Learning Launch of an Automated Guidance / Validation Tool 

 Challenge 

 I work in a high - tech manufacturing environment . A few years ago , we were faced with escalations from   our customers about failures in shipped product . A small team was formed to investigate the issues and   implement corrective actions . The team analyzed all the issues in detail to see if there are any systemic   root causes . The findings were compiled and presented . It was noticed that there were inconsistencies   in the manufacturing and validation processes that were being used . We noticed that even experienced   personnel had a tough time to keep up with the complex requirements and specifications of building the   product . Newly hired personnel were struggling to follow instructions , calculating specifications ,   interpreting results , and ensuring highest quality of shipped product . 

 We needed to build a tool that will help personnel , irrespective of their experience , to build complex   assemblies with ease . We had to change the way we were doing things . Change is not easy - we needed   buy in . We need to build something that both experienced and new people like . The solution should be   scalable and at the same time make economic sense . 

 Selection 

 Design is an Iterative process . It might take years to perfect a product . But in the high tech work we   need to be quick to market . We need to get buy in from all the stake holders fast . We need to convince   management to Invest based on reliable good data . 

 A colleague and I started to work on this problem . We both had a lot of experience ( breadth ) in the   manufacturing domain . We have worked in this area for years and were able to think through the   details . We also had decent programming skills that were needed to develop something that we could   test out . We decided to quickly build something and do a Learning Launch . 

 Application 

 We started to work on a Minimum Viable Product ( MVP ) . We developed basic functionality using C #   coding . The application was completely a simulation . It would simulate build scenario and guide   users / personnel to follow step by step instructions on how to proceed . It will automate processes when   it can , make decisions for users and provide valuable insight into the process . Even though it was   completely a simulation - it was very visual and provided a rich experience for the users to try it out . 

 We recruited a few Testers to try out the application . We carefully selected people based on different   levels of experience and skill sets . We asked them to try out the product for a few hours . We partnered   with the testers and carefully collected their feedback – How long did it take to complete , What they   liked , What they did not like . We also collected data on their progress through the application –   compared and contrasted data - based on their levels of experience . We carefully tabulated all the data   and extrapolated it to the real word scenario . The results & lessons learned were peer reviewed and   shared with management . 

 Insight 

 The feedback and data collected was very helpful . It clearly showed the need and the value of   developing a tool like this . We were able to understand what experienced users were looking for ( more 

 automation ) and what new users were looking for ( detailed guidance ) . We now knew we had to develop   something that can be customized based on user experience . We were able to collect data on savings .   Now we could create a ROI model for management decision to proceed with full - fledged development .   We were also able to show end users what the app might look like – will eventually help us get the buy   in . We got some insights on what data is important , how it should be collected and stored . We were   analyzed Scalability needs and came up with resource needs - Systems , People , Budget etc . 

 Approach 

 I think this approach of use Learning Launch was the right one and it yielded fantastic results . We can   expand the launch now across other business groups to learn more and improve . Every time we will   learn something new , get new data , gain useful insights and better the product . The data collected   during this Learning launch was used to create a compelling story ( Story telling ) for management buy in   and investments . 


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: Learning Launch of an Automated Guidance / Validation Tool 

 Challenge 

 I work in a high - tech manufacturing environment . A few years ago , we were faced with escalations from   our customers about failures in shipped product . A small team was formed to investigate the issues and   implement corrective actions . The team analyzed all the issues in detail to see if there are any systemic   root causes . The findings were compiled and presented . It was noticed that there were inconsistencies   in the manufacturing and validation processes that were being used . We noticed that even experienced   personnel had a tough time to keep up with the complex requirements and specifications of building the   product . Newly hired personnel were struggling to follow instructions , calculating specifications ,   interpreting results , and ensuring highest quality of shipped product . 

 We needed to build a tool that will help personnel , irrespective of their experience , to build complex   assemblies with ease . We had to change the way we were doing things . Change is not easy - we needed   buy in . We need to build something that both experienced and new people like . The solution should be   scalable and at the same time make economic sense . 

 Selection 

 Design is an Iterative process . It might take years to perfect a product . But in the high tech work we   need to be quick to market . We need to get buy in from all the stake holders fast . We need to convince   management to Invest based on reliable good data . 

 A colleague and I started to work on this problem . We both had a lot of experience ( breadth ) in the   manufacturing domain . We have worked in this area for years and were able to think through the   details . We also had decent programming skills that were needed to develop something that we could   test out . We decided to quickly build something and do a Learning Launch . 

 Application 

 We started to work on a Minimum Viable Product ( MVP ) . We developed basic functionality using C #   coding . The application was completely a simulation . It would simulate build scenario and guide   users / personnel to follow step by step instructions on how to proceed . It will automate processes when   it can , make decisions for users and provide valuable insight into the process . Even though it was   completely a simulation - it was very visual and provided a rich experience for the users to try it out . 

 We recruited a few Testers to try out the application . We carefully selected people based on different   levels of experience and skill sets . We asked them to try out the product for a few hours . We partnered   with the testers and carefully collected their feedback – How long did it take to complete , What they   liked , What they did not like . We also collected data on their progress through the application –   compared and contrasted data - based on their levels of experience . We carefully tabulated all the data   and extrapolated it to the real word scenario . The results & lessons learned were peer reviewed and   shared with management . 

 Insight 

 The feedback and data collected was very helpful . It clearly showed the need and the value of   developing a tool like this . We were able to understand what experienced users were looking for ( more 

 automation ) and what new users were looking for ( detailed guidance ) . We now knew we had to develop   something that can be customized based on user experience . We were able to collect data on savings .   Now we could create a ROI model for management decision to proceed with full - fledged development .   We were also able to show end users what the app might look like – will eventually help us get the buy   in . We got some insights on what data is important , how it should be collected and stored . We were   analyzed Scalability needs and came up with resource needs - Systems , People , Budget etc . 

 Approach 

 I think this approach of use Learning Launch was the right one and it yielded fantastic results . We can   expand the launch now across other business groups to learn more and improve . Every time we will   learn something new , get new data , gain useful insights and better the product . The data collected   during this Learning launch was used to create a compelling story ( Story telling ) for management buy in   and investments .
### Output: 