Many factors influence what happens between the time you hit “checkout” and when your package arrives. Let’s explore these challenges and how Minitab provides effective solutions.
Quality professionals are using statistical tools, originally meant for product quality control, to tackle climate change. For example, control charts that monitor manufacturing variations are now tracking energy consumption, identifying spikes, and measuring carbon emissions.
Quality engineers help manufacturing teams identify and resolve production issues through data analysis. They use statistical tools to uncover root causes and apply principles from methodologies like Six Sigma and Lean Manufacturing. Read on to learn more.
Luc Burgard, a leader in pharmaceutical manufacturing with experience in biotechnology and automotive sectors, highlights the importance of data analytics in strategic decision-making and shares insights on building specialized teams using Minitab.
Monte Carlo simulation helps companies understand process variability and make informed decisions. It's beneficial for quality control in manufacturing.
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.
If you're not meeting your NPS goals, try Root Cause Analysis (RCA) to identify and address the reasons behind it. Fixing recurring issues can significantly improve customer satisfaction.
The main difference between projects and programs is their scope and focus. Project management focuses on efficiently executing specific projects, while program management aligns multiple projects with overarching strategic goals.
