As we broke for lunch, two participants in the training class began to discuss, debate, and finally fight over a fundamental task in golf—how to drive the ball the farthest off the tee. Both were avid golfers and had spent a great deal of time and money on professional instruction and equipment, so the argument continued through the lunch hour, with neither arguer stopping to eat. Several other class participants chimed in with approval or disagreement over the points being made on both sides.
Even though I know very little about golf, I thought the problem could be easily solved with a designed experiment as soon as I returned home from the training session. After all, this particular training session was DOE in Practice, and I was the instructor.
Back at Minitab, we found avid golfers who were very much interested in the problem and willing to participate and have data collected on their drives. However, my initial research on this subject revealed that the problem was not that simple. I talked to other statisticians who had attempted to determine how to drive the longest ball from the tee, but they told me they had failed due to high process variation.
Others I talked to found that that the variables they selected did not have a measurable impact. My research also pointed me to conflicting studies that found that keeping the ball high on the tee improved distance, while others recommended keeping the ball low to the ground. Clearly this was not going to be an easy puzzle to solve, making the DOE approach even more appropriate here.
It was easy for me to relate this back to solving manufacturing process engineering problems—the difficult ones have many of the following characteristics in common:
- A complex process with many possible input variables.
- Noise variables beyond the researcher’s control that affect the response.
- Physical roadblocks that prevent executing certain desired run conditions.
- Time, money, and manpower limitations.
- Many experts with competing theories on key drivers of the process (some real and some not).
- Measurement variability making it difficult to measure both responses and inputs.
- Process variation causing different response values even when repeating the same run conditions.
In industry, figuring out how to deal with these kinds of complexities can be overwhelming, often to the point that we wind up just living with an under-performing process for years on end. These are the kinds of entrenched problems that challenge new college grads to give it all they've got, while their more experienced colleagues jeer or offer tales of failed past attempts. But secretly, they’re watching from the sidelines, curious to see what the newcomer’s approach will reveal about the age-old problem.
Does this sound like your life at work? Are you faced with a process engineering challenge that involves many of the same characteristics in the list above? Solving the golf drive-distance problem will require dealing with all seven of these issues. I'll be publishing a series of blog posts over the next few weeks to share our method for breaking this problem into more manageable parts, and our journey towards answering how to drive the ball the farthest off the tee. Our goal is to demonstrate how the steps we’re using here can also be applied to answer questions about your process.
In the next post, I’ll cover how to use process knowledge experts to help define the process and the problem.