Course Content
Define
Identify the problem or process that needs improvement, set project goals, and outline the scope.
0/10
Six Sigma Management at Corporate Level
Six Sigma Project Team Structure
Create team roles and responsibilities
Create a Problem Statement
Create the SIPOC process map
What is CTQ (Critical to Quality)
Create CTQ Tree
Creating a Project Charter
Stakeholder Analysis
Outcome expected after the Define Stage
Measure
Collect data to establish baselines, understand current performance, and quantify the problem. For example, measuring the average turnaround time for policy renewals.
0/4
FMEA (Failure Modes and Effect Analysis) concept
Data collection methods
Introduction to data sampling
Use of Run chart for analysis
Analyze
Examine the data to identify root causes of inefficiencies or risks. This might involve analyzing claims data to uncover bottlenecks or sources of errors.
0/12
5 Whys
Root cause analysis with Fishbone Diagram
Lean 7 Wastes concept
Value chain mapping with Value Stream map
What is a process
Components of process
Process Mapping with Swimlane diagram
Process redesign principles
80/20 analysis with Pareto Diagram
Statistical analysis with Box Plot
Hypothesis Testing
Correlation and Regression Analysis
Improve
Develop and implement solutions to address root causes. For example, streamlining workflows or introducing new digital tools to reduce manual errors.
0/4
Solution Selection Matrix
Cost Benefit Analysis
Risk Assessment
Implementation Planning
Control
Put controls in place to sustain improvements, such as regular monitoring, updated procedures, or dashboards for ongoing performance tracking.
0/4
Create Control Plan
Create Key Performance Indicator (KPI)
Prepare the Process and User Guides
Handover to Process Owner
Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control)

Collecting Data Samples

As it is difficult most of the time to collect data of the entire population of the subject in interest, randomly sampling from the population is important in Six Sigma for teams to draw conclusion from the sampled data that is representative of the entire population.

To ensure samples can be used to draw statistical conclusions, the sampling must be handled correctly and appropriately sized.

 

Types of Sampling Strategies

Simple Random Sampling

Simple random sampling works when there is an equal chance that any item within the population will be chosen.

Random sampling requires that the sample represents similar attributes and percentages as the entire population.

Stratified Sampling

Stratified Sampling occurs when the population as a whole is divided or can be divided into subgroups with different attributes.

For example, when sampling the Voice of Customers for headphone products of a company, the sampling grouped by age groups and demographic would be essential for the company to understand the real feedbacks of the product better.

Sequential Sampling

Sequential sampling involves selecting every X item for inclusion in the sampling. Sequential sampling can be used when teams are collecting data at intervals such as time.

For example, in a outgoing delivery operation in a warehouse, the operator might take a sample inspection of delivery details on the label against the details in the system to ensure the accuracy of the delivery in interval of every 15 minutes. In this manner, the error of labelling would provide information on trends of when the most occurrence happened.

 

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