A Study on Cost Optimization Models in Electronics Manufacturing using Big Data

Abstract

This study, titled "A Study on Cost Optimization Models in Electronics Manufacturing Using Big Data," evaluates focus area allocations, unit manufacturing cost compression, processed sensor data scalability, material scrap waste reduction, and financial feasibility of Big Data analytics engines in electronics production. Electronics manufacturers operate in capitalintensive SMT assembly environments, where supply chain procurement represents 42% and energy/utility optimization accounts for 28% of cost reduction initiatives. A five-year project lifecycle (2021-2025) of a Big Data cost optimization platform is evaluated using capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis indicates that real-time Big Data processing compresses unit manufacturing costs to ₹320/unit compared to ₹1,850 under legacy batch manufacturing. Scaling sensor data throughput to 320 Terabytes/Day delivers cumulative cost savings of ₹850 Crores, expanding analytics penetration to 95.0%, compressing raw material waste to 0.4%, and reducing unplanned plant downtime to 3.2 hours/month by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that deploying Big Data cost optimization engines in electronics manufacturing is highly viable, enhancing operational efficiency and margin growth.