Feedback, KPIs critical to FDA’s case for efficient manufacturing

The U.S. Food and Drug Administration (FDA) is promoting how 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. To do this, the FDA is using total product lifecycle (TPLC) to show how feedback and key performance indicators (KPIs) can make manufacturing more efficient, resulting in lower-c...
By Control Engineering Staff May 1, 2009

The U.S. Food and Drug Administration (FDA) is promoting how 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. To do this, the FDA is using total product lifecycle (TPLC) to show how feedback and key performance indicators (KPIs) can make manufacturing more efficient, resulting in lower-cost medical devices.

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.

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.