DOQS SIX SIGMA SERIES

Design for Six Sigma II

The rigor established in DFSS allows the design process to look at details and opportunities otherwise unavailable to solution design teams. This course covers the creative concept generation techniques of Six Sigma, and the detailed decomposition and experimental techniques needed to define optimal solutions across a variety of design dimensions and concerns that have been mapped to a complete and prioritized voice-of-the-customer. This course results in learners reaching the level required of DFSS Green Belt certification in many organizations. (4 days, $20,000, Prerequisite: Design for Six Sigma I)

Process Coverage

Design for Six Sigma is not independent of the Six Sigma Improvement model. Opportunities for new systems, or components of systems, arise out of the DMAIC analysis process. Implementing a system using DFSS always takes place in the context of the DMAIC-driven improvement and always results in an implementation that is sustained by DMAIC-derived controls, even if the DMAIC efforts are not explicitly associated with the DFSS effort.

Lifecycle Coverage (DFSS)

Design for Six Sigma is based on an Identify-Define-Develop-Optimize-Verify (IDDOV) lifecycle that emphasizes identifying and designing innovative solutions against opportunities defined during traditional DMAIC initiatives. The DMAIC lifecycle is viewed as the driver of scope and direction.

This course covers the entire IDDOV lifecycle, emphasizing detailed activities that were omitted from the Design for Six Sigma I course.

1. Identify the Opportunity - Driven by the measurement and analysis phases of DMAIC, identification activities clarify particular product or service opportunities that can be implemented to help solve the problems being addressed by the DMAIC activity. Whether the need is for a comprehensive systems solution, or a collection of independent components that collectively will solve the problem, their identification initiates the DFSS process.

2. Define the Requirements - Comprehensive requirements engineering is a central theme of DFSS, with integrated models developed for Customer Needs, Business Processes, Functional Systems, Systems Designs, and Operational Support. Emphasis is placed on defining exhaustive requirements with a high level of control and quantification. Customer needs define requirements priorities, leading to a requirements management model that might implement as a single solution, or a series of releases planned over time.

3. Develop the Concept - As requirements progress toward implementation details, various conceptual solutions are continuously being identified and evaluated as stronger and stronger alternatives emerge and drive subsequent engineering activities. Evaluation might include various forms of experimentation, simulation, or prototyping to better understand design parameters and performance relationships.

4. Optimize the Design - Once a best conceptual solution has solidified in response to the prioritized and allocated requirements, the design parameters and performance relationships are driven toward optimal values using further experimentation and analysis. Critical design variables and functional dimensions are designed toward maximizing satisfaction and performance.

5. Verify Conformance - Once a systematic and rigorous design has been optimized, it must be verified and validated against the original requirements, as well as the critical-to-quality control values that have been established at each level of the overall design. The quantification of requirements must be assured, followed by the review and approval of stakeholders to be impacted by the design.

Other common DFSS lifecycle models are also discussed, including Define-Measure-Analyze-Design-Verify (DMADV) and Define-Model-Optimize-Verify-Control (DMOVC). These lifecycles aim to accomplish goals similar to those of the IDDOV lifecycle. They are typically seen in environments trying to implement DFSS on a standalone basis, independent of DMAIC. We believe that DFSS works best if it attempts to integrate the findings and tools of DMAIC into a comprehensive design. DMAIC measurement and analysis activities always lay the groundwork for effective design, and so we integrate them to illustrate and maximize that effect.

Tool Coverage

The tools in this course are all discussed in the broader context of the 4-tier Quality Function Deployment (QFD) model covered in Design for Six Sigma I course:

1. KANO Analysis - Collecting requirements using the voice of the customer often leaves two extreme categories of requirements missing or under represented. KANO Analysis characterizes additional requirements as delighters or dissatisfiers; the former representing opportunities to delight customers with unexpected functions and features, and the latter opportunities to recognize that many customer expectations go beyond the requirements that have been made explicit.

2. Triz Concept Selection - The Theory of Inventive Problem Solving (TRIZ) provides a creative approach for identifying creative and unexpected solutions to problems and design challenges that otherwise appear insurmountable. The 40 principles in TRIZ provide a shopping list of heuristics, one of which will illuminate a creative and relevant solution to virtually any design challenge.

3. Design for "X" - Any number of factors might be relevant to the definition of quality during a systems initiative. DFSS involves being able to incorporate a variety of factors "X" into a design, working toward a solution set that optimizes their interaction against customer needs and requirements. The "X" factors covered in this course include Flexibility, Efficiency, Useability, Portability, Reusability, and Scaleability. (Note: Testability, Reliability, and Maintainability were covered in Design for Six Sigma I.)

4. Design of Experiments (DOE) - Effectively designed experiments to define the relationships among key design parameters is essential to identifying optimal design solutions on multiple levels and scale of design.

5. Conjoint Analysis - When a range of possible design options is evaluated through a weighted analysis customer opinions and reactions to those design options, the relative worth of each individual design option can be calculated. Known as Conjoint Analysis - a specialized form of Design of Experiments - such analysis offers a cost-effective way to quickly prioritize a collection of non-exclusive design parameter settings.

6. Monte Carlo Analysis - Complex systems exhibit nonlinear behaviors because of the interaction of many variables, each of which varies within statistically predictable boundaries. Monte Carlo Analysis provides a simulation of key interactions based on the inherent probabilities associated with each variable in order to predict the frequency and variability of extreme or peak interactions. These simulations are used to ensure that worst case design conditions are accounted for in system designs so that rare interactions do not result in system failures.

7. Dashboards - In order to design systems that are self-aware and capable of self-correction, internal metrics must be made visible to system processes and controls. Collectively, such data defines a dashboard. Designing dashboards requires an analysis of critical metrics and the transfer functions that relate them.

8. Scorecards - To integrate the designed system into the broader Control Plan associated with the initiating DMAIC effort, critical metrics associated with critical to quality customer requirements must be made available for monitoring and control. Collectively, such metrics constitute a scorecard. Effective scorecard design builds upon the included dashboards and transfer functions while being guided by the pattern of design variables that tie back more directly to original customer requirements.