Performance Measurement Metrics: Quality Metrics as a Determinant of Key Performance Indicators in GCP/GLP/Service Areas

Naina Lilly Zachariah, Purdue University


In FDA regulated GCP/GLP/service areas, quality management systems (QMS) are the key drivers that determine organizational compliance to quality, standards and regulations. The effort to utilize CAPA as a potential method of risk assessment and measurement is driven by the draft guidance - Submission of Quality Metrics Data Guidance for Industry. Two of the major intentions of the guidelines are to (a) enable industries to build and maintain innovative quality management systems (QMS) and (b)Support continuous improvement. Both of these can be accomplished through adherence to an efficient quality metrics system. An efficient quality metrics system will address the main factors in consideration for determining the level of risk per nonconformance, which is dependent on FDA requirements and an organization’s QMS while focusing its goal of maintaining patient safety and business integrity. Although the regulatory standards and guidelines call for a robust QMS, methods to its construction, applicability and sustainability along with its required transparency and traceability are not well defined. This introduces a disparity in a highly regulated industry and introduces weakness in its risk management and mitigation system, which then leads into nonconformity. Such nonconformity leads to additional work and could be escalated to a Corrective and Preventive Action (CAPA), which unless mitigated at the organizational or process level, could be detected during audits and escalated to the issue of warning letters. Such a scenario would debilitate the organization’s integrity as well as cost the organization market share. The work presented in this thesis is quasi-qualitative research, using Company X as a study. Procedural and process investigations into the CAPA based risk escalations of non-conformances at Company X highlighted the the traditional method of subjective decision making in risk escalations due to the absence of clarified methods, in addition to procedures that introduced overlap between risk escalation decisions. Company X was used as a study for its procedures, historical data, data from survey using thematic analysis, root cause analysis using a 5 Why method and a DMAIC framework. The above analysis aided in the construction of a risk assessment model. The proposed risk assessment model utilizes a risk ranking method for seven metrics that were resultant from the analysis conducted above. The results of the risk ranking give way to risk escalations of the potential non-conformances in a traceable and transparent manner. The model when studied in parallel to the current risk assessment method at Company X over a five-week period, demonstrated a 100% compliance to the preexisting methods. This indicates that the model is justified in its construction and while being compliant to the standards and procedures followed by Company C. This clarified the decision-making process follows a fact-based decision-making method than the traditional method of subjective decision making in the absence of elucidated methods and procedures that introduced overlap between risk escalation decisions.




Laux, Purdue University.

Subject Area

Pharmaceutical sciences

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