Dome Novak

• Own analytical workstreams related to demand forecasting, inventory optimization and replenishment across 600+ locations and 30,000+ products.

• Develop and evaluate forecasting, segmentation and simulation solutions using Oracle SQL, Python, statistical methods and machine learning.

• Optimize safety stock, minimum stock and replenishment parameters to balance product availability, inventory

• Developed complex Oracle SQL queries to extract, transform and validate multi-million-row datasets across 600+ locations and 30,000+ products.

• Built and automated Excel-based reporting for product availability, inventory, forecasting and replenishment KPIs.

• Analyzed demand, inventory and out-of-stock performance to identify trends, anomalies and root causes, supporting planning and repleni

• Coordinated production planning and material flow using MRP, aligning material availability with production schedules.

• Managed BOM structures and technical documentation within a PLM environment to support accurate production preparation.

• Collaborated with production, procurement and logistics teams to resolve material constraints and improve workflow efficiency.

• Analyzed PET production-line performance using SAP production data and OEE metrics to identify downtime patterns and operational bottlenecks.

• Supported process-improvement activities through data validation, KPI reporting and root-cause analysis.

• Supported manufacturing, assembly and testing activities for a specialized welding manipulator.

• Prepared technical documentation and 3D CAD models using Autodesk Inventor.

Specialist Thesis: Application of Statistical Methods, Machine Learning and Neural Networks in Demand Forecasting

Compared statistical, machine-learning and neural-network forecasting approaches using real-world transactional data, evaluating forecast accuracy, bias and inventory implications.

Focus areas: statistical forecasting, machine learning, multivariate statistics, operations research an

Pursuing a Postgraduate Specialist Degree in Industrial Engineering and Management, with a thesis on comparing statistical, machine-learning, and neural-network forecasting methods.

Master’s Thesis: Inventory Optimization Using Quantitative Methods
•Developed an EOQ-based mathematical-economic model to improve inventory control and reduce total inventory costs.

Relevant coursework: Operations Research, Engineering Statistics, Logistics and Inventory Management.

Bachelor’s Thesis: “Application of Linear Programming in Mechanical Engineering”
• Applied linear programming models to transport optimization, workforce scheduling and material cost minimization.