Reflection – Learning launch 

 CHALLENGE 

 In Pharmaceutical companies assuring reliability of analytical data is very critical . Pharmaceutical   companies use Chromatography a lot for testing of the products . Processing of chromatography data   in compliance to correct standard is very important to get right results . While there are lot of   technological solutions to maintain the integrity of data , still the data processing and audit trails have   to be reviewed for compliance to the procedures and regulatory guidelines . Review of entire data   package for one batch takes considerable time ( 2 - 3 hours ) as the data is often present in multiple   computer systems and the reviewer has to check more than 60 checkpoints . Apart from the quantum   of work , shear boredom due to repetitive nature of work , may result in reviewer missing or skipping   checks , resulting in error going unnoticed . This can have huge implication on the organization because   a batch with incorrect results may get into the market . Also there is risk of regulatory action if such   lapse is detected in regulatory audits which can have huge impact on the business . 

 SELECTION 

 To overcome the above challenge the team decided to use of Robotic Process automation to perform   the review activity . Although the review process is quite complex , entire activity is performed against   set rules and overall activity can be broken down into small tasks and hence can be automated using   RPA . This solution has not been tried in industry yet and there is no readymade solution available .   Considering the significance of the activity and the solution being quite novel , Learning launch   approach was adopted to test the prototype . 

 APPLICATION 

 For first Learning launch , the team focussed on simplest category of test called Assay at one of the   manufacturing site of the company . This test was chosen as there is single peak in the chromatograms   of this test . Single peak makes evaluation of prototype easy although it performs all the checks which   are involved in multi peak analysis . Team defined all the checks which are required to be performed   and all the rules which need to be applied in verification data . Bot was built incorporating all these   rules . After initial trials on dummy data , the Bot was deployed on the live data . The outputs were   verified by performing manual checks of the same data . Challenge studies were also carried out by   intentionally creating incorrect processing situations which a reviewer is supposed to detect . Bot was   made to review such data and its ability to detect all the defective situations was verified by comparing   review findings of same data by qualified reviewers . Minor corrections in the Bot design were done   based on the outcomes of various simulations . 

 After successful deployment in single peak analysis , second Learning launch was performed on more   complex analytical method of Related substance test . While the checks and rules were same , this is   more complex activity due to presence of multiple peaks with varying peak areas and shapes . Due to   these complexities , the design of the challenge studies was more difficult . Also , the number of   challenge studies required to simulate all the possible errors was very high .   As in first Learning launch   the outcome of Bot were compared against the review findings of qualified reviewers 

 INSIGHT 

 Using the Learning launch tool , team could evaluate how the bot will perform in real life scenario . The   Learning launch also brought out some of the shortcomings of the initial design and helped in   optimization . Volume challenge performed as part of the launch proved initial assumptions of number   of bots required to be incorrect . The team could do more accurate estimation of the number of bots   required for deployment across the all the manufacturing sites . It also identified some opportunity   areas for process changes for elimination of Non Value adding Activities . All these insights helped team   to be better prepared for the organization wide roll out . Another important insight from the learning   launch was the team could identify more areas of using RPA in the QC laboratory . 

 APPROACH 

 While all tools of design thinking are useful at different stages of project , Learning launch is the most   appropriate one for testing the prototype before going live or for testing a new concept or design   before implementation on large scale . This can help in quick evaluation of whether the new design   will work . That way the team can decide to proceed further with the design , doing tweaking of the   design or kill the design before deploying too much resource . The insights gained during this launch   helped us in improving the prototype and helped in correct estimation of the resource requirement .   This learning will also help us in testing future designs before their rollouts . 


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 

 In Pharmaceutical companies assuring reliability of analytical data is very critical . Pharmaceutical   companies use Chromatography a lot for testing of the products . Processing of chromatography data   in compliance to correct standard is very important to get right results . While there are lot of   technological solutions to maintain the integrity of data , still the data processing and audit trails have   to be reviewed for compliance to the procedures and regulatory guidelines . Review of entire data   package for one batch takes considerable time ( 2 - 3 hours ) as the data is often present in multiple   computer systems and the reviewer has to check more than 60 checkpoints . Apart from the quantum   of work , shear boredom due to repetitive nature of work , may result in reviewer missing or skipping   checks , resulting in error going unnoticed . This can have huge implication on the organization because   a batch with incorrect results may get into the market . Also there is risk of regulatory action if such   lapse is detected in regulatory audits which can have huge impact on the business . 

 SELECTION 

 To overcome the above challenge the team decided to use of Robotic Process automation to perform   the review activity . Although the review process is quite complex , entire activity is performed against   set rules and overall activity can be broken down into small tasks and hence can be automated using   RPA . This solution has not been tried in industry yet and there is no readymade solution available .   Considering the significance of the activity and the solution being quite novel , Learning launch   approach was adopted to test the prototype . 

 APPLICATION 

 For first Learning launch , the team focussed on simplest category of test called Assay at one of the   manufacturing site of the company . This test was chosen as there is single peak in the chromatograms   of this test . Single peak makes evaluation of prototype easy although it performs all the checks which   are involved in multi peak analysis . Team defined all the checks which are required to be performed   and all the rules which need to be applied in verification data . Bot was built incorporating all these   rules . After initial trials on dummy data , the Bot was deployed on the live data . The outputs were   verified by performing manual checks of the same data . Challenge studies were also carried out by   intentionally creating incorrect processing situations which a reviewer is supposed to detect . Bot was   made to review such data and its ability to detect all the defective situations was verified by comparing   review findings of same data by qualified reviewers . Minor corrections in the Bot design were done   based on the outcomes of various simulations . 

 After successful deployment in single peak analysis , second Learning launch was performed on more   complex analytical method of Related substance test . While the checks and rules were same , this is   more complex activity due to presence of multiple peaks with varying peak areas and shapes . Due to   these complexities , the design of the challenge studies was more difficult . Also , the number of   challenge studies required to simulate all the possible errors was very high .   As in first Learning launch   the outcome of Bot were compared against the review findings of qualified reviewers 

 INSIGHT 

 Using the Learning launch tool , team could evaluate how the bot will perform in real life scenario . The   Learning launch also brought out some of the shortcomings of the initial design and helped in   optimization . Volume challenge performed as part of the launch proved initial assumptions of number   of bots required to be incorrect . The team could do more accurate estimation of the number of bots   required for deployment across the all the manufacturing sites . It also identified some opportunity   areas for process changes for elimination of Non Value adding Activities . All these insights helped team   to be better prepared for the organization wide roll out . Another important insight from the learning   launch was the team could identify more areas of using RPA in the QC laboratory . 

 APPROACH 

 While all tools of design thinking are useful at different stages of project , Learning launch is the most   appropriate one for testing the prototype before going live or for testing a new concept or design   before implementation on large scale . This can help in quick evaluation of whether the new design   will work . That way the team can decide to proceed further with the design , doing tweaking of the   design or kill the design before deploying too much resource . The insights gained during this launch   helped us in improving the prototype and helped in correct estimation of the resource requirement .   This learning will also help us in testing future designs before their rollouts .
### Output: 