What is it about?

This study explores the use of Statistical Process Control (SPC)—a set of engineering and mathematical tools—to monitor the manufacturing of liquid healthcare products. The researcher analyzed 184 batches of medicine, focusing on three critical components: the acidity level (pH) and two essential preservatives, Methyl Paraben (MP) and Propyl Paraben (PP). Because raw manufacturing data is often "non-normal" (it doesn't follow a standard bell curve), the study used a special mathematical formula called the Johnson SU transformation to ensure the results were accurate. The goal was to see if the manufacturing process was not just producing "passing" products, but if it was truly stable and predictable over time.

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Why is it important?

Ensuring that oral medicines are safe and effective is vital for public health. This research is significant for several reasons: Preventing Degradation: If the pH is not strictly controlled, the active ingredients in the medicine can break down or lose their effectiveness. Safety and Comfort: Extreme pH values can cause irritation for the patient. Microbial Protection: Preservatives like MP and PP are crucial because they stop the growth of harmful bacteria and mold during the product's shelf life. Finding Hidden Risks: A key takeaway is that even when a product meets its official specifications, the manufacturing process might still be "unstable". By identifying these hidden shifts, manufacturers can fix problems before they lead to failed batches or safety risks.

Perspectives

The Industrial Engineering Lens: The study treats quality as an engineering challenge, using "sixpack analysis"—a combination of six different charts and plots—to get a holistic view of how a process is performing. Global Quality Improvement: This research was conducted to help develop a better quality management framework for pharmaceutical sectors in economically developing nations, aiming to improve standards within existing production systems. Data-Driven Decision Making: The results showed that while the preservative levels were "capable" (meaning they usually met the targets), all three parameters showed signs of instability, such as sudden shifts in the average or unpredictable variations. This highlights the need for companies to move beyond simple "pass/fail" testing and toward continuous data monitoring.

Independent Researcher & Consultant Mostafa Essam Eissa

Read the Original

This page is a summary of: Enhancing Pharmaceutical Manufacturing through Statistical Process Control: An Industrial Engineering Approach to Quality Assurance, Journal of Engineering Advancements, December 2025, SciEnPG,
DOI: 10.38032/jea.2025.04.001.
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