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Manufacturers know it’s important to modernize equipment and tools. But if they’re not investing in the processes and data that feed continuous improvement, they’re not maximizing their investments.
Think about Amazon, whose website and ordering process is slick. But if the company doesn’t fulfill your order in two days (or less), you won’t be satisfied.
What happens behind the scenes is what really matters. In manufacturing, that means every process must come together perfectly, every time, to deliver on customer expectations. Close isn’t good enough.
Oh, and customer expectations? Those change by the second. Natural disasters, political turmoil, generational trends, and global health crises can upend shopping habits and supply chains—overnight.
It’s a tall order for manufacturing organizations to fill. To meet rising expectations around quality, service, and speed, you need more agile and responsive operations. But how do manufacturers know what to respond to? Or how to respond?
In digitally transformed manufacturing companies, data leads the way. More specifically, quality control data leads the way. Advanced manufacturers apply quality control measurements and data toward big-picture challenges and opportunities. They digitally transform their organizations—not just their lines—to embrace more efficient, accurate, and results-focused business practices.
Quality is central to every step in the manufacturing process, but quality hasn’t connected the steps in a meaningful way until now. That’s because manual methods of gathering, analyzing, and sharing data tend to be siloed—connecting the dots is impossible on modern manufacturing lines. There’s too much data to sift through—and no way to quickly and reliably decipher what it means.
Digital transformation in manufacturing erases those barriers—giving quality teams the platform, tools, and insight they need to quickly respond. Digital transformation can help you build manufacturing resilience—and prepare for whatever comes next.
By leveraging the core elements of quality control, quality management teams should be able to answer business-critical questions such as:
Unfortunately, many manufacturers can’t answer these questions at an enterprise level—not without lots of data manipulation and spreadsheet juggling, anyway. And in those cases, data integrity is questionable. How confident are you that every data point—at every site—was collected and calculated the same way?
In today’s competitive environment, there’s no room for uncertainty. Executives need accurate insights from quality data to make strategic decisions for the company.
Luckily, the data to answer these questions is already being collected on the plant floor. The challenge is making data more reliable, accessible, and actionable.
That’s where digital transformation comes in. To find the answers inside the data, manufacturers need the right tools. Quality data needs to be collected and stored digitally, in a central and standardized repository. From there, modern analytics can help you re-imagine quality and transform your business.
Technology-driven quality management practices provide three key benefits over manual methods:
Many manufacturers have taken preliminary steps toward digital transformation. Perhaps they’ve embraced Industry 4.0 by automating processes or by installing Bluetooth sensors to monitor equipment.
Those are important steps. But to leverage the data that’s being generated, manufacturers need to take digital transformation further. Instead of simply digitizing traditional or manual processes, they need to build stronger connections between systems, processes, and outcomes. Digital tools must talk to one another—using a common language—so data makes sense.
A cloud-based, Software as a Service (SaaS) quality platform brings it all together. Purpose-built, enterprise-wide tools enable more comprehensive and transformative quality management practices.
For example, in a digitally transformed approach to quality management:
These benefits of digital transformation aren’t just conveniences. They lead to “big picture” views of the organization—without losing the opportunity for operators to dig into targeted, in-the-moment metrics. Manufacturers shift from “collecting quality data” to building quality as a competitive advantage.
With analytics-based quality insights, manufacturing leaders elevate the role of quality in their organizations. When decisions about quality performance are driven by quality data:
A comprehensive approach affects more than just quality. Digital transformation in manufacturing supports total manufacturing optimization.
Traditional quality management tools can’t keep up with the demands of modern manufacturing organizations. Yet a recent survey found that 75% of manufacturers still collect data manually. Nearly half still use paper checklists.
Manual processes could be introducing unnecessary risk:
Cloud-based Statistical Process Control (SPC) tools pool all your quality control data—and automatically return extensive, flexible views of performance. With customized reports, notifications, and alerts, your operations and quality teams can save valuable time—and make better use of your quality information.
Take a look at the factors driving digital transformation—and how a tactical move to cloud-based quality management can help you become more agile and efficient.
Digital transformation connects all your quality management data into a comprehensive, purpose-built solution. When compared to home grown or patchwork solutions, true digital transformation is centralized, standardized, and scalable.
A food manufacturer calculated its raw material costs down to the gram, and identified over $3 million in savings. How did they do it? Using line-level quality data they had already collected.
Side-by-side comparisons of production lines and product codes revealed major variances at a top-tier tool manufacturer. By looking at in-spec quality data, they discovered opportunities to reduce scrap and waste—and improve quality, productivity, and profitability.
A metal-forming manufacturer had a 45% scrap rate at one of its plants. By analyzing quality data, they completely eliminated out-of-spec product—and dramatically increased throughput. The key to massive bottom-line cost savings was already within reach: in their quality data.
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