Statistical Process Control (SPC) is a critical methodology within the medicinal chemical industry, employed to guarantee the safety, efficacy, and consistency of chemical products. SPC facilitates the identification of deviations from established specifications, thereby minimizing process variability and waste, and ultimately enhancing customer satisfaction. L-aspartyl-L-phenylalanine methyl ester, an artificial sweetener characterized by its low caloric content, represents a manufacturing process that necessitates diligent monitoring and control. Despite its inherent advantages, the implementation of SPC presents certain challenges, including the judicious selection of appropriate data, the accurate interpretation of analytical results, and the seamless integration with existing quality management systems. Data corresponding to selected inspection attributes were imported into Minitab version 17.1.0 for subsequent statistical analysis. Descriptive statistics, encompassing metrics such as mean, standard deviation, skewness, and kurtosis, were calculated for each parameter to provide an initial characterization of the data distribution. The Anderson-Darling test was employed to formally assess the normality of the data distribution. In instances of non-normal data, various transformations, including square root, logarithm, reciprocal, Johnson, and Box-Cox transformations, were explored. The Anderson-Darling test was reapplied to the transformed data to evaluate the effectiveness of these transformations in achieving normality. For data that remained non-normal after initial transformation attempts, the Box-Cox transformation with a lambda (λ) value of 0.5 was applied using Minitab’s “Identify Distribution” feature. A comprehensive Process Capability Six-pack Report was subsequently generated for each parameter (specifically, optical rotation, loss on drying, and assay) following the transformation process, utilizing Minitab’s “Process Capability Six-pack” functionality. This report comprises six distinct graphical representations and detailed statistical outputs summarizing process performance. Analysis of the optical rotation data indicated a process that, while statistically stable, lacked the necessary capability to consistently meet specifications, suggesting a clear need for process improvement. The study of loss on drying for L-aspartyl-L-phenylalanine 1-methyl ester revealed a process that was neither stable nor capable in the short term, with observed instability and excursions noted in the control chart components of the report. The assay data, which demonstrated a lognormal distribution, indicated a process that was neither statistically stable nor capable of meeting the required specifications, underscoring the imperative for significant process enhancement. To improve long-term process capability for all parameters, it is essential to identify and systematically eliminate the underlying factors contributing to process variation, coupled with the implementation of continuous monitoring and control strategies. The implementation of reinforced monitoring protocols and the application of continuous process assessment utilizing advanced statistical methodologies can substantially contribute to improved quality assurance outcomes and enhanced process efficiency within the medicinal chemical industry.