Digital Transformation of Manufacturing: Opportunities, Challenges and Lessons Learned
Greg Kinsey discusses the digital transformation of manufacturing and shares the opportunities, challenges and lessons learned.
How to Foster a Culture of Innovation: A Q&A with Greg Kinsey
Greg Kinsey has +30 years of experience working with the manufacturing industry. He shares why innovation is so critical, and ways to be more innovative.
Bringing Together IT and Operational Excellence Teams for Successful Digital Transformation
With 30 years of international experience in manufacturing, the thoughts of Industry Expert Greg Kinsey on digital transformation are worth reading!
Minitab Analysis Reveals Surprising Traveler Preferences for Top Vacation Rental Provider
Learn how this Minitab Customer helped a top vacation rental provider find the root cause of its customers' growing dissatisfaction during the pandemic.
How to Predict and Prevent Product Failure
When a reliability issue is reported, you need to identify risks for products still in use. Read this Blog to discover how to predict and prevent failure.
Guest Post: 3 Generations of Machine Learning Models – A New Focus on Business Value
Learn about how machine learning works with Minitab software, and how we are entering a third generation of machine learning capabilities you can tap into.
Why You’re Not Getting Real-Time Visibility Into Continuous Improvement (and What You Can Do About It)
Only 30% of performance improvement initiatives succeed. Explore our top 3 suggestions to manage projects efficiently, consistently and successfully.
Guest Post: Pruning Your Hypothesis Testing Decision Tree
Joel Smith is the Director of Rapid Continuous Improvement at Keurig Dr. Pepper as well as the co-author of the Applied Statistics Manual. He will be hosting a panel on Leading Successful Data Analysis at the 2019 Minitab Insights Conference.
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).