GSS
// Design For Six Sigma (DFSS)
"If you do what you always did, you will get what you always got."

Dr. Albert Einstein




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Design it right the first time!

For most organizations, long-term success is linked to innovation and rapid development of new products, in addition to safe, reliable, ecological and competitive services. In this context, it is imperative to understand which aspects of the product or service are important to the client.  On the other hand, 70 to 80% of operational and quality problems stem from the initial design.  Including the client’s voice and building quality and reliability into the design process are the main objectives of the DFSS approach. 

DFSS is a rigorous methodology (IDDOV) to optimize the design process.  This methodology involves five (5) phases (Identify, Define, Develop, Optimize and Verify) and an array of advanced design tools (including quality function deployment, failure mode and effects analysis, functional analysis, experimental design, simulation, statistical optimization and robust design). 

GSS DFSS Services

GSS supports its clients in the DFSS for process, system, service and product design and redesign. GSS is flexible in its DFSS and includes the assessment of needs in DFSS (2-day workshop spent assessing the design process within the client’s organization), customized training, technical coaching/support in applying the DFSS and implementing the GSS-DFSS Prediction Scorecard (software tool for optimizing the design process).

The GSS-DFSS Prediction Scorecard is a predictive tool for optimizing transfer functions.

It allows the user to modify the nominal values of design parameters in order to reduce variability in the product key quality characteristics. The GSS-DFSS Prediction Scorecard documents and assesses from a quantitative point of view the progress of the DFSS project.  It is a graphical communication tool that allows for follow-up of the learning process and shows all critical elements of a design (functional requirements, design parameters, process variables, tolerances and costs of poor quality) as well as the expected levels of performance. 


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