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.


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 .