Example Reflection – Learning Launch 

 Challenge 

 The pre - launch ramp up of an industrial product ( non - software nor service ) is complex process .   The developed product , which has only seen relatively controlled conditions , faces then an   expensive and too often dramatic reality check . Involving various stakeholders and agendas :   Manufacturing something new in higher scale and lower control conditions in a facility trained   for ‘ always do the same ’ . Field application by specialized users , used ‘ the usual stuff ’ and   observing the newcomer as an unnecessary change . 

 Selection 

 In this context , many factors / properties , not intrinsic to product performance or specifications ,   are key to its correct manufacturing and application success . Both required ( yet not sufficient )   for a commercial success . 

 Instead of gesstimating these factors , it is preferred and more efficient to perform a learning   launch with a minimum viable product ( MVP ) . Which is usually more mature than a SW / Service   equivalent . But still open to take in the lessons and modifications raised by a learning launch   going through both manufacturing and application stages , gathering customer feedback . 

 Application 

 We used learning launch in a new product development to update a product range unchanged   for +10yr . Which involved manufacturing process modification , field application updates and a   lot of organizational expectation – anxiety . 

 Second development iteration ( out of four ) yielded an acceptable MVP . Which was taken to a   series of manufacturing rounds scaling up 104 the usual batch size and taken for field application .   Selection of ‘ close ’ locations allowed not only to adjust the technical parameters , but most   relevant gathering the feedback from operators , shift and site managers . Generally overlooked   stakeholders , but whose insight and buy – in is critical for product quality , performance , and   ‘ reputation ’ . 

 Insight 

 Assistance of product developers to the learning launch allowed to gather and translate   ‘ observational ’ into technical parameters . Many of these ( i.e. block stiffness and internal color )   were not affecting product performance and would have never been identified in standard   product profiling . Moreover , post launch learning would neither be possible since developers   would by then be mobilized to other projects , away from this one . 

 On the flip side , organization anxiety effects were underestimated . The learning launch didactics   and communication did not prevent bad mouthing for product underperformance . Disregarding   that this could be expected for the MVP and the learning launch goal was not the superior   performance of the specific MVP tested . 

 Approach 

 Next time will not overlook but rather take advantage that learning launch also offers learning   possibilities on how the organization will react at political level , what are the undeclared   expectations and positions of the different collectives . 


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 

 The pre - launch ramp up of an industrial product ( non - software nor service ) is complex process .   The developed product , which has only seen relatively controlled conditions , faces then an   expensive and too often dramatic reality check . Involving various stakeholders and agendas :   Manufacturing something new in higher scale and lower control conditions in a facility trained   for ‘ always do the same ’ . Field application by specialized users , used ‘ the usual stuff ’ and   observing the newcomer as an unnecessary change . 

 Selection 

 In this context , many factors / properties , not intrinsic to product performance or specifications ,   are key to its correct manufacturing and application success . Both required ( yet not sufficient )   for a commercial success . 

 Instead of gesstimating these factors , it is preferred and more efficient to perform a learning   launch with a minimum viable product ( MVP ) . Which is usually more mature than a SW / Service   equivalent . But still open to take in the lessons and modifications raised by a learning launch   going through both manufacturing and application stages , gathering customer feedback . 

 Application 

 We used learning launch in a new product development to update a product range unchanged   for +10yr . Which involved manufacturing process modification , field application updates and a   lot of organizational expectation – anxiety . 

 Second development iteration ( out of four ) yielded an acceptable MVP . Which was taken to a   series of manufacturing rounds scaling up 104 the usual batch size and taken for field application .   Selection of ‘ close ’ locations allowed not only to adjust the technical parameters , but most   relevant gathering the feedback from operators , shift and site managers . Generally overlooked   stakeholders , but whose insight and buy – in is critical for product quality , performance , and   ‘ reputation ’ . 

 Insight 

 Assistance of product developers to the learning launch allowed to gather and translate   ‘ observational ’ into technical parameters . Many of these ( i.e. block stiffness and internal color )   were not affecting product performance and would have never been identified in standard   product profiling . Moreover , post launch learning would neither be possible since developers   would by then be mobilized to other projects , away from this one . 

 On the flip side , organization anxiety effects were underestimated . The learning launch didactics   and communication did not prevent bad mouthing for product underperformance . Disregarding   that this could be expected for the MVP and the learning launch goal was not the superior   performance of the specific MVP tested . 

 Approach 

 Next time will not overlook but rather take advantage that learning launch also offers learning   possibilities on how the organization will react at political level , what are the undeclared   expectations and positions of the different collectives .
### Output: 