Green Belt Certification Employs a Set of Tools and Methodologies
Published by Jacquelyn Donner in Leadership, 1 month 3 weeks 16 hours 4 minutes 37 seconds ago
Design for Six Sigma employs a set of tools and methodologies in the design of new products or services and their supporting processes. This is to ensure that the outcome remains as simple and cost effective as possible while serving the needs of the customer.
Products and services are defined such that they can be produced at a Six Sigma level utilizing processes that are operating at their optimum technological capability level of entitlement. Empowered cross functional teams are the most effective to utilize all applicable tools.
The guiding principles ensure product/service definition decisions are data based, judgment guided, and experience guarded, rather than the traditional mostly judgment based all within an IPDT environment.
Customer requirements must be rigorously identified, prioritized, and translated into design requirements rather than just making broad-based assumptions. This allows proper cost and performance trade-off decisions to be made. Specific component or process step targets can then be established as a basis for product/service or process definition. Some of the primary application tools are Quality Function Deployment, Design for Manufacturing and Assembly Robust Design, Risk Analysis, and Key Critical Characteristics key characteristics in engineering. The DFSS tools can be used to design products, services, and manufacturing and transactional processes to meet customer requirements with minimum steps and parts to minimize opportunities for defect and improve sigma levels.
How would we use DFSS for a transactional process? asks a project team member. The Black Belt says Well, if we design a transactional process up-front with a minimum level of process steps, all the while meeting customer requirements, we will reduce the opportunities for defects. The team member says Yes, I remember, the goal is to decrease the probability of defects occurring on each unit, item, or service provided. In order to reduce the likelihood of the event that a unit will not meet the specification of a CT, we must reduce the opportunities for defects and the events we measure that provide a chance of not meeting a customer requirement.
So, consider the various processes that exist functionally and across sites to collect and maintain employee information says the Black Belt. The existence of many processes and process steps increases opportunities for defects in information accuracy. Therefore, the rolled through yield of correct information is not as high as it should be and we need to design a simple, standard process. After identifying high-level process maps and requirements, we could standardize and optimize the selected process to meet the established targets for information accuracy. In fact, if we have data for each process step, we could use various statistical tools to assign estimated RTY targets for each step. In this case, statistical analysis techniques would enable us to determine which steps are not adding value to the process. We may also want to look at cycle-time. Here, process simulation tools would be a very effective way to optimize cycle-time in the process. We could also determine how to make this design more robust by determining important interactions between input variables. We can identify variables that cause uncontrollable variation and then design the process to be robust to these variables. As you can see, the possibilities are endless.
Products and services are defined such that they can be produced at a Six Sigma level utilizing processes that are operating at their optimum technological capability level of entitlement. Empowered cross functional teams are the most effective to utilize all applicable tools.
The guiding principles ensure product/service definition decisions are data based, judgment guided, and experience guarded, rather than the traditional mostly judgment based all within an IPDT environment.
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Customer requirements must be rigorously identified, prioritized, and translated into design requirements rather than just making broad-based assumptions. This allows proper cost and performance trade-off decisions to be made. Specific component or process step targets can then be established as a basis for product/service or process definition. Some of the primary application tools are Quality Function Deployment, Design for Manufacturing and Assembly Robust Design, Risk Analysis, and Key Critical Characteristics key characteristics in engineering. The DFSS tools can be used to design products, services, and manufacturing and transactional processes to meet customer requirements with minimum steps and parts to minimize opportunities for defect and improve sigma levels.
How would we use DFSS for a transactional process? asks a project team member. The Black Belt says Well, if we design a transactional process up-front with a minimum level of process steps, all the while meeting customer requirements, we will reduce the opportunities for defects. The team member says Yes, I remember, the goal is to decrease the probability of defects occurring on each unit, item, or service provided. In order to reduce the likelihood of the event that a unit will not meet the specification of a CT, we must reduce the opportunities for defects and the events we measure that provide a chance of not meeting a customer requirement.
So, consider the various processes that exist functionally and across sites to collect and maintain employee information says the Black Belt. The existence of many processes and process steps increases opportunities for defects in information accuracy. Therefore, the rolled through yield of correct information is not as high as it should be and we need to design a simple, standard process. After identifying high-level process maps and requirements, we could standardize and optimize the selected process to meet the established targets for information accuracy. In fact, if we have data for each process step, we could use various statistical tools to assign estimated RTY targets for each step. In this case, statistical analysis techniques would enable us to determine which steps are not adding value to the process. We may also want to look at cycle-time. Here, process simulation tools would be a very effective way to optimize cycle-time in the process. We could also determine how to make this design more robust by determining important interactions between input variables. We can identify variables that cause uncontrollable variation and then design the process to be robust to these variables. As you can see, the possibilities are endless.
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