ECONOMETRIC MODELING AND MATRIX PLANNING OF INNOVATIVE SPARE PARTS PRODUCTION THROUGH ECONOMIC ENGINEERING

This thesis investigates the mechanisms of diversifying automotive spare parts manufacturing based on economic engineering, matrix resource planning, and quantile regression modeling with time-varying parameters (QR-TVP). Unlike conventional evaluations focused solely on capacity utilization or environmental compliance, this study examines the micro-technological reconfiguration of raw material processing—specifically template cutting geometry, 30-degree panel storage, and thermal spiral synchronization— alongside long-term econometric forecasting up to 2036. Using empirical data from “Avtooyna” LLC (2020–2025), the study proves that matrix planning reduces inter-operational technological losses from 5–10% to 1–3%, while quantile regression reveals a saturation effect in high-innovation segments (q75) and identifies robotization (X4) as the primary statistically significant driver in lower (q25) and median (q50) segments.