Next Generation SPC & Quality Analytics

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Making a process map presentation-worthy is less about picking a trendy color and more about empathizing with your audience. Learn how to use the design elements in Minitab Workspace to your advantage when creating a process map.

We understand that spreadsheets simply won't cut it anymore. Continuous improvement teams need to track and report on things beyond what project managers or technical leaders do. Learn how to get a dashboard view into projects and get the reports and insight needed to make CI a reality.

Takeda has had a longstanding relationship with Minitab and has used analysis to, identify and execute on improvement opportunities.

We are pleased to announce the release of three new add-on modules to improve your ability to visualize, analyze, and harness the power of your data. Minitab’s new modules will help companies make key decisions and improve analytical capability, backed by the power of Minitab Statistical Software. 

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.

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.

The Xbar/R Control Chart is a method of Statistical Process Control (SPC) manufacturers can use to reduce fill weight variation, maintain centerlines and avoid rework. Here are best practices to keep in mind:

Statistical Process Control’s impact on industry has been enormous. It has branched out beyond the manufacturing realm and can be applied to any process with quantifiable outputs.

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.