Items Tagged with 'statistical process control (SPC)'

Zontec Inc. introduced its AI-enabled statistical process control software, Synergy™️ 2000 v14.0 with Smart SPC Intelligence, featuring generative AI analysis and recommendations for process improvement.

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.

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.

Manufacturers can tackle production issues by accurately defining problems, using root cause analysis to identify key factors, implementing data-driven corrective actions, and continuously monitoring processes to ensure ongoing improvements and maintain a competitive edge.

Statistical process control (SPC) charts are crucial for process stability, especially with improved data collection. Introduced by Walter Shewhart in the 1920s, SPC uses random samples to estimate statistics and assess variation. With better data availability, techniques like the Rbar method, which estimates standard deviation using subgroup ranges, need reevaluation.

Statistical Process Control (SPC) is evolving to not just detect defects, but also to predict and prevent issues. Modern factories use more sensors and collect more data, allowing SPC to analyze real-time patterns and forecast potential issues.

In today's tech-driven world, companies use software to collect data, but the analysis can be flawed. Charts with only specification limits, arbitrarily chosen warning and action limits, and misused Process Behavior Charts contribute to misinterpretation.

Design of Experiments (DOE) helps improve products and processes more efficiently, providing a comprehensive understanding of influences on the end result.

Process mapping is a method to visualize and understand manufacturing processes, similar to a flowchart. It helps identify inefficiencies and delays in the workflow. For example, it can pinpoint the source of delays in an assembly line.

In manufacturing, operators, inspectors, engineers, and systems measure characteristics to ensure product quality. This data helps monitor real-time processes, tracking performance indicators like cycle time, throughput, and efficiency.