Statistical Process Control (SPC) and Data Analysis Course

Drive continuous process improvement by reducing deviations from standards and requirements, while maintaining a stable level of quality to increase customer satisfaction. Improve data entry, analysis, reporting, and productivity.

Learn how SPC uses data analysis to classify data into variables and attributes, with variables measured on a scale (length, temperature) and attributes as discrete counts (blemishes, faulty products).

Analyze how a disturbance in the process creates variation and shapes control charts, and identify the two types of violations—common prosecution and special prosecution—in SPC.

Identify common cause variation as the natural, random fluctuation around the data average caused by unknown factors intrinsic to the process, with sources like materials, method, measurement, machine, and conditions.

Identify special cause variation as an unexpected, assignable shift in outputs caused by known factors or environmental changes, signaling nonrandom patterns that can be traced and removed.

Explore control charts in statistical process control, with center line, upper and lower control limits, and subgroup data to distinguish in-control versus out-of-control processes and trigger corrective actions.

Use control charts to distinguish special from common cause variation, with a central line and upper and lower limits set at three sigma to guide investigation and improvement.

Explore how variable and attribute control charts monitor process variation, including s-bar charts, the Irish chart, and u, c, and p charts for defects.

Explore types of control charts for attributes, including RN, NP, SNP, and MP charts, to monitor proportion defective and number of items over time and across varying sample sizes.

Study variable control charts, including x-bar charts for means, s charts for variability, and range charts for small samples, with standard deviation guiding variation for large datasets.

Explore attribute control charts for counts data, including c, u, and p charts, and mp charts, to monitor defects, pass-fail measurements, and varying sample sizes.

Learn how to assess process capability in SPC using CP, CPK, P and PPK indices to meet specifications, with data assumptions and random sampling for valid results.

Assess a process's ability to meet specifications using Cp and Cpk, with Pp and Ppk, and relate specification limits and sigma to overall capability.

Understand process capability indices, including Cp and Cpk, and how to interpret moving range, D2, and six sigma context to assess whether a process meets customer specifications.

Explain the scatter diagram as a tool to assess relationships between two data sets and identify correlation types, including positive strong, negative strong, positive moderate, and absence of correlation.

Identify the most impactful few defects with a Pareto chart and the 80/20 rule, then collect, order data, and plot cumulative percentages to guide improvement.

Study how a histogram depicts data distribution and dispersion to assess quality characteristics of a product or service, while noting it does not reflect process behavior over time.

Design check sheets to collect data easily and systematically, enabling efficient inspection and verification of all items. Define the data aim and the method for stratification.