What is statistical process control (SPC) and is it for you?

Statistical process control quality (or SPC for short) is considered the industry standard when it comes to measuring and controlling quality during your production runs.

SPC manufacturing comes in the form of gathering data on your products or processes in real-time using a graph with pre-determined control limits to measure its efficiency.

You can determine what your control limits are by the capability of your production lines.

This means, if the data you gather falls within your control limits, it’s a sign that everything is running as it’s supposed to.

But, if the data falls outside of your control limits, this suggests that issues are occurring, and something needs to change along your production line before it leads to bigger problems.

Using the information you gather, you can identify the areas of inefficiency and waste in your business and quantifying the value of an SPC solution by asking yourself the following questions:

• What are your quality costs?
• Can you make improvements with the data you’re gathering?
• Are you collecting the right data in the right areas?
•  Are business decisions being made with up-to-date information?
• Can you easily identify the cause of quality issues?
• Do you know when to perform predictive maintenance? And
• Is your demand forecasting correct?

Now you know what statistical process control quality improvement is, what’s the objective of SPC manufacturing?

You can think of SPC quality as conformance to your production’s specifications, although the issue is that products or characteristics are often not the same, due to processes containing sources of variability.

In the traditional mass-manufacturing process, the finished products (or samples in batch manufacturing) are inspected to determine the quality of the goods.

With SPC tools, you can observe the performance of production to identify variations before they lead to defective products.

fall into one of two categories:

Common causes

Sometimes referred to as a ‘non-assignable’ or a normal source of variation. A common cause is a variation (or issue) that consistently acts on the process and is a reoccurring problem that is predictable.

Statistical process control examples for common variations could be an employee who takes longer than forecasted to finish a task, or the working conditions being poor.

Special causes

Sometimes referred to as ‘assignable’ sources of variations. This means there’s a factor that’s affecting production, but intermittently and unpredictably.

Statistical process control examples for special variations could be equipment malfunctions or even an accident that happened on the way to work, which impeded a worker’s journey.

Knowing these variations can help you adopt SPC lean manufacturing since some minor issues can be ignored, but the larger ones need to be identified and addressed. Otherwise, you’re going to drive up your production costs.

Before we move onto the seven steps to implementing statistical process control quality, let’s first look into the two phases you need to follow if you wish to practice SPC lean production.

Following these following steps is going to be crucial for getting set up with SPC manufacturing, starting with:

1. Choosing the Critical-to-Quality (CTQ) Product Characteristics

CTQ in SPC manufacturing is the process of identifying the needs of the customer and translating that information into measurable production and process requirements.

Statistical process control examples of a CTQ could be a baker who makes sweet pastries.

To make the pastries sweet, the baker will need sugar, which will be a defining characteristic of this product.

2. Choosing Your Critical Processes

You can determine if a process is critical or not by knowing that if something was to go wrong with this process, would it have a big impact on at least one of the CTQ characteristics.

You’ll need to monitor these critical processes to achieve SPC lean manufacturing.

An example of a critical process, once again in the context of a baker, could be when the pastries go into the oven, they need to be set to a specific temperature.

3. Deciding Whether Your Machines Can Calculate SPC Autonomously

Can the machines on your production line or your production scheduling software help you collect and analyze SPC data, and also issue alerts when things are starting to get out of control?

If you’re using inefficient Excel spreadsheets, it’s going to be difficult to dedicate your time or team members to performing an analysis each day to make sure production is running smoothly.

In 1974, Dr. Kaoru Ishikawa put together a collection of SPC tools manufacturers can use to improve their processes, called Guide to Quality Control.

These SPC tools can be found below:

• Cause-and-effect diagram;
• Check sheet;
• Control chart;
• Histogram;
• Pareto chart;
• Scatter diagram; and
• Stratification.