Reflection – Learning Launch 

 Design Thinking - A new perspective in Analytical Research and Development in Generic Industry 

 Challenge 

 Being an analytical research scientist in Generics industry , agility is the new normal to survive in the   current cut throat competition . The motto is First to market to cater to unmet needs of patients in the   world . 

 Generics product development contains making a bio - equivalent product of the innovator product   which is already existing in the market . The pharmaceutical companies has to identify the marketing   opportunities and develop new generic products . The faster the development , better are the returns . 

 The critical chain for the development of generic products is the analysis time and solvent   consumption . The analysis methods should be robust and be able to identify the variations which may   occur in different batches of the same product . The solvent consumption leads to heavy cost to the   company . There is chance of reducing the solvent consumption through shorter run time methods . 

 Selection 

 We focus on implementing the following to develop robust products by applying agile concepts 

 1 . Design Expert tool ( Quality by Design tool ) – This will help us in understand the robustness of   the analytical method . We can build the quality into the process leading to reproducible   methods at quality control .    2 . Multi - Dose dissolution apparatus – This equipment helps us to screen 8 different batches   simultaneously    3 . Fibre Optics in dissolutions – For online monitoring of dissolution results .   4 . Smart knowledge transfer – Usage of google glass technology for effective knowledge transfer   between R&D facility and manufacturing locations .    5 . Application of data management tools ( Information mapping ) – Mining of Vast data and   consolidating into information .   6 . Applying new separation techniques ( HPLC vs UPLC ) to reduce overall run time of analysis . 

 Application 

 The target of above techniques are 

 1 ) Reduced solvent consumption by reducing run time for analysis .    2 ) Simultaneously screening multiple batches .   3 ) Quality by design as a tool for monitoring the robustness of the product .   4 ) Instead of re - inventing the wheel , leveraging the vast data and mining quality information .    5 ) The R&D process has been recorded real - time by using google glass and sharing the same to   quality control to prevent the manual intervention . The process at quality control while   knowledge transfer can be monitored real - time by sitting at R&D.    6 ) As a pilot project , two products were selected for improvement by applying new tools . The   products were run in fibre optics dissolution apparatus and the results are matched with 

 conventional process . The run time in HPLC has been reduced by following new equipment of   UPLC where the run times are shorter further saving the solvent consumption for the analysis .    7 ) Now these techniques has been rolled out in all products . 

 Insight 

 By application of knowledge tools and new technology tools , we are able to successfully develop the   generic products in very short time . This has saved time and cost for the product development . The   friction between quality control and R&D department has also reduced a lot . 

 Approach 

 By application of the learning launch strategy of Design Thinking , we can have a strategic advantage   of resource optimization . The quality can be built into products instead of firefighting at the shop floor . 

 These methods helped us in removing common cause variation ( variation inbuilt in the process ) which   is very difficult to tackle .   Due to this , it has become easy to identify special cause variation ( Variation   due to external factor ) . 


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 

 Design Thinking - A new perspective in Analytical Research and Development in Generic Industry 

 Challenge 

 Being an analytical research scientist in Generics industry , agility is the new normal to survive in the   current cut throat competition . The motto is First to market to cater to unmet needs of patients in the   world . 

 Generics product development contains making a bio - equivalent product of the innovator product   which is already existing in the market . The pharmaceutical companies has to identify the marketing   opportunities and develop new generic products . The faster the development , better are the returns . 

 The critical chain for the development of generic products is the analysis time and solvent   consumption . The analysis methods should be robust and be able to identify the variations which may   occur in different batches of the same product . The solvent consumption leads to heavy cost to the   company . There is chance of reducing the solvent consumption through shorter run time methods . 

 Selection 

 We focus on implementing the following to develop robust products by applying agile concepts 

 1 . Design Expert tool ( Quality by Design tool ) – This will help us in understand the robustness of   the analytical method . We can build the quality into the process leading to reproducible   methods at quality control .    2 . Multi - Dose dissolution apparatus – This equipment helps us to screen 8 different batches   simultaneously    3 . Fibre Optics in dissolutions – For online monitoring of dissolution results .   4 . Smart knowledge transfer – Usage of google glass technology for effective knowledge transfer   between R&D facility and manufacturing locations .    5 . Application of data management tools ( Information mapping ) – Mining of Vast data and   consolidating into information .   6 . Applying new separation techniques ( HPLC vs UPLC ) to reduce overall run time of analysis . 

 Application 

 The target of above techniques are 

 1 ) Reduced solvent consumption by reducing run time for analysis .    2 ) Simultaneously screening multiple batches .   3 ) Quality by design as a tool for monitoring the robustness of the product .   4 ) Instead of re - inventing the wheel , leveraging the vast data and mining quality information .    5 ) The R&D process has been recorded real - time by using google glass and sharing the same to   quality control to prevent the manual intervention . The process at quality control while   knowledge transfer can be monitored real - time by sitting at R&D.    6 ) As a pilot project , two products were selected for improvement by applying new tools . The   products were run in fibre optics dissolution apparatus and the results are matched with 

 conventional process . The run time in HPLC has been reduced by following new equipment of   UPLC where the run times are shorter further saving the solvent consumption for the analysis .    7 ) Now these techniques has been rolled out in all products . 

 Insight 

 By application of knowledge tools and new technology tools , we are able to successfully develop the   generic products in very short time . This has saved time and cost for the product development . The   friction between quality control and R&D department has also reduced a lot . 

 Approach 

 By application of the learning launch strategy of Design Thinking , we can have a strategic advantage   of resource optimization . The quality can be built into products instead of firefighting at the shop floor . 

 These methods helped us in removing common cause variation ( variation inbuilt in the process ) which   is very difficult to tackle .   Due to this , it has become easy to identify special cause variation ( Variation   due to external factor ) .
### Output: 