Process Mapping and Gap Identification
Learning Objective
By the end of this lesson, the learner will be able to:
- Explain the importance of systematic data collection in Six Sigma.
- Distinguish between a population and a sample.
- Explain the importance of representative data.
- Understand how sampling can reduce the effort required to study a process.
- Recognize common sources of sampling bias.
- Explain random, stratified, and systematic sampling.
- Use check sheets to support structured data collection.
- Understand how appropriate sampling supports reliable Six Sigma analysis.
1. Introduction
Data is the foundation of Six Sigma decision-making.
A Black Belt must collect data in a systematic and appropriate manner so that the information accurately represents the process being studied.
When it is not practical to collect information from every item or person in a population, sampling can be used.
The objective is to obtain useful information while reducing unnecessary data-collection effort.
2. Understanding the Population
A population is the complete group of items, transactions, people, or observations relevant to the study.
Examples include:
- All products manufactured during a month
- All customer transactions during a year
- All patients visiting a hospital
- All service calls received by a call centre
The population defines the complete group about which the Black Belt wants to understand process performance.
3. Understanding a Sample
A sample is a subset of observations selected from the population.
For example:
Population: 100,000 customer transactions
Sample: 1,000 selected transactions
Studying the sample can provide information about the population without examining every individual observation.
However, the sample should be selected appropriately so that it represents the population being studied.
4. Representative Data
Representative data reflects the important characteristics of the population or process being studied.
A Black Belt should consider whether the collected sample adequately represents:
- Different time periods
- Different shifts
- Different locations
- Different customer groups
- Different operating conditions
If important groups are excluded, the analysis may provide a misleading view of process performance.
5. Sampling Bias
Sampling bias occurs when the method used to select observations systematically favours some observations or groups over others.
For example, suppose a retail organization wants to understand customer waiting time but collects data only during quiet periods.
The resulting sample may not represent the actual customer experience during busy periods.
Therefore, the Black Belt should consider when, where, and how the data is collected.
6. Random Sampling
Random sampling selects observations so that members of the population have an appropriate opportunity to be selected.
For example, a company may randomly select customer transactions from a complete transaction list.
Random sampling can help reduce selection bias when properly designed and implemented.
7. Stratified Sampling
Stratified sampling divides the population into relevant groups, or strata, and then samples from those groups.
Examples of strata may include:
- Geographic regions
- Customer categories
- Product types
- Production shifts
Application Example
A company operates in four regions:
- North
- South
- East
- West
Instead of collecting data only from one region, the Black Belt can structure the sample to include the relevant regions.
This can provide a more representative view of the process.
8. Systematic Sampling
Systematic sampling selects observations according to a defined interval from an ordered population or process.
For example, an organization may inspect every 10th transaction from an appropriate transaction sequence.
The sampling interval should be selected appropriately for the process and study design.
9. Check Sheets for Data Collection
A check sheet provides a structured method for recording observations.
It can help the Black Belt consistently capture information such as:
- Defect types
- Defect frequency
- Process occurrences
- Time-related events
- Categories of problems
The important principle is to define what will be collected, how it will be recorded, and under what conditions before beginning data collection.
10. Tools & Techniques
The main tools and techniques associated with this lesson include:
- Population definition
- Sample selection
- Random sampling
- Stratified sampling
- Systematic sampling
- Check sheets
- Data-collection planning
- Bias identification
These techniques help the Black Belt obtain data that is useful for subsequent analysis.
11. Application Example — Customer Survey
A company wants to understand customer satisfaction across several geographic regions.
The customer population includes customers from:
North, South, East and West.
The Black Belt uses stratified sampling so that customers from the different regions are represented in the study.
The resulting data provides a broader view of customer experience than collecting responses from only one region.
12. Case Study — Retail Chain
A retail chain wants to understand customer waiting times.
Initially, the organization collects observations mainly from urban stores.
The Black Belt recognizes that the sample does not adequately represent the organization’s wider operations.
The study is expanded to include both urban and rural locations.
The analysis identifies longer waiting times in rural locations.
The organization responds by adjusting staffing arrangements.
The case illustrates the importance of selecting data that represents the process being studied.
13. From Data Collection to Analysis
A Black Belt should follow a logical sequence:
Define the Population
↓
Determine the Required Data
↓
Select an Appropriate Sampling Method
↓
Collect the Data Systematically
↓
Check for Bias and Representativeness
↓
Analyse the Data
Poor sampling can affect the reliability of subsequent analysis, even when the statistical method itself is correctly applied.
14. Black Belt Perspective
A Black Belt should not simply ask:
“How much data do we have?”
The more important questions are:
“Where did the data come from?”
“How was it selected?”
“Does it represent the process?”
“Could there be sampling bias?”
Good statistical analysis begins with good data collection.
15. Lesson Practice
Scenario
A retail company wants to understand customer waiting time across its stores.
The company operates in:
- Urban locations
- Rural locations
- Different regions
- Different operating shifts
Questions
- What is the population in this study?
- What would constitute a sample?
- Why is representative sampling important?
- Which sampling method could help ensure different regions are represented?
- How could collecting data only during quiet periods introduce bias?
- How could a check sheet support systematic data collection?
16. Key Learning Points
After completing this lesson, the learner should be able to:
- Explain the purpose of systematic data collection.
- Define a population.
- Define a sample.
- Explain representative data.
- Recognize sampling bias.
- Explain random sampling.
- Explain stratified sampling.
- Explain systematic sampling.
- Understand the role of check sheets.
- Select an appropriate sampling approach for a process situation.
- Recognize the importance of data representativeness before analysis.
17. Lesson Conclusion
Effective Six Sigma analysis depends on reliable and representative data.
The Black Belt should understand:
Population → Sample → Sampling Method → Data Collection → Representativeness → Analysis
Appropriate sampling helps obtain useful information without necessarily studying the entire population.
The key principle is:
Good analysis begins with good data collection.