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Data Collection Lean Training on PowerPoint
Data collection is a fundamental Lean skill that lays the foundation for successful improvement efforts. Poor data collection sets managers and their teams up for failure. Good data collection makes improvement faster and easier.
This overview covers the basics of data collection and provides a solid foundation for more advanced training on the subject. There is an optional hands-on data collection training exercise as well.
Data collection is a fundamental Lean skill that lays the foundation for successful improvement efforts. Poor data collection sets managers and their teams up for failure. Good data collection makes improvement faster and easier.
This overview covers the basics of data collection and provides a solid foundation for more advanced training on the subject. There is an optional hands-on data collection training exercise as well.
Data Collection is a critical skill for anyone using Lean to improve their operation. Every facet of Lean requires knowing where you are starting, and if the changes you make are actually improving the process.
This PowerPoint presentation shows the basics of data collection, and has a few built in exercises to drive points home. There are also a few optional companion exercises that can increase the engagement and retention of your trainees.
1. Purpose of data collection
a. Provide basis for decision making
2. Costs of data collection
a. Planning costs
b. Collection cost
c. Usage costs
3. How much data should you collect
a. Too little
b. Too much
4. Where to determine data needs
a. Project Charter
b. Current Process
c. Quality information
5. Types of data
a. Quantitative data
i. Continuous (or variable) data
ii. Discrete data
b. Qualitative date
i. Attribute data
1. Ordinal data
2. Nominal data
ii. Open data
6. Data collection methods
a. Tools for collecting data
i. Enterprise software systems / databases
ii. Checksheets
iii. Travelers
iv. Logs
7. Turning data into information
a. Identifying data needs
b. Reviewing existing data
c. Developing a data collection plan
i. Balancing the need to predict data requirements with the risk of steering teams toward specific solutions
8. Data collection steps
9. Avoiding bias in data collection
a. Sources of bias
10. Sampling
11. Statistical significance
12. How people react to data
13. Data perception exercises
14. Pitfalls to avoid
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