Six Sigma Green Belt is two weeks of training for process improvement leaders. The focus of the projects, at this level of proficiency, is typically on stabilizing the process - getting rid of the firefighting mentality, and having consistent performance. Note that this may not be optimal performance, but significantly reduced volatility.
CEU and PDU credits will be provided to students after successfully completing the training and required exam or a live project for certification. In addition, attendees must attend all training and participate in all classroom exercises. If a student misses more than one day of training, make up work must be completed before the final exam may be taken and a training certificate, CEU’s or PDU’s will be issued at this time or after the successful completion of all the training and a live project. A live project is required for certification. Project should be submitted to Six Sigma.us within 6 months of the last day of training for certification. Projects should illustrate an understanding of the tools and concepts of the appropriate training level (i.e. Black Belt). Individuals interested in receiving PDU’s or CEU’s only my take a comprehensive online exam in lieu of a submitting a project with the instructor’s approval. The instructor will determine, if testing or completion of a project is the appropriate means of assessing the student’s understanding of the subject matter. Students wanting to receive Six Sigma certification will be required to complete and submit a project for Master Black Belt review.
Upon completing this course, students will be able to:
A brief history of both Lean and Six Sigma process improvement methodologies are introduced to help set the stage for the various techniques and tools to be taught throughout the course. The process improvement structure, the DMAIC roadmap, is explained and shown how it applies to various process performance issues. The concepts of inefficiency and ineffectiveness of processes are linked to the Lean Six Sigma methodology to affirm the application of the methods to all types of processes.
Proper definition, scoping and selection are critical in the success of any process improvement project. Participants are challenged to define their project including defining the Problem, the Process associated with the Problem, Metrics for measuring success, and the business value of the project.
For an effective and beneficial deployment and integration of Lean Six Sigma methods there needs to be a defined support structure along with various roles and responsibilities. Presented here are attributes of the various roles, expectations of each role, and success factors for effectively completing meaningful projects.
Establishing the existing process flow, the steps involved in the process, and the various contributing factors to the process performance is a foundational tool in determining the root cause of the variability in the baseline process. This is not a standard “flowchart” - it is a deeper evaluation, searching for Root Cause.
It is absurd to expect each factor identified to be deeply researched with its regards to the overall process performance. The Cause and Effects matrix is a detailed, systematic prioritization tool to help establish which factors to address, based on their relationships to the process performance measures.
Once factors are prioritized, an in-depth view of the top ranked factors is taken to determine if they are susceptible to failure. The relationship between factor failure, the effects on the process and customer, the causes for the failures, and current controls in the process are evaluated to establish high risk environments. Actions are then identified for the high risk relationships to help mitigate the risk of failure. Participants are led through the idiosyncrasies of this complex tool to make it effective for evaluating potential failures in their process.
Six Sigma methods are fundamentally based on data, and thus use statistical approaches to solve the underlying performance problems. Participants are introduced to methods for describing data statistically with regards to the Shape, Center, and Spread of the data distribution. In addition, the properties of the Normal distribution are explained and linked to the concept of variability.
Analysis of data is made far easier with the advent of software. Participants are exposed to the basic functions, structure, and capabilities of the chosen software package before utilizing it in future training modules.
There are a variety of graphical and analytical techniques used to conduct basic analysis of the process performance data in an aggregate form (all data together). Participants will begin to analyze data sets, searching for root causes of variability, using tools including:
Investigating the variability of process performance data over time can identify key events contributing to the variability of the process and help set direction for process improvement. Participants will learn about the different general types of variability, the application of the Normal distribution to time series data, and how to interpret a standard Control Chart.
Six Sigma focuses on the use of data to determine the root causes of process performance variability. The trustworthiness of the performance data is crucial in making decisions throughout the process, thus the reliability of the method for measuring it must be established. Participants will learn how to construct and complete a Measurement Systems Analysis (MSA) for a variety of measurement situations.
Understanding the natural variation of the process is important for determining what contributes to it, but determining how the process performs in comparison to the customer expectations (specifications) provides insight to expected levels of out-of-specification occurrences. Participants will learn the difference between Short and Long Term variability, and various capability indices for variables type data.
Every process can benefit from efficiency improvement. This introduction to basic Lean tools will provide the participants insight on basic Lean tools including, 5S, the Theory of Constraints, and Value Stream Mapping.
When application of the knowledge-based tools (Process Variables Map, Cause and Effect Matrix, and FMEA) does not result in the performance impact desired, taking a more data-based approach is likely the next step in understanding the root cause of the variability. Participants will learn key elements for designing and conducting a successful passive data collection study, and practice graphical analysis techniques for prioritizing which factors to investigate with advanced analytical methods.
Hypothesis tests are a fundamental set of techniques for evaluating the relationships between process factors and the performance measures. Understanding the steps for designing and properly analyzing the various types of hypothesis tests is essential for making well informed decisions. Participants will gain insights on risk levels for decision errors, how to determine the proper test to utilize, and threats to the practical significance of the evaluation may exist.
ANOVA is probably the most used hypothesis test as it evaluates the impact of a categorical factor on a performance measure. Participants will use the roadmap for conducting this test and for evaluating the various underlying assumptions affecting the method including the Test for Equal Variances.
Establishing the relationship between two continuous type variables can be accomplished through use of correlation. Participants will learn how to evaluate the causal relationship using basic Linear Regression techniques and their use in developing a prediction equation.
There exists the condition where count type data is all that is available for both the factor and the performance measure of the process. This test method is beneficial is testing the proportions of various groups to establish if there is an unusual relationship that exists. This method shows to be applicable to many transactional type processes.
Participants will apply the various Multi-Vari Study data analysis techniques to a challenging case study showcasing the decision process based on information provided using a simulation tool.
Once stability in a process is achieved, characterization and optimization of the process may be desired. Participants will be given an overview and high-level understanding of the applicability, concepts, methods for designing and analyzing simple designed experiments. This provides insight into tools expanded upon in the Black Belt level training course.
Once critical factors are determined and proven to have a significant effect on the process performance, effective controls must be put in place to maintain the process performance. Participants will consider methods to prevent factors from varying uncontrollably through the use of Mistake Proofing methods.
The Control Plan documents the established and validated critical factors in the improved process. This document is crucial in order for project leaders to transfer understanding to the process owners and to establish ‘the formula’ for future predictable process performance.
In order for the project leader to be certified, and for the project to be considered completed, proper documentation is necessary providing a history of the methods, assumptions, analysis, and decisions made throughout the process. In addition, the documentation should establish the final impact and value of the project on the business.