IJEBM | Application of Statistical Process Control Chart in Food Manufacturing Industry

Indexing and Abstracting

Application of Statistical Process Control Chart in Food Manufacturing Industry

Noora Shrestha

International Journal of Engineering, Business And Management(IJEBM), Vol-4,Issue-5, September - October 2020, Pages 82-87 , 10.22161/ijebm.4.5.2

Download | Downloads : 21 | Total View : 1443

Abstract:

Statistical Process Control (SPC) charts are simple graphical tools that allow monitoring of process results. They are used to describe the type of variance that exists within the process. They highlight areas where further investigation could be needed. The purpose of the study was to use statistical control charts to examine the production process of buff sausages in the food manufacturing industry. The mean (X-bar) chart and range (R) chart were used in the study to assess if the production process was under control. The weight of 100 packets of buff sausages was measured (in grams) using a weighing scale. In order to collect information about the manufacturing process of the buff sausage, the interview of operations manager was followed by the observation of the buff sausage production plant. The result of mean chart shows that the within subgroup variation is consistent because all the sample points fall within the three-sigma upper and lower control limits. The range chart displays that all the sample points are within the two-sigma control limits. The buff sausage manufacturing process is therefore in a state of statistical control. However, the production unit can still seek to further improve performance to maintain consistent quality of the product.

Keywords:

Statistical Process Control (SPC), Mean Chart, Range Chart, Control Limits, Sausage, Food Industry, Three-sigma

References:

[1] Fitzsimmons, J. A., & Fitzsimmons, M. J. (2006). Service management: Operations, strategy, and information technology. Boston: McGraw-Hill/Irwin.
[2] Bamford, D. R., & Forrester, P.L. (2010). Essential guide to operations management: Concepts and case notes. Hoboken, NJ: John Willey & Sons.
[3] Shewhart, W. A. (1987). Statistical method from the viewpoint of quality control, Edited by Deming, W.E. New York: Dover Publication.
[4] Johnston, R., Clark, G., & Shulver, M. (2012). Service operations management: Improving service delivery. 4th ed. Harlow, England: Pearson.
[5] Lásztity, R.R. (2009). Food quality and standard. In: Encyclopedia of life support systems. Oxford: Eolss Publishers.
[6] Aung, M. M., & Chang, Y.S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172-184. https://doi: 10.1016/j.foodcont.2013.11.007
[7] Montgomery, D.C. (2000). Introduction to statistical quality control. 2nd edition, New York: Willey.
[8] Gupta, K. P., & Hira, D. S. (2014). Operations Research. 7th ed. New Delhi: S. Chand Publishing.
[9] Dunchan, A. J. (1986). Quality control and industrial statistics. 5th ed. Illinois: Irwin.
[10] MOICS. (2019). Industrial statistics 2018/2019. Department of Industry, Ministry of industry commerce and supplies, Government of Nepal.
[11] Lawrence, S. A. (1998). Fundamentals of industrial quality control. 3rd ed. New York: CRC Press.
[12] Boler, D. D., & Woerner, D. R. (2017). What is meat? A perspective from the American Meat Science Association. Animal Frontiers, 7 (3), 8-11. https://doi.org/10.2527/af.2017.0436
[13] Dramm, J. R. (1997). Statistical process control and other tools for continuous improvement. In: Proceedings, Wood Technology Clinic, and Show. San Francisco, CA: Miller Freeman.
[14] ASQ. (2020). Control chart- Statistical process control charts. https://asq.org/quality-resources/control-chart
[15] Slack, N., Chambers, S., & Johnston, R. (2010). Operations management. 6th ed. England: Pearson.
[16] ITC. (2016). Managing quality in Nepal: A directory of services for SMEs, Switzerland: International Trade Center.