Modern manufacturing systems increasingly include parallel production branches, shared resources, and synchronization-dependent assembly operations. Under such conditions, delays generated at specific operations may propagate across multiple production stages and influence overall manufacturing lead time. Conventional Value Stream Mapping (VSM) approaches remain limited in analyzing these interactions because they primarily provide static flow representation and focus mainly on localized waste identification. This research proposes the Hierarchical Governing-Path Optimizing Value Stream Mapping (HGP-OVSM) model to support the analysis and improvement of manufacturing performance in complex production environments. The framework combines hierarchical production representation, process-precedence network analysis, the Critical Path Method (CPM), mixed-integer linear programming (MILP), and multi-objective optimization to evaluate governing-path behavior and operational improvement priorities. The model was implemented computationally using Python and applied to a real manufacturing system consisting of interconnected production lines and synchronization-sensitive assembly stages. The optimization model selected two paths with 37 operations for improvement from 104 processes. The governing-path lead time decreased from 142,409.61 to 103,820.19 seconds, corresponding to a 27.10% reduction, reducing lead time from 1.65 days to 1.20 days, which considers the first path. The second path lead time decreased from 125,887.6 to 93,300.86 seconds, corresponding to a 25.88% reduction, reducing lead time from 1.46 days to 1.07 days. Total non-value-added (NVA) activity decreased from 560,739.51 to 392,517.66 seconds, representing a 30% reduction under process-improvement eligibility constraints associated with implementation feasibility and improvement capacity constraints. In addition, governing-path process-cycle efficiency increased from 9.68% to 13.27%. The results showed that system-level improvement was governed primarily by operations located on dominant production paths rather than by isolated local improvements alone. The analysis further demonstrated that changes in improvement feasibility and optimization priorities may alter governing-path dominance and redistribute bottleneck pressure across interconnected production branches. Overall, the proposed HGP-OVSM framework extends VSM toward optimization-oriented manufacturing analysis capable of supporting improvement prioritization and lead time control within complex multi-path production systems.

Keywords

Manufacturing Optimization, Value Stream Mapping, Critical Path, Bottleneck, Multi-Objective Optimization.