Data Collection Plan Tutorial

Tutorial

When to use this tool

Use the Data Collection Plan template to create a systematic framework for collecting data to be used in tests that will facilitate the identification of significant process inputs (X's).

The plan provides for identification of the following elements of data collection for a particular process input:

• Description of the measure/metric and data that is to be collected
• A related Operational Definition or Specification
• A description of how the measurement is made
• Whether or not the measurement system is capable
• Where the data comes from
• Who collects the data
• The frequency of data collection
• The sample size for each data collection event
• Where the collected data is stored

How to use this tool in EngineRoom

1. Open the template by going to DMAIC > Measure or Standard > Planning depending on the chosen menu structure.
2. Enter the data collection information into the table - the suggested titles are can be edited to align with your data collection needs and goals.
3. Proceeding by column, list your process inputs that are to be measured, along with related information for collecting the data, such as the measurement or metric representing the input, its operational definition, the measurement device used to measure the metric, its reliability, the source of the data, the responsible party, collection frequency, sample size and data storage location.
4. You can add rows by clicking the ‘plus’ buttons where you want the new row to appear and delete them by clicking on the row to be deleted, and clicking the trash can icon button that appears next to it.

The Data Collection Plan can be used as a stand-alone template or can be populated from an Input Screening Report.

A Data Collection Plan for a customer support call-center is shown below:

Some tips to keep in mind:

• Set realistic measurement priorities – target feasible measurements where the knowledge gained will be most helpful.
• Evolve your measurements over time – evaluate your plans regularly and learn from the data you collect and tweak your plan accordingly.
• Don’t be afraid to stop measuring data that does not prove useful.