Data Ends the Debate

A few years ago we were having a discussion on who's product caused a problem. A wood products manufacturer created the box and a plastics supplier moulded the cap. The cap and box did not fit very well. The wood products guy said the plastic caps were distorted and each one was different. His stance was "not my problem." The plastics fellow said the plastic parts would distort out of the mould but not enough to cause the problem we were seeing. Going nowhere.

A couple of fellows took a load of the wood boxes and started measuring them. One particular dimension was tied to the problem. The Excel chart shows the distribution of this dimension over dozens of boxes. Clearly, one of the holes was being drilled too far from the edge.

With data, the debate was over and we found the root cause quickly.

A simple example but it is not common. Many of our problem solving efforts fail because we just don't have the right data.

Why We Don't Solve Quality Problems

The reason why problems do not get solved is that the decision makers do not have the right data to work with. We sit in a meeting room with a team of people discussing the problem and all we get are opinions. Mostly, they're wrong. Opinions are formed in the absence of data.

Other times we have someone in the discussion who may have authority or who may just be assertive. They will present a good case but it's just another opinion. Assertiveness isn't always a good thing. Organizations have spent lots on solutions that weren't.

The name we give to a problem often suggests a root cause. One example comes from a high volume packaging line. A particular problem was termed "feed problem" which suggests that the feeder was the root cause. But the real problem turned out to be orientation of the item. Because the center of gravity was not the center of the item, some items wobbled. Once this was identified and shown to be true, the fix was easy. But for months, the "feeder problem" was not solved because people were looking at the conveyor system and not the item on the conveyor.

Labelling a defect incorrectly is common and it causes delays.
Another reason why we misdiagnose problems is because we use the wrong sample size. This happens a lot. If you find two defects and trace them back to supplier A and not supplier B, we all know that isn't enough confidence to call it a supplier defect. But what sample size is right? How many do we need to be 95% confident? 99% confident. Most people don't know how to determine the required sample size. And that also causes delays as solutions are implemented that don't solve the problem.

There are a lot of reasons why we are not so effective at problem solving and we've all experienced solutions that proved to be wrong. Collecting evidence and using statistical methods can make your problem solving efforts a lot more successful.

Productivity in Problem Solving

I've been a black belt for many years now and have been involved with a lot of problem solving assignments. These projects require charters, meetings, checklists and reports. You tend to have conversations about the problems with dozens of people, one at a time. There's a lot of routine work involved, as with any project.

But nearly everything I learned about the problem came from analysis of data. Collecting evidence about process behaviour, or running a designed experiment provides new insights almost every time. So if you're tackling a difficult problem, you need to be on the floor, or in the lab, measuring parts and collecting data.

Solving quality problems is a scientific endeavour. If there's no data, it isn't science.