In the last hundred years, several people have developed and enhanced key concepts of SPC.
While the concept of SPC has been around since the 1920s, the mathematics driving it are relatively young and cutting edge.
SPC can be used in conjunction with other problem-solving methods like Root Cause Analysis (RCA) to help uncover additional insights and helps answer operational questions that other data approaches may not perfectly target, such as “by how much does the product vary?” Statistical process control (SPC) is a way to use data to improve and control systems and processes. As the global markets grow more complex, it’s crucial for manufacturing organizations to hone processes that access and interpret data in real-time to control variances and target quality. From plant facilities to entire supply chains, costly nonconformances can eat away at profit margins at a time when competition is high. Leveraging data holds the key to driving greater efficiency and transparency.
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