An Introduction to Design and Development Design for Six Sigma An Introduction to Design and Development Introduction This course is designed to give you an overview explanation of Design for Six Sigma, and how these concepts lead to the implementation of an effective design and development process for both manufacturing and service This class is best taught using your company’s specific examples, allowing for interaction among the students Feel free to modify and change this course any way you like. Simply click View  Master  Master Slide to edit the master slide so that all slides show your company logo and name. You may also teach it exactly as it is. We recommend about 4 hours to teach the course. We recommend that someone with prior experience and training with DfSS teach the course. If not being used as a course, valuable information can still be obtained by following through the presentation and reading the notes section. BPI provides more specific training on Lean and Six Sigma tools and concepts, if you aren’t familiar with these already. If you have any questions, please visit or contact us at http://www.biz-pi.com

What is Design for Six Sigma (DfSS)? Benefits of DfSS DMADV Tools Course Content Introduction What is Six Sigma? What is Design for Six Sigma (DfSS)? Benefits of DfSS DMADV Tools Deployment Structure Summary References Introduction What is Six Sigma? What is Design for Six Sigma (DfSS)? Benefits of DfSS DMADV Tools Deployment Structure Summary References

Translate into CTQ’s Customer Voice: “Fast Service!” CTQ: Cycle Time How to Measure: Time Client Requests To The Time Client Receives Service Let’s summarize: Your customer gives you a need such as “quick service” That gets translated that into a metric called “cycle time” A more specific measurement is defined as Time client requests service until the time client receives the service The expectation is established by the customer that this service should occur in about 3 days, with a max of no more than 5 days Target/Limits: Goal of 3 days, with max upper limit of 5 days

Measurement System Analysis Example: CTQ = Cell Phone Luminance Ex: Take 2 technicians, using same photometer, measure 4 cell phones (3 times each) Study will: compare technicians to each other Measure repeatability within each technician Determine if overall measurement variation is acceptable for this process and spec limits Let’s continue our cell phone example. Let’s say that the luminance (brightness) of the cell phone display is a CTQ. In order to validate our ability to measure luminance, we take 2 technicians, using the same photometer, and measure 4 different cell phones 3 times each by each technician. This study will allow us to compare how close the technician measurements are to each other (reproducibility), and how well they can repeat the luminance reading on each repeat reading (repeatability). This will not result in perfect and exact measurements. There will probably be some differences, so the study will also look at the spec limit range to determine if the overall measurement variation (repeatability and reproducibility) is acceptable or not.

Technical Requirements Customer Requirements Roof of Correlation NEGATIVE CORRELATION! “More Functions” is good, but it will reduce “Battery Life” How to resolve this contradiction? TRIZ! ROOF Technical Requirements Let’s now go back to the QFD. In the roof, we look at how the requirements relate to each other. The dash line means no correlation/interaction. The plus sign means a positive correlation (good thing), but a downward arrow implies a contradiction or interaction. In other words, If I increase the number of features/services, it will probably run down the battery much faster. Therefore, how can I improve both requirements, if they work against each other? TRIZ Customer Requirements INTER RELATIONSHIPS Planning Matrix Product Targets

DOE Example Send postcards during week, letters on weekend To look at interactions and understand them, we see that the day in which they receive the letter has an impact on response rate, but it depends on another variable (interaction). In this case, the format of the mailer. Therefore, if the postcards are sent during the week, and the letters are sent on the weekend, then the response rate is optimal. Another interaction exists with personalized and format. If the letter is personalized, the response is better, but not on the postcard. This information can be used to minimize production costs of the postcards, instead of spending extra on personalized features. DOE is the best analysis tool for studying and determining interactions. Personalize the letter, not the postcard INTERACTION PLOTS DOE IS BEST TOOL TO TEST FOR INTERACTIONS!

Helps identify shifts and trends Machine Wear Problem Let’s look at an example. Our process for controlling machine wear has set control limits (based upon historical data) set at 9.4 and 10.6. Somewhere around the 20th of October, the machine starts to experience a problem, and we see that our chart starts to trend upward, and it eventually exceeds our upper control limit. This is the indication needed to highlight that there is most likely a problem that needs to be investigated. Without the control chart, it might take much longer (and at more cost) to detect the same issue.

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