Does your manufacturing organization think of statistical process control (SPC) as a necessary evil? Is it used only for customer and regulatory compliance? Do you collect data only to file it away? Or does SPC mean “Start Producing Charts” at your company? You’re not alone! Let’s change SPC into your partner in productivity.

Infinity QS Infocenter

SPC is a powerful tool for manufacturing optimization, enabling you to reduce costly waste, scrap, and rework. You achieve those benefits by eliminating defects as early as possible in your process. And eliminating defects starts when you have effective sampling strategies. Let’s take a look at how to target your sampling strategies to get the information you really need.

 

Aiming for Higher Quality

Have you ever spent time and effort collecting SPC data only to realize that the information in your control charts and histograms was not very helpful in reducing costs, eliminating waste, identifying defects, or reducing variation?

Eliminating defects at their source is the key to higher quality, increased efficiencies, more throughput, happier employees, happier customers, lower warranty costs, higher profit margins, and more. To find the source of defects you must ask the right questions. Then you can structure your data collection strategies to give you the answers you need.

 

Getting Started: What Are Your Questions?

You can put all the data you want into your database, but if it doesn’t answer the questions that are important to you, then it’s useless. The more you know about the questions that need answers, the better you’ll be able to construct the right sampling strategies.

Many manufacturing organizations start with questions about data collection:

  • How am I going to collect this data?
  • How often do I collect it?
  • What sort of measurement device or instrument do I need to collect the data?

However, the first question should be:

What questions do I need to ask and answer with my data?

To discover that foundational question, it’s helpful to review warranty claims or customer complaints. Here’s an example.

In this simple Pareto chart, we can see that most complaints in our example company are due to a faulty hinge on widget A.

Image: Analysis Pareto—widget returns analysis

Analysis Pareto—widget returns analysis

This visual representation makes it easier to ask meaningful questions and discover answers in the SPC data.

  • First, we might ask, “Do we have any functional data on that hinge?”
  • We discover that widget A has two hinges.
  • The complaints are related to the left hinge.
  • Our inspection plan includes testing on just one of the hinges.

You already see where the problem might be.

The original inspection plan was designed to answer different questions than what the warranty investigator needs.

Doing a deeper dive, the investigator discovers that the hinge is made from two sub-assemblies. There are three machines that make the components and two suppliers that do the plating. There are also seven assembly stations (employees) where the hinges are attached to the widget. Other sources of variation include batch numbers and raw material suppliers.

Analysis Pareto –widget returns analysis 2

Analysis Pareto –widget returns analysis 2

Given all these inputs, an effective sampling strategy would create a test that simulates the failure mode on the left hinge. Then, you can associate each test result value with the appropriate assembly station, the machines that made the sub-assemblies, the plating supplier, component batch numbers, and raw material suppliers.

SPC tools aid in analyzing data and providing a visual representation that makes it easier to recognize patterns. Given the right information, something in that data will expose the path to where we want to go to next.

 

Being Proactive

For your own manufacturing organization, you can extend this type of questioning to any kind of process. Many times, the important factor that will crack the case is buried in the data. That’s why it’s so important to do your homework ahead of time.

Really strategize about what data you collect and how you want to collect it. For example:

  • If fill volume is critical, then you need to make sure the fill data that you collect is associated with the specific filling machine in question AND the appropriate fill nozzle.
  • If weight of an injected molded part is important, make sure to capture the press number, mold number, and cavity number, plus any inserts. As well as the process input parameter set points—like flow rate, temperature, and pressure.
  • If you’re using different measurement gauges to verify a lot of material, the problems might be with the gauges. If the gauge ID is not associated with the value, then the data is confounded. You’ll never know which gauge to look at if the gauge ID is not included in your sampling strategy.
  • If surface flatness uniformity is important, make sure multiple flatness measurements across the surface are collected. If flatness problems can manifest themselves on certain zones on the surface, make sure the precise zone location is associated with the flatness measurement.

Overall, having the numbers is great, but capturing the conditions surrounding those numbers is what can turn a typical SPC control chart into the best informational tool you have.

Learn how a leading seafood manufacturer leveraged real-time SPC to improve intake processes in their most important area: raw materials. Read the case study King & Prince Seafood—In-spec Processes: Upstream & Downstream.