Modern manufacturers collect mountains of data every day. But how much of that data is ever studied, beyond the small percentage that raises red flags?
Spending too much (time or money) on part disposition? Start with an analysis of your measurement system. Even a marginal measurement system could contribute up to 30% of the variation seen in your control chart.
Quality professionals know the value of good measurement systems. They know that without trustworthy, high quality data you cannot make good business decisions. Unfortunately, most business people and many engineers don’t understand this value.
How will this image be used? Do I anticipate any changes? What are your tolerance requirements? All these questions are paramount in determining the successful path of the data output and each are mutually exclusive of one another.
The company, headquartered in Nottingham, is among the top 1 percent of the UK’s SMEs after being profiled in the London Stock Exchange Group’s 1000 Companies to Inspire Britain.
Socrates said, “The only true wisdom is in knowing you know nothing.” Another great mind, Ed Morse (in his keynote address at this past year’s Coordinate Metrology Society Conference), said, “Data is only as good as what you can do with it.” If you were so inclined to put these two thoughts together, you could see the current dilemma regarding Big Data.
Over the past several years we have seen the far reaching impact of cloud, mobile, IoT, and other emerging technologies on enterprise resource planning (ERP) systems.
The manufacturing sector is currently saturated with Industry 4.0 (aka the Industrial Internet of Things or IIoT) hype and jargon. This is no surprise given the evidence showing that the connectivity of systems and exploitation of data can add significant value to modern manufacturing processes and supply chains.