Over the past few decades, terms such as Lean Six Sigma, Total Quality Management, and Continuous Improvement Strategy have become increasingly common in discussions of manufacturing plans. These techniques have one goal in common: to develop processes that maximize productivity and consistency and minimize risk to workers, consumers, and to the company. But each of those methods addresses specific aspects of manufacturing quality and efficiency. At an advisory board meeting with the FDA in 2011, one pharmaceutical industry executive concluded, “The industry started by controlling variability using by Six Sigma, and moved onto reducing waste with Lean Manufacturing. It’s possible to do both without QbD (Quality-by-Design). However, without QbD, one can apply Lean and Six Sigma to everything rather than focusing on issues that are critical to product quality”. The essential value a QbD approach brings to process design is in identifying what process parameters most affect product quality and then devising control strategies to assure consistent quality production.
Early adopters of such evidence-based statistical strategies for process control and improvement include aerospace, automotive, and electronic industries, among many others. These industries have strived for efficiencies far exceeding 6σ, as the risk of not doing so far outweighs the cost of implementation of these process control strategies.