Next Generation SPC & Quality Analytics

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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. 

Manufacturers use statistical and rule-based analysis of manufacturing data to better understand and improve their processes. They also use it to pinpoint and strengthen best practices, react quickly, and foresee potential problems before they disturb product quality, yield, or cost.

Manufacturers need proper automation, machines, and software to manufacture products faster and keep up with evolving customer demands. As production capacity increases, these businesses must boost their quality control capacity while reducing quality costs. 

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.

Greg Hitachi has over 30 years of experience working with the manufacturing industry, mainly in the automotive, electronics, aerospace, and consumer goods sectors around the world. Learn about his vision for digital transformation.

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 when 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.

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

When faced with potential reliability issues, original equipment manufacturers must quickly find the root cause and determine the risk for other equipment still operating in the field.