Learning Launch – Banco Santander Río de 

 Argentina ( India Business Unit ) 

 Challenge – I was working as an Intern with Banco Santander Río de 

 Argentina during the summers of 2013 . I was working in   the commercial planning department and the core challenge which the company was facing at that   time was to improve the accuracy of their demand planning . As the Alcohol Beverage industry is   influenced by a lot many internal factors ( supply , marketing , sales ) as well as external factors ( legal ,   regulations , climate , seasonality and other macro - economic factors ) , it was becoming difficult for the   company to forecast their demands accurately . Also , since the company ’s business unit was new in   India , so the processes development of various department was in the early phase . And hence the   demand planning was not based much on the data rather on experience and field knowledge . I   devoted my time working on data and finding out a suitable model to make the demand forecasting   process data oriented . 

 Selection – I choose the Learning launch tool here because , although after going through a lot of   secondary research ( doing online research for existing forecasting techniques and studying the alco   beverage industry ) and primary research ( talking to multiple stakeholders in company ) , and then   followed by brainstorming and funnelling the ideas through selection criteria filters , I had already   shortlisted a few of the models which could be tested for future forecasting . Now the next task was   to test the efficiency of these models and to see whether which model will work best for the company .   For this part of the project , I used Learning launch tool , the idea was to first proof test the model in a   particular state of India ( Maharashtra in our case ) and if the accuracy tested for this turns out to be   good . This model can be further launched for the pan India business or if it fails , insights can be drawn   to further improve the model . 

 Application – So the model I finalized which was showing the best accuracy till date was ARIMA   forecasting model . I went ahead for the Learning Launch using this model in one of the states of India   ( Maharashtra ) . We based the demand forecast for the month of May , based on solely this model . All   our sales , marketing and supply planning was based on this forecast and our next key goal was to   check whether our actual sales for the month of May , fits how closely with this model ’s forecast . 

 Insight – The model turned out to be 80 % accurate for the month of May . We further went down to   analyse the data for day to day accuracy of forecast , and then we drew insights looking into each of   the SKU ’s and then looking at the forecast accuracy number for each of the cities . The insights we   obtained from this learning launch in Maharashtra , helped us understand the limitations of our ARIMA   forecasting model . I identified certain variables which were missing in the model , going ahead we will   be working to include these variables in our model , so that the forecasts become more accurate for   the model . For example , we were not factoring in the local strikes and constraints happening at the   breweries due the licencing issues from the government end and the competitor ’s behaviour , like a   new brand being introduced in the market or the pricing was changed by the competitor which   affected the actual sales of our product . These factors were further included into the model to further   refine the forecasting . 

 Approach – As already described earlier , the whole process of problem solving was based on using   different design thinking tools / activities . And at this stage when I needed to decide which   mathematical model will work best for forecasting , using Learning launch tool was the best idea .   Further after this learning launch was successful , insights were drawn to be incorporated in developing   a full - fledged model for the pan India launch . Alternate approach would have been to rather go with   the analytical analysis of the data already available with us and on basis of forecast accuracy obtained   for previous years we could make a decision whether which model to go forward for a Pan India   launch , but in my opinion that might not have been the best idea , as we recorded a lot many 

 interesting insights from the learning launch and understood the limitations of the model , which were   actually incorporated in the final pan India launch . 

 This is one of my experience where I used Learning Launch , although at that time I was not aware that   I was blindly in a way following the Design thinking model . Now that I have completed this course , I   am quite aware of all these tools and going ahead I am quite sure I would be using these tools more   consciously while working on my future projects . 


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 – Banco Santander Río de 

 Argentina ( India Business Unit ) 

 Challenge – I was working as an Intern with Banco Santander Río de 

 Argentina during the summers of 2013 . I was working in   the commercial planning department and the core challenge which the company was facing at that   time was to improve the accuracy of their demand planning . As the Alcohol Beverage industry is   influenced by a lot many internal factors ( supply , marketing , sales ) as well as external factors ( legal ,   regulations , climate , seasonality and other macro - economic factors ) , it was becoming difficult for the   company to forecast their demands accurately . Also , since the company ’s business unit was new in   India , so the processes development of various department was in the early phase . And hence the   demand planning was not based much on the data rather on experience and field knowledge . I   devoted my time working on data and finding out a suitable model to make the demand forecasting   process data oriented . 

 Selection – I choose the Learning launch tool here because , although after going through a lot of   secondary research ( doing online research for existing forecasting techniques and studying the alco   beverage industry ) and primary research ( talking to multiple stakeholders in company ) , and then   followed by brainstorming and funnelling the ideas through selection criteria filters , I had already   shortlisted a few of the models which could be tested for future forecasting . Now the next task was   to test the efficiency of these models and to see whether which model will work best for the company .   For this part of the project , I used Learning launch tool , the idea was to first proof test the model in a   particular state of India ( Maharashtra in our case ) and if the accuracy tested for this turns out to be   good . This model can be further launched for the pan India business or if it fails , insights can be drawn   to further improve the model . 

 Application – So the model I finalized which was showing the best accuracy till date was ARIMA   forecasting model . I went ahead for the Learning Launch using this model in one of the states of India   ( Maharashtra ) . We based the demand forecast for the month of May , based on solely this model . All   our sales , marketing and supply planning was based on this forecast and our next key goal was to   check whether our actual sales for the month of May , fits how closely with this model ’s forecast . 

 Insight – The model turned out to be 80 % accurate for the month of May . We further went down to   analyse the data for day to day accuracy of forecast , and then we drew insights looking into each of   the SKU ’s and then looking at the forecast accuracy number for each of the cities . The insights we   obtained from this learning launch in Maharashtra , helped us understand the limitations of our ARIMA   forecasting model . I identified certain variables which were missing in the model , going ahead we will   be working to include these variables in our model , so that the forecasts become more accurate for   the model . For example , we were not factoring in the local strikes and constraints happening at the   breweries due the licencing issues from the government end and the competitor ’s behaviour , like a   new brand being introduced in the market or the pricing was changed by the competitor which   affected the actual sales of our product . These factors were further included into the model to further   refine the forecasting . 

 Approach – As already described earlier , the whole process of problem solving was based on using   different design thinking tools / activities . And at this stage when I needed to decide which   mathematical model will work best for forecasting , using Learning launch tool was the best idea .   Further after this learning launch was successful , insights were drawn to be incorporated in developing   a full - fledged model for the pan India launch . Alternate approach would have been to rather go with   the analytical analysis of the data already available with us and on basis of forecast accuracy obtained   for previous years we could make a decision whether which model to go forward for a Pan India   launch , but in my opinion that might not have been the best idea , as we recorded a lot many 

 interesting insights from the learning launch and understood the limitations of the model , which were   actually incorporated in the final pan India launch . 

 This is one of my experience where I used Learning Launch , although at that time I was not aware that   I was blindly in a way following the Design thinking model . Now that I have completed this course , I   am quite aware of all these tools and going ahead I am quite sure I would be using these tools more   consciously while working on my future projects .
### Output: 