<p>Indoor positioning systems (IPS) enable the tracking of assets, people, and processes, forming a foundation for smart factory operations. Ultra-wideband positioning offers high accuracy but comes at a significant cost. In contrast, low-cost Wi-Fi fingerprinting offers meter-level accuracy, but its use in industrial environments with poor network infrastructure is limited. Factory layouts are typically structured in zones, making zone-based Wi-Fi fingerprinting feasible to apply. However, fingerprinting is highly sensitive to environmental factors, making it challenging to apply in complicated factory settings. To address these, this study proposes a comprehensive analysis pipeline based on a zone-based IPS architecture, utilizing machine learning models to evaluate the system configuration and zone-related factors for system implementation. In the case study, the zone-based IPS was integrated with a noise map that visualized the real-time noise distribution and tracked object movement in a factory. Two dynamic noise maps demonstrate a hitting rate exceeding 70% for the practical inspection trajectory. This method, which only relies on low-cost Wi-Fi routers and antennas, provides a feasible solution for indoor localization in manufacturing settings, offering a practical and cost-effective alternative IPS.</p>