ASQ Lean and Six Sigma Conference
The American Society for Quality (ASQ) will host their Lean and Six Sigma Conference virtually on March 1–4, 2021. The theme for this year’s conference is New Opportunities for a New Decade. 2020 was marked by the most disruptive event in recent history, as a global pandemic forced the adoption of innovative production, logistics, and workforce solutions. The impact of artificial intelligence, the Internet of Things, and virtual tools was unmistakable, as organizations harnessed digital technologies as they pivoted in response to both local and global restrictions. At this conference, attendees will learn how they can harness the benefits of these new methods to identify improvements, gather and respond effectively to data, and create an ongoing culture of excellence through quality.
Join Nathan R. Soderborg, Ph.D., CRE, CSSBB, Exponent principal scientist, as he presents “Maximizing Insight from Data Mining and Analysis through Traceability” on Tuesday, March 2, as part of the on-demand sessions. Most organizations today strive to run their operations on data-based decisions. As trends in data growth continue and publicity regarding “big data” expands, software tools for data mining and AI get a lot of attention. However, these tools are only useful if data contain the characteristic, location, and timing information needed to effectively solve an organization’s most pressing problems. This kind of traceability is an essential ingredient of data quality. By proactively working to improve traceability in their organization’s data systems and experimental plans, Lean and Six Sigma professionals can greatly increase their ability to efficiently identify and solve quality problems. This presentation explains the importance of traceability in the age of big data and covers practical approaches to improving traceability in manufacturing and transactional organizations. Examples across multiple industries illustrate how better traceability leads to more meaningful insights over a range of applications, e.g., manufacturing process control, sampling and test plans, reliability and warranty analysis, and machine learning cases for root-cause analysis.
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