DCU-Final-Year-Projects-Booklet-2025

57 75. Comparative Analysis of Kanban-Like InventoryManagement Strategies in Industrial Engineering This project explores the continuous monitoring of Kanban and CONWIP inventory management strategies through adaptive systems that track on-time delivery and service level targets. By replacing static management approaches, these evolving systems dynamically adjust to order levels and aim to enhance the efficiency and responsiveness of these inventory management strategies within manufacturing facilities. Student Programme Mechanical andManufacturing Engineering (Year 5) Project Area Digital Signal Processing, Information Retrieval, Internet of Things, Operating Systems Development, Simulation Project Technology Excel/VB, ExtendSIM Student Name(s) Órla Fitzpatrick Email orla.fitzpatrick8@mail.dcu.ie Supervisor Dr John Geraghty 76. Integration of Ignition andMQTT for Cloud-Based Automation Applications This project explores the integration of Ignition SCADA, developed by Inductive Automation, with the MQTT communication protocol for cloud-based automation. The goal is to demonstrate the potential use cases and cost-effectiveness of a cloud-based Industrial Internet of Things (IIoT) automation stack, thereby advancing Industry 4.0 solutions. Awater management system is developed, where an Arduino Opta PLC controls water flow using sensors, relays, and a control loop. The systempublishes real-time data to a cloud-basedMQTT broker, which is then processed and visualised on Ignition SCADA. By leveraging the efficiency of Ignition andMQTT, the project highlights a scalable, cost-effective, and lightweight alternative to traditional automation stacks. Student Programme Mechatronic Engineering (Year 4) Project Area Arduino, Automation, Cloud Computing, Control Systems, Instant Messaging, Internet of Things, Mechatronic Systems, Wireless Technology Project Technology C/C++, PLC Programming Student Name(s) Fergal Reilly Email fergal.reilly8@mail.dcu.ie Supervisor Dr Nigel Kent 77. SafeFish Phishing remains amajor cyber threat, exploiting human error. SafeFish is a toolset that educates users by simulating spear-phishing attacks. It analyses the user’s internet browsing behaviour (with the user’s consent), and generates phishing emails usingMachine Learning. Users receive simulated attacks to improve detection skills in a safe environment. A reporting platform tracks interactions, highlighting vulnerabilities and areas for improvement. SafeFish ensures GDPR compliance, allowing users to toggle data collection and delete data. Targeted at individuals and organisations, it enhances cybersecurity awareness and training. Student Programme Computer Science Project Area Artificial Intelligence, Data Analytics, Educational, Security, Human-computer Interaction, Phishing, Social Engineering Project Technology CSS, HTML5, JavaScript, Python, SQL, Machine Learning, Google Cloud API, Typescript Student Name(s) Carlos Conde  |  Evun Grant Email carlos.conde3@mail.dcu.ie   |  evun.grant24@mail.dcu.ie Supervisor Mr Renaat Verbruggen