,500 words
Big Data and Cloud Computing: FieldVision Cloud-Based Big Data Solution for AgroNova
This assessment is a 2,500-word individual report for the Big Data and Cloud Computing module within the MSc Management with Data Analytics programme at BPP University. The report is based on a fictional agricultural technology company, AgroNova, and its FieldVision project. AgroNova provides climate change and crop monitoring services to farmers in the UK and intends to expand internationally. However, its existing ageing infrastructure and manual processes present challenges to international expansion and the development of data-driven decision-making. The FieldVision project aims to use Internet of Things (IoT) technology and cloud-based big data solutions to collect, store and analyse real-time information from agricultural environments. The report requires students to act as a big data and cloud solution consultant and provide recommendations to senior management at AgroNova. The project involves IoT devices such as CropCam aerial cameras supplied by HydroSense and SmartHarvester solar-powered sensors supplied by SoilTech. These technologies collect information including crop imagery, soil moisture, temperature and humidity. The resulting data can support crop monitoring, risk assessment, early warning alerts, irrigation decisions, crop-failure claims validation and other farming-related insights. The first task focuses on Big Data Requirements and Storage Solutions. Students must identify the requirements arising from the scenario and critically evaluate a range of cloud-based big data storage solutions. The evaluation should consider factors such as capacity, functionality and costs. The second task requires students to propose one appropriate cloud-based solution architecture supported by an architecture diagram showing the essential components from data sources through to reporting. The selected architecture must be analysed in relation to AgroNova's requirements and the storage solutions considered in Task 1. The third task addresses Project Risks and Issues. Students must critically appraise the risks and issues associated with deploying the proposed cloud-based big data solution and identify appropriate mitigation approaches. These issues should be connected directly to the storage solutions and proposed architecture. The scenario highlights concerns from AgroNova's CISO, CFO and Chief Reputation Officer regarding potential data breaches, high costs and poor returns on investment, making security, financial viability and organisational risk important considerations. The report should contain an approximately 200-word introduction, an 800-word analysis of Big Data Requirements and Storage Solutions, a 500-word Proposed System Architecture section supported by relevant diagrams, an 800-word Project Risks and Issues section, and an approximately 200-word conclusion. Harvard referencing, academic research and appropriate supporting appendices are also required. The assessment addresses three learning outcomes: designing an architecture that supports complex data collection, critically evaluating data storage solutions from an enterprise systems perspective, and critically appraising issues involved in enterprise-system deployment.
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Global Supply Chain Management
4,200 words
Global Supply Chain Management – Gatwick Airport Northern Runway Expansion
This individual report examines several strategic and operational aspects of the global supply chain associated with the Gatwick Airport Northern Runway expansion. The assessment requires students to investigate the organisation and its current global supply chain challenges, while applying relevant supply chain management theories, frameworks and models to analyse its operations and develop strategic recommendations. The report begins by providing a background of the organisation, with particular attention to its current global supply chain issues and strategic supply chain relationships. Key areas of investigation include risk management, supply chain resilience, outsourcing, supplier planning and selection, supply chain integration, network design, leadership, benchmarking, decision-making, and the application of lean and agile approaches. These areas provide the basis for understanding how the organisation can manage its supply chain effectively within a complex and changing global environment. A critical evaluation of external factors affecting the organisation's global supply chain is also required through the use of a PESTEL analysis. This enables consideration of the political, economic, social, technological, environmental and legal factors that may influence supply chain performance, risks, costs and strategic decisions. The report must then develop key strategic recommendations aimed at optimising the organisation's global supply chain. The recommendations should focus on reducing costs, supporting sustainability, improving operational efficiency and enhancing supply chain resilience. Each recommendation should be supported by appropriate supply chain management theories and models, such as agile supply chain approaches, lean supply chain principles and global sourcing strategies. Students are expected to provide a clear rationale for their recommendations using theoretical insights and relevant academic and professional literature. The assessment places emphasis on critical reasoning, wider reading and the ability to connect supply chain theory with practical organisational issues. A logically structured, professional and business-like report is required, supported by appropriate evidence and Harvard referencing. The marking criteria assess the use of theory and frameworks, critical analysis and evaluation, conclusions and recommendations, and the overall structure, presentation and referencing of the report. The assignment is worth 70% of the unit assessment and has a required length of approximately 4,200 words, with a permitted variation of ±10%. The report should demonstrate an understanding of global supply chain management practices and the ability to apply relevant theories and frameworks to analyse supply chain challenges and develop realistic strategic recommendations.
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Computational Algorithms and Paradigms
1,000 words
Computational Algorithm Analysis – Research Paper Algorithm
This individual coursework for the Computational Algorithms and Paradigms module requires students to thoroughly analyse a computational algorithm proposed in a research paper selected from the list of research papers provided on Canvas. The purpose of the assessment is to develop students' ability to understand, explain and critically evaluate computational algorithms presented in academic research. Students must first identify and describe the computational problem addressed by the selected research paper and clearly state the research questions investigated by the authors. They must then extract the main computational algorithm proposed in the paper and present it in pseudocode. The assessment also requires students to clearly identify the inputs required by the algorithm and the outputs produced by it. The coursework consists of six main analytical sections. The first section focuses on the computational problem and research questions addressed in the selected paper. Students are expected to provide an accurate description of the problem and explain the research questions that the proposed algorithm attempts to address. The second section requires the proposed computational algorithm to be represented using suitable pseudocode. The third section identifies and explains the algorithm's inputs and outputs. The fourth section requires students to explain the proposed algorithm using simple and understandable language. The explanation should demonstrate a clear understanding of how the algorithm operates rather than simply reproducing the description provided in the research paper. The fifth section focuses on analysing the time complexity of the proposed algorithm. Students should evaluate the computational cost of the algorithm and explain its time complexity appropriately. The final section requires a critical evaluation of the algorithm's strengths and weaknesses. Students should identify the advantages and limitations of the proposed approach and discuss potential improvements where appropriate. This section should demonstrate critical thinking about the effectiveness, efficiency and practical applicability of the algorithm. The coursework has a total word count requirement of 800–1,000 words. The inputs and outputs, pseudocode and time-complexity analysis sections are excluded from this word-count limit. The template requires approximately 250 words for the computational problem and research questions, approximately 250 words for the simple explanation of the algorithm, and approximately 300 words for the strengths and weaknesses evaluation. Students must report the word count for sections 1, 4 and 6 after completing the assignment. The submitted work must be original and is subject to plagiarism and collusion checks through Turnitin. The assessment brief also states that generative AI tools may be used for proofreading but are not permitted for creating the coursework content. No figures or images are permitted, and the coursework must be submitted using the provided Word template in DOC or DOCX format.
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Enterprise Business Start-up: Sustainable Business Plan, Fundraising Strategy and Entrepreneurial Reflection
Entrepreneurship, Business Start-up, Business Plan, Business Idea, Innovation, Sustainable Business, Market Gap, Market Research, Competitor Analysis, Customer Segmentation, Customer Profiles, Product Development, Marketing Strategy, Marketing Communications, Business Model, Costing, Pricing Strategy, Sales Forecasting, Revenue Model, Financial Planning, Founding Team, Core Competencies, Fundraising Strategy, Venture Capital, Angel Investment, Entrepreneurial Tendency, GET Test, Entrepreneurial Self-Assessment, Start-up Finance, Business Pitch
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Calculus and Optimisation: Python Implementation of Gradient Descent, Derivatives and Polynomial Regression
This Calculus and Optimisation technical exercise demonstrates the implementation of mathematical and machine-learning concepts using Python. The document is organised as a set of code fragments that must logically work together to perform symbolic calculus, numerical optimisation, physical modelling and polynomial regression. The material uses libraries including NumPy, SymPy, Matplotlib and Scikit-learn, linking mathematical theory with practical computational implementation. Jigsaw_Puzzle_Calculus_Original… A central component is the implementation of gradient descent for linear regression. The supplied function calculates predictions, evaluates prediction errors, computes gradients and updates model parameters iteratively using a configurable learning rate. Mean squared error is recorded during optimisation, and a tolerance-based stopping condition is used to terminate the algorithm when successive cost values change by only a very small amount. Jigsaw_Puzzle_Calculus_Original… Synthetic linear-regression data is generated using a fixed NumPy random seed to support reproducible experimentation. A bias column is added to the feature matrix, initial parameter values are randomly generated, and the custom gradient-descent function is then executed to estimate the intercept and slope of the relationship. Jigsaw_Puzzle_Calculus_Original… The document also demonstrates symbolic differentiation and optimisation using SymPy. A cubic polynomial is defined and differentiated to obtain both its first and second derivatives. Critical points are identified by solving where the first derivative equals zero, while the second derivative is evaluated at each critical point to determine whether the point represents a local minimum, local maximum or saddle point. Jigsaw_Puzzle_Calculus_Original… Jigsaw_Puzzle_Calculus_Original… A further section applies mathematical formulas to projectile motion. Using a specified initial velocity, launch angle and gravitational acceleration, the code calculates both maximum projectile height and horizontal range. This component illustrates how calculus-related mathematical relationships can be translated directly into executable computational models. Jigsaw_Puzzle_Calculus_Original… The final major element explores polynomial regression. Synthetic nonlinear data is generated from a cosine-based function with added random noise. Scikit-learn pipelines are then used to compare polynomial models of degrees 1, 4 and 15. The models are fitted to the synthetic dataset and visualised against the underlying true function, allowing comparison of model complexity and illustrating concepts such as underfitting and overfitting. Jigsaw_Puzzle_Calculus_Original… Jigsaw_Puzzle_Calculus_Original… Overall, the exercise integrates calculus, optimisation, numerical methods and machine-learning modelling through Python. It provides practical experience with differentiation, critical-point analysis, iterative optimisation, mathematical simulation, regression modelling and visualisation. Important: because this file does not identify a university, assessment weighting, academic level, reference style or required word count, those fields should remain Not specified / Not applicable rather than being invented.
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Digital Marketing / Marketing Analytics / Data-Driven Marketin
Data-Driven Marketing Analytics: Google Analytics 4 and Google Merchandise Store Campaign Analysis
This Data Driven Marketing assessment requires students to develop practical digital analytics skills and apply them to the evaluation of a real-world marketing campaign. The assessment combines professional training through LinkedIn Learning with a recorded executive-level presentation based on data from the Google Analytics 4 Demo Account for the Google Merchandise Store. The aim is to analyse campaign performance, interpret relevant marketing KPIs and present actionable recommendations that senior executives and managers can use for decision-making. CW 1_ LinkedIn Learning and Rec… The first task requires completion of two LinkedIn Learning courses: Google Analytics 4 (GA4) Essential Training and Advanced Google Analytics. Students must submit the certificates of completion as evidence of developing the foundational and advanced skills required for digital marketing analysis. CW 1_ LinkedIn Learning and Rec… The second task places the student in the role of a digital marketing executive for the Google Merchandise Store. Students select and analyse a digital marketing campaign using GA4 demo-store data and then create a 10-minute recorded presentation, with a tolerance of ±2 minutes, providing evidence-based recommendations for improving campaign performance. CW 1_ LinkedIn Learning and Rec… The main presentation is limited to 12 slides and should include an executive summary, key findings and insights, recommendations, actionable steps, and references or appendix material. An additional 4–6 appendix/reference slides may be used for supporting charts, graphs and data. Recommendations must be linked directly to analytical evidence and aligned with the campaign's overall marketing objectives. CW 1_ LinkedIn Learning and Rec… The analysis should examine relevant marketing performance indicators, including click-through rate, conversion rate, cost per acquisition, return on ad spend and user-engagement measures such as bounce rate, session duration, pages per session and retention patterns. CW 1_ LinkedIn Learning and Rec… Students are expected to move beyond descriptive reporting and develop strategic recommendations concerning budget reallocation, creative optimisation, audience targeting and funnel performance. The presentation should identify underperforming and high-performing channels, evaluate audience segments, examine points of user drop-off and propose practical interventions to improve conversion and campaign efficiency. CW 1_ LinkedIn Learning and Rec… The marking scheme gives 20 points for LinkedIn Learning certification, 35 points for executive communication and structure, 35 points for data analysis and insight generation, and 10 points for references and appendices. CW 1_ LinkedIn Learning and Rec… CW 1_ LinkedIn Learning and Rec… Overall, the assessment integrates GA4 skills, campaign analytics, KPI interpretation, data visualisation, strategic recommendation development and executive presentation skills within a practical digital-marketing context. The brief also requires Cite Them Right Harvard referencing. CW 1_ LinkedIn Learning and Rec…
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4,200 words
Global Supply Chain Strategy for the Gatwick Airport Northern Runway Expansion
This individual Global Supply Chain Management assessment requires students to prepare a professional strategic report on the supply chain implications of the Gatwick Airport Northern Runway expansion. The task focuses on applying contemporary supply chain theories, models and analytical techniques to a complex infrastructure development context, with particular attention to strategic relationships, operational efficiency, risk and resilience. Assignment Brief 2 - MBA013 Glo… The report begins by establishing the background to the organisation and examining its current global supply chain context. Students are expected to consider issues including strategic supplier and customer relationships, risk management, resilience, outsourcing, supplier planning and selection, integration, supply chain networks, leadership, benchmarking, decision-making, lean principles and agile supply chains. These areas provide the basis for evaluating how supply chain performance can be improved in a major airport expansion project. Assignment Brief 2 - MBA013 Glo… A key analytical component requires a PESTEL analysis to critically assess how external political, economic, social, technological, environmental and legal factors may affect the organisation’s global supply chain. The purpose is not only to identify external pressures but to evaluate how those factors may influence sourcing, supplier management, cost, risk exposure, resilience and long-term strategic decision-making. Assignment Brief 2 - MBA013 Glo… The final part of the report develops strategic recommendations aimed at optimising supply chain performance. Recommendations should focus on reducing costs, supporting sustainability, improving operational efficiency and strengthening resilience. Students are expected to justify these proposals through explicit application of relevant theories and models such as lean supply chain, agile supply chain and global sourcing strategies, supported by appropriate academic and practical evidence. Assignment Brief 2 - MBA013 Glo… The marking criteria place substantial emphasis on theoretical application, critical analysis and the connection between theory and practice. The rubric allocates 25% to theory and frameworks, 30% to analysis and evaluation, 20% to conclusions and recommendations, and 25% to structure, presentation and referencing. Assignment Brief 2 - MBA013 Glo… Assignment Brief 2 - MBA013 Glo… Overall, the assessment develops competence in strategic supply chain analysis, infrastructure logistics, risk and resilience, supplier management, lean and agile operations, sustainability and evidence-based recommendation development. Overview word count: approximately 350 words. Important: the brief explicitly states that the report must be original and that students should not use AI to produce the work. Assignment Brief 2 - MBA013 Glo…
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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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Web Applications / Artificial Intelligence / Software Development
Smart Clinic Appointment and Patient Management System with AI-Based Demand Prediction
This Web Applications and AI coursework requires students to design, implement and evaluate a Smart Clinic Appointment & Patient Management System for a small healthcare clinic. The application combines conventional web-development functionality with an artificial-intelligence component for predicting appointment demand. The system is expected to use Java EE technologies, including Java Servlets, JSP, Web Services and JDBC, together with a relational database such as MySQL or PostgreSQL. f3855340dabd17407efd386c38cfdc3… The patient-facing side of the application should allow users to browse and search clinic services by department or specialty, price, availability and duration. Patients must be able to view detailed service information, select a clinician where appropriate, choose an available date and time, enter their details and confirm an appointment. The system should also provide a booking reference and basic appointment-history functionality. f3855340dabd17407efd386c38cfdc3… The administrative interface focuses on operational management. Staff should be able to add, update and remove services, configure consultation duration and pricing, manage clinician working hours and appointment-slot availability, and generate basic reports. f3855340dabd17407efd386c38cfdc3… A separate machine-learning component requires students to implement appointment-demand prediction using WEKA regression embedded in Java. The provided sample dataset contains Year, Month, Promotions Cost and Booking Requests. Students must expand this dataset to at least 60 realistic rows, including seasonal changes and plausible variation in marketing expenditure and demand. A regression model is then trained to predict booking requests for the following year based on promotional spending, including estimation of future demand if promotions expenditure increases by 10%. f3855340dabd17407efd386c38cfdc3… The assessment also requires evidence of professional software-development practice. Students must provide application-design artefacts such as design patterns, ER diagrams, wireframes and sketches, document the development process, demonstrate correct use of JSP, Servlets, Web Services and JDBC, and provide evidence of implementation through code, database content and screenshots. Regular GitHub commits are required to demonstrate ongoing development. f3855340dabd17407efd386c38cfdc3… f3855340dabd17407efd386c38cfdc3… The final submission includes a DOCX or PDF report containing system-design and implementation information, links to a private GitHub repository and a demonstration video of no more than five minutes. The assessment is classified as Green for AI use, meaning AI tools may support tasks such as generating example datasets, suggesting code snippets and brainstorming features or tests, provided their use is clearly declared in the report. f3855340dabd17407efd386c38cfdc3… Overall, the coursework integrates full-stack Java web development, relational database design, web services, software engineering and machine-learning regression within a healthcare appointment-management scenario. Important: the uploaded brief states that it is for Coventry University Group students' own use and must not be passed to third parties or posted publicly. f3855340dabd17407efd386c38cfdc3… So for a public Reference Library, use an original summary like the one above rather than publishing the original brief itself.
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Artificial Intelligence / International Business
3,000 words
AI Innovation Consultancy: Evaluating Artificial Intelligence Solutions for Business Problems
This individual consultancy assessment requires students to act as an AI Innovation Consultant and evaluate how artificial intelligence could address a significant real-world business problem. Students select one industry—such as healthcare, retail, FinTech, manufacturing or agriculture—and concentrate on a single clearly defined organisational challenge rather than comparing multiple sectors. Potential issues include long waiting times, high operating costs, fraud and risk, poor customer experience or inefficient supply chains. The report develops a practical AI solution by identifying suitable technologies such as machine learning, natural language processing or computer vision and explaining how they would operate within the chosen organisational context. Students are not required to build an AI system; instead, the emphasis is on demonstrating business-level technical understanding, critical thinking and the ability to assess whether the proposed technology can realistically integrate with existing organisational processes. The analysis considers the capabilities and limitations of AI, technical feasibility, integration requirements and the skills or organisational capabilities required for implementation. Students must also critically examine ethical, legal and social implications, including issues such as algorithmic bias, transparency, accountability, privacy and regulatory obligations such as UK GDPR. Appropriate risk-mitigation measures should be proposed. A substantial element of the report develops the business case for AI adoption. Students evaluate implementation costs and expected benefits, estimate return on investment, identify assumptions and commercial risks, and assess the overall strategic value of the solution to the organisation. The report concludes with clear recommendations, implementation priorities and a final judgement on whether the proposed AI initiative is feasible and worthwhile. The assessment places strong emphasis on critical analysis, technical understanding, business acumen and professional communication. Students are expected to support arguments with credible academic, industry and government evidence and include at least two professional visualisations such as frameworks, diagrams or tables. Harvard referencing is required throughout. Overview word count: approximately 330 words. The brief also allows authorised use of generative AI for idea generation, drafting/structuring and proofreading, provided the student verifies accuracy, references appropriately and submits the required GenAI declaration.
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Data Science / Data Management / Professional Practice
4,500 words
Tesla Data Science Professional Case Study: Data Management, Leadership, Entrepreneurship and Ethics
This composite case-study assessment for The Data Science Professional module requires students to critically analyse Tesla from several interconnected professional perspectives, including data management, artificial intelligence ethics, leadership, organisational development, entrepreneurship and business risk. The coursework is designed to combine technical data-science capability with strategic, ethical and managerial decision-making. a82a1aa6a2e7a3268ad021c4f5b3d44… Part A – Database Design and Distributed Frameworks focuses on data management. Students design an Entity-Relationship model and relational schema for a Tesla-related vehicle-hire business, identifying entities, relationships, cardinalities, identifiers, primary keys and foreign keys. The accompanying guidance specifies entities relating to vehicles, employees, outlets, clients, hire agreements, insurance, faults and employment records. a82a1aa6a2e7a3268ad021c4f5b3d44… 39cdc24cc46a13ddf444b8e7af838eb… The SQL component uses an Online Music database to examine Tesla customer preferences. Students create relational tables using Oracle standard SQL and write queries involving users, music, publishers, categories and download records. Evidence of implementation and query results must be provided using Oracle Live SQL. 39cdc24cc46a13ddf444b8e7af838eb… Part A also requires a critical assessment of the security, privacy and ethical implications of Tesla’s Full Self-Driving technology, connecting technical development with responsible data and AI practice. a82a1aa6a2e7a3268ad021c4f5b3d44… Part B – Leadership and Developing People requires critical evaluation of Tesla’s leadership model, organisational culture and their effects on employees and organisational performance. Students must propose a leadership and people-development strategy capable of supporting the organisation as it expands. a82a1aa6a2e7a3268ad021c4f5b3d44… Part C – Entrepreneurial Practice and Managing Risk examines a proposed Tesla spin-out venture developing innovative low-cost green hydrogen production systems. Students critically assess management support for the venture, propose an evidence-based approach to entrepreneurial risk, develop an entrepreneurial leadership role descriptor, and evaluate how GDPR and AI/data ethics may support or constrain entrepreneurial practice. a82a1aa6a2e7a3268ad021c4f5b3d44… Overall, the assessment integrates technical database design, SQL, data ethics, organisational leadership, entrepreneurship, sustainability and professional decision-making within a single Tesla-focused case study. Overview word count: approximately 360 words. Important: the assessment brief itself explicitly states that it must not be passed to third parties or posted on any website. So for a public Reference Library, use the metadata and your own finished work where permitted, but do not upload the assessment brief/guidance PDFs themselves publicly. a82a1aa6a2e7a3268ad021c4f5b3d44… The AI status is Amber: generative AI may be used for limited inspiring/planning purposes, but usage must be acknowledged with the tool, prompts and relevant evidence; the brief also specifically prohibits using LLMs to generate the Part A(3) essay. a82a1aa6a2e7a3268ad021c4f5b3d44… a82a1aa6a2e7a3268ad021c4f5b3d44…
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Data Science / Data Management / Leadership and Entrepreneurship
4,500 words
The Data Science Professional: Tesla Data Management, Leadership and Entrepreneurial Practice
This interdisciplinary Data Science Professional assessment uses Tesla as the central organisational case study to examine how data management, leadership, entrepreneurship, technology and ethics interact within a contemporary technology-driven business. Students produce an individual report of 4,500 words equivalent, combining technical data-science competencies with strategic and managerial analysis. The Data Science Professional A… The Data Science Professional A… Part A – Database Design and Distributed Frameworks focuses on data management. Students design an Entity-Relationship model for Tesla-related organisational data, translate the model into a relational schema and justify the database structure. They also work with a specified database to produce Oracle SQL queries for analytical requirements. A further component critically evaluates the security, privacy and ethical implications of Tesla's Full Self-Driving technology, supported by relevant academic evidence. The Data Science Professional A… Part B – Leadership and Developing People requires critical analysis of Tesla's leadership model and organisational culture. Students examine current leadership challenges and their implications for organisational and employee performance before proposing a leadership and people-development strategy designed to support future organisational growth, employee engagement and sustained performance. The Data Science Professional A… Part C – Entrepreneurial Practice and Managing Risk investigates the proposed creation of a Tesla spin-out venture focused on innovative, low-cost green hydrogen production systems. Students critically evaluate management support for the entrepreneurial initiative and recommend an evidence-based course of action. They must also propose a multidimensional approach to mitigating entrepreneurial risks and barriers, critically assess entrepreneurial leadership characteristics, and develop a role descriptor for the person who would lead the new venture. The Data Science Professional A… The final element examines whether GDPR and data or AI ethics constrain or support entrepreneurial practice among employees. Overall, the coursework integrates database modelling, SQL, data governance, leadership development, organisational culture, entrepreneurship, innovation, risk management and ethical decision-making within a single applied case study. The Data Science Professional A… Important for the public Reference Library: the brief explicitly states that it is for Coventry University Group students' own use and must not be passed to third parties or posted on any website. The Data Science Professional A… So publish an original high-level overview like the one above, but do not upload the original assessment brief itself to the public library.
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Machine Learning / Cloud Computing / Artificial Intelligence
4,004 words
Cloud-Based Machine Learning for Financial Fraud Detection
This Machine Learning on Cloud assessment requires students to design, implement and critically evaluate a cloud-oriented machine-learning solution for financial fraud detection. Working as a group, students address a scenario in which a financial-services organisation requires an automated model capable of detecting fraudulent transactions, reducing financial losses and improving customer security. The project combines machine-learning development with critical evaluation of cloud infrastructure, data preparation, model performance and responsible AI considerations. NUL - LD7187 -Assessment Brief … The project begins with a cloud feasibility study comparing at least two major machine-learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Students evaluate factors including performance, scalability, cost, compliance, system integration and vendor lock-in before providing a justified recommendation. The project then moves into Exploratory Data Analysis, where patterns, anomalies and correlations within the fraud dataset are investigated using visualisations such as heatmaps, histograms and boxplots. NUL - LD7187 -Assessment Brief … A substantial part of the assessment focuses on data preprocessing and class imbalance. Students are expected to clean and transform the dataset, apply scaling and encoding, perform feature engineering and investigate techniques such as SMOTE, undersampling and cost-sensitive learning. These decisions must be justified in terms of their potential effect on predictive performance. NUL - LD7187 -Assessment Brief … Students must then select and train at least two machine-learning models. Suggested algorithms include Logistic Regression, Random Forest, XGBoost and Neural Networks. Appropriate cross-validation and hyperparameter-tuning procedures should be applied, followed by systematic evaluation using precision, recall, F1-score, AUC and precision-recall curves. Supporting visualisations should include confusion matrices, ROC curves and feature-importance analysis. NUL - LD7187 -Assessment Brief … The final component addresses professionalism and ethics in cloud-based AI, including bias, fairness, transparency, data privacy and environmental sustainability. Overall, the project integrates cloud-platform selection, exploratory analytics, preprocessing, imbalanced-data handling, predictive modelling, model evaluation and ethical AI into an applied financial fraud-detection solution. NUL - LD7187 -Assessment Brief … Note: the uploaded brief does not explicitly name a referencing system. If your portal requires a selection, I would use Not specified rather than assume Harvard.
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Environmental Sustainable Engineering
3,000 words
Environmental Sustainable Construction & Logistic Sources – EG7037
This coursework assesses Environmental Sustainable Engineering and Logistic Sources through a large-scale construction project scenario. The student is appointed as the project manager for a £320 million design-and-build project involving the development of a new shopping centre in central London. The project is planned over a 48-month period and is presented as a flagship initiative focused on environmentally friendly and sustainable construction practices, with government support for its sustainability commitments. The project manager is responsible for protecting the client's interests while ensuring that project standards, schedules, budgets, procurement requirements and quality expectations are achieved. The assignment is structured around environmental sustainability, construction logistics, procurement, renewable energy, health and safety, and sustainable design. Part A requires investigation of the environmental, economic, historical and cultural factors that could contribute to significant project costs and delays. It also requires discussion of four renewable or sustainable energy sources that could be used within the project, together with justification for each selected energy source. These activities require consideration of the project's location, sustainability objectives and practical construction requirements. Part B focuses on supply chain management and health and safety. Students must outline the supply chain management and procurement process for the material resources required for the construction project. This includes consideration of how appropriate materials and suppliers can be sourced and managed. The section also requires identification of key health and safety considerations during the production phase and discussion of the associated risks. Part C focuses on sustainable design systems. Students must discuss the need for implementing passive or active systems during the design stage and identify appropriate tools or techniques that can support their application. The potential environmental impacts of these systems must also be considered. The assignment therefore requires students to connect sustainable design decisions with environmental performance and the wider objectives of the construction project. The completed coursework should be approximately 3,000 words, excluding appendices and labelled diagrams or sketches. Students are expected to provide a well-researched and clearly structured written account using appropriate textbooks and other relevant sources. Images, photographs and diagrams should include captions and be cross-referenced within the text. The submission should include a conclusion and a properly cited bibliography or reference list. References must follow the Cite Them Right requirements. The coursework is submitted electronically through the designated Turnitin link.
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Entrepreneurship / Entrepreneurial Marketing
1,200 words
Reflective Analysis of Entrepreneurial Capabilities and Development: The GymBuddy Venture
This reflective Entrepreneurial Marketing assessment evaluates the development of the student's entrepreneurial capabilities through the GymBuddy business concept, a proposed fitness application designed to improve motivation and accountability among beginner gym users. The reflection examines how entrepreneurial learning, customer research and creativity frameworks changed the student's original approach from developing a highly complex product toward a more focused and testable business proposition. Entrepreneurial_Marketing_Refle… A central theme of the report is entrepreneurial self-assessment. Opportunity recognition is identified as an important strength, supported by interviews with beginner gym users and competitor analysis involving platforms such as Strava, MyFitnessPal and Nike Training Club. At the same time, the reflection identifies areas requiring development, particularly financial understanding, customer acquisition cost, lifetime value, conversion-rate analysis and negotiation capability. These strengths and weaknesses are used to establish priorities for future entrepreneurial development. Entrepreneurial_Marketing_Refle… The report also evaluates the evolution of the GymBuddy product using SCAMPER, Design Thinking and Lean Startup principles. Rather than pursuing a feature-heavy application, customer feedback leads to greater emphasis on simplicity, peer accountability, social competition and iterative validation. SCAMPER is applied to eliminate unnecessary features, substitute complex recommendations with simpler motivational mechanisms, combine useful functionality and adapt gamification to the needs of beginner users. Entrepreneurial_Marketing_Refle… Another major area of reflection concerns teams, networks and collaborative innovation. The student considers how group creativity exercises challenged the assumption that a venture should be developed independently and highlights the importance of mentors, developers, industry professionals and potential investors. The report links entrepreneurial networks with access to resources, opportunity identification, legitimacy and emotional support and proposes deliberate networking activities as part of future venture development. Entrepreneurial_Marketing_Refle… The visual development framework on page 3 summarises the entrepreneurial journey in five stages: GymBuddy concept and market research, capability assessment, product development and validation, teams and networks, and a time-based action plan. The reflection is structured using the Gibbs Reflective Cycle, linking experience with evaluation, analysis and future action. Entrepreneurial_Marketing_Refle… Overall, the assessment demonstrates reflective learning through the application of entrepreneurial theory to a practical start-up concept. It shows a shift from perfectionism toward rapid validation, customer-centred development, collaborative working and concrete action planning, while identifying financial management, negotiation and network development as important areas for continued improvement. Entrepreneurial_Marketing_Refle… Note: this file is a completed reflective submission rather than the original university assessment guideline, so the university, academic level and academic year should remain Not specified unless you also upload the official brief.
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Entrepreneurship / Business Management / Enterprise
5,000 words
Enterprise Business Start-up: Innovative Business Plan, Fundraising Strategy and Entrepreneurial Reflection
This Enterprise Business Start-up assessment requires students to generate an innovative and sustainable business idea and develop it into a comprehensive start-up plan capable of guiding venture creation, attracting customers and supporting external fundraising. The module is assessed through two connected components: a 10-minute business pitch presented to an audience representing angel investors and venture capitalists, followed by a detailed individual business-plan portfolio. Assessment Brief Feb-May 2026 L… The principal written component is a 4,000-word detailed business plan. It begins with an executive summary before examining the proposed business idea, identified market gap, market research and competitor landscape. Students must then define appropriate customer profiles and segmentation, explain the development of the proposed product or service, and construct suitable marketing and communications strategies for reaching the intended market. Assessment Brief Feb-May 2026 L… A substantial part of the business plan focuses on the venture's financial and commercial viability. Students are expected to develop costing, pricing, sales and revenue projections alongside an appropriate business model. They must also identify the founding team, explain relevant roles and demonstrate the core competencies required to launch and develop the business successfully. Financials and business models receive particularly strong emphasis within the marking criteria. Assessment Brief Feb-May 2026 L… The written portfolio also includes a 1,000-word reflective component. Half of this section focuses on the student's fundraising strategy, including suitable funding sources and how potential investors or funders would be targeted, approached and secured. The other half critically reflects on the student's entrepreneurial tendency, personal experience and future skills development. Students are specifically required to complete the General Measure of Enterprising Tendency (GET) self-assessment and incorporate the results into their reflection. Assessment Brief Feb-May 2026 L… Assessment Brief Feb-May 2026 L… Overall, the assessment integrates entrepreneurial opportunity recognition, market analysis, customer segmentation, product development, marketing, start-up finance, business modelling, team capability, fundraising and reflective entrepreneurial development. The accompanying pitch additionally assesses visual quality, creativity, clarity of message, presentation skills, preparedness and professionalism. Assessment Brief Feb-May 2026 L… Assessment Brief Feb-May 2026 L… Important: the document does not explicitly specify a referencing style or academic level, so I would keep those fields as Not specified rather than assume Harvard or Masters.
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Emerging Technology and Cloud Computing
5,000 words
Emerging Technology and Cloud Computing – SafeCloud Project
This MSc Management coursework for BPP University’s Emerging Technology and Cloud Computing module is a 5,000-word formal business report based on the SafeCloud project at AlwaysUp Ltd., a manufacturing company specialising in power-electric equipment for buildings and critical installations. The assignment examines how emerging technologies, Big Data and cloud computing can support AlwaysUp Ltd.’s international expansion, real-time equipment monitoring, personalised preventive maintenance, data-driven decision-making, and secure management of equipment and client information. The company is seeking to expand from the UK into European markets through distributor partnerships and ultimately achieve global reach. The report requires the identification and critical evaluation of two emerging technologies that can improve AlwaysUp Ltd.’s support and equipment-care services. Students must evaluate the benefits and limitations of each technology within a manufacturing context and use real-world examples to support the analysis. The assignment specifically assesses the ability to demonstrate a comprehensive understanding and critical evaluation of emerging technologies in business. The second task focuses on designing and evaluating a cloud-based Big Data architecture that integrates the two selected emerging technologies. The proposed architecture must include an architecture diagram and should be evaluated in terms of scalability, security, cost-effectiveness, real-time data processing and decision-making. The solution should address AlwaysUp Ltd.’s business requirements and enable the collection, storage and analysis of equipment-related data. The third task examines data protection, ethical considerations, project risks and resource requirements associated with implementing the proposed solution. This includes consideration of data protection and ethical issues arising from the selected technologies, implementation risks, and the human, technological and other resources required. The fourth task analyses the strengths and weaknesses of the combined emerging technologies and Big Data architecture and considers their application to AlwaysUp Ltd.’s business strategy. The report must conclude with a proposed route forward based on the findings. The required report structure consists of an introduction of approximately 500 words, four main tasks of approximately 1,000 words each, a conclusion of approximately 500 words, Harvard-style references and optional appendices.
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Consultancy Project
1,464 words
Enhancing Sustainable Supply Chain Practices through Digital Transformation: A Case Study of Unilever Plc
This consultancy project proposal examines how digital transformation can enhance sustainable supply chain practices at Unilever Plc. Unilever is presented as a multinational fast-moving consumer goods organisation operating across more than 190 markets, with a complex global supply chain and sustainability commitments linked to the United Nations Sustainable Development Goals. The proposal focuses on the challenges associated with achieving supply chain transparency, managing fragmented sustainability data, and balancing sustainable sourcing and operational costs. The project investigates how emerging digital technologies, particularly blockchain, the Internet of Things (IoT) and Artificial Intelligence (AI), could support greater transparency, traceability, operational efficiency and sustainability within Unilever's supply chain. The organisational analysis identifies issues including the complexity of tracing raw materials across multi-tier supply chains, fragmented procurement and sustainability information systems, and the tension between sustainability investments and short-term operational costs. The proposal also considers infrastructure readiness, data security and employee upskilling as factors affecting digital transformation. The research aims to examine how digital transformation technologies could improve sustainable supply chain planning at Unilever while supporting long-term business and environmental goals. Three objectives are established: examining sustainability challenges affecting Unilever's global supply chain, assessing how blockchain, IoT and AI could improve transparency, traceability and efficiency, and proposing strategic recommendations for integrating digital innovation into Unilever's sustainability framework. The central research question examines how digital transformation can contribute to an ethical, transparent and sustainable supply chain while maintaining operational efficiency. A qualitative exploratory case-study research design is proposed. The main emphasis is on secondary data, including Unilever's annual reports, sustainability information, digital transformation publications, company news, industry articles and analyst reports. Where feasible, primary data may be collected through semi-structured expert interviews and a short qualitative questionnaire. The proposed analysis includes thematic analysis, document review and descriptive statistics using relevant secondary numerical data. Validity is supported through triangulation, while reliability is addressed through consistent coding and an audit trail. The proposal also addresses ethical considerations, including research integrity, transparency, confidentiality, informed consent, anonymity, GDPR requirements, plagiarism avoidance, Harvard referencing and potential corporate bias or greenwashing. Overall, the project explores the potential contribution of digital innovation to Unilever's sustainability objectives and the wider responsible digitalisation of the FMCG supply chain.
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Data Visualisation / Business Intelligence
2,500 words
Data Visualisation (BS664) — Portfolio: Tableau and Power BI Dashboard Development for Evidence-Based Decision-Making
This Level 7 portfolio accumulates work across a full semester of data visualisation study and is submitted as one continuous document combining reflective writing, tool evaluation and an applied dashboard project. The emphasis throughout falls on interpretative rigour — showing not just that a visualisation was built, but why each design decision serves the decision-maker who has to act on it. The first activity documents sustained critical participation across the module's weekly discussion topics. Five or more substantive responses are evidenced with dated screenshots, each paired with the corresponding workshop date, and each grounded in both academic literature and practitioner sources rather than opinion alone. Live URLs to the Power BI and Tableau dashboards built during the module are included, and the activity closes with a consolidated review of how understanding developed across the units. The second activity covers formal training completion in both platforms, with certificates and badges reproduced, and then turns to accessibility. It examines the visualisations produced during that training against accessibility principles — colour contrast and colour-blind safe palettes, text alternatives, chart type legibility, cognitive load — and argues why accessibility is not a compliance afterthought but a determinant of whether a dashboard actually informs decisions inside a global organisation with a diverse user base. The third activity is a structured comparison of Tableau and Power BI, conducted through the evaluative frameworks introduced in workshops rather than a feature checklist. Benefits and limitations are weighed across data connectivity, calculation capability, visual flexibility, licensing and cost, collaboration and governance, and organisational fit. The section ends with a reasoned recommendation of one platform for the applied work that follows, with the trade-offs of that choice acknowledged openly. The fourth and largest activity is the applied project. A dataset is selected from an approved open source and its provenance, quality and limitations discussed. The target business, its stakeholder groups and the varying analytical literacy of the intended audience are analysed, because these determine what the dashboard must show and how plainly. The report then walks through the full development sequence — data preparation and cleaning, chart selection and justification, layout and interactivity, iteration in response to identified weaknesses — with screenshots evidencing each stage. Findings are reported and translated into specific, evidence-based decisions the business could take on the strength of them. The submission follows the prescribed structure with title page, contents, introduction, business problem, main analysis, findings, Harvard reference list and appendices, presented as a single file.
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Data Management / Business Analytics
2,500 words
Data Design Management (BS514) — Data Strategy Consultancy: Relational Database Design, SQL Implementation and Pipeline Transformation
This Level 7 assessment places the writer in the role of a Data Strategy and Analytics Consultant appointed by an organisation operating in a realistic industry sector. The brief is entirely simulated, so the work sets out a defensible set of assumptions about the organisation's data environment before any design begins, and populates the resulting database with synthetic but realistic records. The deliverable is a slide deck carrying full explanatory notes, submitted as a single PDF, and weighted across three connected tasks. Task one establishes the business case. It describes how the chosen organisation currently collects, stores and uses data across customer interactions, sales transactions, operational processes and digital channels, and identifies where that fragmented picture costs the business in efficiency, resource use, retention and decision quality. A SWOT analysis benchmarks the organisation against a named real-world competitor in the same sector, drawing on publicly available market information rather than assertion. The section closes with a critical evaluation of modern relational database advancements — cloud-hosted SQL services, distributed architectures and data warehousing — assessed not in the abstract but against what each would actually change about this organisation's business model. Task two carries the heaviest weighting and is the technical core. Key business entities are identified from the scenario, a current-state data flow diagram traces how data moves from collection points through to storage and reporting with the existing ETL approach made explicit, and a future-state ER diagram is then built with full attributes, primary and foreign keys, relationships and cardinality. The design is normalised to third normal form with the decomposition reasoning shown. Implementation follows in SQL: tables created with appropriate integrity constraints, at least ten realistic sample records inserted per table, and five business questions answered through working queries — highest-performing product or campaign, average conversion by category, workload distribution across staff, accounts with overdue or pending items, and most effective service channel. Outputs accompany every script. A transformation demonstrating query optimisation is included with before-and-after samples so the improvement is evidenced rather than claimed. Task three steps back to the technology decision. Two widely used data processing platforms are compared in tabular form across integration, cleaning, transformation and automation capability, judged specifically against this organisation's constraints, with a reasoned justification for the tool finally selected. The transformed dataset is then used to answer two management-level questions — where investment should be prioritised and how retention might be improved from observed behavioural patterns — each interpreted briefly and tied back to a concrete recommendation. Slide structure follows the prescribed layout, SQL scripts sit in the notes section, and the complete script file is reproduced in the appendix.
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Supply Chain Management / Strategic Sourcing
1,800 words
Strategic Sourcing and Supply Chain Resilience Under Global Uncertainty
This postgraduate supply-chain assessment examines how organisations operating in the UK are adapting their strategic sourcing and supply-chain practices in response to sustained global uncertainty. The assignment is situated within an environment shaped by major disruptions including the Covid-19 pandemic, geopolitical conflicts, trade tensions, tariff volatility and Brexit, all of which have challenged the reliability, cost efficiency and resilience of globally distributed supply networks. Students select either a British organisation or a multinational enterprise with significant UK operations and critically analyse its documented supply-chain strategy. Potential areas of investigation include insourcing and outsourcing, offshoring, reshoring and nearshoring, single versus multiple sourcing, supplier selection and monitoring, supplier and customer relationships, inventory management, digitalisation, sustainable sourcing and supply-chain risk management. The assessment requires more than description. Students must provide a critical and theoretically informed evaluation of the organisation's decisions, identifying both opportunities and risks associated with its strategic response. At least one relevant theoretical framework or analytical tool must be applied to assess the company's sourcing or supply-chain decisions. The analysis must also include at least one company practice or decision from 2025 and conclude with a detailed recommendation explaining actionable steps, implementation challenges, expected outcomes and alignment with organisational objectives. Research must draw on a minimum of 10 credible sources, including at least five academic journal articles and five non-academic sources such as news articles, industry reports or corporate publications. At least two of the non-academic sources must have been published during 2025–2026. Students may present the work as either a business report or academic essay, with consistent in-text citation and referencing. Norwich Business School normally requires the Harvard referencing system. Overview word count: approximately 340 words. Note: the document header states PG Coursework 2025–26, but one later line gives a submission deadline of 15 May 2025, which appears inconsistent with the stated academic year. I would therefore use 2025/26 for the library record and avoid publishing the deadline unless it is verified.
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Business Strategy / Digital Business and Innovation
3,125 words
Safaricom Business Hub: Strategic B2B Digital Payment Platform for East African SMEs
This strategic business case proposes the Safaricom Business Hub, an integrated B2B digital-payment platform designed to support small and medium-sized enterprises across East Africa. The proposed solution responds to Safaricom’s need for digital diversification as traditional voice revenues mature, while leveraging the growth of M-Pesa and the wider transition toward digital financial services. The platform combines invoicing, bulk payments, cash-flow analytics and integrations with accounting systems such as Xero, QuickBooks and SAP. The report begins by analysing Safaricom’s current strategic position, including subscriber growth, revenue performance, declining voice services, M-Pesa transaction growth and market saturation. It then assesses the wider East African B2B digital-payment opportunity, considering market size, digital adoption, the SME population, regulatory developments, smartphone penetration and operational inefficiencies associated with manual invoicing and reconciliation. Competitive analysis evaluates Safaricom against established banking solutions, international fintech providers and specialist payment gateways. The strategic case identifies several potential advantages associated with Safaricom’s existing M-Pesa infrastructure, agent network, brand position and payment-services capabilities. The proposed Business Hub addresses fragmented SME financial processes through automated invoicing, bulk payment processing, real-time cash-flow analytics and integration with existing accounting platforms. The business plan also provides a four-phase implementation roadmap, covering MVP development, pilot testing, Kenyan market launch, regional expansion and ecosystem development. Financial projections consider customer growth, revenue, EBITDA, customer acquisition cost, lifetime value, payback and return on investment, alongside sensitivity scenarios for different levels of market adoption. Finally, the report evaluates competitive, regulatory and market-adoption risks and links the proposed investment to Safaricom’s broader strategic diversification objectives. Overall, the work integrates strategic analysis, fintech innovation, market opportunity assessment, implementation planning, financial modelling and risk evaluation to present a commercial case for expanding Safaricom’s digital financial-services ecosystem. Overview word count: approximately 340 words. One useful note for the public Reference Library: I would upload the clean original assignment/business plan rather than the AI-detection or similarity-report versions if you have it, because these two PDFs contain checker-report pages in addition to the coursework itself.
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Machine Learning / Data Mining / Text Mining
Machine Learning Analysis of Classification Models and Text Mining on Furniture Review Data
This technical machine-learning report demonstrates the practical application of predictive modelling and text mining using WEKA. The work is divided into two major tasks. The first evaluates and compares Support Vector Machine and Decision Tree classification models, while the second applies text-mining techniques to furniture-review data and compares multiple classifiers after preprocessing, feature selection and class balancing. The first task uses the Screenshots.arff dataset to investigate the performance of libSVM and J48 Decision Tree classifiers. A 70% training and 30% testing split is applied, and the models are manually tuned to examine how different parameter settings affect predictive performance. For libSVM, an RBF kernel is used while different gamma and cost values are tested through grid-search-style experimentation. The report identifies gamma 0.03 and cost 2 as the strongest tested combination, producing approximately 91.67% accuracy on the test split. The J48 model is also optimised by adjusting the confidence factor used for pruning. Several confidence-factor values are examined, with 0.09 producing the strongest reported result of 80% accuracy. The optimised SVM and J48 models are then compared using five-fold cross-validation, where libSVM achieves 89.75% accuracy compared with 81.25% for J48. The second task focuses on text mining of Furniture Reviews. Text preprocessing includes TF-IDF term weighting, stopword removal, stemming, conversion to lowercase and word-count generation. The resulting textual dataset is transformed into a numerical feature representation suitable for machine-learning classification. Dimensionality reduction is performed using InfoGainAttributeEval with Ranker, selecting the 900 most informative attributes. The dataset is then balanced using WEKA techniques including Resample and SpreadSubsample to reduce class bias before classification. Finally, three classifiers—Naive Bayes, libSVM and J48—are evaluated on the balanced text dataset. The reported accuracies are 90.52% for Naive Bayes, 58.62% for libSVM and 78.45% for J48. The analysis concludes that Naive Bayes performs strongest for the processed furniture-review dataset, while the wider exercise demonstrates the importance of preprocessing, parameter tuning, feature selection, class balancing and appropriate model evaluation in producing reliable classification results. Important: this upload appears to be the completed student report, not the actual assessment guideline. Because the document does not state the university, module name, academic level, academic year, required word count or prescribed referencing style, I would leave those fields as Not specified rather than guessing.
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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Operations and Supply Chain Management
1,987 words
Supply Chain Analytics and Quantitative Data Analysis for Organisational Decision-Making
This postgraduate individual report focuses on the application of quantitative data analysis to Operations, Logistics and Supply Chain Management decision-making. Students are required to select an organisation from the private, public or third sector and investigate a relevant operational or supply-chain issue using quantitative data. The purpose is to demonstrate how data can be collected, prepared, analysed and interpreted to generate evidence-based insights that may support managerial decision-making. The selected dataset must relate to the organisation's operations or supply chain and may include variables such as revenues, product orders, sales, transportation costs, procurement expenditure, inventory levels or other appropriate quantitative measures. The dataset must contain at least 60 observations, and the analysis must involve at least two variables. Data may be obtained directly from organisations or from recognised secondary-data platforms and databases. The assessment consists of two equally weighted components. Part A – Motivation and Justification for the Analysis requires students to formulate relevant analytical questions and explain their practical importance by linking them to Operations and Supply Chain Management theory and business practice. Appropriate academic, industry and practitioner evidence should be used to justify the selected issue. Research questions may also be translated into testable hypotheses where appropriate. Part B – Execution of the Analysis requires students to answer the identified questions through appropriate statistical techniques. Potential methods include tables, charts, summary statistics, t-tests and regression analysis. Data may first need to be cleaned, transformed and structured before analysis. The results must then be interpreted clearly for a managerial audience such as the organisation's board, owner or CEO. The statistical analysis is expected to be conducted using Stata, with all data-cleaning, manipulation and analytical commands recorded in a reproducible do-file. The report must also demonstrate explicit links between theory and practice and contain a suitable mixture of academic and professional evidence, including at least five academic journal articles. Harvard referencing is required throughout. The resulting work demonstrates practical competence in business analytics, statistical interpretation, supply-chain decision support, reproducible analysis and evidence-based managerial communication. Overview word count: approximately 370 words.
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Computer Science / Algorithms and Optimisation
Genetic Algorithms for the Balanced Spanning Tree Problem
This technical research work investigates the Balanced Spanning Tree Problem, an optimisation problem that seeks to construct a spanning tree capable of balancing two competing network objectives: the low overall cost associated with a Minimum Spanning Tree and the short source-to-destination distances provided by a Shortest Path Tree. For an undirected, weighted and connected graph with a designated root vertex, a balanced spanning tree is defined using two parameters, α and β. The first limits the distance between the root and each vertex relative to the corresponding shortest path in the original graph, while the second limits the total tree weight relative to the Minimum Spanning Tree. Finding an optimal balanced spanning tree is computationally challenging because determining whether a graph contains an (α, β)-balanced spanning tree is an NP-complete problem. The research therefore proposes genetic algorithms as heuristic optimisation techniques for two variants of the problem: minimising β while α is fixed, and minimising α while β is fixed. The proposed genetic algorithm represents individual spanning trees as chromosomes composed of graph edges. An initial population of valid spanning trees is generated before evolutionary operations are repeatedly applied. The approach incorporates chromosome selection, crossover, mutation, fitness evaluation and stopping criteria. Four selection strategies are examined: Random Selection, Roulette Wheel Selection, Stochastic Universal Sampling and Tournament Selection. The fitness function is based on the relationship between the Minimum Spanning Tree weight and the total weight of the candidate chromosome. Experimental evaluation is performed using randomly generated weighted graphs containing 6, 10, 15 and 20 vertices. The experiments investigate different values of the balancing parameters, selection mechanisms and population sizes. The implementation uses a population size of 30, a maximum of 300 generations, crossover probability of 0.9 and mutation probability of 0.01 in the principal experiments. The reported results show that the genetic approach can generate high-quality balanced spanning trees and, for the tested instances, produced solutions matching the corresponding optimal balanced spanning trees. The study also examines how balancing parameters and population size influence execution time and convergence, demonstrating the practical use of evolutionary computation for complex graph-optimisation problems.
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Global Supply Chain Management / Operations Management
2,494 words
Global Supply Chain and Operations Performance: Company Case Analysis
This Level 7 individual assignment requires students to produce a 2,500-word company case analysis examining an important operations performance issue within a real organisation. Students may select an organisation of any size, type, industry or country and must concentrate their analysis on one operations performance objective: cost, dependability, flexibility, speed or quality. The report requires students to identify the current challenges faced by the chosen organisation in relation to the selected performance objective and critically examine those challenges using relevant operations and supply chain management theories, concepts and analytical tools. Rather than providing only a descriptive account of organisational activities, students are expected to apply appropriate theoretical frameworks to the case and evaluate how these concepts could contribute to improved organisational and supply chain performance. A significant component of the assignment involves critically discussing appropriate best practices for performance improvement. These may include digital technologies, sustainability practices or other relevant operational and supply chain approaches. Students should connect these practices directly to findings from their chosen company case and assess their practical relevance, opportunities and limitations. Academic literature and appropriate real-world evidence should be used throughout to substantiate the analysis and recommendations. The assignment evaluates students' ability to analyse supply chain and operations problems, apply academic theory to organisational practice, and develop evidence-based conclusions and practical recommendations. The marking rubric places substantial emphasis on analysis and discussion (30%), application of theory (30%), and conclusions and recommendations (30%), with the remaining 10% allocated to presentation, logical structure, English expression and correct referencing. The report must use Harvard referencing, include a contents page, and follow the specified academic formatting requirements, including Arial size 12, 1.5 line spacing and A4 pages with 2.54 cm margins.
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Marketing
4,000 words
Strategic Recovery Marketing Report — Diagnosing Underperformance and Building a Sustainable Turnaround Plan
This is a case-based marketing assessment in which the student selects a real business that is currently underperforming or failing, diagnoses the causes, and builds a full marketing recovery strategy for it. The company choice is the student's own but requires module team approval, so the selection itself carries risk: a firm with thin public disclosure will starve the analysis, while an over-documented household name invites description rather than diagnosis. The deliverable is a formal report of around four thousand words, excluding references and appendices, referenced in APA 7. Appendices can carry supporting data, models and supplementary analysis, but not core content — a common way marks are lost is pushing substantive argument into an appendix to stay within the word limit. The report has two linked halves that students often treat as separate. The first is diagnostic: why is this business underperforming? This demands explicit application of marketing theory rather than a narrative of the company's troubles assembled from press coverage. The second is prescriptive: a recovery strategy that follows from the diagnosis. Recommendations that could have been written without the analysis — refresh the brand, invest in digital, improve customer experience — score poorly regardless of how well they are expressed. Where this module differs from a generic strategic marketing assessment is the sustainability and ethics dimension. The learning outcomes centre on sustainable marketing, social responsibility, and the intersection of marketing technology with sustainable practice. The marking criteria reward conclusions that show awareness of ethical and sustainability dimensions at every band above a pass. A recovery plan built purely on cost and revenue logic will therefore underperform against the rubric even if it is commercially sensible. The strongest submissions treat sustainability as part of the recovery mechanism rather than a section appended at the end. Assessments of this type increasingly sit within a tiered AI policy. Where a permissive tier applies, students may use AI tools for idea generation, structuring, source discovery, summarising notes, and proofreading or feedback — but not for producing the analysis itself — and must declare which tools were used and how, usually in a table placed before the reference list. Students are also expected to retain evidence of how their thinking developed, such as version histories or drafts, which can be requested if misconduct is suspected. Our support on assessments of this type is guidance-based. Typical areas of help include: advising on company selection and data availability, explaining how a diagnostic framework should structure an argument, showing the difference between descriptive and evaluative use of theory, clarifying how sustainability criteria are actually assessed in a marking rubric, checking APA 7 consistency, and reviewing a completed draft against the published criteria.
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Entrepreneurship and Business Start-up
5,000 words
Enterprise Start-up Portfolio — Business Plan, Fundraising Strategy and Entrepreneurial Self-Assessment
An enterprise start-up portfolio assessment asks students to originate a business idea and build the full supporting case for it, then present that case both in writing and as a recorded investor pitch. It is a pass/fail module structure in which every component must be passed individually — a strong business plan cannot compensate for a weak pitch, or vice versa. The written portfolio divides into two unequal halves. The larger part is the business plan itself, built to a fixed section structure with prescribed word allocations: an executive summary; the business idea set against an identified market gap, with market research and competitor analysis; customer profiles and segmentation; product development; marketing and communications; financials and business models covering costing, pricing, sales and revenue; and the founding team with its core competencies. The word allocations are not decorative — financials carry the single largest weighting in both the word budget and the marking rubric, which tells students where analytical depth is expected and where concision is. The smaller part is reflective. It covers the fundraising strategy — which funding sources are realistic for this venture, and how each would be targeted, approached and secured — and the student's own entrepreneurial tendency, informed by a standardised self-assessment instrument taken online. The rubric here rewards critical self-reflection over description: reporting a test result scores poorly; interpreting what the result means for how this founder should build a team and where they need support scores well. The pitch component is assessed on visual and audio quality, content and message, comprehension, delivery, and evident preparation. Technical execution carries real marks, which students routinely underestimate. Two things separate strong submissions. The first is internal consistency: the revenue model must follow from the pricing, the pricing from the customer segment, the segment from the identified gap. Plans that read as seven separate essays under seven headings lose marks even when each section is individually competent. The second is specificity in the financials — costing assumptions stated and justified, rather than round numbers presented without derivation. Note that assessments of this type commonly require the student to retain all drafts and earlier versions of their work, and to sign a detailed declaration itemising exactly how any AI tools were used. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining what a market gap argument needs to be credible, showing how competitor analysis should be structured rather than listed, clarifying how costing and pricing assumptions should be built and presented, reviewing whether a fundraising strategy matches the venture's actual stage, explaining how reflective writing is assessed at postgraduate level, and checking a completed draft against the published rubric.
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Operations and Supply Chain Management
1,800 words
Strategic Sourcing and Supply Chain Resilience Under Global Uncertainty — A Firm-Level Critical Evaluation
This type of postgraduate coursework asks students to take a single real firm — a British company, or a multinational with substantial UK operations — and critically evaluate how its sourcing and supply chain strategy has adapted to a decade of sustained disruption: the pandemic, geopolitical conflict, tariff volatility, and departure from the European single market. The analytical scope is broad but the depth expectation is narrow. Students choose one or two themes rather than surveying all of them: insourcing versus outsourcing and location decisions; single versus multi-sourcing across supplier segments or regions; how supplier selection and monitoring criteria have shifted toward reliability and geographic proximity; alignment between sourcing decisions and wider corporate objectives; supplier and customer relationship management under cross-border friction; inventory tactics such as stockpiling and safety stock repositioning; digital adoption for visibility and compliance; sustainable sourcing under cost pressure; or risk management around currency, customs and compliance. The evidence base is deliberately mixed. Alongside academic literature, students are expected to draw on annual reports, shareholder briefings, company statements, practitioner journals and recent news coverage — with a minimum spread of credible sources across both categories, and some of the non-academic material drawn from the most recent year so the analysis reflects current firm behaviour rather than historical commentary. At least one theoretical framework from the module or the wider literature must be applied to the firm's decisions, and at least one recent documented practice must be examined with specific sourcing. Two requirements distinguish strong submissions. The first is criticality: the brief explicitly separates evaluation from description, and marks weight critical analysis most heavily. Reporting what a firm did is not the task; assessing whether it was the right response, and what opportunities and risks it created, is. The second is the recommendation — at least one specific, actionable proposal with steps, anticipated obstacles, expected outcomes, and a clear line back to the firm's own stated goals. A generic suggestion to "diversify suppliers" fails this test; a costed, sequenced proposal grounded in the firm's actual constraints does not. Word-count conventions are stricter than students often expect: in-text references, tables, illustrations and front matter typically count, while appendices and the reference list do not. Our support on assessments of this type is guidance-based. Typical areas of help include: narrowing a firm and theme so the analysis fits the word limit, explaining the difference between description and critical evaluation with worked examples, clarifying how a theoretical framework should structure an argument rather than sit decoratively in a paragraph, checking source mix and Harvard referencing consistency, and reviewing a completed draft against the published assessment weightings.
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