MES 101 Quality Management
Welcome to our MES 101 series. We are going to cover the basics of a variety of MES components. You will get an overview of what is available in an MES implementation, how to leverage each piece in your process, and how you will get an ROI on the investment.
In the previous installments of this series, we covered Downtime Tracking and Performance Management. This post will wrap up the components of OEE with Quality. The Quality component of OEE is the ratio of the good parts produced to the total parts started. Parts can be defined as actual units of a discrete part or pounds through a process depending on how you measure your production counts.
Figure 1: Quality Calculation
The Quality calculation requires two pieces of data. First, we need to know how many parts we started. For discrete manufacturing processes where you are making an object, this is usually accomplished with a photo eye on a conveyor line. As raw materials pass by the photo-eye, a counter is incremented, and the value at the end of the shift is your parts started. If you are pulling raw material out of a warehouse or using pounds of raw material, this data may come from operators or an ERP system. One OEE system we did for a chocolate manufacturer used the scaled on their raw material mixers to generate the pounds started.
The next piece of data is the good parts produced. Good parts are just everything that was started minus the quantity of parts not meeting quality specs. The complicating factor here is usually where this data is generated. A lot of companies implementing OEE for the first time are using an Excel document to track quality information. We’re not saying you need to implement a full-blown LIMS system, but you do need at least a way to capture the number of scrap and rework parts to get a useful quality value.
For this post, let’s assume we can get this value into our database.1
Breaking It Down
There are a few common quality contributors. Startup Rejects, raw material issues, equipment failure, and operator error are at the top of the list.
Startup rejects assume there will be some loss while your process is getting to steady state after starting a new production run. Raw Material and equipment failure are more obvious, and operator error can arise from any number of causes. Understanding how each of these, and any other contributors, affect quality and how to reduce them in the first place most companies will realize value.
After you have the basic quality information acquisition in place you can start to move into the wonderful world of Statistical Process Control. This will also open up the doors to things like Six Sigma, some parts of Lean Manufacturing, and beyond. Those are separate series in and of themselves.
Given the scope of what surrounds quality this is going to be a relatively short post.
The basics are simple:
- Track what comes into your process.
- Track what comes out of your process.
- Reduce the difference between the 2 to improve the quality factor of your process
Improving quality lets you spend less time, money, energy, and operator effort to produce goods you can’t send out the door, saving you all of those avenues of spending along the way.
Coming up in this series we will dive into how you can move the flow of information into your process from Excel and printouts to your ERP system and begin to realize the full capability and power of Manufacturing Execution Systems.
Ready to get started? Email Dave@CorsoSystems.com
1If for some reason this data isn’t availalble your quality will be 100% and you can address this in Phase 2. Unless fixing Quality issues is your #1 productivity increase this should be sufficient for now.