The Hidden Quality Cost Resulting from Food and Drug Administration Inspection Failures in Mid to Large Companies

Abstract

An approach to identify and validate hidden quality costs associated with regulatory non-compliance from inspections by the Food and Drug Administration (FDA) has not been previously researched. This dissertation presents a robust quantitative methodology to analyze the daily cumulative abnormal returns (CAR) on populations of publicly traded mid-to-large cap companies around FDA inspection events having negative outcomes. Three hypotheses have been tested focusing only on inspection events from companies falling under the biologics product type category. Hypothesis 1 states that there is no statistically significant correlation between the sum of CAR around inspection windows for such companies when a negative inspection observation is issued by the FDA. Hypothesis 2 states that there is no measurable hidden quality cost that can be associated with a negative inspection observation for such a company. Hypothesis 3 states that there is no consistent methodology that can be developed to calculate hidden quality costs from this research. Also, the use of the statistical results from this research cannot be integrated into the current cost of quality models to provide a more accurate and comprehensive estimate of total quality costs for publicly traded mid to large companies regulated by the FDA.

Financial data was obtained from python libraries that have access to Yahoo’s publicly available finance APIs. The time frame of 2009 to 2019 was chosen to avoid abnormal variation in 2008 from the financial crisis and in 2020 due to the global Covid-19 pandemic. The time frame was later expanded to include data from 2009 to September 1, 2023 as a means to highlight the potential for predictive capabilities. The study presents a methodology using multiple statistical tests for determination of significance. These tests include the One Sample Wilcoxon Signed-Rank Test, Binomial Test, and Mann-Whitney U Test. This methodology provides empirical evidence by rejecting all three null hypotheses in order to validate hidden quality costs and quantify the financial impact of regulatory non-compliance for companies in the product type category of biologics demonstrating quantifiable 'hidden quality cost'.

This dissertation introduces a novel methodology which it calls the “Walston Regulatory Impact Cost Estimator” as well as a proposal to create an aggregated list called the “Walston R.I.C.E. Index.” Both the methodology and index enhance traditional Cost of Quality (COQ) models by incorporating regulatory impact costs to identify, measure, and predict hidden quality costs related to regulatory noncompliance events. The research concludes by using real-world examples and recommendations to advocate for the use of the Walston R.I.C.E. Index as a comprehensive tool for both academic and industry applications to better manage and mitigate risks associated with regulatory compliance failure hidden quality costs.

The research establishes a Python code-based methodology that can empower Quality Systems through Technology Management to democratize future research in this field. This research offers a practical model that enables organizations to quantify and manage hidden quality costs related to failure costs resulting from regulatory noncompliance.