It begins with a patterned set of experiments in all of the interesting variables.
John draper tools for data analysis full#
(See reference 6 for a full textbook presentation.)įinally, Charles Hendrix states, "Another popular method of optimization works very much like a game of leapfrog. A further discussion with an extensive example can be found in chapter 15 of Box, Hunter, and Hunter (5).Ī second design approach uses a simplex or triangle as the basis of the data collection. The designs in Box and Draper are based on full and fractional factorials. The combined intersections of process analytical technology (PAT), risk analysis, quality by design (QbD), the ICH troika of Q8, Q9, and Q10, and the strong emphasis of continuous improvement may provide a window of opportunity. Even if that level of performance was poor, management was not going to let anyone, particularly floor operators, start experimenting with an accepted process.Īre we still at that point today? Yes, but changes in the industry and at FDA may make EVOP a tool whose time has come. Why was this theory not wildly and immediately accepted in the 1970s? Companies had often spent years working to make their processes achieve a certain level of performance. And it should be said that there would be an increase in quality as well. "What originally motivated the introduction of EVOP, however, was the idea that the widespread and daily use of simple statistical design and analysis during routine production by process operatives themselves could reap enormous additional rewards" (4). George Box and Normal Draper, both highly regarded and pragmatic statisticians, stated the goal in the preface of Evolutionary Operation.
This achievement is the ultimate in continuous improvement philosophy. Also, for processes that vary with input materials and environment, it is possible to track and maintain optimality over time. This process is repeated until no further optimization is achieved. The designs are simple factorials which, when analyzed, direct the process to a new point of operation that is more optimal for the critical quality attributes.
The small changes are compensated by the large amount of data collected. The changes are small enough that the process still makes acceptable products and remains in a state of control. Small changes are made to the current process, and a large amount of data is taken and analyzed.
EVOP is experimentation done in real time on the manufacturing process itself. Published in 1946, the experiments were not appreciated and used until the early 1980s (2).Īnother valuable tool that has yet to gain wide acceptance is an optimization technique known as evolutionary operation (EVOP) (3). An example is the Plackett-Berman designed approach to experimentation. Other tools languish forgotten for years and even decades before being accepted by mainstream users. Exploratory data analysis (EDA), for example, was quickly accepted within and outside the field of statistics (1). Statisticians and nonstatisticians alike have readily adopted some of these tools and theories. Since the mid to late 1800s, statisticians and mathematicians have been developing increasingly useful statistical tools and statistical theory.