For nearly 100 years, the central focus of quality efforts in the U.S. has been on the control of variation. It has proven to be a valuable and effective approach. However, the view that “variation is the enemy”  is so deeply ingrained in our thinking that we may frequently overlook some simpler and dramatically effective ways to solve quality problems.
Working with Professor Phil Barkan at Stanford in the early 1990s, we set out to identify what could be done during product concept development to achieve the greatest improvement in quality . Based on the variation quality model, we sought data on the relationship between product level defect or non-conformance rates and the control of variation measured by process capability indices (Cpk).
We were surprised to learn that most organizations had never tried to estimate product quality in this manner, and even more surprised when we could not find any correlation between system level defects and the control of variation. This suggested that variation is not the dominant quality problem in modern production!
We discovered that system defect rates were consistently correlated only with product and process complexity, pointing to the key role of mistakes in product quality . Subsequent studies substantiated our initial conclusions. Although this relationship does not in any way suggest that controlling variation is unimportant, it shows that control of variation has advanced to the point where mistakes are the dominant quality problem today.
In contrast to companies that focus on Six Sigma and rarely achieve defect rates below 1000 to 5000 parts per million (ppm), companies like Toyota maintain defect rates below 30 ppm at a fraction of the cost of traditional quality control methods . Most importantly, the link between complexity and defects points to the two most effective methods for improving quality as: a) mistake-proofing processes, and b) simplifying products and processes.
Mistake-proofing in Pharmaceutical Applications
In a brief review of On-the- Job Training (OJT) documents and Standard Operating Procedures (SOPs) for one pharmaceutical batch process during April 2008, less than five mistake-proofing controls were identified while over 130 mistake-proofing opportunities were found.
Mistake-proofing was virtually non-existent in the process. A three-hour tour of the facility identified yet another potential 20 to 30 mistake-proofing opportunities. Perhaps the best way to illustrate the differences between traditional quality control and mistake-proofing is to provide some specific examples of what we saw during this plant visit for this batch manufacturing process.
Case 1: Guess Which Scale is Which?
In one set up, controls for two digital scales were positioned one above the other at the center of the wall, when the associated scales were located at the left and right corners of the room. As a result, the relationship between the scales and the controls was ambiguous.
Regardless of how much operator training takes place in this environment, errors will occur and time will be wasted in selecting the correct control. After the fact, arrows could be placed by the controllers showing the link between the scale and the controllers. However, if the controls had been placed side-by-side with the left control operating the left scale and the right control operating the right scale,the use of the controls would be more efficient and errors in selecting the controller would have been virtually eliminated.
Case 2: Identify the Water Source
Another case in point. At this pharma facility, three different sources of water at various temperatures are available in the processing room. The controls for each are identical and adjacent to each other. This situation will inevitably lead to errors and waste, since a batch can be scrapped if the wrong water source is selected. Solutions would be simple.
For example, by using a unique tip shape on each nozzle and a matching nozzle restriction in the tank, much as our automobiles do to prevent the use of leaded bas, errors in using the wrong water source could be eliminated. As an alternative, features that hold the hose in the tank during filling could be modified so that only the correct hose could be held in place for each specific operation.
Case 3 : Guess When to Turn Off the Water?
Currently, at this same facility, operators are required to turn off the water manually when the correct amount has been put in a tank. The operators are often performing other tasks while the tank is slowly filling. Getting the correct weight depends on operator diligence.
Because operators may be distracted at the time the water should be shut off, errors can occur. Operators may misread the scale, or even when reading gauges correctly, they may not shut off the water at the correct time, resulting in costly corrections or scrapped batches. Every problem can be solved in more than one way.
One approach that could prevent filling errors would be to use an alarm that starts beeping as the correct water weight is approached. The frequency of the beeping could increase until the correct weight is reached. When the correct weight is reached a steady tone would sound. Thus, the operator would have a warning in advance, allowing time to move to the water control knobs, and would know when the tank is full without even looking at the gauge.
A different approach may be to interlock the scale with a shutoff valve, so the water is shut off when the correct weight is added. A third approach may be to adapt a nozzle like those used to refuel vehicles that shuts down when a specified fluid level is reached. Superior solutions are developed by generating a variety of ideas and comparing them to find the best one.
For example, the nozzle shutoff might not be the right solution for this application if there is a risk of contaminating the nozzle tip, or generating variability in the fill quantity.