Design Thinking Reflection Using Learning Launch Tool 

 Challenge 

 I work in a bank organization as a resilience team member . Our team mainly provide solutions for   recurring problems and the personnel who affected by those problems . Our challenge is increase the   efficiency and effort management for the branch personnel of the bank by identifying their problems   and create quick / automatic solutions for them . In our scope there are 900 branches and 1800   personnel exist . All the problems encountered in branches need to be solved by these personnel at   most within one week . There are two personnel responsible in each branch . One works for the   customers of the bank and the other one works with the ATM devices in the branch building . There are   30 million customers and 5 thousand ATM devices within branch buildings . We need to quickly figure   out what kind of problems we might see , which are related to branch customers and/or which are   related to the ATM devices . When we determine the problems we will focus on possible solutions and   precautions for the problems before they cause bigger ones . 

 Selection 

 Since there are many customers and devices per branch personnel , being quick has has a crucial role   in this job . We must face and solve the problems when they first emerge and less customers effected .   Thus , we will use learning launch method , this ensure our problem detection and create solution ideas   will be easily implemented and experimented quickly . Also this tool will provide us fast and cheap test   environment . 

 Application 

 In the first learning launch we asked these questions . Who is our customer ? As internal , branch   personnel , as external , customers of the bank . What are their main problems ?   For personnel , ATM   device problems that are ATM balance difference , ATM money loss . For customers , ATM money jam   while performing transaction . How we can detect these problems ? The best option for our assumption   is would be automatically problem detection mechanism . The faster we detect the problem the faster   we solve it . So , the customers would not have always need to report problems to us . We would have   been informed about the problem even the solving progress could have started . After all , we have   learned that from Chapter-4 , “ The purpose of a learning launch is to get the truth and then the truth   either confirms or dis - confirms your assumptions . ” Our assumption seemed more likely be true , so we   could go on second phase of the learning launch . Hereby , we could test our assumption by measuring   problem detection and solving times , also effort and time effiency of the personnel , and problem   numbers reported by customers . For our second learning launch , we determined   an experiment group   consists of   ten different branches to make this progress fast and cheap as possible . For balance   difference and money loss problems , we defined and implemented error keywords to ten specified   ATM devices to have it indicated a possible problem . When the specific error keyword detected , an   automatic message generated to the personnel that contained " There is a problem that can cause   balance difference or money loss problem . Check the related transaction . " Also , for the money jam   problem of the customers , we defined and implemented different error keywords with auto test feature   to the ten specifed ATM devices . When the specific error keyword detected , this time ATM device   started auto test and count all the money that customer deposited . After the jammed money counted   and specified , automatic money refund process to the customer account started . Also out team has   conducted a two surveys for ten personnel from different branches and one hundred customers who   had interaction with these branches and related ATM devices . The surveys were conducted within one   week and included questions about benefits of our new auto error detection method , time efficiency of 

 personnel , and feelings about the change . Also for customers , additional questions about customer   satisfaction were added . 

 Insight 

 The learning launch tool , helped our team to make assumptions and test them in a fast , cheap and   easy way . Our expectation from results to see increased efficiency of branch personnel and increased   customer satisfaction . When we evaluated the actuals results by comparing problem numbers   reported by per branch personnel and solution time of problems between the experiment branches   and the branches that learning launch assumption have not applied , we realized that auto error   detection has helped branch personnel to solve problems faster . Also , the personnel in these ten   branch reported less number of problems to our team than the personnel in other branches . However ,   by the customer side , survey results show that some of the customers have refunded lesser the exact   amount of the money they intend to deposit . In future launches this insight will be reviewed . Changing   or improving the idea would seem to be useful . 

 Approach 

 We can say with the peace of the mind the learning launch tool is suitable and easily applicable for   improvement of our work processes , especially by enabling to test and see the results immediately . We   have obtained important insight from learning launches , and with this information we will get one step   closer to desired truth in our reviews . In future launches insight will be improved , we will made better   progress and be more ready for full rollout . 


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: Design Thinking Reflection Using Learning Launch Tool 

 Challenge 

 I work in a bank organization as a resilience team member . Our team mainly provide solutions for   recurring problems and the personnel who affected by those problems . Our challenge is increase the   efficiency and effort management for the branch personnel of the bank by identifying their problems   and create quick / automatic solutions for them . In our scope there are 900 branches and 1800   personnel exist . All the problems encountered in branches need to be solved by these personnel at   most within one week . There are two personnel responsible in each branch . One works for the   customers of the bank and the other one works with the ATM devices in the branch building . There are   30 million customers and 5 thousand ATM devices within branch buildings . We need to quickly figure   out what kind of problems we might see , which are related to branch customers and/or which are   related to the ATM devices . When we determine the problems we will focus on possible solutions and   precautions for the problems before they cause bigger ones . 

 Selection 

 Since there are many customers and devices per branch personnel , being quick has has a crucial role   in this job . We must face and solve the problems when they first emerge and less customers effected .   Thus , we will use learning launch method , this ensure our problem detection and create solution ideas   will be easily implemented and experimented quickly . Also this tool will provide us fast and cheap test   environment . 

 Application 

 In the first learning launch we asked these questions . Who is our customer ? As internal , branch   personnel , as external , customers of the bank . What are their main problems ?   For personnel , ATM   device problems that are ATM balance difference , ATM money loss . For customers , ATM money jam   while performing transaction . How we can detect these problems ? The best option for our assumption   is would be automatically problem detection mechanism . The faster we detect the problem the faster   we solve it . So , the customers would not have always need to report problems to us . We would have   been informed about the problem even the solving progress could have started . After all , we have   learned that from Chapter-4 , “ The purpose of a learning launch is to get the truth and then the truth   either confirms or dis - confirms your assumptions . ” Our assumption seemed more likely be true , so we   could go on second phase of the learning launch . Hereby , we could test our assumption by measuring   problem detection and solving times , also effort and time effiency of the personnel , and problem   numbers reported by customers . For our second learning launch , we determined   an experiment group   consists of   ten different branches to make this progress fast and cheap as possible . For balance   difference and money loss problems , we defined and implemented error keywords to ten specified   ATM devices to have it indicated a possible problem . When the specific error keyword detected , an   automatic message generated to the personnel that contained " There is a problem that can cause   balance difference or money loss problem . Check the related transaction . " Also , for the money jam   problem of the customers , we defined and implemented different error keywords with auto test feature   to the ten specifed ATM devices . When the specific error keyword detected , this time ATM device   started auto test and count all the money that customer deposited . After the jammed money counted   and specified , automatic money refund process to the customer account started . Also out team has   conducted a two surveys for ten personnel from different branches and one hundred customers who   had interaction with these branches and related ATM devices . The surveys were conducted within one   week and included questions about benefits of our new auto error detection method , time efficiency of 

 personnel , and feelings about the change . Also for customers , additional questions about customer   satisfaction were added . 

 Insight 

 The learning launch tool , helped our team to make assumptions and test them in a fast , cheap and   easy way . Our expectation from results to see increased efficiency of branch personnel and increased   customer satisfaction . When we evaluated the actuals results by comparing problem numbers   reported by per branch personnel and solution time of problems between the experiment branches   and the branches that learning launch assumption have not applied , we realized that auto error   detection has helped branch personnel to solve problems faster . Also , the personnel in these ten   branch reported less number of problems to our team than the personnel in other branches . However ,   by the customer side , survey results show that some of the customers have refunded lesser the exact   amount of the money they intend to deposit . In future launches this insight will be reviewed . Changing   or improving the idea would seem to be useful . 

 Approach 

 We can say with the peace of the mind the learning launch tool is suitable and easily applicable for   improvement of our work processes , especially by enabling to test and see the results immediately . We   have obtained important insight from learning launches , and with this information we will get one step   closer to desired truth in our reviews . In future launches insight will be improved , we will made better   progress and be more ready for full rollout .
### Output: 