Articles Tagged with ''statistical process control (SPC)''

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.

Process capability measures, such as Cpk and Ppk, measure how well your process is performing relative to your customer’s specifications. Let’s review some capability analysis basics and then delve into another capability estimate.

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Why the demand for short production runs? Your customer’s application of Lean principles causes them to request smaller batch sizes which results in the need for shorter production runs and short run SPC.

Globalization and digitalization have intensified competition in manufacturing. Some companies are using Bayesian hypothesis testing to optimize processes and make informed decisions. For example, a production manager could use it to improve the engine-cylinder-head-machining process.