While Motorola was developing six-sigma (6σ) practices in the 1980’s, Pharma R&D remained at best, “naïve” if not openly hostile to the idea. Literally, 6σ gets its name from “reducing defects to 3.4 per million”; statistically six sigma’s from the mean. It would take another fifteen years before Pharma embraced the practice, since few saw how it could apply to R&D’s decade-long, variable and complex process. “Defects” in research problems did not make sense to R&D leaders (except for drug manufacturing), and they resisted 6σ until the practice evolved. Pharma now views six-sigma as an umbrella term for removing causes of problems within various stages of research and development. An important variant is “lean six-sigma”, which places a perhaps more proper emphasis on how value flows throughout the R&D process.
The practice has its roots in earlier techniques pioneered by others including Juran and Deming. Experts argue the differences between 6σ and TQM (Juran’s Total Quality Management), or SPC (Deming ‘s advanced Statistical Process Control.) Regardless, our focus is what these operational excellence tools can do for Pharma R&D. In the early 1990’s I tested and applied TQM and SPC-driven improvements across the analytical chemistry labs I managed. Ironically those labs focused on making the Discovery practice more effective and faster. Ironic because the real industry-wide acceptance of these tools first came into Development after they had proven their value in Pharma Manufacturing. In 2010, firms are still “dipping their toes” in how 6σ might work within Discovery.
One Principle – Consider End to End Effects: We all started by addressing small sections of the overall R&D value chain. At Bristol-Myers, I applied the evolving toolset to reengineer how we launched new drugs in the mid-1990’s. Later, while consulting to the fourth-largest Pharma at the time, I won over their leaders to use those techniques to redesign a larger piece of R&D; their end-to-end clinical process. Over a dozen such projects in BioPharma, I found several key “Architectural” principles to guide such efforts.
First, we have to understand that R&D is complex and intertwined. My work in the preceding ten years proved that unless we design from a “holistic” perspective, simple spot solutions just fail to create any evidence of creating value.
This is one reason “Electronic Data Collection” of clinical data failed to deliver its promised value for nearly ten years. It merely pushed problems up- and down-stream, and barely “moved the meter” on end goals of R&D. The graph to the right is an example of how point-solutions fail to raise overall performance.
Good, high-performing design requires familiarity with the many disciplines across all of R&D. But “architects” of such good design cannot have, and indeed do not need deep expertise in all areas. Instead, internal experts must join and participate, while the redesign architect connects the dots across that breadth. They understand what questions to ask that are “in the gaps”. Holistic design that creates greater performance also comes from understanding how teams, intellectual property, competition, market forces and other “value levers” will combine to create the value and benefits. This begins to frame “R&D Business Architecture” as a discipline (which is the overall purpose of this blog.) Future posts will further detail fully what that discipline involves, and what it delivers.
Best Practices moving “Upstream”: Over the past ten years, the various pieces of the “six sigma” umbrella have come together more and more within BioPharma R&D. Coloring that legacy is how such operational excellence practices came “upstream” from drug manufacturing into drug development. In the late 1990’s operational control tools like SPC and SAP transformed drug manufacture, reducing costs, improving quality and yield. Some of those involved huge investments and organizational commitment – far larger than just the software purchase. So many participants learned process analysis and troubleshooting techniques, bringing new thinking and new leaders into the R&D arena. The emphasis on new, novel drugs affected many of the “handshakes” between R&D and Manufacturing. This in turn prompted exchanges of ideas and increasing demand for better information and material flow between the two arenas. Root-cause analysis and constraint-based planning were just some of the early techniques that moved “upstream”.
In an upcoming post: Evaluating examples of 6σ on real R&D performance and how R&D Business Architecture should complement this and the full range of Operational Excellence techniques. NOTE: This side of the home page addresses past improvement levers in Pharma R&D. A nice summary looking at current state overall for Lean and Six Sigma in Pharma is here: http://www.pharmamanufacturing.com/articles/2011/005.html