Research Strategy 2024-2027

CCT\u27s Research Strategy effective from 2024-2027

CCT College Teaching, Learning and Assessment Strategy 2024-2027

CCT\u27s Institutional Strategic Plan in respect of Teaching, Learning and Assessment effective from 2024-2027

Tesla-Addressing eco challenges globally

This dissertation examines Tesla Inc.\u27s contributions to global environmental sustainability through its renewable energy solutions and managerial approach to regulations. Aligning with Tesla\u27s mission as an electric car and energy solutions provider, the study employs secondary qualitative research and thematic analysis to explore the company’s role in sustainability, its market niche, and the regulatory challenges it faces. Key findings highlight differences in legal systems across major markets, the challenges Tesla encounters in meeting diverse environmental standards, and the significance of technology in advancing sustainability initiatives. The research underscores the critical role of multinational enterprises (MNEs) in achieving global sustainability goals and advocates for harmonised regulations, industry-specific sustainability standards, and technological innovation to sustain competitive advantage. This study contributes insights into international business, legal frameworks, and environmentalism

Supply Chain Optimisation with Machine Learning and Neural Networks: Applications to Demand Planning, Supply Planning, and Inventory Planning.

This thesis explores the impact of machine learning (ML) on supply chain planning, particularly in demand forecasting, supply planning, and inventory optimisation. By analysing literature on supply chain management, data flow, and the intersection of ML and competitive advantage, the author contextualises the research within a globalised market\u27s demands. Case studies, interviews with industry professionals, and raw data collection provide empirical support for evaluating the research objectives and documenting the integration of ML in supply chain processes. The findings reveal that optimised ML models, particularly those using model stacking (autoregressors, GRUs, and Random Forests), significantly outperform traditional demand forecasting methods, achieving a 70% MAPE improvement over a 45% benchmark. The integration of advanced techniques like XGBoost further optimised supply and inventory planning. The research concludes that leveraging ML not only enhances forecast accuracy but also strengthens supply chain competitiveness through superior planning outputs. By critically relating empirical data to literature insights, the author demonstrates that ML-driven approaches enhance supply chain management in a Central European wholesale clothing business. This research validates the transformative potential of advanced data analytics for achieving a competitive edge in the supply chain

Maize Crop Pests and Diseases Classification Using Hybrid Models.

This research focuses on improving the detection and classification of maize crop pests and diseases to enhance agricultural yield and food security. A dataset comprising 5389 images of maize conditions (healthy, pest-affected, and disease-affected) across seven classes was used. The images underwent preprocessing, including resizing to 299x299, class balancing using augmentation techniques, and noise reduction with Gaussian filtering. Feature extraction utilised EfficientNetB0 and InceptionV3 architectures, with PCA employed for feature selection. Classification was conducted using a Support Vector Machine (SVM) with a One-vs-One strategy, alongside a baseline 2D CNN model. Data engineering included label encoding, standardisation, and an 80:10:10 train-test-validation split. Hyperparameter optimisation was performed via Grid Search CV for SVM and Random Search for the 2D CNN. The best-performing model, EfficientNetB0+SVM (299x299), achieved an accuracy of 93%, outperforming other models, including the standalone 2D CNN, which reached 78%. This underscores the advantage of hybrid models over standalone CNNs in classification tasks for pest and disease detection in maize crops

Accelerating the Transition - Understanding and Addressing Barriers to Consumer Adoption of Tesla Electric Vehicles.

This research investigates the key influences on adoption of electric vehicles (EVs): focusing on consumer perceptions, advances in technology and government incentives characteristics through Tesla. The study looks at the challenges to EV acceptance, such as charging infrastructure, cost and range anxiety through both qualitative and quantitative means along with potential motivators for adoption such as environmental benefits, financial incentives, technology advances. Consumer awareness, government policies and brand influence are identified having a High effect on EV adoption whereas work-place incentives have an Intermediate effect with high priority to Next. Key challenges are limited charging infrastructure and high upfront costs, however increasing incentives combined with technological innovation provide opportunities to facilitate rapid market development. Suggestions range from the enlargement of infrastructure and better government-supported incentives to targeted information campaigns. This research suggests that to encourage the new vehicle purchasing decision of electric vehicles, it is necessary not only countering practical but also psychological barriers in a holistic approach supporting potential buyers contributing toward more sustainable transport and environmental targets

Evaluation and Implementation of Machine Learning Models to Predict Customer Churn in the Telecommunications Sector.

This research addresses customer churn in the Telecom industry by utilizing Machine Learning (ML) models to predict customers at risk of leaving and provide data-driven retention strategies. The study highlights the effectiveness of ML, particularly in churn prediction, while noting the need for further exploration into the ethical implications of AI, such as potential biases towards vulnerable groups. Using the CRISP-DM framework, the study develops and compares three Supervised Learning (SL) models: Random Forests (RF), LightGBM (LGBM), and XGBoost (XGB), incorporating class resampling techniques to manage data imbalance. The findings identified five key features as the most significant predictors of churn at Viatel Technology Group (VTG), including customer billing, service retention efforts, and product offerings. Among the models tested, LGBM-SMOTETomek delivered the best performance with a precision of 97.92%, recall of 95.25%, and an F1-score of 96.57%. The research concludes with recommendations to promote automatic payment methods, reward loyal customers, and proactively engage with customers who frequently contact the company

Bord Bia: Global marketing challenges

This research examines global marketing challenges faced by Bord Bia, an Irish agency in the food, drink, and horticulture sector, as it seeks to increase market share in the European Union (EU). The study combines primary and secondary research, utilising a case study approach to analyse Bord Bia’s branding, marketing strategies, and digital transformation efforts. Key areas of focus include leveraging sustainable production as a unique selling point, enhancing the Country of Origin (COO) effect, and addressing cultural diversity in marketing activities. Findings highlight the value of a blended marketing strategy, integrating traditional and digital methods, and recommend adopting AI-driven analytics to support business expansion. This research provides actionable insights for Bord Bia and similar organisations navigating competitive foreign markets