June 8, 2017
Using Quality Data to Solve Issues? That’s Just the Tip of the Iceberg
What would happen if you read only 2% of your emails? You’d miss a lot, right? Meeting notices, requests from your colleagues for information or feedback, Cubs tickets. . . . You’d be blindly navigating throughout your work day.
Now, consider what would happen if your manufacturing quality team looked at only 2% of the data they collected. Sound unlikely? Unfortunately, it’s not. In fact, ignoring 98% of quality data is the norm, not the exception.
What Happens to the Other 98%?
In most manufacturing operations, the business acts on only 2% of the quality data it collects—the out-of-spec data, and any data that indicates the presence of a problem.
But that problem-related data is just the tip of the information iceberg you’re collecting. What about the other 98%? On the busy shop floor, quality professionals have a tendency to ignore the data that’s in spec, focusing instead on solving problems specific to data that does not meet specifications.
Dealing with critical fixes is important—obviously—but it won’t make huge improvements to overall costs or make substantive improvements in process and product quality. If you want to make positive, systemic, wide-ranging improvements, you need to aggregate all that in-spec
data, and then convert it to information that you can use to transform the business. Let’s look at an example.
Example: Overfilling at a Distillery
A distillery wanted to maximize its bottom-line profitability, so the company contacted InfinityQS®.
We suggested installing ProFicient™
software on one of the distillery’s many production lines as a pilot program.
While the distillery already checked bottle sanitation, cap torque, flow rate, and case counts, the management team wanted to focus on cost savings. Therefore, the team focused on gathering and analyzing net contents (volume) data—that is, the actual amount of distilled spirit in finished bottles. Line operators randomly selected five bottles every 30 minutes to check the volume.
In beverage manufacturing, the fill amount is printed on each container, and regulatory and consumer organizations check foods and beverages closely to ensure that packages contain what they say they should. Manufacturers work hard not to fall out of compliance with the stated fill volumes, typically erring on the side of caution and adopting an approach of filling at least
the stated amount.
With ProFicient, the distillery gained enormous actionable insight into the filling process, including fill variances from shift to shift, from product to product, and from filling nozzle to filling nozzle. Rather than just reacting to problems, the management team aggregated its in-spec quality data
, and used ProFicient to highlight opportunities for reducing overfill.
The result of this insight? An annual fill-volume savings in excess of $800,000 per year on just one production line – while still complying with stated fill volumes. By replicating what it learned on this one line, the distillery can truly transform their bottom-line business results, turning quality data into financial success.
A Tip for Maximizing Your Quality Data
So, what’s the takeaway from this example?
Don’t just focus on out-of-spec data, and don’t just use quality data to search for problems.
Instead, aggregate your in-spec data regularly—weekly, monthly, quarterly, yearly—and apply InfinityQS’s easy-to-use, yet powerful analysis tools to compare performance across lines, parts, plants, and more. These reviews lead to wide-ranging, systemic process improvements that can lead to huge improvements in efficiencies and cost savings.
Quality Data Isn’t Just for Fixing Problems
Quality data isn’t just about identifying and fixing problems and ensuring traceability. It’s about getting a return on your quality investment, maximizing customer loyalty, reducing costs, and improving profitability. When you dive deeper and see the information that’s under the surface, you can re-imagine how quality can become a strategic advantage to your organization.