Statistical Quality Control as a Tool for Monitoring and Improving Dimensional Accuracy in Soap Manufacturing
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619-631
Digital Object Identifier:
10.58578/mjms.v3i3.6431
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Authors:
Alfred Ayo Ayenigba1* 
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Abstract
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.
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References
El-Din, M. A. S., Rashed, H. I., and El-Khabeery, M. M. (2020). Quality measurement in a manufacturing supply chain system using statistical process control. International Journal of Industrial Engineering & Production Research, 31(2), 143 152. https://www.researchgate.net/publication/342143896
Geprom. (2024). Statistical process control (SPC). Geprom. https://www.geprom.com/en/statistical process-control-spc
Kim, S. and Park, J. (2022). Statistical methods and inspection techniques in quality control: An introduction. ResearchGate. https://www.researchgate.net/publication/361670444
Smith, J., and Lee, T. (2024, August 17). Applying statistical quality control in manufacturing processes. Editverse. https://editverse.com/applying-statistical quality-control-in-manufacturing-processes
Tests and Trials. (2022). Statistical quality control in production processes. Tests and Trials. https://www.testsandtrials.com/en/statistical-quality control-in-production-processes
Wang, L., and Zhang, Y. (2023). A new statistical approach to automated qual ity control in manufacturing processes. Procedia Manufacturing, 59, 123–130. https://www.sciencedirect.com/science/article/pii/S2351978923001234
Xu, Y., Zhang, Q. and Liu, H. (2022). Enhancing manufacturing quality through integrated statistical process control and machine learning. Journal of Manufacturing Systems, 65, 789–798. https://doi.org/10.1016/j.jmsy.2022.07.012