1. Six Sigma Green Belt Training
Lecture 5
Measure:
Data Collection

2. Six Sigma Introduction
Green Belt Training
Six Sigma Introduction
Define
Measure
Improve
Control

3. Measure the current process to understand the performance

4. Measure
Data Collection
MSA
Process Stability
Process Capability

5. Measure Phase Deliverables
Mapped Process
Quick wins Implemented
A confirmed Operational Definition
Analyzed Measurement System
Determine process stability & process capability

6. Measure
Data Collection
MSA
Process Stability
Process Capability

7. Data Collection Objectives
Why Collect Data?
Populations & Samples
When to Collect Data?
How to Collect Data?

8. Why collect data? Data is the Voice of the Process (VoP)
VoP and VoC need to speak the same language
An operational definition is essential
Data
Can validate root causes
Can be used for trending
Helps prove anecdotal statements
Can be graphed to identify sources of variation

9. When to collect Data Define Phase Measure Phase Analyze Phase
Quantify the business case
Establish a SMART metric
Measure Phase
Voice of the Process
Measurement System Analysis
Process Capability
Analyze Phase
Understand the root cause of the problem
Improve Phase
Pilot
Design of Experiments
Control Phase
Repeat the same measurements as done in Measure phase
Control Charts

10. Collecting data is a key step in the process of making good decisions
Data Decision Cycle
????
Need for decision
Good decision
What are right questions?
Measure phase questions:
Project Y stable?
Project Y capable?
Type of problem?
The
DMAIC
way
Answer questions
based on data analysis
Analyze the data using graphs and statistical methods
What data do we need?
Collect the data
Collecting data is a key step in the process of making good decisions

11. How much data to collect?

12. Samples & Populations

13. How much data to collect
Since it is not always practical, possible or economic to measure everything in the population, we usually take a random sample

14. Taking a “Peek” at a Few to Make Estimates about the Total
Sampling
Taking a “Peek” at a Few to Make Estimates about the Total

15. Key Terms
Population: EVERY data point that has ever been or ever will be generated from a given characteristic.
Sample: A portion (or subset) of the population, either at one time or over time
Observation:
An individual measurement.

16. Ways of Sampling Process Sampling Population Sampling Sample
Process in Motion
Helps understand nature and conditions of the process
Population Sampling
Sample
Determines characteristics of the population

17. Methods of Sampling Stratified Random Sampling Random Sampling
each item has equal
probability of being selected
population “stratified” into groups
random selection within each group
Systematic Random Sampling
Subgroup Sampling
Process in Motion
every nth item
3 samples
at this point
each hour

18. Sampling Exercise

19. Types of Sampling Errors
Error in sampling
Error due to differences among samples drawn at random from the population (luck of the draw).
This is the only source of error that statistics can accommodate.
Bias in sampling
Error due to lack of independence among random samples or due to systematic sampling procedures (height of horse jockeys only).
Lack of measurement validity
Error in the measurement does not actually measure what it intends to measure (placing a probe in the wrong slot measuring temperature with a thermometer that is just next to a furnace).
Error in measurement
Error in the measurement of the samples.

20. How to Collect Data Automated Data Collection Manual Collection IBEX
ClinStar
HED
HSM
Manual Collection
Manual Forms
Patient Charts/Sheets
Time Studies
Observation Studies

21. Manual data collection forms
Unfortunately, most projects do not have data and data collection forms need to be created by the project team
Create the form such that it can capture all the relevant information that may be required to solve the problem
Train all those involved in the data collection as to how the form is to be completed and test the form for a day
Work out all of the issues prior to full implementation to ensure the correct data is being collected
Ensure the data from the forms is entered into a database as quickly as possible

22. Manual Data Collection
Identify elements of data that will be collected
What are we measuring?
When are we collecting data? All shifts? All days?
How long will we track this metric(s)?
Who is responsible for collecting the data?
Who is asking us to do this?
WIFM?
Confirm Operational Definitions with your team and train your data collectors
This step is essential to make sure that everyone is measuring the same thing
This will be pertinent later on in this phase when we talk about Measurement System Analysis (MSA)
Create a Data Collection Plan

23. Obtaining the measurements
There are several simple methods to collect initial data
The most common are
Check sheet: a simple log of “tick marks” representing the volume and type of work
Time stamps: a recording of the time that each activity begins and ends
Check sheet example: applications returned for missing data
© 2002 IBM Corporation (unpublished). All rights reserved. Version 1.0.

24. Example data collection form
Identification of the largest contributors
Identifying of data is normally distributed
Identifying sigma level and variation
Root cause analysis
Correlation analysis
Pareto chart
Histogram
Control chart
Scatter diagrams
How will data be used?
How will data be displayed?

25. Summary Describe when & why we collect data
At this point, you should be able to:
Describe when & why we collect data
Understand the different ways and methods to sampling
Explain the different types of sampling errors
Create and implement a data collection plan