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.

Know Your Process

In an injection molding factory, most parts will have a mold and cavity ID molded on the back of the part. In this way, when there is a quality problem we can quickly know where the parts came from. Not many processes are set up with such convenience.

I was able to work with a high tech company that had a high reject rate, 2.7% coming from a long and complex process. There were several workstations for each process step so the number of possible paths that each product could have taken was in the hundreds. This is a problem in many high volume manufacturing companies. Not many can run one-at-a-time serial production.

One of the process steps was a heating cycle. Thousands of parts were soaked at an elevated temperature for 24 hours. When they were removed from the ovens, the parts were handed to several people for more work before making their way to a final test station. It was here at final test where the 2.7% failed.

In this assignment I had each product labelled (Sharpie marker) with the shelf number from the oven (from 1 to 12). We were very surprised to see that nearly all failures were labelled 1, 2 or 3. The bottom three shelves in the oven were responsible for almost all the failures! The temperature in the oven dropped of sharply near the bottom and these parts were not getting processed adequately.

There were so many opinions on a possible root cause and the real root cause wasn't on the list! Collecting data may be tedious but there's no other way to know your process.

An Expensive Opinion

Over a year ago, I was involved in a project to solve a weld problem. This company performed thousands of spot welds every day and some (approx one in 300) just didn't take. The failure wasn't obvious unless you tugged at the weld and even that wasn't a reliable test. But a poor weld could be serious if it failed in the field so this had to be solved.

A department manager was convinced that a current monitor would take care of things. In his opinion, the weld fails when the current drops below some critical number. So when the current is too low, the operator discards that last welded part. My argument was that there are many things that could cause a weld to not take; part cleanliness, surface geometry, applied force, squeeze time and more factors that are part of the welding process. The project required much more effort and some data. But he was adamant and he told us that this was what they do in the automotive industry. So we backed off and accepted this as a problem nearly solved.

A year later, the process has a reject rate of one in 300.

Collecting evidence to learn about a root cause takes so little time. Solving a problem based on an opinion can set you back months.

Clue Generating Tools

A client had a high reject rate on one step in the manufacturing process. This is a complex item with several parts and at this stage, near the end, rejects were costing just over $15,000 every month. The defect was causing a leak at a particular location on the part and many efforts over many months were not successful. 

I was invited to assist in this problem solving project and in my line of work, I never accept things as facts without evidence. I've seen too much waste. We rented a high speed camera to record what was happening at the point in the process where the defect was created. To everyone's surprise, we saw the leak start in a different location. It was centrifugal force that moved the fluid to the spot that everyone had thought was the leak. 

The high speed camera showed us where the problem started.  Tools that enhance human perception can often provide clues. 

Old Machinery

We often blame the old machine when we have a quality problem. This old thing doesn't work like it used to. At some point, we end up buying a new machine. But the problem wasn't the entire machine. Some part of it had failed or worn out. The machine ought to have a much longer life when you replace worn parts. The trouble is, a lot of people give up the hunt for the elusive broken part of the machine.

A company I worked with was dispensing fine powders as a part of one of their processes. The reject rate was too high because the amount of powder dispensed was sometimes too much, other times too little. The problem was in the variability of the dispensing process. A new, half million-dollar machine was purchased to replace the old machine and for a while, both machines worked side-by-side. Now get this. The new machine was worse than the old one! More overweight rejects. More underweight rejects.

I was able to work with the company on this specific project and we determined the root cause of the old machine's variability problem. It was corrected and was producing over 99% quality product (up from 85%!) in a few weeks. The new machine was significantly different and required some pretty serious redesign work. My contribution was over and I moved onto other assignments but I hear from people still there that the new machine is in the corner. No longer needed.

Old machines don't fail. Parts of it wear. The root cause of many old machine problems turns out to be a hundred dollar part. It's well worth spending the time to figure this out.