7 Tools Of Quality

Visualize a process with a process map to make it easier to follow and analyze. Use process maps, one of the seven basic quality tools, to understand and improve workflows.

Discover process map symbols for input and output, including digital inputs like passwords and non-digital outputs like a cake, and the idea of designed-in delays.

Explore the four basic process map symbols: start and finish, activities, decision, and subprocess, to model workflows with practical examples and a preview of more advanced symbols.

Explore digital process maps by creating diagrams with drag-and-drop symbols, connecting steps, and labeling decisions, whether by hand, PowerPoint, or a website; this example illustrates basic workflows and customization.

Explore an example process map for handling website complaints, using a digital input, a hygiene-related decision, vendor escalation, and a subprocess investigation with recording and communicating outcomes in a database.

Translate the written cleaning process into a process map or flowchart. Use start and end symbols, decision points about chairs on tables, and a document symbol for the written warning.

Balance activity and decision symbols in process maps, note when an activity can be implied, and recognize variations among authors using chairs on the table as an example.

Intro to cause and effect diagrams teaches how to structure information during brainstorming, enabling a focused analysis of the various causes of a problem.

Explore the cause and effect diagram, also known as the Ishikawa or fishbone diagram, to brainstorm and structure causes and their details across categories like food, vendors, and facilities.

Explore scatter plots as a tool to visualize relationships between variables and decide actions. Devote time to understanding scatter plots and their insights for quality analysis.

Explore scatter plots, showing the relationship between two variables like temperature and ice cream sales, and learn to build them in Excel with axis titles and the correlation coefficient r.

Explain how the correlation coefficient r indicates the strength and direction of a linear relationship between two variables on a -1 to 1 scale, helping interpret scatterplots with many dots.

Learn three major cautions about correlation and scatterplots: r only captures linear relationships; outliers can distort the coefficient; and correlation does not imply causality, even with hidden variables.

Compute the correlation coefficient in Excel using two data arrays (temperature and ice cream sales), interpret R from -1 to 1, and explore positive and negative linear relationships.

Compute correlations in Excel with the Analysis Toolpak to produce a correlation matrix that links satisfaction to waiting time, discounts, temperature, and budget.

Learn how control charts help Claude see developments over time by organizing quality related data into a structured view.

Learn how control charts display the development of a variable over time, detect trends, cycles, and shifts, and trigger alarms to improve quality.

We visualize the distribution of data with histograms to reveal insights; future videos explore histograms in more detail.

Explore how histograms visualize data distributions and frequencies, illustrated by Claude's budget data, and distinguish normal versus bimodal distributions to guide audience diversity, with Excel-based creation in a future video.

Create a histogram in Excel with the 2024 budgets data, adjust the bin width to two, and add axis titles for budget in euros and frequency.

Explore how tallies and Pareto charts work together to create an overview of quality problems and identify the main root causes.

Analyze complaints using tallies and the Pareto chart to identify the most frequent problems, order causes from high to low frequency, and assess cumulative impact for root cause focus.

Master constructing a Pareto chart in Excel versions after 2016 with a few clicks, including inserting the histogram with a line and adding axis titles for percentage data.