Manufacturers can unearth valuable insights from test data from various sources by employing statistical algorithms and machine learning to establish patterns and predict future outcomes and trends.

Statistical process control (SPC) helps organizations monitor and control quality. It enables manufacturers to boost efficiency, minimize waste, and detect problems early. Quality and process monitoring is key to efficiency in manufacturing.

Machine downtime can be expensive. It eats up profits, repair costs, and time that could otherwise be devoted to labor. Organizations can avoid this by proactively observing the condition and performance of their equipment, enabling them to predict and prevent machine failures. 

How LVHN used Lean Six Sigma tools and Minitab statistical analysis to achieve improvements in patient-centered care.

In situations where resources are limited to gathering real data would be too expensive or impractical, Monte Carlo simulations can help forecast results and probabilities. 

Testing potential improvements can get complicated then you’re working with multiple suppliers in different steps of a process. Using a Monte Carlo Simulation can help illuminate the results you’d like to see, while saving time and money compared to running tests on real parts.

A world leader in rubber compounding needed to meet increasing customer expectations and heightened sustainability recommendations while continuing to grow and improve their business.

Using Minitab’s Predictive Analytics with Auto ML in manufacturing enables quality improvement and warranty prediction, practically in real-time.