Statistical Process Control with Real-World Water Quality Data: An Illustrative MEC Control Chart Approach for Mathematical Science - Serly Malinda; Muhammad Alwi (Universitas Cokroaminoto Palopo, 2026)
Statistical Process Control (SPC) constitutes an important topic in applied statistics and mathematical science, particularly in the context of monitoring process stability and detecting small shifts in process parameters. While SPC methods are widely discussed in the quality engineering literature, illustrative applications using real-world data remain valuable for strengthening conceptual understanding in statistics and mathematics-oriented journals. This paper presents an illustrative application of SPC using residual chlorine measurements from a water production process. The analysis employs Mixed Exponentially Weighted Moving Average–Cumulative Sum (MEC) control charts enhanced with Fast Initial Response (FIR) features, namely Basic FIR (BFIR) and Modified FIR (MFIR). The results show that MEC charts incorporating MFIR features consistently produce narrower control limits during the initial monitoring phase and detect early deviations more effectively than BFIR-based charts. This study contributes an applied and conceptually clear example of advanced SPC methodology using authentic environmental data, which may serve as a reference for applied statistics and mathematical science audiences.