The three major success factors in manufacturing processes? Performance, availability and quality. Learn how to tackle all of them to increase overall equipment effectiveness (OEE), create transparency and ultimately reduce cost by using process mining and data that is already available in this blog post by Gerrit Kohrs.
When we take a bird’s eye view on manufacturing, there are three main questions that decide about success or failure:
When we talk about these questions, we are talking about one of the most popular KPIs in manufacturing: OEE or Overall Equipment Effectiveness. Let’s take a minute to wrap our minds around the underlying concept. OEE is a combined score that is made up of three components:
Now that we have calculated these three scores (or losses, if you look at it the other way around), we combine all three into one total score by multiplying:
OEE = 95% availability x 92,6% performance x 97% quality = 85,33 %
The perfect score of 100% is pretty much impossible to achieve. What is a good value for your operation, depends a lot on your type of industry and various environment parameters. Therefore, benchmarking with similar companies is probably a good idea. With the 85,33% in our example, we’d already be “world class” as stated by Seiichi Nakajima, who introduced TPM in the 1980’s . So, for most companies, this would already be a very ambitious target.
The plain OEE scores above will neither tell you how well you use your potential, nor can you deduct how or where you could improve. In order to do that, you need to understand which parameters have an impact on these KPIs. This is where process mining comes in.
With process mining technology, you can analyze the digital footprint of your production. Process mining tools take logfiles from various source systems like your ERP, shop-floor management, machinery and equipment controls etc. and translate them into a visual representation of your process. Assuming these systems are connected or at least share some common data like order numbers, you can even visualize surrounding processes like sales order management or intra-company logistics, which might help explain dips in the availability-part of your OEE.
The visualization and analysis of the processes in your production delivers content to the bare numbers and thus helps not only with their interpretation, but also directly to process steps or sequences that concern probable problems.
In our experience, when process owners think of their processes, they quite often have the picture in their head as they designed it. For example, a simple process for manufacturing a plastic part of some sort might look like this:
Reality of course is rather more complex and visualizing this complexity is one of the main objectives of process mining.
All the sudden we see a completely different picture with all those extra steps, loops, a sometimes seemingly strange order of activities as well as some unexpected manual work. At first glance, we see those time-consuming steps (many tools show them in red) and all those situations that drive machine operators crazy, like a cancelled order when they already had everything prepared for it. We also see a problem with intra-company logistics right away because we always need to wait for material.
In short, this graph – along with numerous other features of common process mining tools – will already give you answers why your scores for performance, availability or quality look the way they do. Most of the tools in their ready-made templates offer numerous detailed analyzes that provide you with further insights in the second step. Common findings include, but are not limited to:
Bottlenecks in certain steps of the production process:
Idle times:
Re-work:
What you see above is a rather simple example, of course, but imagine we would include the logs from machine controls to show error codes, sensor data, maintenance activities and many more. This would make potential trouble visible even before one of the three scores go down and give you time to react. And the best part: You would know exactly where to start improving.
Our experience shows that a short pilot project or proof of concept (PoC) is a good start. In order to see first results, there are basically four simple steps, after you have identified the first process you want to examine:
Still sounds like a complicated endeavor? Guess what, it’s our daily business and we’re here to support you every step of the way.
Obviously, manufacturing processes are not the only ones worth analyzing. Pretty much every digital process in your organization will have more or less potential for optimization and the message is the same: Getting started is simple, the data is already there, and insights are almost instantaneous.
Process mining technology offers full process transparency and data-supported decision-making aids for your process optimization. Think about how you could bring its advantages to use within your organization or how you can fully exploit the potential of your existing process mining tool. Feel free to contact us.