This article is the second in a two-part series about demonstrating CGMP compliance during inspections by establishing a successful quality culture and related systems. Part 1 discussed the importance of “top-down” quality management and “bottom-up” communication of compliance risks and concerns. Here in Part 2, we turn our attention to the input data for an effective QMR.
Many people in our industry have had root cause analysis (RCA) training. It is aimed at helping people understand an issue and the underlying reasons it happened. Once you have those reasons (the “root causes”), you can act on them. This is the most effective way of trying to stop the same issue from recurring. And RCA is now a requirement for serious issues per ICH E6 (R2).
This is the first in a five-part article series, Identifying And Resolving Errors, Defects, And Problems Within Your Organization. This article will enhance your understanding and prime you for visual detection of real trends in your organization by looking at a couple of examples. (You will understand the relationship between this picture and your quest for the quantitative truth by the end of this article.)
Probably the most significant concern for anyone responsible for implementing, deploying, and maintaining a quality management system (QMS) is the integration of risk-based thinking. While the concepts of risk-based thinking and management are not new, previous practice was more reactionary, primarily focusing on detection after the fact, root cause analysis, corrective actions, and preventing recurrence of the failure. Contemporary thinking places the emphasis on considering risks up front (prevention) and having a solid approach to address risk in planning, managing, and driving actions.
While other industries have learned from the principles of quality and statistical control methodology, (bio)pharma has been slow on the uptake. This article proposes tactic designs that utilize collaboration tools to facilitate Kaizen/continuous improvement in quality management systems (QMSs).
Ever-increasing amounts of data generated throughout the product life cycle can be hard to utilize to the organization’s advantage because of silos, misalignment, and complexity.