Feedback, KPIs in the FDA’s total product lifecycle help manufacturing efficiency
Knowledge acquired in the development, manufacture, and use of one generation of product can be applied to future generations that a company plans to market. U.S. Food and Drug Administration (FDA) is using total product lifecycle (TPLC) to show how feedback and key performance indicators (KPIs) can make manufacturing more efficient, resulting in
|Also read from Axendia, FDA-related white papers:
– The Future of the FDA: Operating in an Electronic World ;
– Quality Management System Trends in Life Sciences ; and
– Pursuing a Future Where all Regulated Product Information is Electronic, interview with Dr Armado Oliva, FDA Deputy Director, Bioinformatics .
Providing a feedback loop between shop floor data and the design of the next generation product is indispensible in TPLC. Incorporating strategies such as robust design and designing for manufacturability enable product designers to take into consideration process capabilities. This could have a significant impact in increasing product quality while reducing manufacturing costs.
Build in quality
FDA also has been very proactive is in communicating its support for an industry-wide shift towards a focus on control theory and away from testing to document quality.
The agency defines the “desired state” as follows:
Product quality and performance achieved and assured by design of effective and efficient manufacturing processes;
Product specifications based on mechanistic understanding of how formulation and process factors impact product performance; and
Ability to effect continuous improvement and continuous “real time” assurance of quality.
To meet FDA’s stated “desired state,” manufacturers must attain real-time visibility into their processes to separate the wheat from the chaff. This requires deep knowledge of critical-to-quality variables and KPIs.
To move towards operational excellence, device manufacturers must identify “golden” (optimal) and “lead” (first) units, lots or batches and analyze key parameters that produced them. They must also compare and contrast such batches to identify parameters that are inconsequential to product quality. This approach can provide a deep understanding of the process to enable the development of KPIs to reduce variability, increase yields, and lower costs.
Also see April Control Engineering , “
Daniel R. Matlis is president of Axendia, a life-sciences and healthcare consulting and strategic advisory firm. www.axendia.com
– Edited by Mark T. Hoske , editor in chief
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