This course has been designed to provide the student with:
- Strong theoretical knowledge of the Six Sigma Green Belt Body of Knowledge.
- Practical, hands-on, experience with the Six Sigma methodology.
- A Katz Six Sigma Green Belt Certificate, for students earning a grade of “B” or better.
Six Sigma is a disciplined, data-driven approach to process improvement aimed at the near-elimination of defects from every product, process, and transaction. Six Sigma utilizes the following five-phase problem solving methodology known by the acronym DMAIC:
1. Define the projects, the goals, and the deliverables to customers (internal and external). Describe and quantify both the defect and the expected improvement.
2. Measure the current performance of the process. Validate data to make sure it is credible and set the baseline.
3. Analyze and determine the root cause(s) of the defects. Narrow the causal factors to the vital few.
4. Improve the process to eliminate defects. Optimize the vital few and their interrelationships.
5. Control the performance of the process. Lock down the gains.
To integrate theory and practice, students will be grouped in teams and work under the mentorship of a Six Sigma Black Belt on an industry client field project. The flow of the lecture topics and hands-on class labs will mirror the DMAIC methodology; providing just-in-time knowledge; balancing delivery, quality, and cost for our clients. In summary, BQOM 2139 Six Sigma Theory and Practice promises a dynamic and engaging experience based learning opportunity for MBA students who are equal to the challenge of applying the DMAIC methodology to a real-world project in partnership with an industry client.
The following Clients partnered with Katz for this course over the 2018-2019 Academic Year. Please contact Prof. Jim Kimpel (firstname.lastname@example.org) with any additional question regarding clients.
- City of Pittsburgh
- Haemonetics Blood Management Solutions
- Pitt Ohio
- Siemens Mobility
- UPMC St. Margaret Hospital
Prerequisites: BQOM 2401 Statistical Analysis.