What is DAMAIC (Define, Measure, Analyze, Improve, and Control?)
DAMAIC stands for define, measure, analyze, improve, and control. It’s a methodology used to improve processes with 5 steps in total. For the sake of this blog, we’re going to refer to the DAMAIC process in regards to the manufacturing industry. So, basically, the DAMAIC method is a process to improve and optimize processes throughout the plant, more often than not, in conjunction with Six Sigma.
The middle of that acronym – measure and analyze – is essentially Mingo. Our product measures the data and has the tools to analyze and provides insights for you to improve. The control part is making sure that whatever your improvement was, remains, well, improved. The idea is to do this in a repetitive manner.
Which, really, is the methodology we talk about with our own customers. We provide all of the “plumbing” necessary to make the DAMAIC method work. “How’s that?” you ask?
Well, we can pull data directly from the machines, and by doing that, measure what’s happening. We can give you good analytic information that can help you with root cause analysis and all of the other things needed to solve problems. Once you go to improve something in the plant, you also have the data that will tell you if that actually worked and quantifiably measure that you improved something. If it did, great, and you keep going through the DAMAIC framework and improve more. If it didn’t, you can stop and use Mingo to look at the controls to make sure things are running how they should be.
For example, with statistical process control, you need to be able to look at data and know whether or not things are out of spec. We give you all of that information. Which begs the question, why would you even use the DAMAIC process in the manufacturing environment if you didn’t have something in place like Mingo? It wouldn’t be nearly as effective without manufacturing analytics.
Without a Mingo, or really any manufacturing analytics solutions, it’s likely you’re relying on whiteboards, pen and paper, or Excel. Maybe you’re even putting together big war rooms of sticky notes and posters where you analyze processes and improvements, but this is tedious, right? It’s a very manual process. All of which works fine, to a degree, but it’s not going to keep up with a fast-paced, growing company.
Using Mingo Within the DAMAIC Process
So, this is the part where we get down to the nitty-gritty, really explaining what parts of the Mingo system apply to the DAMAIC method and how you would use it in this process.
Defining Your Problems
Defining your problems can actually come from Mingo if you think about it. If you’re looking at the system and seeing your defect rate on one particular line is really high, there’s a lot of unplanned downtime in one particular area, maybe there’s a ton of work in progress that piles up in a certain location, or something is a bottleneck and really shouldn’t be, you know you have a problem.
You can then use that knowledge to help you define your problems and figure out what your goals are, all with Mingo dashboards.
Most importantly, you want to map out your entire process, so you know, going into the project, exactly how your plant flows. Think of it as a value stream map. This will help organize the rest of the steps as you work through the DAMAIC method.
Measuring and Analyzing
The next part is determining (or in other words, measure and analyze) how to improve the goal you’ve set. Say you want to reduce unplanned downtime or reduce changeover and setup times for a particular product on a particular line. Now, you have to actually be able to measure those things. How are you going to do this, effectively?
Well, with Mingo you can.
To determine actual changeover or setup times, data is pulled directly from the machines and operators, and the system tells you in exact terms. Or, if you’re trying to fix a quality issue, you can look at process data on a machine that’s telling you that maybe the pressure, temperature, or current is too high or the flow rate is too low, to help you match up against the goal and the problem you’re trying to solve.
While only two examples, there are many instances in which Mingo can help to measure and analyze the problems at hand, to then, improve upon those problems.
Improving the Problem
So, we’ve measured and gathered all of this data (This method is oddly familiar to our IIoT guide, we recommend you check it out.), and now you can start looking at Mingo and all of the different tools within the software to figure out, at a high level, the root causes. Tools like Pareto Charts and root cause analysis will be instrumental in this step of the DAMAIC process.
Let’s say you have long changeover times. You find it typically happens at this particular time of day, changing from this particular part to another part. This is all information you get out of Mingo. And, then maybe you can go and talk with the operators and supervisors on the floor to figure out what’s happened. What is it about changing from Part A to Part B that takes so much longer than changing from Part B to Part C when they’re similar parts?
Once you’ve gathered information on why a problem is occurring, you can begin to make improvements.
Yes, improvements are offline activities that need to be enacted without the help of Mingo, but you do need Mingo to monitor the improvements to make sure they actually improved something. Think of this as a Kaizen event, used to monitor and map out your plan for improvements.
We’re saying “improve” a lot in this blog, and yes, we feel like broken records ourselves, but hopefully, it’s really driving the point home.
After you’ve improved and measured that the problem has indeed been fixed, you go back to the beginning of the DAMAIC process to improve something else. You keep going around until you’ve solved all of the problems you need to.
Controlling the Improvements
Once, you’ve gotten to that point, you need to sustain the gains.
Let’s go back to the changeover time example. You’ve had all these gains and reduced the changeover time, now you need to make sure it stays down. What’s the plan to control the process? How do we know when something is going awry? You have to collect data and monitor it.
You can use things like alerts, dashboards, scoreboards, to tell you when those things are going on, in addition to the more manual, traditional processes like 5s, mistake proofing, statistical process control, or a quality control plan.
And, then, you work through the DAMAIC process again. And again. And again. Until you’ve made all of the improvements you want to make. But, realistically, this will be a continuous improvement project because improvements never stop. Things always need to be improved upon.
With the help of manufacturing analytics, using the DAMAIC method just became that much easier.
