Challenge 

 I would like to talk about an opportunity in my organization where we applied the learning launch   process . I work in the global data center operations space for a large the firm and my group was   responsible for implementing new process improvement strategies every year . We wanted to decrease   the time to install new cable by 7 - 10 % or more and we believed that implementing a new design of fiber   cabling that was more robust , easier to handle and faster to install would help get us to that very target . 

 Selection 

 We knew that we needed a tool from the design thinking toolbox that would push us to test our ideas ,   frame our hypothesis , engage customers , quantify the impact and iterate . The learning launch tool was   the optimal one to assist us with this approach . As we had a few potential design ideas we knew that we   would have to experiment quickly and compare each alternative design and also to the current status   quo . The cable manufacturers had sample data that asserted efficiency improvements with the new   design of fiber cabling compared to the current one , however , it was under different conditions and an   unknown data set . What really made learning launch an applicable tool in our project was that we had   to quantify and test the hypothesis that we would see an efficiency improvement with experimentation ,   reducing the possibility for confirmation bias . We also knew that having pictures , anecdotes and   feedback would be important too so we coupled learning launch with visualization . We aligned on this   approach and proceeded . 

 Application 

 I start the process by answering the basic foundational questions of any problem - what is the problem   and what are we trying to accomplish . I was careful not to over index or over commit to a specific   number . As mentioned earlier , we had four designs that could be tested for efficiency improvements .   We had to come up with a way to test these designs . We answered the following learning launch   questions : 

 1 . Idea- implementing one of the 4 new cable manufacturer designs would yield a 7 - 10 % efficiency 

 gain 

 2 . Specific person - this was for global data techs   3 . Has specific need - to work as effectively and efficiently as possible delivering capacity to 

 customer 

 4 . Idea met the need- ?   5 . Better than current alternative- ? 

 The last two questions , we did n’t have the answer to initially . I proceeded to create pilot plans with the   right amount of samples , experimentation conditions and data tech users . In addition to capturing time   study data , in my pilot forms I also encouraged the techs to put feedback , use pictures and be as   descriptive as well to capture anecdotal feedback . After few months of testing we evaluated the results- both qualitatively and quantitatively . There was a clear winner - a figure 8 design sample that met the   need . It was quicker to install with 10 % efficiency gain and much easier to use , the users had very   positive feedback .   To re - validate the design I had a few more users test it . Their feedback echoed what   was previously stated and confirmed the improvement . In essence we were able to answer the   questions of the learning launch process 

 1 . Idea- implementing one of the 4 new cable manufacturer designs would yield a 7 - 10 % efficiency 

 gain 

 2 . Specific person - this was for global data techs   3 . Has specific need - to work as effectively and efficiently as possible delivering capacity to 

 customer 

 4 . Idea met the need - yes , it met the efficiency and usability targets   5 . Better than current alternative - yes , there was a 10 % improvement in efficiency , 

 Insight and Approach 

 The learning launch method proved to be a very valuable process in selecting the right design for the   installation job . The specific elements of the tool encouraged to develop hypothesis , experiments ,   validate and select the optimal answer . As mentioned in my pilot study I encouraged testers to use   pictures , photos and anecdotal feedback which in many ways added the visualization component to the   initiative this was also very important so that new testers and other stakeholders could see for   themselves the impact and confirm that the new design while faster was not more cumbersome to   handle . Hence learning launch coupled with visualization is a powerful tool . 

 I would say that in some cases that combing learning launch with six sigma and statistical qualification   tools can help especially since there are some elements in sampling and experimentation elements   involved that design thinking may not fully address . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Challenge 

 I would like to talk about an opportunity in my organization where we applied the learning launch   process . I work in the global data center operations space for a large the firm and my group was   responsible for implementing new process improvement strategies every year . We wanted to decrease   the time to install new cable by 7 - 10 % or more and we believed that implementing a new design of fiber   cabling that was more robust , easier to handle and faster to install would help get us to that very target . 

 Selection 

 We knew that we needed a tool from the design thinking toolbox that would push us to test our ideas ,   frame our hypothesis , engage customers , quantify the impact and iterate . The learning launch tool was   the optimal one to assist us with this approach . As we had a few potential design ideas we knew that we   would have to experiment quickly and compare each alternative design and also to the current status   quo . The cable manufacturers had sample data that asserted efficiency improvements with the new   design of fiber cabling compared to the current one , however , it was under different conditions and an   unknown data set . What really made learning launch an applicable tool in our project was that we had   to quantify and test the hypothesis that we would see an efficiency improvement with experimentation ,   reducing the possibility for confirmation bias . We also knew that having pictures , anecdotes and   feedback would be important too so we coupled learning launch with visualization . We aligned on this   approach and proceeded . 

 Application 

 I start the process by answering the basic foundational questions of any problem - what is the problem   and what are we trying to accomplish . I was careful not to over index or over commit to a specific   number . As mentioned earlier , we had four designs that could be tested for efficiency improvements .   We had to come up with a way to test these designs . We answered the following learning launch   questions : 

 1 . Idea- implementing one of the 4 new cable manufacturer designs would yield a 7 - 10 % efficiency 

 gain 

 2 . Specific person - this was for global data techs   3 . Has specific need - to work as effectively and efficiently as possible delivering capacity to 

 customer 

 4 . Idea met the need- ?   5 . Better than current alternative- ? 

 The last two questions , we did n’t have the answer to initially . I proceeded to create pilot plans with the   right amount of samples , experimentation conditions and data tech users . In addition to capturing time   study data , in my pilot forms I also encouraged the techs to put feedback , use pictures and be as   descriptive as well to capture anecdotal feedback . After few months of testing we evaluated the results- both qualitatively and quantitatively . There was a clear winner - a figure 8 design sample that met the   need . It was quicker to install with 10 % efficiency gain and much easier to use , the users had very   positive feedback .   To re - validate the design I had a few more users test it . Their feedback echoed what   was previously stated and confirmed the improvement . In essence we were able to answer the   questions of the learning launch process 

 1 . Idea- implementing one of the 4 new cable manufacturer designs would yield a 7 - 10 % efficiency 

 gain 

 2 . Specific person - this was for global data techs   3 . Has specific need - to work as effectively and efficiently as possible delivering capacity to 

 customer 

 4 . Idea met the need - yes , it met the efficiency and usability targets   5 . Better than current alternative - yes , there was a 10 % improvement in efficiency , 

 Insight and Approach 

 The learning launch method proved to be a very valuable process in selecting the right design for the   installation job . The specific elements of the tool encouraged to develop hypothesis , experiments ,   validate and select the optimal answer . As mentioned in my pilot study I encouraged testers to use   pictures , photos and anecdotal feedback which in many ways added the visualization component to the   initiative this was also very important so that new testers and other stakeholders could see for   themselves the impact and confirm that the new design while faster was not more cumbersome to   handle . Hence learning launch coupled with visualization is a powerful tool . 

 I would say that in some cases that combing learning launch with six sigma and statistical qualification   tools can help especially since there are some elements in sampling and experimentation elements   involved that design thinking may not fully address .
### Output: 