Looking for a job

Nicola Sigismondi

@nicolasigismondi

Message

I optimize supply chain processes using machine learning, simulation, and automation analysis.

Italy

Message

What I'm looking for

I'm looking to improve supply chain and operational processes through quantitative analysis, machine learning, simulation, automation, and digital tools.

At Aptar Italia, I analyzed and simulated supply chain processes to assess the operational and economic impact of LLM-based automation. I built a Random Forest model to predict order-confirmation lead times and developed discrete-event simulations comparing process performance before and after automation.

I'm a Management Engineering graduate focused on quantitative process improvement through data, automation, and digital tools. My work combines machine learning, techno-economic analysis, ROI evaluation, and supply chain analytics to identify low-value-added activities and evaluate practical automation opportunities.

Experience

Work history, roles, and key accomplishments

Supply Chain Intern, Operational Excellence

Aptar Italia S.p.A.

Nov 2025 - Jul 2026 (8 months)

Analyzed and simulated supply chain processes to assess the operational and economic impact of automation through Large Language Models. Developed a Random Forest model to predict order confirmation lead times and built Discrete Event Simulation models to compare performance before and after automation.

Python SQL SAP Dataiku Machine Learning Discrete Event Simulations Process Analysis Process Automation Supply Chain Analytics Operational Excellence

Education

Degrees, certifications, and relevant coursework

University of L'Aquila

Master of Science, Management Engineering

2023 -

Grade: 107/110

Activities and societies: Developed machine learning models, DES simulations, and cost-benefit analyses for process automation.

Pursuing a Master's Degree in Management Engineering, focusing on supply chain analytics and operational excellence. Thesis on identifying low-value-added activities and assessing automation feasibility using Large Language Models.