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.


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 .