Linking Process Capability Improvement to Carbon Reduction in SME Die Casting: A Case Study of Aluminum Alloy Components

Abstract

High shrinkage-related defect rates in aluminum die casting reduce effective production capacity. They also create energy-intensive re-melting loops, which weaken production planning reliability and environmental performance. This study examines how Green Lean Six Sigma can stabilize a resource-constrained Small and Medium-Sized Enterprise (SME) die-casting process and translate quality improvement into measurable capacity recovery and Scope 2 electricity-related carbon savings. Based on a 10-month case study, the Define–Measure–Analyze–Improve–Control (DMAIC) framework was integrated with factorial ANOVA, the Response Surface Methodology (RSM), one-way analysis of variance (ANOVA) and statistical process control (SPC). These methods supported process parameter identification, operating-window development and shop-floor process stabilization. The analysis identified the filling speed and mold temperature as significant shrinkage drivers, developed a mold temperature control map, and determined the standardized filling speed at 800 mm/s. The intervention reduced the shrinkage defect rate from 7.19% to 1.46%, reduced the overall scrap rate from 7.60% to 2.71%, and improved the overall sigma level from 2.93 to 3.42. This yield improvement generated a 4.89 percentage-point yield-equivalent capacity gain, avoided 4401 kWh of re-melting electricity, reduced Scope 2 emissions by 2.36 t CO2e, and generated gross annualized savings of RMB 249,214 (USD 35,783). Considering a one-time implementation cost of RMB 31,445 (USD 4515), the first-year net saving was RMB 217,769 (USD 31,268). The findings show that accessible statistical process control methods can provide SMEs with a resource-efficient pathway to improve process stability, capacity utilization and electricity-related environmental performance before investing in advanced digital technologies.