The Devil Is In The DetailsByRaymond D. Matkowsky
It has been a known fact that 80% of Research and Development projects fail. The world spent in 2010 nearly US$1 trillion on R&D. This says that US$800 billion was wasted on unsuccessful projects. Some of this money has been wasted needlessly. If we increase our success rate just 1%, this translates to about a more productive US$8 billion. It dosen't have to be this way. The problem is that many R&D teams don't pay enough attention mundane details. These mundane details are source of a great deal of projects being dropped as "failures."
Let me give you two examples of what I am saying. First, a customer was getting a great deal of variability in his product's ability to meet standards. After trying a number of options, he decided that he could just not meet standards using his present raw materials. It turned out that his curing temperature was near his catalyst's decomposition point. His variability was do to the various levels of decomposition his catalyst underwent. The catalyst was such a small portion of the entire formula that it was never considered as significant. It was!
The second example I have deals with with employees and production practices. The effectiveness of a certain product depended on polymer chain lenght. The shorter the chain the less affective it was. Studies showed that a maximum chain lenght was achieveable within a narrrow pH range. In this case it was on the basic side and the manufacturing specifications reflected that. However, it did not take into account the rate of addition and localized effects. Rather than add the basic solution slowly, the production staff would dump in a pale of sodium hydroxide solution. The pH would shoot up within the immediate area cleaving the polymeric chains. What was worse, if they overshot the proper pH range for the entire batch, they would adjust it with the addition of acid. The final product was within the proper pH range, but the damage was already done.
From my own experience, many times it is a combination of factors that creates a problem that leads to false conclusions. Interactions are not prevalent but they do exist. Look for them!
Do you have any other suggestions, please share them with your fellow readers. Email me at rdm@datastats.com.
Copyright © 2015 Raymond D. Matkowsky
Raymond D. Matkowsky is the Chief Executive Officer of Data Stats, a consulting firm specializing in system or product improvement through mathematical and scientific modeling. He can be reached at rdm@datastats.com or through Data Stats’ web site at www.datastats.com |
