Statistical Quality Control as a Tool for Monitoring and Improving Dimensional Accuracy in Soap Manufacturing - Ayenigba, Alfred Ayo (Mikailalsys Journal of Mathematics and Statistics, 2025)
This study examines the application of Statistical Quality Control (SQC) techniques to enhance dimensional consistency, specifically in length and weight in the production of Sunlight Soap at Unilever’s Aba Plant. Data were collected from 20 production batches in January 2024 and analyzed using X̅ and R control charts, along with process capability indices (Cₚ and Cₚₖ). Analysis revealed that both dimensions were statistically in control, with no significant variation across batches (p = 0.9875 for length; p = 0.939 for weight). However, while weight measurements exhibited excellent process capability (Cₚ = 2.35, Cₚₖ = 2.31), length measurements reflected poor capability (Cₚ = 0.412, Cₚₖ = 0.301), indicating excessive variability. To address this inconsistency, the study recommends equipment recalibration, real-time monitoring, and targeted staff training. The findings contribute a replicable quality control framework aimed at improving product uniformity within fast-moving consumer goods (FMCG) manufacturing environments.