Addressing market complaints through learning launch for Autoinjector device . 

 Introduction 

 I am a pharmaceutical supply chain management professional focused on external manufacturing   supplies and operational excellence related to the same . I was assigned a task to reduce the market   complaints for an injectable device for US market . This device has plastic based components with a   prefilled syringe made of high quality glass and autoinjector system that will be used to deliver the   dose to patients suffering from migraine . As the product design suggest it is intended for use directly   by the patients , i.e whenever the migraine attack happens the patient can use the device immediately   without any assistance from medical professional . Giving the patients convenience to use the product   and portability to carry made the product successful and of high value . However as the product sales   increased the market complaints also increased and majority of the complaints when classified   categorically as device malfunctioning . 

 The Challenge : 

 This Autoinjector device product was unique and novel in design hence the device assembly machine   was also custom made to order to fit the requirement . Since this product was already in the market   for several years now and given the highly regulated environment of US market it was imminent to   address the issue . 

 Thorough evaluation of the nature of market complaints revealed several possibilities of the device   not working . Hence rather unconventional it was decided to quickly address these issues through   learning launch process . 

 Learning launch 1 : 

 As the device was new and unique and very different in design than the innovator product it was   essential to educate the customers the right way to use it . Several educational sessions and webinars   were conducted with prominent pharmacist and medical practitioner as well as field force on how to   use the device properly . Step wise diagrams on proper usage of device were also introduced in the   patient information leaflets which helped a lot at first . We observed reduction in the complaints but   that was not significant and with the addition of new user , the trend line were maintained . 

 Learning launch 2 : 

 Although it was important to educate the front end on correct usage and ensuring new user were also   able to use the device correctly , it did n’t yield significant result , learning launch 2 phase was designed   to check and control the release of faulty device . There were systematic procedure for product release   and until the device passed those test will not get released to market . Although these test were   significant to batch release and the sample size just enough to justify the same , It did n’t offer anything   on the prediction of failure . After several brainstorming session with the team it was decided to put a   camera system that will check and reject the device at the source itself . This was working although   not in its best way but significant enough to get accepted . At the same time our product yield was also   compromised as the camera system and was attributed to the camera system rejecting the good   devices too . 

 Learning launch 3 : 

 Across learning launch 1 and 2 our entire focus was on either educating the customer and ways to   reject the faulty device , both these learning launches proved to be important and paved the way for   the third phase of the process . For learning launch 3 we directed our focus on understanding what   made the device to malfunction in the first place . This helped us to look at the design of Assembly   machine itself since changing the design of autoinjector device product was a long process given to   regulatory environment . Once again the team assembled for brainstorming but this time I brought in   the Assembly machine manufacturer to capture their insight . 

 Further detailed investigation into the design function of device and its compatibility with the   assembly machine revealed some surprizing but significant fact . We discovered more or less the issue   to be with the breakages of more than one components of Autoinjector device components   internally   which was not visible to the camera system that led to device malfunction . These breakages were   accounted to the incompatibility of the plastic and Glass components of the Autoinjector device and   the robotic pneumatic arm of the assembly machine . This robotic arm was hydraulic powered with no   control on the force it exerted on the device component while it fixes the Glass syringe in the bottom   assembly of the device . This was eureka moment for us as we were significantly close to remedy the   issue . Immediately we worked on changing the Pneumatic robotic arm of the assembly machine from   hydraulic to servo powered . This servo powered arm will provide the exact amount of force that is   required to fix the glass syringe in the bottom assembly of the device without breaking any of the   components . With the help of experts we exacted on the force that need for the device to be   assembled without damaging . We implemented this and are expecting significantly reduction in   market complaint in upcoming 3 to 4 months ’ time . 

 Conclusion : 

 We did address the actual root cause and expecting to see some major improvement as far as market   complaints are concerned . We are also expecting yield improvement by improving the design . Across   the three learning launches there were several realization about the device manufacturing process   and the challenges it can pose to the end user as well as the manufacturer . We learned one significant   aspect about device manufacturing that less the number of components the lesser will be the chance   of error or malfunction . 

 This use of learning launch to quickly test the theory and implement the solution and then iterating   the process till we achieve the best possible solution has proven significantly on how issues can be   addressed , a rather unconventional way to put this tool to use but it immensely helped in arriving to   the best possible solution . 


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: Addressing market complaints through learning launch for Autoinjector device . 

 Introduction 

 I am a pharmaceutical supply chain management professional focused on external manufacturing   supplies and operational excellence related to the same . I was assigned a task to reduce the market   complaints for an injectable device for US market . This device has plastic based components with a   prefilled syringe made of high quality glass and autoinjector system that will be used to deliver the   dose to patients suffering from migraine . As the product design suggest it is intended for use directly   by the patients , i.e whenever the migraine attack happens the patient can use the device immediately   without any assistance from medical professional . Giving the patients convenience to use the product   and portability to carry made the product successful and of high value . However as the product sales   increased the market complaints also increased and majority of the complaints when classified   categorically as device malfunctioning . 

 The Challenge : 

 This Autoinjector device product was unique and novel in design hence the device assembly machine   was also custom made to order to fit the requirement . Since this product was already in the market   for several years now and given the highly regulated environment of US market it was imminent to   address the issue . 

 Thorough evaluation of the nature of market complaints revealed several possibilities of the device   not working . Hence rather unconventional it was decided to quickly address these issues through   learning launch process . 

 Learning launch 1 : 

 As the device was new and unique and very different in design than the innovator product it was   essential to educate the customers the right way to use it . Several educational sessions and webinars   were conducted with prominent pharmacist and medical practitioner as well as field force on how to   use the device properly . Step wise diagrams on proper usage of device were also introduced in the   patient information leaflets which helped a lot at first . We observed reduction in the complaints but   that was not significant and with the addition of new user , the trend line were maintained . 

 Learning launch 2 : 

 Although it was important to educate the front end on correct usage and ensuring new user were also   able to use the device correctly , it did n’t yield significant result , learning launch 2 phase was designed   to check and control the release of faulty device . There were systematic procedure for product release   and until the device passed those test will not get released to market . Although these test were   significant to batch release and the sample size just enough to justify the same , It did n’t offer anything   on the prediction of failure . After several brainstorming session with the team it was decided to put a   camera system that will check and reject the device at the source itself . This was working although   not in its best way but significant enough to get accepted . At the same time our product yield was also   compromised as the camera system and was attributed to the camera system rejecting the good   devices too . 

 Learning launch 3 : 

 Across learning launch 1 and 2 our entire focus was on either educating the customer and ways to   reject the faulty device , both these learning launches proved to be important and paved the way for   the third phase of the process . For learning launch 3 we directed our focus on understanding what   made the device to malfunction in the first place . This helped us to look at the design of Assembly   machine itself since changing the design of autoinjector device product was a long process given to   regulatory environment . Once again the team assembled for brainstorming but this time I brought in   the Assembly machine manufacturer to capture their insight . 

 Further detailed investigation into the design function of device and its compatibility with the   assembly machine revealed some surprizing but significant fact . We discovered more or less the issue   to be with the breakages of more than one components of Autoinjector device components   internally   which was not visible to the camera system that led to device malfunction . These breakages were   accounted to the incompatibility of the plastic and Glass components of the Autoinjector device and   the robotic pneumatic arm of the assembly machine . This robotic arm was hydraulic powered with no   control on the force it exerted on the device component while it fixes the Glass syringe in the bottom   assembly of the device . This was eureka moment for us as we were significantly close to remedy the   issue . Immediately we worked on changing the Pneumatic robotic arm of the assembly machine from   hydraulic to servo powered . This servo powered arm will provide the exact amount of force that is   required to fix the glass syringe in the bottom assembly of the device without breaking any of the   components . With the help of experts we exacted on the force that need for the device to be   assembled without damaging . We implemented this and are expecting significantly reduction in   market complaint in upcoming 3 to 4 months ’ time . 

 Conclusion : 

 We did address the actual root cause and expecting to see some major improvement as far as market   complaints are concerned . We are also expecting yield improvement by improving the design . Across   the three learning launches there were several realization about the device manufacturing process   and the challenges it can pose to the end user as well as the manufacturer . We learned one significant   aspect about device manufacturing that less the number of components the lesser will be the chance   of error or malfunction . 

 This use of learning launch to quickly test the theory and implement the solution and then iterating   the process till we achieve the best possible solution has proven significantly on how issues can be   addressed , a rather unconventional way to put this tool to use but it immensely helped in arriving to   the best possible solution .
### Output: 