5 Steps to Quality–Driven Profits
Shift from reactive quality control to a proactive, profitable approach
Imagine driving down the interstate at full speed—when you can see only 2% of the road.
Nerve-wracking? Nightmare-inducing? Sure. But manufacturing companies of every size and in every industry hinge product quality, customer satisfaction, and ultimately profitability on only about 2% of the available quality data. No wonder quality managers are losing sleep.
Most quality professionals are problem-solving experts. We leap into action when quality data shows that a process or product has fallen outside the acceptable range of operation. But by focusing on only what falls out of spec, we’re ignoring about 98% of our quality data. We’re metaphorically hurtling down the highway, dodging disasters as they crop up but not getting where we—and our companies—want and need to go.
When you increase visibility to include all your quality data, you begin a shift from reactive quality control to proactive quality transformation. And that road can lead to literally millions of dollars in increased cost efficiencies and profits—not a bad destination.
Getting started is easier than you might think. Take these five steps and you’ll be well on your way.
Step 1: Switch your focus from quality problems to quality excellence
As quality professionals, we’re used to looking for and solving problems. Of course, you always need to keep an eye on processes that fall out of spec or out of control. But what about the 98% of quality data that fall within the acceptable range?
If you’re like most quality teams, you probably don’t give that data a second glance. It might be recorded somewhere—likely on paper, in a spreadsheet, or in a database—but that’s about it. I have found that unless quality data indicates a problem, it is generally ignored.
Yet that data holds a wealth of information. It can take your manufacturing quality from “good enough” to “nobody’s better.” It can reveal new opportunities for cost savings or even help you overcome the type of blind spots that threaten the health of manufacturing plants (as you’ll see in a moment). By including this data in your manufacturing worldview, you shift into proactive gear—a vital part of getting out of “firefighting” mode.
Step 2: Aggregate all your data
If you want to make big improvements, you need to take a “big picture” view of quality:
- Want to determine the best way of running a particular product? Aggregate all the quality data for that product across every production line that runs it. Doing so lets you see which production line runs the product most efficiently, readily providing a benchmarking opportunity.
- Want to identify the greatest opportunities for improving quality? Aggregate all product quality data across all production lines. By doing so, you can determine where best to run specific part families, which production lines are ideal for specific products, and which lines to avoid.
Reacting to quality problems as they occur is certainly important. But unless quality professionals are careful, focusing only on fighting daily quality “fires” can divert our attention from big opportunities. Bottom line: Reacting to problems and out-of-spec situations isn’t enough.
Step 3: Compare to reveal opportunities for improvement
Now comes the fun part: comparing performance. After data aggregation, run comparisons across processes, shifts, products, lot codes, and more. Take advantage of comparative analysis tools such as box-and-whisker plots to highlight your greatest improvement opportunities, using data that is actually within specifications.
Looking at this normalized data (i.e., standardized to specification limits), you can suddenly see variances that were previously invisible—or ignored. Perhaps one lathe is operating differently from the other, or different parts behave differently on each lathe, or the features for each part behave differently between parts and lathes. Using these insights, engineers, managers, and operators can make changes that can dramatically reduce variation and improve yield.
Step 4: Review regularly
Regularly review your aggregated data: daily, weekly, monthly, or quarterly. By doing so, you can uncover insights that can cut costs and improve consistency in processes, product quality, and overall manufacturing operations. Companies generally discover systemic, ongoing issues that are never uncovered during the heat of daily manufacturing activities. Stepping back from the daily grind and summarizing your quality data is the crucial step. Whether you do so each month or each week, you’ll be surprised by the meaningful and profitable insights you can gain by reviewing aggregated data.
Step 5: Prioritize actions for high-value returns
Once you get a grip on everything that’s happening with your process quality, you can begin to use this information strategically:
- Identify opportunities for systemic improvement. You never know what information might crop up when you aggregate quality data. Aggregated data might highlight machinery inconsistencies that occur during certain times of the week. Tooling or dies might perform better or differently on specific manufacturer’s machinery. Materials supplied from various vendors might generate significant differences in production-line performance or product quality results. As opportunities become apparent, identify and prioritize them.
- Create action plans to address the issues and opportunities you’ve found. This task isn’t designed to add to your workload. Rather, you’ll save more time than you spend by heading off bigger problems that might necessitate a halt to production, product waste, and so on. (As an added benefit, digitizing and aggregating all your quality data can also save time when you’re faced with an audit or reporting demands from customers or regulatory agencies.)
- As you begin to see improvements, expand your vision across lines, products, and even plants. By using quality data to increase operational performance, you do more than save time for the quality team—you boost cost efficiencies and profitability across the entire company.
See what’s possible
Do these benefits seem extreme for such a simple change in direction? Consider some real-life examples of quality teams that have moved to this type of proactive quality approach.
A distillery was looking for potential cost savings—no easy task after years of belt tightening. Each production line comprised six main quality checkpoints: bottle washing, filling, capping, labelling, packaging, and palletizing. Throughout the day, operators gathered data on breakage, sanitation, temperature, leaks, net contents, and so on. For the most part, the machinery was well maintained, operators were well trained, and excellent engineering support was provided on the shop floor. Very few specification violations ever occurred. So far so good.
But corporate management wanted bottom-line cost savings. Therefore, the team decided to focus their cost-saving activities on net contents: the measured volume of spirits placed in bottles.
To meet regulatory demands and consumer expectations, machinery was calibrated to meet minimum fill volumes for all bottles. More than 30 rotary heads filled thousands of bottles each day. The team randomly sampled five bottles every 30 minutes and recorded each bottle’s volume. This process was performed for weeks across several product codes. When the team began aggregating and comparing the net contents data, they found that bottles weren’t just being filled to the required minimum volume—they were being filled beyond the minimum. Even so, nearly all the volumes were within specifications. Armed with this information, the team made a variety of changes, modifications, and process adjustments that greatly reduced overall volume variability on the line. These actions, in turn, enabled the company to lower overall volumetric fills while still meeting the minimum requirement. The result was an annual product savings of more than $800,000 – on just one production line. This was an immense saving for the company, and if replicated across all their production lines, would generate massive bottom-line cost savings.
Another astounding transformation occurred for a folding carton company that was on the verge of closing one of its plants. In this challenging industry, packaging needs to be just right—colors, shapes, and carton design must all be precise and consistent. For one plant, quality problems had become a serious barrier to success. Defects had become such an issue that not only were they causing waste at the plant, but customers—food and beverage companies using the cartons to package their own products—were suffering frequent production shutdowns as they dealt with subpar carton quality. As a result, the plant was about to lose several of their largest, most lucrative contracts. Such a loss would force a shuttering of the facility, leaving more than 300 members of the plant’s small community out of work.
The quality team began by aggregating defects across all products and all production lines. Using a variety of sophisticated Pareto charts to examine defect summaries, the team uncovered many underlying problems that were causing the chaos.
Armed with the information generated by these analyses over many weeks, the management team—working in concert with maintenance, operations, engineering, quality, and operators—was able to dramatically improve product quality and performance. They were able to pinpoint and resolved issues, all by relying on aggregated, big-picture data and the information it revealed.
In less than six months, the plant achieved six sigma quality levels on primary characteristics, reduced overall product defects by 86%, virtually eliminated customer complaints (down from more than 100 per month), and achieved the lowest parts per million defect rate across all corporate facilities. In summary, the plant went from the worst performing to the highest quality producing plant in the entire company—and did so in only six months.
Set your sights on excellence
Ready to expand your vision and lead your company to competitive excellence? If you want to make big leaps in quality, aggregate and analyze the 98% of your data that you usually ignore. Start thinking about quality as more than fixing problems and scrambling to meet traceability, compliance, or audit demands as they pop up. Switch gears and begin to look at the big picture by aggregating and analyzing data and seeing quality as the basis for better ROI, cost reductions, improved profitability, and optimal customer loyalty.
The quality experts at InfinityQS are committed to supporting your efforts. Visit us online to access useful resources such as these:
- Can Quality Protect Your Brand? Absolutely!
- Put your Quality Data to Work and Transform Your Manufacturing Business
- Re-Imagine the influence of the Quality Professional
- 11 Skills your Quality Team needs to Have
- The First life of Data: Optimizing the Value of Live Industrial Data
- The Second Life of Data: Quality Analytics and Manufacturing Efficiency
- Quality Data: From Cost to Profit Center
- Using Quality Data to Achieve Manufacturing Excellence
- Re-Imagining the Role of Quality in Manufacturing
White papers and eBooks:
Doug Fair is the Chief Operating Officer at InfinityQS International, Inc. A statistician at The Boeing Company before joining InfinityQS in 1997, Doug holds a BS in Industrial Statistics from the University of Tennessee and is the co-author of the books Innovative Control Charting (ASQ Quality Press, 1997) and Quality Management in Health Care: Principles and Methods (Jones & Bartlett Learning, 2004). He is a Six Sigma Black Belt and a senior member of the American Society for Quality (ASQ).