Quality Toolbox: Data Collection and Analysis Tools
Customer Benchmark Survey: You might be wondering why the customer benchmark survey has to do with quality tools, to which I would argue that a quality system can only be effective after both measuring and understanding your customers’ needs, wants, and expectations. The KISS Principle (Keep It Simple Steve) most certainly applies to surveys; when designing the survey it is of paramount importance to base the questions on actionable strategies and make it a quick, painless process for your customers. With today’s technology, a Web-based survey can be completed by a customer in 3 minutes without having to leave their desk, and is the preferable medium for ease of use, demographic data capturing, and automated scoring/reporting.
Check Sheet: An easily created, simple tool that can be adapted for a wide variety of purposes, the check sheet is a process-specific prepared form for collecting and analyzing data. Check sheets are typically kept at the applicable process or machine, and contains information on process data collected, validation of activities completed, and/or tracks patterns or trends.
Control Charts: Control charts are a graphical representation of the current state of a process, and should be implemented at the operator level to maximize effectiveness. A control chart’s true function is to provide real-time feedback to control and improve a process, which means that the data displayed on the charts must help front-line operators make better process decisions.
Scatter diagram: A diagram that graphs pairs of numerical data against one variable on each axis that is used for analyzing relationships and trends between two variables. If the variables are correlated, the points will fall along a trend line or curve, and the higher the correlation, the tighter the data points around the trend line.
Stratification: This tool is not widely used or understood, and is generally used in conjunction with other techniques. Stratification separates data gathered from a variety of sources so that patterns can be seen, and is particularly useful when data from a variety of sources or categories have been lumped together and the meaning of the data can be impossible to see. This technique separates the data so that patterns can be seen. When using this technique, a couple of rules need to be followed:
- The strata must be mutually exclusive (every element in the population must be assigned to only one stratum) and
- The strata should also be collectively exhaustive (no population element can be excluded).