Learning Launch Tool

1. Challenge:

Working as a leader in R&D organization of machining software company, I am never short of  ideas in our group. Hundreds are generated when we get team together to brainstorm on any  topic. Challenge is always selecting right few to proceed with and doing that selection without  technology, execution or individual biases.

In this specific case, we had gathered ideas on simplifying steps in providing tolerance values on  the part, so that machining allowance / errors wont exceed the limits even after considering worst  possible scenario. Let me take help of below figure to explain the process. Each plate has  tolerance as mentioned below. If you take worst case, i.e. each tolerance limit staying on positive  max, then overall tolerance for X will be summation of all positive worst-case tolerances. In  assembly, typically you have overall tolerance and then need to reverse calculate permissible  tolerance for each part to stay within limits.

Now imagine complex assembly with multiple parts which have holes, fillets, or shapes which are  not rectangular. Tolerance calculation keeps becoming complex and it becomes very  cumbersome process for user of our software to input it meaningfully. In machining, you have to  compute and adjust for these tolerances to meet overall fitment of the part within assembly for  given functionality.

2. Selection:

Team came out with multiple approaches that can possibly simplify this interaction and we  shortlisted three of them as most promising based on our internal criteria. These approaches  varied on controls it provided to user to finetune their inputs for various scenario (maximum  functionality) versus asking minimum intervention from user and product making assumptions on  their behalf (minimum UI).

Then we decided to select Learning Launch tool to experiment with these three approaches to  come out with best possible one for final implementation.

3. Application:

We build quick and dirty simulated prototype of these approaches in less than 2 weeks of efforts  and exposed them to ~18 customers / users who had volunteered to work with us to improve our  productline.

We developed ‘customer satisfaction index’ (CSI) survey for evaluation with criteria like if  proposed solutions meets their need, is it easier to use, what they most liked about this particular  approach. We exposed our quick and dirty simulated prototypes to them through cloud VM and  also observed their usage through video calls. We asked them to fill these CSI survey after  roughly allowing 20 mins play time for them. Each user had to spend roughly 30 min of its total  time for the same including documenting their feedback for ~10min.

4. Insight:

This exercise resulted in surprise findings for us. None of the three was clear winner in terms of  distinct leader in CSI index. Users like simplicity of the approach where we didn’t ask them too  many inputs, but wanted deeper control over functionality for their real world parts and their  variations. Due to insight gained from our learning launch experiment, we developed new  approach altogether where we provided simplicity of making assumptions on behalf of users to  generate default output for most cases, but then allowing to go back and launch detailed controls  for few cases. This detailed UI was required to be invoked only in rare cases to provide different  values than what software has calculated.

This approach was like combining best of all worlds – no UI and default output for 80% of  scenarios, but ability to go back provide details for 20% cases where unique inputs are required,  and more functionality needs to be exposed. Our users loved this approach when it was launched  finally after few more confirmation trials.

5. Approach:

Clearly keeping open mind in Learning Launch phase and allowing customers / users provide all  possible feedback is important. We quickly learned none of our shortlisted ideas were great, but  could develop new one based on feedback from these experiment that allowed us to provide  innovative solution that could help our customer.

References:

1. Above figure, examples taken from real work done during project and publicly published material

related to this product at: http://hogan.com/app/categoriesindex.php


Learning Launch Tool 1 . Challenge : Working as a leader in R&D organization of machining software company , I am never short of ideas in our group . Hundreds are generated when we get team together to brainstorm on any topic . Challenge is always selecting right few to proceed with and doing that selection without technology , execution or individual biases . In this specific case , we had gathered ideas on simplifying steps in providing tolerance values on the part , so that machining allowance / errors wo nt exceed the limits even after considering worst possible scenario . Let me take help of below figure to explain the process . Each plate has tolerance as mentioned below . If you take worst case , i.e. each tolerance limit staying on positive max , then overall tolerance for X will be summation of all positive worst - case tolerances . In assembly , typically you have overall tolerance and then need to reverse calculate permissible tolerance for each part to stay within limits . Now imagine complex assembly with multiple parts which have holes , fillets , or shapes which are not rectangular . Tolerance calculation keeps becoming complex and it becomes very cumbersome process for user of our software to input it meaningfully . In machining , you have to compute and adjust for these tolerances to meet overall fitment of the part within assembly for given functionality . 2 . Selection : Team came out with multiple approaches that can possibly simplify this interaction and we shortlisted three of them as most promising based on our internal criteria . These approaches varied on controls it provided to user to finetune their inputs for various scenario ( maximum functionality ) versus asking minimum intervention from user and product making assumptions on their behalf ( minimum UI ) . Then we decided to select Learning Launch tool to experiment with these three approaches to come out with best possible one for final implementation . 3 . Application : We build quick and dirty simulated prototype of these approaches in less than 2 weeks of efforts and exposed them to ~18 customers / users who had volunteered to work with us to improve our productline . We developed ‘ customer satisfaction index ’ ( CSI ) survey for evaluation with criteria like if proposed solutions meets their need , is it easier to use , what they most liked about this particular approach . We exposed our quick and dirty simulated prototypes to them through cloud VM and also observed their usage through video calls . We asked them to fill these CSI survey after roughly allowing 20 mins play time for them . Each user had to spend roughly 30 min of its total time for the same including documenting their feedback for ~10min . 4 . Insight : This exercise resulted in surprise findings for us . None of the three was clear winner in terms of distinct leader in CSI index . Users like simplicity of the approach where we did n’t ask them too many inputs , but wanted deeper control over functionality for their real world parts and their variations . Due to insight gained from our learning launch experiment , we developed new approach altogether where we provided simplicity of making assumptions on behalf of users to generate default output for most cases , but then allowing to go back and launch detailed controls for few cases . This detailed UI was required to be invoked only in rare cases to provide different values than what software has calculated . This approach was like combining best of all worlds – no UI and default output for 80 % of scenarios , but ability to go back provide details for 20 % cases where unique inputs are required , and more functionality needs to be exposed . Our users loved this approach when it was launched finally after few more confirmation trials . 5 . Approach : Clearly keeping open mind in Learning Launch phase and allowing customers / users provide all possible feedback is important . We quickly learned none of our shortlisted ideas were great , but could develop new one based on feedback from these experiment that allowed us to provide innovative solution that could help our customer . References : 1 . Above figure , examples taken from real work done during project and publicly published material related to this product at : http://hogan.com/app/categoriesindex.php