Are your quality teams suffering from data overload? Technology, in general, is advancing at a breathtaking pace. And manufacturing technology is no different. In fact, in many ways, it is probably leading the way—utilizing the cloud, mobility, AI, and more. 

However, all those technologies generate extraordinary amounts of information. In today’s manufacturing environment, there is oftentimes so much information that it becomes mere noise. That noise can block our ability to understand the important messages buried in the data we capture.

The information you need is still in there—and it’s still accessible through the tried-and-true Statistical Process Control (SPC) methods you rely on. But today, you need an easier way to get through to those insights. 


Heads are Spinning

The progression of data overload has been strange and sometimes confusing. Equipment manufacturers started coming out with their own versions of data output; sometimes these new machines even came with their own databases to which they could write. Others had the ability to configure and export spreadsheet files. Even the smallest devices seem to have some sort of data output. And every equipment provider is pushing you to stream your data from their new device into your system.

In a short time, manufacturers had a lot of data in a lot of different formats. If these various machines and databases don’t all speak the same language or output the same format, that’s a challenge. 

It’s easy to see why quality professionals are scratching their heads and wondering what to do with all this data—from all these disparate machines. Can we make sense of it all?


Enter the Process Model

There’s an aspect of Enact®, the InfinityQS Quality Intelligence platform, that interconnects all these disparate data streams: the process model. 

Process model functionality greatly benefits operators and quality professionals by helping them visualize your processes, make sense of the sequence of things like data collections, and understand the interconnectivity of your data streams.

The process model allows you to build a “map” of your process in a step-by-step fashion (which is convenient). It’s expandable, it helps you error-proof your processes, and it centralizes and simplifies the ways in which you manage your operations.


Can Does Not Equal Should

Without a tool like a process model, it’s easy to fall into a sort of manufacturing “paralysis,” whereby you’re overwhelmed by the data coming in and not sure what’s useful and what isn’t.

This paralysis is prevalent in today’s manufacturing world when organizations collect all the data they can—without really considering whether they should. Sometimes called “data gluttony,” this habit is expensive for many organizations. Data collection has associated costs. In addition, too much data can distract you from discovering important things about your processes, such as cost savings. Finding those opportunities is possible if you focus on the right data. 

The key is to collect only what you need and not everything you want, just because you can.


Where Do We Start?

Simplifying data collection, focusing on the information in the data, and getting to the root of your problems sounds great. But where do we start?

Dr. W. Edwards Deming—the master of continuous quality improvement—advised, “Start with the data. Look at what the data is telling you. Analyze the data and it will tell you what direction to go.”


Pick Up the Stream

That advice still requires you to start somewhere. One good place way to start is to focus on a single data stream. Then, if there’s anything of interest there, follow it. 

How do you know what’s important? How do you know you’re starting with the right data and not wasting your time on something that’s not so important?

It’s common knowledge that fixing a problem upstream is the cheapest way to go. Fix the problem before it gets further down the value-add stream. This mindset is “let’s start at the beginning.”

However, we recommend starting at the end. Look for places at the end of the process where problems appear, where you’re catching issues just before they escape and cause issues with customer relationships. When you start at the end of the data stream and trace it back step by step, you begin to be able to prioritize the improvements that will make the biggest impact in your processes.


Ask the Next Question…and the Next

At this stage, the aim is to start asking better questions than you could without the data. It really doesn’t matter where you start. Just get started. 

For example, imagine that at final inspection, your product is over the weight in the specification. You can begin asking questions such as: 

  • What’s going on upstream that keeps adding unwanted weight? 
  • Where exactly in the process is weight in some form added to the product?
  • If I analyze input/output delta weight changes, can I isolate the most likely sources of the problem? 

Once you’ve implemented changes that effect weight, watch what happens downstream and see whether you fixed the problem. 

This advice is fundamentally obvious but in reality, methodical problem solving does not happen as much as it should. When you start at the end, the data inevitably drives you upstream. The important thing is to get started.


Too Much Noise to Hear the Signal

Getting started shouldn’t be too much of an issue for quality pros. We love data and usually can’t wait to get our hands on it so we can begin figuring things out. But sometimes getting started seems daunting because there is just so much data.

And taken as a whole, it all seems important. The health of the equipment; the health of the parts that are running; or the metrics we are measuring on this equipment. You don’t want to miss anything. Ah, the noise!

Fortunately, modern SPC software help you cut through the noise. SPC is the foundation of your data analysis. When it’s the foundation of your quality management software, there is an element of understanding and validity in your analysis.

Modern quality management software starts by making data collection easier and more accurate. Then, you know the data is trustworthy. Elements such as the process model help you ensure all the data are collected for the right things, by the right devices, in the right sequence. And built-in dashboards and automated notifications help you ensure that data is collected on time and processes are standardized.

If you believe in the science of statistical analysis, then a modern software solution is going to help you get past all the noise of all that data. When you really understand all the nuances of your manufacturing processes, then you—and your organization—will make better business decisions based on process knowledge.

We invite you to visit our website to learn more about the benefits of modernizing your SPC toolset.