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How to measure Wishes - The QFD Method in the ExtraEnergy Test

ExtraEnergy started using the Quality Function Deployment (QFD) method for evaluating e-vehicles at the end of 2009. The QFD method matches a list of consumer wishes to (...)

(...) values measured by ExtraEnergy in its test. This makes subjective and supposedly difficult-to-measure wishes for pedelecs and e-bikes objectively measurable. The results are then summarized in the c-factor, where "c" stands for "client-satisfaction".

At first glance the wishes (or demands) customers have for pedelecs seem varied and subjective. But, ExtraEnergy managed to reduce the customer wishes down to fourteen, thanks to its years of experience, surveys, and visionary thinking. The 27 values measured in the ExtraEnergy test were then matched to the 14 wishes , thereby uniting (or merging) subjective values with objective measurements.

For example, the wish for better support was matched with the s-factor (support factor) calculated in the test from the measured values. ExtraEnergy introduced the s-factor in 1995 and has since established the indicator in numerous tests as a strong guideline. In the latest test the three s-factors were calculated for each vehicles - one for the tour sub-section of the test track, another for the hill section and a third for the city section. The s-factor tour was matched to the customer wish for support, as it best represents the high support required by customers. Support on the hill is only experienced by cyclists in special, difficult conditions. Therefore, the s-factor hill cannot substitute for general support - only for the wish to have top-load support. If a pedelec supports strongly on a hill, it also supports well when accelerating, with cargo, or when taking off. These are all situations demanding top-load support. Some wishes are also matched to several measured values. The 3 portability tests in the ergonomic test together provide an answer to the wish that the vehicle should be light. The ergonomic test is a user test in which the suitability for everyday use is measured. Apart from portability tests, there are battery removal and replacement and short cycling tests, to name a few. All the values calculated for vehicles in the test are based on both qualitative and quantitative information gathered in the ergonomic and riding tests.

Since the market for electric vehicles develops constantly, pedelecs and e-bikes are becoming more diversified and differentiated, meaning there are more bikes for more target groups. A pedelec cannot satisfy all 14 customer demands fully, ie. 100 %. Instead, it satisfies some demands better than others. That doesn`t mean it is worse than other pedelecs, but only that it is better (or maybe even best) suited for a particular use.

The different uses were distilled into 11 product groups by the test team. The categories were created from exclusion criteria (eg. minimum levels for support, elaborateness of fittings, such as the display). In addition, all groups contain the same 14 customer wishes, but with different weights for the wishes, reflecting how important a specific feature is for a specific user group. For example, sporty cyclists emphasize speed more than a family man, who needs a very reliable pedelec to bring him and his child home safely. So, the Sport Pedelec has a heavier weighting for the wish for speed, than the Family Pedlec, which weighs peak-load support and reliability heavier.

In this way the 14 needs were prioritized within each of the 11 product groups. This was done by comparing pairs of wishes. One wish was compared to one other wish - and then the next one, and the next. Until the particular wish was compared to 13 others - one after the other. Every comparison produced one of three possible answers, namely more important (2), equally important (1) or unimportant (0). The points awarded (2/1/0) were then entered into a matrix and the rows summarized to produce an importance ranking of wishes for each product group.

A relations matrix is at the core of the QFD method. It shows all the relationships and performance levels between wishes, or demands (WHAT) and the actual product characteristics (HOW), as calculated from the test data. A strong correlation is given 9 points (a performance of 100 %), no correlation gets zero points and a negative correlation -9. For example, the vehicle with the best s-factor tour receives 9 points and all other bikes are measured against that bike. Thanks to the logarithmic transfer, the differences between the e-vehicles become clearer. It also becomes possible to allocate a number to the correlation between test bike and customer demand, even though the values were previously given in kilometers per hour, school grades, or similar.

The standardized numbers are then multiplied with the weights of a particular group. This is done for every product group. Should several values match with a particular wish, the importance points are divided between them. For instance, if the wish for higher support in the group Family Pedelec carries a value of 7, and assigned as the representative value for s-factor tour and s-factor city, the values are calculated in this example by multiplying with 1.4 and 5.6. In this example the Family Pedelec is primarily to be used in city environments.

By adding all points of a specific vehicle (that is to say, how it fulfills individual wishes) and doing that for all vehicles in the product group, a product group winner is determined, and the result expressed in a c-factor. This c-factor (client satisfaction factor) runs from 1 to 10, with 10 going to the pedelec in a particular group which satisfies consumer wishes best. All other vehicles in this group are then measured against the winner and the test seals allocated accordingly. A c-factor of 8 to 10 receives a "Very Good" seal, while 5 to 7 receives a "Good" seal.

>> Product Groups

Text: Frieder Herb and Nora Manthey
Photo: Harry F. Neumann

Translation: Christoffel Volschenk

Online publication: Angela Budde

27. March 2012



7 - 12 September 2021, IAA, Munich, Testtrack

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