Could Pressure for Covid-19 Drugs Lead the FDA to Lower Its Standards?
Given the death, suffering, social disruption and economic devastation caused by Covid-19, there is an urgent need to quickly develop therapies to
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Given the death, suffering, social disruption and economic devastation caused by Covid-19, there is an urgent need to quickly develop therapies to
Each article in this series presents new tools for increasing return on investment (ROI), enhancing customer satisfaction, creating process excellence, and driving risk from an ISO 9001:2015-based quality management system (QMS).
Researchers at the National Institute of Standards and Technology (NIST) have used state-of-the-art atomic clocks, advanced light detectors, and a measurement tool called a frequency comb to boost the stability of microwave signals a hundredfold.
What is quality intelligence, exactly? It’s more than marketing spin. More, even, than the sum of its many control charts. It’s not collecting data simply to further go/no-go actions.
For many manufacturing quality professionals, the thought of updating their statistical process control (SPC) solution is like getting an extra birthday.
An organization can achieve great results when everyone is working together, looking at the same information generated from the same data, and using the same rules.
Blame it on Moore’s law. We live in a digital Pangaea, a world of borderless data driven by technology, and the speed and density with which data can be transmitted and handled.
It’s no secret that manufacturing companies operate in an inherently unstable environment. Every operational weakness poses a risk to efficiency, quality, and ultimately, to profitability.
For nearly a century, statistical process control (SPC) has been the cornerstone of quality management and process control. But traditional SPC can’t keep up as the pace of manufacturing accelerates.
Quality data are the heart and soul of statistical process control (SPC), the industry standard methodology for measuring and controlling quality in manufacturing processes. Asking manufacturers to give up data is like asking them to give up water.
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