Six Sigma Online Training

Six Sigma Introduction

Six Sigma is a disciplined, data-driven approach and methodology for identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes. It uses a set of quality management methods, including statistical methods, and creates a special infrastructure of people within the organization (“Champions”, “Black Belts”, “Green Belts”, “Yellow Belts”, etc.) who are experts in these methods. This module will:

  • Define Six Sigma and discuss its origin and evolution.
  • Describe how it differs from Lean and Six Sigma.
  • Explain how sigma levels are determined, and how they are used to indicate process capability.
  • Describe the roles of Six Sigma team members.
  • Discuss key factors of Six Sigma success.
  • Discuss important elements of the Six Sigma process, including key inputs and outputs and the role of “Critical to Xs”.
  • Describe the five phases of the DMAIC improvement cycle.

Managing the Project

  • Describe the GRPI Model and how to use it throughout the project.
  • Apply the ARMI tool to clearly define stakeholder roles.
  • Illustrate and complete a Project Charter.
  • Conduct a stakeholder analysis.
  • Plan the project, identify necessary resources, and discuss the different project roles.
  • Explain the team dynamics necessary to be a Change Leader.

Voice of the Customer

Voice of the customer (VOC) is used to describe the in-depth process of capturing a customer’s expectations, preferences and aversions. This process is all about being proactive and constantly innovative to capture the changing requirements of the customers with time. It produces a detailed set of customer wants and needs, organized into a hierarchical structure, and then prioritized in terms of relative importance and satisfaction with current alternatives. Voice of the Customer studies typically consist of both qualitative and quantitative research steps. This module will:

  • Describe methods on how to translate the Voice of the Customer (VOC) into measurable requirements.
  • Explain how to apply a 5-step method for setting up and conducting a VOC study.
  • Discuss techniques that are used to identify process variables which are correlated to customer requirements.

Pareto Analysis

Pareto analysis is a formal technique useful where many possible courses of action are competing for attention. In essence, the problem-solver estimates the benefit delivered by each action, then selects a number of the most effective actions that deliver a total benefit reasonably close to the maximal possible one. This module will:

  • Explain how to create a Pareto Chart, including a cumulative relative frequency line.
  • Given data and a Pareto Chart, describe how to use a variable to weight the original data and produce another Pareto Chart.
  • Discuss how to use stratification methods to perform in depth Pareto analysis of the data.
  • Explain how to interpret a Pareto Chart to make a business decision

 

SIPOC

SIPOC is a tool that summarizes the inputs and outputs of one or more processes in table form. The acronym SIPOC stands for suppliers, inputs, process, outputs, and customers which form the columns of the table. This module will:

  • Define SIPOC and describe its components.
  • Discuss the purpose of SIPOC.
  • Explain how to construct a SIPOC diagram.
  • Describe how the information gained from a SIPOC analysis can be used.

Mapping the Process

Mapping the Process is a way to visually represent the sequence of actions that comprise a process. It helps to document, analyze, and improve on processes. This module will:

  • Define a process and a process map.
  • Describe the benefits of process mapping.
  • Describe the differences between relationship maps, swim lane charts, and process maps.
  • Discuss the three levels of detail used to describe a complex process.
  • Walk through the five steps of process mapping.
  • Demonstrate how to apply a process map.

 

Process Based Costs:

This module will:

  • Discuss how the overall cost of quality relates to both the cost of poor quality and the cost of good quality.
  • Define the Cost of Poor Quality (COPQ) and identify components of COPQ as they relate to the process.
  • Explain how to calculate the Cost of Poor Quality.
  • Identify the benefits derived by a company when they are able to reduce COPQ.

Measurement System Analysis

A measurement systems analysis (MSA) is a specially designed experiment that seeks to identify the components of variation in the measurement. Just as processes that produce a product may vary, the process of obtaining measurements and data may have variation and produce defects. A measurement systems analysis evaluates the test method, measuring instruments, and the entire process of obtaining measurements to ensure the integrity of data used for analysis (usually quality analysis) and to understand the implications of measurement error for decisions made about a product or process. This module will:

  • Identify the characteristics of a good measurement system.
  • Identify the benefits of using a Gauge R&R study to validate the measurement system.
  • Discuss the steps used to conduct a Gauge R&R study.
  • Use the results of the Gauge R&R study to determine how effective the measurement system is.

Cause and Effect Diagrams

Cause and effect means that an action or event will produce a reaction or response in the form of another event. Cause and effect diagrams are used for root cause analysis of what factors are creating the risks within the project. The goal is to identify and treat the root of the problem, not the symptom. This module will:

  • Explain the three basic steps for identifying and preventing problems.
  • Apply basic cause and effect principles in order to identify the root cause of a problem.
  • Teach techniques for gathering information for cause and effect analysis, including Five Whys and Brainstorming.
  • Organize data and information for analysis using the Affinity Diagram and the Fishbone (or Ishikawa) Diagram.
  • Analyze a process using Root Cause Analysis and The XY Matrix

Failure Mode and Effects Analysis

Failure Mode and Effects Analysis (FMEA) is a systematic technique for failure analysis. An FMEA is often the first step of a system reliability study. It involves reviewing as many components, assemblies, and subsystems as possible to identify failure modes, and their causes and effects. For each component, the failure modes and their resulting effects on the rest of the system are recorded in a specific FMEA worksheet. This module will:

  • Define FMEA and discuss its use as a project risk assessment tool.
  • Describe the 10 steps for constructing a process FMEA.
  • Explain the FMEA scoring criteria.
  • Discuss how to translate FMEA results into action.

Scatter Diagrams

Scatter Diagrams are graphs in which the values of two variables are plotted along two axes. The pattern of the resulting points will reveal if there is any sort of relationship between the variables. This module will:

  • Show how to determine if two variables plotted on a scatter diagram appear to be correlated and to what degree.
  • How to build a scatter diagram.
  • How to avoid errors in analyzing scatter diagrams.
  • How to use stratification to further explore the relationship between variables

Introduction to Process Capability

A process is a unique combination of tools, materials, methods, and people engaged in producing a measurable output; for example a manufacturing line for machine parts. All processes have inherent statistical variability which can be evaluated by statistical methods. The Process Capability is a measurable property of a process to the specification, expressed as a process capability index (e.g., Cpk or Cpm) or as a process performance index (e.g., Ppk or Ppm). The output of this measurement is usually illustrated by a histogram and calculations that predict how many parts will be produced out of specification (OOS). Two parts of process capability are: 1) Measure the variability of the output of a process, and 2) Compare that variability with a proposed specification or product tolerance. This module will:

  • Determine how well a process is able to meet customer requirements by measure of process capability.
  • Identify when one process is more capable than another.
  • Distinguish capable from non-capable processes.
  • Identify how sample measurements are used to estimate population values.
  • Determine which Control Chart type is most appropriate for monitoring a particular process parameter.

Process Capability Assessments

This module will:

  • Compute Cp, Cpk, Pp, and Ppk values for processes using continuous data.
  • Interpret Cp, Cpk, Pp and Ppk and relate them to a defect level.
  • Take relevant process information for a process using discrete data.
  • Calculate process assessment measurements.
  • Determine how well processes are meeting customer requirements.
  • Look at a powerful operation metric called Rolled Throughput Yield.

Selecting the Solution

Once the real root cause of a problem has been isolated, the team uses the information gathered to creatively generate potential solutions. It then evaluates the alternate solutions, assesses the risks, and makes its selection. This module will:

  • Examine the process of selecting a solution for an improvement project.
  • Discuss how potential savings affect a project’s Return On Investment (or ROI).
  • Describe the purpose and application of common tools used to generate and analyze potential solutions and to assess risk.
  • Explain how all these components come together in the implementation plan

Control Charts

Control Charts are a tool for distinguishing between the two types of variation causes (Common Cause and Special Cause). They are used to determine if a manufacturing or business process is in a state of statistical control. This module will:

  • Define Control Charts and discuss their purpose.
  • Explain how to determine whether to use an Attribute or a Variables Control Chart.
  • Describe the steps for setting up a Control Chart.
  • Discuss the basic rules for using Control Charts.
  • Explain how to identify which Control Chart type is most appropriate for monitoring a given process parameter

Controlling the Process

The last phase of the DMAIC process is Control. Once a solution has been selected and implemented, the team must make sure that the process improvements will be sustained in the future, and the people or system, will not revert to the old way of doing things. The purpose of the Control phase is to maintain a stable and predictable process that meets customer requirements; to make adjustments to meet any changing business requirements, and close the project. This module will:

  • Discuss the purpose of the Control phase in a Lean Six Sigma DMAIC project.
  • Walk through the steps for controlling the process.
  • Describe the basic elements of a Control Plan, discuss its importance, and explain how to create and implement it.
  • Describe the key components required for effectively closing the project, including documentation, handoff, and leverage.