Manufacturers can prevent backorders and keep their inventory on track by predicting customer demand, otherwise known as using predictive analytics.

The topic of Quality 4.0 is rapidly becoming a legend due to its mysterious nature; it seemingly has no formal description. Very few people can confidently say that they have seen it or used it. 

Artificial intelligence and predictive modeling still require a human element: Especially staff to capture data, manage insights, deploy the software, guarantee production quality and more.

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. 

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. 

A global leader in the food and beverage industry faced a complex challenge in their process of refining corn sugars.

Greg Kinsey, Industry Executive at Hitachi, reveals how creative uses of the DMAIC process with digital transformation are helping factories to unlock the next level of process improvement.

Real-Time SPC provides data collection, monitoring, alerts, and analysis that allow organizations to identify actionable insights immediately.