Mohamed Amer

Results-oriented professional with experience as a Student Researcher at Volvo SML, focusing on data analysis and machine learning. Demonstrated success in improving project outcomes through effective collaboration and innovative solutions. Proficient in Python and automation scripting, emphasizing efficiency and time management in high-pressure settings.

An Integrated Room Booking and Access Control System for Public Spaces with BankID integration. This thesis presents a solution for challenges faced by public spaces, especially universities, in managing room bookings and access control. The proposed system integrates two mobile apps for room reservations and access control to address issues like conflicting bookings and unauthorized entry. Developed using NodeJS, Android Studio, and PostgreSQL, the system includes Mobile BankID for secure user authentication. Consultation with Halmstad University's IT department ensured a comprehensive understanding of common problems, leading to a successfully tested solution in a simulated environment. 

Prediction Intervals for ML-driven Automotive Service Market Logistics (Volvo SML). This thesis investigates how combining demand forecasting models with prediction intervals (PIs) can improve spare parts inventory management in Volvo Group's Service Market Logistics (SML), focusing on Volvo Trucks Sweden. Using real operational data and discrete-event simulation, the study evaluates the impact of forecast accuracy and uncertainty on inventory performance metrics such as service levels and costs. The research demonstrates that incorporating prediction intervals, especially via Beta-PERT and triangular distributions, can substantially reduce inventory costs while maintaining high service levels for low-demand parts, offering actionable insights for automotive and other industries facing similar inventory challenges.