Performance and Perception Expectations Technique

Ideal Solution Elements (ISEs) are the features or functions that your innovation must have to meet customer expectations (for more information, see Performance and Perception Expectations, Technique 30). 

To undertake robust design, you ll definitely need help from an experienced engineer or statistician familiar with this approach to testing and analysis. You ll also need to know how to apply several other techniques in this book including Performance and Perception Expectations (Technique 30), Axiomatic Design (Technique 31), Design FMEA (Technique 40), and Design of Experiments (Technique 50). 

Robust design starts with conceptual system, design, during which you define the ideal performance for your innovation, and make a list of measurable system features that are critical to the customer. You may have already done this using Performance and Perception Expectations (Technique 30). For our skin patch example, the ideal design will consistently dispense a dose of 1.0 mg/hr 0.2, regardless of patient fat content, skin condition, or other environmental factors. 

For more on customer types, see Performance and Perception Expectations (Technique 30). 

Do not confuse this exercise with cataloguing performance and perception expectations (see Technique 30), which are solution-specific performance characteristics, such as candle bum time (target = 32 hours), or PC battery life (target = 6 hours). Outcome expectations are solution-neutral and reside at a higher level they are JTBD-specific desires, such as maximize duration of illumination (using any solution), or maximize operating time (in whatever way possible). 

Customers have three types of expectations—outcome, performance, and perception. Outcome expectations (see Technique 2), are specific to the/oZ the customer wants to get done. Performance and perception expectations,... 

Customer attributes are synonymous with performance and perception expectations, so see Technique 30 to help with forming the front end of your axiomatic design activity. 

Use Paired Comparison Analysis when you need to compare either more upstream innovation ideas or more downstream design concepts. This technique is especially helpful when you don t have objective data regarding how different ideas could meet your customers outcome expectations (see Technique 2), or when you re uncertain about how different design concepts could meet customer performance and perception expectations (see Technique 30). 

If you want a more rigorous method of comparing multiple design concepts against performance and perception expectations, use a Pugh Matrix (Technique 36). 

Even when it s not a life-or-death situation, variation leads to customer dissatisfaction and the inability to reliably meet performance and perception expectations (see Technique 30). As you near the completion of your innovative solution design, you can use MSA to identify and correct measurement system error, resulting in a higher quality, more reliable design. To be successful with MSA, you will need some experience with statistics. 

List the customer-driven outputs across the top of the C E Matrix (Exhibit 54. i). The outputs should be synonymous with the customers performance and perception expectations (see Technique 30 for more information). Underneath each output, rank it on a scale of 1 (less important to the customer) to 10 (more important). If it takes too long to get consensus, use 1, 5, and 9 for the rankings to give the outputs more relative separation. 

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