7 Steps To Set Up Statistical Process Control (SPC) On Production Processes

1. Select critical-to-quality (CTQ) product characteristics

For example, if the metal hardness is lower than expected on a screwdriver, that product might not be able to perform its intended task. Hardness is a CTQ characteristic.

2. Select critical processes

What qualifies a process as “critical”? If something goes wrong with that process, it will probably have a sizable impact on at least one CTQ characteristic.
Critical processes are often indicated with a ‘*’ on the control plan.

3. Determine if machines can calculate SPC by themselves

Many modern pieces of equipment collect and analyze data, and then issue an alert when they get out of control. However, this is very uncommon in China, so let’s assume this is not the case; it means you will need to carry out steps 4 and 5.

4. Gather data and process knowledge of what impacts the output of the process

For example, the variables that might affect the output of a gluing process are listed below:
- Process input: glue
- Process settings: cycle time; fixture
- Environment: outside temperature, humidity, etc.

Based on this, you might conclude that the viscosity of the glue and the ambient humidity are two variables that need controlling.

But how can you get that information? It often comes from engineers, chemists, physicians, etc. but it can also come from operators who have been working on that process for some time and may have noticed some cause-and-effect relationships.

For more information on this step, ‘Reducing Production Costs’ by Don Wheeler is an excellent resource.

5. Control independent variables that have a sizeable impact on the process output

There are 2 ways you can ensure that your production does not deviate too much:

1. Gather data on the variables you identified previously on a regular basis, e.g., 5 random samples every 4 hours.
2. Teach the production operators and leaders necessary calculations and how to plot them on a chart, e.g., on the “Xbar – R” chart, the most popular tool. For simplicity, we tend to set a goal for the process capability index (Cpk) and let operators plot the evolution of that index over time.

6. Look for ways to reduce variation

Now that you have made variations visible, the next step is to find ways to reduce it. If you use statistical process control charts, ideally it looks somewhat like this:

As long as the Cpk index is within the control limits, engineers and production leaders are encouraged to test different approaches. A Cpk target of 1.0 is often attainable within a few weeks. 1.33 is more challenging. 1.66 is much, much harder!

If necessary, you can use another statistical technique called Design of Experiments (DoE) to help you get close to the optimal values for variables that impact your process output. But, be prepared – there are several available approaches here, and it can get relatively complex.

 

7. Keep it up in the long term

Let’s say you manage to reduce variation. Set a new target, and keep controlling the key variables. If your process characteristics drift in one way or another, your statistical process control system will alert you. Otherwise, don’t tamper with the process, constant little adjustments will mechanically make the process “unstable” in statistical terms.