REFLECTION- Learning Launch 

 CHALLENGE:- 

 As a part of a Mechanical Design Engineering team in a Research and Development centre ( which   specialises in Special Purpose Machinery ) , I am accustomed to solve unique problems . The sole   purpose of our job is to conceptualise an idea to solve our customer ’s problem , prototype or   conform the concept / theory developed and validate the prototype by rigorously testing it   ( subjecting the prototype to conditions it is likely to face during the course of the products life ) . In   order to solve a problem , we deem it necessary to understand the customer ’s requirements . This is   done by means of a URS ( User Requirement Speciﬁcation ) which is provided by the customer . A   thorough study of the URS paints a clear picture of the customer ’s wants and whether or not   achieving these is feasible or not . 

 As mentioned above , majority of the projects we encounter are unique , which require us to test out   new theories or concepts . Since there is uncertainty about the outcome of these concepts or   theories , LEARNING LAUNCHES is the tool we use to tackle the uncertainty . A learning launch is a   perfect way of failing fast and cheap , if possibility of failure in the initial attempts is high . These   constitute the reasons for us to select LEARNING LAUNCH as a design thinking tool to solve the   prevalent customer problem . 

 SELECTION:- 

 LEARNING LAUNCHES are essentially “ fast and cheap ” experiments . These two words suit   perfectly when one works in a Research and Development ﬁeld . As mentioned earlier the customer   provides us with a User Requirement Speciﬁcation . Based on the URS , collection of data relevant   to the customer ’s problem becomes mandatory . The data includes:- • Literature review on relevant subject matter . • Consulting experts in the relevant ﬁeld . • Studying the ﬁnancial and manufacturing feasibility of the project . 

 Accumulation of the aforementioned data , facilitates a smooth execution of a LEARNING   LAUNCH .   Apart from practically testing the hypothesis , the LEARNING LAUNCH also paints a rough picture   of how the ﬁnal product might look like . It indicates whether ours and the customer ’s thought   processes are aligned . 

 APPLICATION:- 

 Since conforming a prototype could be an expensive affair , we have purchased a rapid prototyping   machine . This machine uses additive manufacturing technique to build a product from scratch . The   raw material used for manufacturing the prototype via rapid prototyping is cheap . The machine can   be operated by a semi skilled labourer , which makes the operating it an economical affair . 

 The prototype built is scaled downed to half scale ( half the size of the ﬁnal product ) . This helps   reduce time , cost and effort utilised in the process . The data generated from the rapid prototyped ,   scaled down model of the product is then studied . 

 This data is then extrapolated to gauge the ﬁnal performance . Since the parameters studied during   this phase are independent of volume , extrapolating the data was generated is a right decision . 

 INSIGHT:- 

 The insights gained from the entire experiment are a by product of the data generated during the   learning launch phase . This data is then shared with the customer . A detailed study reveals that   there is a performance void between the results generated and the results expected by the   customer . The Learning Launch revealed that certain parameters affecting the performance of the   product were not taken into consideration . This could possibly be the reason for the performance   gap , which will be incorporated in the further learning launches till satisfactory results are obtained . 

 APPROACH:- 

 Before initiating the project , the team had studied various design thinking tools available to them .   Having brainstormed over which tool to implement , the team shortlisted Learning Launch . After   sharing the data generated from the Learning launch with the customer , the customer shared their   insights . Incorporation of additional features which were n’t mentioned within the initial User   Requirement Speciﬁcation , was one of the many insights shared . 

 The request , though made in the initial design phase , could change the the   performance affecting parameters of the product . Such a situation could have been avoided , if   the team decides to use VISUALISATION as its design thinking tool , prior to using a learning   launch . A virtual model of the product could be conformed as per the new requirements . This can   then be shared with the customer , and only after we have a GO AHEAD from the them , would be   start with the LEARNING LAUNCH . This would ensure the data generated from the next learning   launch is relevant , and could be constructively used to Launch the ﬁnal product . 


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: REFLECTION- Learning Launch 

 CHALLENGE:- 

 As a part of a Mechanical Design Engineering team in a Research and Development centre ( which   specialises in Special Purpose Machinery ) , I am accustomed to solve unique problems . The sole   purpose of our job is to conceptualise an idea to solve our customer ’s problem , prototype or   conform the concept / theory developed and validate the prototype by rigorously testing it   ( subjecting the prototype to conditions it is likely to face during the course of the products life ) . In   order to solve a problem , we deem it necessary to understand the customer ’s requirements . This is   done by means of a URS ( User Requirement Speciﬁcation ) which is provided by the customer . A   thorough study of the URS paints a clear picture of the customer ’s wants and whether or not   achieving these is feasible or not . 

 As mentioned above , majority of the projects we encounter are unique , which require us to test out   new theories or concepts . Since there is uncertainty about the outcome of these concepts or   theories , LEARNING LAUNCHES is the tool we use to tackle the uncertainty . A learning launch is a   perfect way of failing fast and cheap , if possibility of failure in the initial attempts is high . These   constitute the reasons for us to select LEARNING LAUNCH as a design thinking tool to solve the   prevalent customer problem . 

 SELECTION:- 

 LEARNING LAUNCHES are essentially “ fast and cheap ” experiments . These two words suit   perfectly when one works in a Research and Development ﬁeld . As mentioned earlier the customer   provides us with a User Requirement Speciﬁcation . Based on the URS , collection of data relevant   to the customer ’s problem becomes mandatory . The data includes:- • Literature review on relevant subject matter . • Consulting experts in the relevant ﬁeld . • Studying the ﬁnancial and manufacturing feasibility of the project . 

 Accumulation of the aforementioned data , facilitates a smooth execution of a LEARNING   LAUNCH .   Apart from practically testing the hypothesis , the LEARNING LAUNCH also paints a rough picture   of how the ﬁnal product might look like . It indicates whether ours and the customer ’s thought   processes are aligned . 

 APPLICATION:- 

 Since conforming a prototype could be an expensive affair , we have purchased a rapid prototyping   machine . This machine uses additive manufacturing technique to build a product from scratch . The   raw material used for manufacturing the prototype via rapid prototyping is cheap . The machine can   be operated by a semi skilled labourer , which makes the operating it an economical affair . 

 The prototype built is scaled downed to half scale ( half the size of the ﬁnal product ) . This helps   reduce time , cost and effort utilised in the process . The data generated from the rapid prototyped ,   scaled down model of the product is then studied . 

 This data is then extrapolated to gauge the ﬁnal performance . Since the parameters studied during   this phase are independent of volume , extrapolating the data was generated is a right decision . 

 INSIGHT:- 

 The insights gained from the entire experiment are a by product of the data generated during the   learning launch phase . This data is then shared with the customer . A detailed study reveals that   there is a performance void between the results generated and the results expected by the   customer . The Learning Launch revealed that certain parameters affecting the performance of the   product were not taken into consideration . This could possibly be the reason for the performance   gap , which will be incorporated in the further learning launches till satisfactory results are obtained . 

 APPROACH:- 

 Before initiating the project , the team had studied various design thinking tools available to them .   Having brainstormed over which tool to implement , the team shortlisted Learning Launch . After   sharing the data generated from the Learning launch with the customer , the customer shared their   insights . Incorporation of additional features which were n’t mentioned within the initial User   Requirement Speciﬁcation , was one of the many insights shared . 

 The request , though made in the initial design phase , could change the the   performance affecting parameters of the product . Such a situation could have been avoided , if   the team decides to use VISUALISATION as its design thinking tool , prior to using a learning   launch . A virtual model of the product could be conformed as per the new requirements . This can   then be shared with the customer , and only after we have a GO AHEAD from the them , would be   start with the LEARNING LAUNCH . This would ensure the data generated from the next learning   launch is relevant , and could be constructively used to Launch the ﬁnal product .
### Output: 