Finding the “what” is half the battle
September 5, 2013
Cycle time? Labor performance? It’s easy just to pick a random area for improvement, but if a metal fabricator really wants to change operations dramatically, time and effort are needed to identify the root causes in the greatest need of correction.
The old saw “Where there’s a will, there’s a way” is at least sometimes true. But in the realm of continuous, focused improvement, after having established a will, there certainly must be a “what” before there’s a “way.”
We want to improve cycle times and on-time delivery. We want to improve throughput and efficiency. And we want to improve responsiveness. Or quality. Or any number of things that we detected and decided needs improving. We want to improve results.
Improvement initiatives often fail not because of a lack of will, effort, or resources, but simply because the wrong “what” was chosen. This can be frustrating and expensive.
After the “will to improve means will to invest” hurdle I discussed last month, this problem is one of the biggest impediments to continuous improvement I have encountered. In consulting with fabricators and specialty manufacturers for years, and in my own companies, I found that getting to the right “what” was the key to making real progress. It was at least as important as deciding how to improve, and often more complex.
Why is this? There are a number of reasons, but they are all related to the fact that most results—that’s what we want to improve—have multiple causes. These causes can interact, and some are not root causes. If we choose the wrong possible cause, or we don’t get to the root causes, we can spend a lot of time and money and get, basically, nothing. The results barely change.
Consider cycle time. We know it’s related to average process throughput, which is related to labor availability and performance; machine availability and uptime; material availability and handling; master scheduling; shop floor scheduling and expedite control; quality and “first-time good”; plant layout; metrics; and shop functional organization. It’s also related to average work-in-process (WIP), which relates to some factors in the previous list as well as a few others, such as batch sizes and produce and move triggers.
But let’s say we need to improve cycle time, and we think we need to do this by improving throughput. What gives us the biggest bang for the buck? What is the “what”? This is where many companies run into a lot of trouble and frustration. Looking at the previous list, where do we start?
Most companies first look at labor performance because that is the most apparent result we see. Operators fill in time sheets. We can gather this data by job, compare it to the standard or estimate, and calculate some efficiency number. But is labor performance a root cause? Maybe. But more often it’s not. Labor performance is driven by not just how hard and well someone works, but by machine capability and performance (including uptime), material availability, training, scheduling, and other factors, including some very subtle ones.
A few years back I consulted with a large aluminum extruder facing serious competitive threats that were degrading margins significantly. Two hugely apparent issues were poor labor performance and awful throughput, and the company had spent a lot of money on various initiatives to improve both.
The results were completely underwhelming, to say the least. None got to the root causes. To make a long story short, what I found was that the real causes were poor quality that required a lot of re-runs and, even worse, serious delays (two days) in detecting the quality problems. They were discovered at packing, just before shipment, which caused unplanned schedule interruptions and more setups for the extrusion mills upstream.
But even those were not the root causes. The root causes were poor die maintenance (somebody had decided that there were too many high-priced folks in the toolroom) and metrics. Yes, metrics. The extrusion mill crews were measured on pounds per hour—not good pounds per hour. This measure drove the mill crews and their department supervisor to push out as much metal as possible. The people downstream got to worry about the consequences. The quality checks at the mills were cursory, at best. The result was systemized poor performance that essentially was designed into the process.
It took a month to figure out the real problems, and another month to convince management that their department-style organization, prior decisions, and metrics were totally killing them. And it took just one week to fix the entire problem. In this case, the “what” was way more than half the battle.
So how do you uncover the root causes of the results you want to improve? Well, first, turn into a 3-year-old and keep asking “Why?” The famous 5 Whys approach is always useful, as is the equally famous fishbone chart that helps detail the possible causes of something. With a lot of practice and experience, you can make these things become second nature. If you are looking to train your workforce on some powerful tools for improvement, these two techniques are a good place to start.
But frankly, these techniques need augmentation and framing to be completely effective, because they can really get off track, argumentative, and even political. They assume a commonality and rationality in people that are sometimes not realistic. The best way to augment and frame the root cause exercises is through simple observation, followed by measurement.
In a prior column I referred to your “search radar.” It’s not something you have to buy. It’s something you already have. You just have to turn it on and use it. The search radar mechanism is simply observation by you and virtually everyone else in the organization. Everyone notices something that’s not working as expected; sometimes they note it or say something, sometimes they don’t. As an individual, you notice and observe some things, and other things just pass you by.
The point is that every problem in every operation is probably noticed by someone. Observation works automatically. To make it work effectively, though, the observations have to be recorded. So there has to be a way to gather those observations. Pre- or postproduction all-hands meetings are useful. They help you record observations: The light’s out; the machine keeps shutting down; I can’t find the tool when I need it. It sounds like clutter, but radar has clutter—and critical facts.
A related task is to find patterns and “densities” in these observations. Say you see this in your recorded observations: We keep changing the schedule. There are too many job interruptions. I am constantly setting up. They may be stated differently in the observation recording, but they mean the same thing. Now the search radar is really working to its full capabilities.
Once patterns and densities are found, interviews and conversations provide not only detail, but also some possible solutions. Primarily, they can be mapped to the results that you are trying to improve. They provide the augmentations and frames that make root cause analysis really effective.
Next you need to gauge the problems’ severity and impact, and then compare them to other issues that need improvement, so you can focus on the ones that bring the most benefit. By using root cause techniques augmented by search radar, sorting, analysis, interviews, and conversation—at all levels of the organization—you can uncover a very sound list of “whats.”
From this list you need to get to the one “what” you will focus on to improve the end result. Remember that the causes can be interrelated. So you need to look at how improving each of the candidate “whats” affects not just the result, but also other nominal causes and, possibly, other results.
You do this by constructing a cause-effect chart that is essentially interconnected fishbone diagrams, but usually drawn in a color-coded matrix arrangement for clarity. This shows how improving one result can bring about improvements elsewhere too. For example, if we did things to improve machine uptime, which improves throughput and, therefore, cycle times, we also improve asset turns and labor performance. It also shows if you are just pushing on a balloon: improving one and worsening another.
I wish I could agree with some who say that finding the “what” is a simple, mechanical, cookbook affair. It’s definitely not. I’ve described some methods that work, for sure, but employing them effectively takes practice and experience. It is decidedly a serious skill, but one that you and your company need in order to continuously, and economically, improve.