Despite the mountains of paperwork and sophisticated systems, occasionally a glitch makes all of that effort appear to have been a waste. It happens to the best of us with the only winners being therapists and system developers claiming to have the answers that will save us from a repeat in the future.
Since the economy climbed out of the last recession, “Help Wanted” signs have become a common fixture near manufacturing facilities all over the United States. With 10,000 baby boomers reaching 65 each day, retirements are leaving a significant experience gap to be filled.
ARaymond, an industrial supplier of fastening and assembling solutions, rose from humble beginnings. Founded in Grenoble, France in 1865, the family business “started out making fastening elements for the glove and footwear industry,” says Jake Fox, senior quality engineer at ARaymond’s Brunswick, Ohio location.
Over a career that spans 40 years, Steve Gruler has struggled with the fact that quality seems to be a soft term. “Companies often say, ‘We’ve got great quality. We’ve got the best quality,’ and they’re looking at customer complaints and certification systems as their primary metrics,” he attests.
Statistical process control (SPC) charts are used in quality-focused facilities to monitor process output on a continual basis and alert process operators, managers and the support staff in real-time when the process is shifting towards an undesirable condition.
Quality engineers use statistical process control (SPC) to eliminate process variation and ensure that final products meet customer expectations. Their job includes rooting out problems during production and ideally preventing issues before products become rejects.
Looking into new software for statistical process control (SPC) can be challenging and often confusing at the outset. This is because many providers claim to have similar features like real-time data collection and easy installation, though results may vary.
On every trade show floor featuring additive manufacturing, there’s a growing selection of additive processes for making production parts that stand alongside machines more commonly associated with prototyping. An essential aspect of additive’s transition from prototyping to production is data collection.
The obvious reasons for automating quality in manufacturing are to reduce scrap, rework, overtime and costs while simultaneously increasing productivity and customer satisfaction. The non-obvious reasons include employee satisfaction, customer referrals and market growth. Automation also give managers and line workers insights into ongoing production.