Evaluation of the deviation between ideal time and real time in quality control analyses in the pharmaceutical industry

Introduction: Efficient management of performance and effectiveness indicators in pharmaceutical quality control is essential for optimizing resources, minimizing waste, and ensuring the timely release of batches, thereby enhancing the reliability of immunobiological production.The release of a single batch may require more than 500 analyses, subject to operational constraints that cause deviations between the ideal and actual execution times.These deviations impact the predictability of the production chain and the industry's ability to meet demand.Objectives: This study evaluates the deviation in quality control analysis time and its impact on the release of immunobiology batches.Methodology: Data from 94 batches of four immunobiological products were analyzed between 2023 and 2024 (Product A: 15 tests; B: 4; C: 14; D: 25).A deviation rate was used as a parameter: Deviation rate = 100 x (Actual time -Ideal time)/Ideal time.This calculation was applied to 58 distinct tests, and the causes of delays were categorized using the Ishikawa Diagram and quantified with the Pareto Chart.Tests 13A, 14A, and 15A (A), 3B (B), 8C (C), and 18D (D) were considered process delimiters, as they are critical and require longer analysis times. Results:The deviation amplitudes were as follows: Product A (345 pp), B (1279 pp), C (220 pp), and D (1664 pp).The dispersion of Product C was found to be eight times lower than that of Product D. Tests 3A (21%) and 1C (18%) exhibited the lowest deviation, while 6D (1664%), 1B (1300%), 16D (938%), 2D (721%), and 18D (515%) demonstrated the most significant deviations.Test 12C (2200%) was disregarded as it was conditioned by a delimiting test.Consequently, a 562% shift in the release time of Product D was observed, with issues related to critical input shortages identified.Conversely, the same issue impacted only 21% of the ideal time for Product C, indicating that the impact of these factors varies across different products.The Pareto chart was employed to identify the primary causes of delays, which included lack of input (46.7%), competition for laboratory equipment (33.3%), and operational labor constraints (20%). Conclusion:The efficacy of analytical tests in quality control depends on the quality of logistics management.To this end, strategies such as active input monitoring, laboratory equipment optimization, and predictive logistics management are essential for reducing variability in execution times and improving production flow in the immunobiological industry.Additionally, these strategies help mitigate the risk of shortages and enhance public health outcomes.