Learning Launch Toll for fairs

Challenge:

I studied Mechanical Engineering, and our final assignment for one course was to create a device  that could help the people that works at neighbourhood fairs with a problem that we could detect.  The main constraints of the project were that we had a little under three months, and the solution  had to be affordable for the customers.

My team found that the volume of organic waste produced at the fair was a problem for many of  the stakeholders because it was difficult to handle it, and if they left the street with trash, it could  result in a fine that would hurt them financially.

Selection

By using design thinking tools, such as an ethnographic approach and visualization, we came to  the concept of "reducing the volume" of the waste as a solution to their problem. So we  developed different alternatives on how to reduce the size of this type of garbage.

Through the use of the learning launch tool, we expected to get the feedback on the early  prototypes we manufactured, which focused on different things, like size, weight, cost, reduction  mechanism and user interaction.

Application

To test our different prototypes, created different prototypes that we grouped considering the  feature we wanted to test. The first time we did a test with the user, the difference between the  models was substantial. For example, the mechanisms for the reduction of the volume would be  a type of shredder with cylinders that you would put over the trash can, or a machine that  smashed the trash with pistons and makes blocks or even a rotating axis that would cut the trash.

We invited people from various fairs to use the devices, and we recorded the interactions, the  commentaries and the suggestions they had. Because the designs focused on different aspects of  the machine, each person needed to experiment with as many models as possible. This way, we  could check our assumptions, such as the weight the users tolerated, the more intuitive  mechanism for them, and if it was within their budget or willingness to pay.

We did a total of three iterations with different groups of people (around 20 each time). The first  test helped us to discover the traits that the customers prefered, for the second one we did other  cheap prototypes, with small changes (mostly on the shape and the weight) on the base design of  the selected traits of the first iteration. Finally, in the third iteration, we combined the different  features in a single prototype and tested it again to see if the sum of the selected parts of the  previous tests was well received.

Insight

The results were pleasing for the customer because they were participants in the process for the  creation of the device. They choose each iteration, what was the type of solution that was more  comfortable for them, and by doing that, they saved us a lot of time and effort. Because if we had  not tested the devices so often, we would have developed prototypes that were too complex, had  too many attributes to rate at the same time, and probably would have failed to fulfil the  requirements of the stakeholders.

Having a broad look at the problem, helped us to deliver many different alternatives for the first  iteration. This way, we were able to show vastly different alternatives and make them compete  with the final user as the centre of the next iterations. I think this also was key to the success of  this assignment.

Finally, the use of low-cost and fast-building models was important when creating a product. The  idea of narrowing the designs, to what your customer feels comfortable with was essential to  deliver the product.

Approach

The main problem that we faced was that because the groups were so large, getting people to  agree on some of the different features that they liked was no easy task, and many times there  was not a total agreement at all.

In the future, when we do more field tests with the learning launch tool, I think it would be  beneficial to have smaller groups for the tests. This way, people are more comfortable  investigating the devices and feel free to speak what they think, and the data we can collect  would have a higher quality and diversity.


Learning Launch Toll for fairs Challenge : I studied Mechanical Engineering , and our final assignment for one course was to create a device that could help the people that works at neighbourhood fairs with a problem that we could detect . The main constraints of the project were that we had a little under three months , and the solution had to be affordable for the customers . My team found that the volume of organic waste produced at the fair was a problem for many of the stakeholders because it was difficult to handle it , and if they left the street with trash , it could result in a fine that would hurt them financially . Selection By using design thinking tools , such as an ethnographic approach and visualization , we came to the concept of " reducing the volume " of the waste as a solution to their problem . So we developed different alternatives on how to reduce the size of this type of garbage . Through the use of the learning launch tool , we expected to get the feedback on the early prototypes we manufactured , which focused on different things , like size , weight , cost , reduction mechanism and user interaction . Application To test our different prototypes , created different prototypes that we grouped considering the feature we wanted to test . The first time we did a test with the user , the difference between the models was substantial . For example , the mechanisms for the reduction of the volume would be a type of shredder with cylinders that you would put over the trash can , or a machine that smashed the trash with pistons and makes blocks or even a rotating axis that would cut the trash . We invited people from various fairs to use the devices , and we recorded the interactions , the commentaries and the suggestions they had . Because the designs focused on different aspects of the machine , each person needed to experiment with as many models as possible . This way , we could check our assumptions , such as the weight the users tolerated , the more intuitive mechanism for them , and if it was within their budget or willingness to pay . We did a total of three iterations with different groups of people ( around 20 each time ) . The first test helped us to discover the traits that the customers prefered , for the second one we did other cheap prototypes , with small changes ( mostly on the shape and the weight ) on the base design of the selected traits of the first iteration . Finally , in the third iteration , we combined the different features in a single prototype and tested it again to see if the sum of the selected parts of the previous tests was well received . Insight The results were pleasing for the customer because they were participants in the process for the creation of the device . They choose each iteration , what was the type of solution that was more comfortable for them , and by doing that , they saved us a lot of time and effort . Because if we had not tested the devices so often , we would have developed prototypes that were too complex , had too many attributes to rate at the same time , and probably would have failed to fulfil the requirements of the stakeholders . Having a broad look at the problem , helped us to deliver many different alternatives for the first iteration . This way , we were able to show vastly different alternatives and make them compete with the final user as the centre of the next iterations . I think this also was key to the success of this assignment . Finally , the use of low - cost and fast - building models was important when creating a product . The idea of narrowing the designs , to what your customer feels comfortable with was essential to deliver the product . Approach The main problem that we faced was that because the groups were so large , getting people to agree on some of the different features that they liked was no easy task , and many times there was not a total agreement at all . In the future , when we do more field tests with the learning launch tool , I think it would be beneficial to have smaller groups for the tests . This way , people are more comfortable investigating the devices and feel free to speak what they think , and the data we can collect would have a higher quality and diversity .