How to manage your process using statistical Excel Tools

Explore how to create a process map for an orange juice plant, tracing flows from unloading to pasteurized filling and by-products, to extract data for process improvement and SPC.

Calculate overall yield and uncover the hidden factory through first time yield analysis of packaging, inspection, and rework, revealing 70.7 percent first time yield and 27.9 percent hidden factory.

Explore how to map a three-step process with a flowchart, track scrap and rework, and compute first-time yield or throughput yield to reveal true process performance.

Learn to calculate rolled throughput yield across process steps, accounting for scrap and rework, and build a process flowchart to reveal true yield and defects.

Identify complexity in processes, enable communication, and guide quality improvement by capturing current work and evolving to a final map created by those closest to the process, using Microsoft Bisio.

Learn how to create basic to cross-functional process flowcharts in Excel using built-in templates, customize shapes and connectors, update data through refresh, and export the diagram with Snipping Tool.

Learn to build flowcharts in Excel using shapes, including rectangles and diamonds for steps and decisions, add connectors, customize color and text, and group items for flexible diagrams.

Identify continuous and attribute data in statistical process control, and learn how data type choices—measurements, fractions, and pass/fail counts—shape analysis in Excel tools.

Compare the center and spread of distributions by calculating the mean from samples across three machines. Use standard deviation to assess variation; a lower value means more consistent sugar percent.

Learn to calculate average and standard deviation in Excel using sample data from multiple machines, selecting ranges, and analyzing variation to compare performance.

Analyze a class score distribution with a histogram, compute mean and standard deviation, and compare to a normal distribution to assess data spread and 95% confidence range in Excel.

Learn descriptive data analysis in Excel by creating a histogram and a box plot from 50 samples to explore moisture percentage, particle size, and density and identify data distribution.

Explore histogram and boxplot analyses to assess data distribution, detect outliers, and distinguish normal vs non-normal shapes while relating mean and standard deviation to specification limits for quality improvement.

Explore hypothesis testing in Excel by comparing a sample mean to a target with the data analysis toolpak and t-test, interpreting p-values to assess if the target is met.

Install data analysis toolpak in excel and run two-sample t-tests via the data analysis tool. Learn hypothesis testing concepts, p-values, and when to use before-and-after, peer data, or normal data.

Explore process control charts in Excel, including the i-mr and x-bar charts, to monitor hour-by-hour samples, track variation, and detect assignable causes with upper and lower control limits.

Learn when to use c chart or p chart for defect data, set control limits, and choose appropriate charts based on data type and sample size, with root cause analysis.

learn to create control charts in excel using formulas, including calculating mean and standard deviation, and plotting upper and lower control limits with three standard deviations.

Explore free Excel templates for statistical process control, including run charts, P charts, and descriptive statistics with normality tests, to assess data normality and automatically compute control limits.