The companies that have developed the most efficient manufacturing processes are committed to two kinds of continuous improvement:

• First, transformation processes are analytically monitored, well maintained and minor, operator initiated, improvements are made on a regular basis.
• Second, the processes are periodically subjected to rigorous, traditional industrial engineering scrutiny: Can/should the process be modified, eliminated, combined, simplified or should the sequence be changed?

One of the critical elements of managing manufacturing material transformation is aggressively optimizing existing processes with economic performance as the key optimizing parameter. Sophisticated statistical and mathematical tools are an essential part of the optimization process. These tools are commonly known as Taguchi techniques and Design of Experiments (DOE).

Traditional process analysis involved changing one variable at a time and examining the impact of the change. It is difficult to discover or understand interactive effects using traditional techniques. In a manufacturing process analysis the power of statistical tools includes:

• The ability to expose relationships among interacting parameters. For example in a metal forming process there may be a link between the metal temperature, the type of forming lubricant and the surface finish of the raw material.
• Statistical tools allow several variables to be changed simultaneously, dramatically reducing the number of trials necessary. Both time and cost are reduced.
• Occasionally a parameter that was considered to be immaterial will be discovered to be important.
Understanding the relationships and impact of process parameters is the key to improving and economically optimizing processes. It is common for the analysis of early sets of experimental trials to reveal unexpected results. For example, in some metal forming operations, lubricant temperature and thickness have proven to be unexpectedly important. Metal forming speed has also proven to be a surprising variable with specific materials.

Successful manufacturing process control and optimization experiments proceed through a proven series of steps as follows:

• Select a multi-disciplined team familiar with the manufacturing process, materials, experimental techniques and economic analysis.
• Analyze the process, i.e. choose the independent and dependent variables At initial stages it is important to capture all of the potential variables. It is often tempting, especially for experienced people, to dismiss factors that are "intuitively" irrelevant. In the course of the experimental process and analysis surprises are usually discovered.
• Design the experiment, the range of independent variables to be studied. This is the "art" of the process. The most robust design will include multiple "levels" of every independent variable. The number of experimental runs required is equal to the number of levels for each factor times the number of levels for every other factor. The matrix can get large (therefore expensive) quickly. Partial or fractional level factorial designs (Taguchi) are often used to reduce the cost and time required for experiments.
• Design/build the experimental equipment Production equipment is rarely either adequately instrumented or adjustable for good experimenting. Measurement and adjustment systems must be calibrated. This can be time consuming, expensive and can require sophisticated instrument engineering skills.
• Perform the experiment(s)
• Analyze the data
• Iterate the process based on the results of the previous experiment.
• Reach technical conclusions about the critical factors and their impact.
• Rationalize the chosen factors based upon their economic impact. This often leads to short term factors based upon the capability of existing production equipment, and longer term plans based upon modifying or replacing the equipment to achieve better economic performance.
• Establish process "run" parameters.

There are also two differing basic approaches to executing the experiment. Either can be equally effective.

• Run the experiment as a non-production exercise, as a research activity - "off-line." This is the most common approach
• Perform the experiment during production, on production equipment, using natural process variations to adjust the process and "modify" it in "real time." This is best accomplished when the process parameters can be automatically changed and monitored. Injection molding is an example where this approach has proven valuable.

The techniques of Design of Experiments are designed to reveal what factors are important in a manufacturing process and what their settings should be. The techniques are not designed to reveal any underlying theory.

Design of Experiments is largely an analytical process. Once the key factors are understood an economic analysis is usually necessary as part of the process of "optimization." The synthesis of the final "run" factors follows.

Design of Experiments leads to optimizing a process. It does not assure that the process optimized is the best process for the job. It can be seductive and can lead to "sub-optimization" of the entire manufacturing process.