Example Reflection – Learning Launch 

 Challenge     I am a member of my company ’s process development team for one of our core products . Our   customers gave us the challenge to aggressively improve the performance of a widely adopted product   in the field . This heavily used product has been used in the field for over a decade and has reached the   limits of its performance as customer specifications have tightened . Our challenge is to develop a   hardware improvement package that could retrofit the existing product to improve the performance of   the tool . Our customers have given us 6 months deadline to develop , test , and validate improved   performance of the new hardware in order for them to consider adoption of the new part . The   hardware improvement package has constraints due to the existing product framework in the field , so   we are limited in the manners that we may modify the existing tool .      Selection     Because the process , hardware , and modeling team had already done thorough characterization of the   existing product , we already had ideas on how we might want to improve the hardware . We decided to   use the learning launch tool because of the time constraint placed on us by the customer . As mentioned   in the video , “ the key here is to do experiments that are fast and cheap ” . So , we used the learning   launch tool to test multiple hypotheses and paths to improve the hardware . Due to time delays of   acquiring hardware , we knew that we had to model , order , and test competing hypotheses of hardware   modifications in parallel to find the best path to achieve our performance benefit and meet the time   constraint by the customer .     Application     In order to apply the learning launch tool to our hardware improvement project , we gathered all the   relevant stakeholders in a meeting . During this meeting , each stakeholder proposed their individual   hypothesis on how to improve the hardware and a rough sketch of the proposed hardware design . This   resulted in a list of 8 - 10 disparate modifications of the existing hardware with reasons on why each   potential modification would deliver the desired outcome . None of the proposed modifications were   meant to be a final product solution ; they were only meant to aggressively test each hypothesis to help   us determine which path would lead us in the right direction for process improvement .     After the initial discussion , we had our modeling group run coarse ( cheap and fast ) simulations which   would give us a better understanding of which hardware modification plan showed a potential for   performance improvement . After 2 weeks of running simulations on all of the different hardware   modifications , we met again to review the simulation data and refine our ideas . This meeting resulted in   narrowing our potential hardware modifications to four separate ideas . We then had the mechanical   engineers working on the project design the hardware part needed to test each hypothesis .     After all of the designs were complete , the team met one last time to align on the final details of the   designs . After a few slight modifications , we sent the designs out for manufacturing . We chose to use   the learning launch tool to test multiple ideas in parallel in order to find our pathway as quick as   possible rather than simulating , designing , and testing ideas in a serial manner . 

 Upon receiving all the parts , we tested them as quickly as possible over a wide process window to   determine which pathways would give us the most improvement and which pathway lead us in the   wrong direction . As the video mentions , this was our method of generating data as quickly as possible to   help guide our development of the final hardware improvement package that we would eventually   deliver to the customer .     Insight :     By using the learning launch tool , we were able to quickly determine which hypotheses were correct and   delivered performance improvement and which hypotheses were invalid . The learning launch tool   helped us drop our bias on which method of hardware modification was best and enabled us to rapidly   generate data to test multiple hardware modifications . In testing our hardware modifications , we were   able to narrow down our initial 8 - 10 ideas into two potential paths that would both deliver performance   improvement . Also , in generating data using all the disparate hardware modifications designs , we were   able to gain a better understanding of the overall process and hardware . The final solution we ended up   providing to the customer was a combination of the two paths that showed improvement and met the   aggressive specifications established by the customer in the 6 - month deadline .     Approach     Overall , I was quite pleased with the success of the implementation of the learning launch tool .   However , if I were to use this tool again , I would invite people outside of the stakeholders for hypothesis   generation . Because we were all surprised by some of the findings and data generated , I think we would   have benefited by getting some out - of - the - box ideas from people who were less familiar , and therefore   biased , by our existing knowledge . This would have enabled us to do some inexpensive and rapid   simulations of concepts that may have not been obvious or expected . 


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: Example Reflection – Learning Launch 

 Challenge     I am a member of my company ’s process development team for one of our core products . Our   customers gave us the challenge to aggressively improve the performance of a widely adopted product   in the field . This heavily used product has been used in the field for over a decade and has reached the   limits of its performance as customer specifications have tightened . Our challenge is to develop a   hardware improvement package that could retrofit the existing product to improve the performance of   the tool . Our customers have given us 6 months deadline to develop , test , and validate improved   performance of the new hardware in order for them to consider adoption of the new part . The   hardware improvement package has constraints due to the existing product framework in the field , so   we are limited in the manners that we may modify the existing tool .      Selection     Because the process , hardware , and modeling team had already done thorough characterization of the   existing product , we already had ideas on how we might want to improve the hardware . We decided to   use the learning launch tool because of the time constraint placed on us by the customer . As mentioned   in the video , “ the key here is to do experiments that are fast and cheap ” . So , we used the learning   launch tool to test multiple hypotheses and paths to improve the hardware . Due to time delays of   acquiring hardware , we knew that we had to model , order , and test competing hypotheses of hardware   modifications in parallel to find the best path to achieve our performance benefit and meet the time   constraint by the customer .     Application     In order to apply the learning launch tool to our hardware improvement project , we gathered all the   relevant stakeholders in a meeting . During this meeting , each stakeholder proposed their individual   hypothesis on how to improve the hardware and a rough sketch of the proposed hardware design . This   resulted in a list of 8 - 10 disparate modifications of the existing hardware with reasons on why each   potential modification would deliver the desired outcome . None of the proposed modifications were   meant to be a final product solution ; they were only meant to aggressively test each hypothesis to help   us determine which path would lead us in the right direction for process improvement .     After the initial discussion , we had our modeling group run coarse ( cheap and fast ) simulations which   would give us a better understanding of which hardware modification plan showed a potential for   performance improvement . After 2 weeks of running simulations on all of the different hardware   modifications , we met again to review the simulation data and refine our ideas . This meeting resulted in   narrowing our potential hardware modifications to four separate ideas . We then had the mechanical   engineers working on the project design the hardware part needed to test each hypothesis .     After all of the designs were complete , the team met one last time to align on the final details of the   designs . After a few slight modifications , we sent the designs out for manufacturing . We chose to use   the learning launch tool to test multiple ideas in parallel in order to find our pathway as quick as   possible rather than simulating , designing , and testing ideas in a serial manner . 

 Upon receiving all the parts , we tested them as quickly as possible over a wide process window to   determine which pathways would give us the most improvement and which pathway lead us in the   wrong direction . As the video mentions , this was our method of generating data as quickly as possible to   help guide our development of the final hardware improvement package that we would eventually   deliver to the customer .     Insight :     By using the learning launch tool , we were able to quickly determine which hypotheses were correct and   delivered performance improvement and which hypotheses were invalid . The learning launch tool   helped us drop our bias on which method of hardware modification was best and enabled us to rapidly   generate data to test multiple hardware modifications . In testing our hardware modifications , we were   able to narrow down our initial 8 - 10 ideas into two potential paths that would both deliver performance   improvement . Also , in generating data using all the disparate hardware modifications designs , we were   able to gain a better understanding of the overall process and hardware . The final solution we ended up   providing to the customer was a combination of the two paths that showed improvement and met the   aggressive specifications established by the customer in the 6 - month deadline .     Approach     Overall , I was quite pleased with the success of the implementation of the learning launch tool .   However , if I were to use this tool again , I would invite people outside of the stakeholders for hypothesis   generation . Because we were all surprised by some of the findings and data generated , I think we would   have benefited by getting some out - of - the - box ideas from people who were less familiar , and therefore   biased , by our existing knowledge . This would have enabled us to do some inexpensive and rapid   simulations of concepts that may have not been obvious or expected .
### Output: 