Synthesized research on AI-enabled inspection, forecasting, and scheduling to quantify impacts on cost, throughput, and sustainability. Discussed integration challenges and workforce implications to guide adoption strategies for advanced manufacturing.

Collected dimensional data; built X̄–R control charts; performed ANOVA/DOE and nested Gage R&R. Confirmed overall process stability; identified measurement repeatability as primary source of residual error; proposed calibration and sampling improvements for tighter tolerances in additive manufacturing.

Explored foundational methods in manufacturing systems including assembly line balancing, Materials Requirement Planning (MRP), Bills of Materials (BOM), and reliability modeling. Developed process flow diagrams, evaluated workstation allocation strategies, and applied dependent demand scheduling to understand how manufacturers optimize efficiency and reliability. This study emphasizes lean principles, process planning, and systems thinking as applied to real-world production challenges.