The Future of Operational Excellence according to Industry Expert Greg Kinsey
Greg Kinsey, Industry Expert, shares his view on the Future of Operational Excellence and the new role of Lean Six Sigma – and how you can be part of it.
Guest Post: Location Matters – Data Mining Research to Enhance Accuracy, Benefit Future Studies
Research institutions and museums seek to provide the most accurate data possible to record the past and choose where to explore in the future. Modeling the accuracy of data is essential and nailing down geographical locations of specimen samples as exactly as possible is vital to do it effectively.
Guest Post: It’s Tough to Make Predictions, Especially about the Future (even with Machine Learning)
At its core, all Machine Learning algorithms follow a two-part process. First a sequence of increasingly complex functions is fit to part of the data (training data set). Then each model in the sequence is evaluated on how well it performs on the data that was held out (the holdout set).
Beyond the Hype: Machine Learning for Manufacturing Performance
Machine learning utilizes analytical data to discover insights that can create a more efficient manufacturing process and solve problems in a matter of seconds. These tools can be used in countless constructive ways in the manufacturing industry.
Guest Post: Predictive Analytics Accelerates Problem Solving
Adam Russell, Global Operations Master Black Belt explains how Tate & Lyle deploys Continuous Improvement (CI) tools, including Minitab and Salford Predictive Modeler (SPM), to challenging engineering and manufacturing problems.
The Journey Toward Industry 4.0
The convergence of the widespread deployment of low-cost sensors, cloud and greater computer power has brought together a multitude of connected devices which can monitor, collect, exchange, analyze and deliver insight like never before.
Guest Post: It’s Tough to Make Predictions, Especially about the Future (even with Machine Learning) (Insights America 2019)
At its core, all Machine Learning algorithms follow a two-part process. First a sequence of increasingly complex functions is fit to part of the data (training data set). Then each model in the sequence is evaluated on how well it performs on the data that was held out (the holdout set).
TEST 3 Generations of Machine Learning Models – A New Focus on Business Value (Clone)
When the data science buzz began around a decade ago, creating a predictive model was complicated work that only skilled programmers could accomplish. Toda