Reflection - Learning launch tool

Challenge    In Indian railways, the Mumbai local trains are considered to be the lifeline of the local people who relies  heavily on them to commute for work, schools and colleges, basic travel for shopping, entertainment etc.  on a daily basis. Two of the drawbacks which the customers often complain about is the long queue for  the ticket service and frequent overcrowding of trains especially during peak travel hours such as morning  and evening when students and working class people commute to and fro to their schools and offices  respectively. Normally, the ticket purchase service is not online and one has to buy from the counter  section (monthly/yearly pass option available) or automated machine. However, a significant part of the  population relies on a daily purchase of tickets depending on the frequency with which they travel, for e.g.  some schools and colleges will demand only 2-3 days/week of classes, some rely heavily on entertainment  during weekends depending on the stops they have to get off, some labour workers and a lot of other  examples that demands daily purchase of tickets. Only two partitions are available, special one for the  handicapped and general one for all class of people ranging from young, adult and senior citizen. The idea  is to come up with a system which will not only harmonize the ticketing system but also reduce  overcrowding as the former one is hypothesize to affect the later one, and the customer will able to catch  a train he/she is intended to, as the train spans a travel time of 2hrs in her whole journey with more than  15 stops to reach her destination from Karjat to Mumbai. The challenge is to make the system as simple  as possible, easy and quick to rely on and most important it must be cheap for the railways to successfully  implement and test it in real scenario. Finally, the outcome of the project should be, less waiting time to  buy tickets, efficient for all class of people from young to senior citizens, reduced ticket price and less  crowds in trains.     Selection    One of the tool that we think that best suits the problem is ‘Learning launch tool’ nicely and clearly  explained in module 4. The reason we selected this, as it involves an idea in the form of hypothesis which  directly targets the need of large number of customers. Coming back to our challenge, the idea is to create  a ticketing system for trains where the waiting hours are reduced and the hypothesis is that this can have  a significant effect in reducing the crowd in a running train. The idea has the potential to meet both the  needs of the customers i.e. to save time while buying tickets and also to reduce crowd to some extent.  The project also involves quick frequent interaction with customers belonging to wide class of age group  and to test the customer assumptions made to execute the idea.     Application    The first thing we did, is, we created a hypothesis which is described above believed to solve two specific  need of large group of customers. We then divided our teams in to various sub teams (hiring some  volunteers to fill up the space) and began to conduct some mock interviews for the customers. The sub- teams were designed to target different age-class of people such as young, adult and seniors.     The basic questions asked are as follows: How frequent do they travel? Do they buy tickets on daily basis  or monthly? Will they prefer to buy them online? Whether they use smartphones? Due to long queue’s,  how often they miss their trains they intend to catch? If they catch the train on time, are there any  differences in the crowd inside as compared to when they miss their train? On an average, are they able  to predict or guess the waiting time they might have to spend on the queues? How many of them travels

on a specific time daily, or are there any irregularities in their schedules? What are the main purpose of  travel? Are they willing to accept a change in the way of buying tickets? How many of them will volunteer  to participate in an experimental trial of a new ticketing system? How many of them are willing to undergo  a trial test, where they will be asked to notice a difference (waiting time, crowd etc) when they bought  ticket through both system i.e. old and new one. The focus was to collect both the quality of data and the  quantity, so that it can be enough to test and simultaneously lauch the system. Few questions were also  targeted towards the staff working over the counter to sell tickets. Finally, the data collected was  organized and categorize depending on the task set up for the respective sub-teams.     When the data collected was sufficient enough to analyze by all of our group teams, we then quickly  started conducting some small experiments. The very first thing we did, was, we launched an online  application for smartphones suitable for all platforms. One of our groups installed a smart machine on  each stations (just in front of platforms) where the trains stops. The no of smart machine per stations was  4. The smart machines will perform the following functions simultaneously: gives a map for the whole  journey, train timings for the current day, options to buy daily/monthly/yearly tickets from the current  station to the designated station, a scanner to scan the respective tickets and also for someone who wish  to buy tickets via application. At the same time, the old system for over the counter tickets will be kept as  it is. For smart machines, two person will be employed to guide some customers unable to follow the  instructions on the screen. We planned to conduct the experiments for a minimum duration of 2 months,  in which our sub-teams and volunteers did the following trials:

-  some volunteers (who have access to smartphones) were asked to use only online service for one  month

-  Those who don’t have access to smartphones were asked to use the smart machines for one  month

-  Then they were asked to use the old system for the next one month

-  A few volunteers were asked to use the old system for 2 months

-  The volunteers chosen for each stations are frequent travelers and rely on daily purchase of  tickets.

-  The quantity was good enough given the fact that there was a huge demand to solve the particular  problem.

-  The volunteers in all the stations were divided in to different age groups and they were asked to  keep a track of the following without disturbing their daily schedule: Based on the two months  experiment, how easier is it for them to buy tickets as compared to old one? Do they wait for long  time in the queue? Do they see the differences in the crowd inside the train? Is the difference less  or large? If less, are they comfortable inside when all the seats are filled and they have to stand in  the aisle to travel? Now, are they able to catch the intended train? How often? Are they able to  predict the waiting time to buy tickets from the counter, smart machines or just to scan the ticket  from the application?

-  The observations from the whole trials will then be drawn into a final conclusion by the sub-teams  who will then decide to pass the hypothesis that changing the ticketing system can have an effect  on the crowd gathered inside the train on each platforms, thereby making the travel easier. When  the project is finished, by further brainstorming on the some specific questions by the teams such  as what do we know? What do we don’t know? What do we need to know? How do we learn what  we need to know? The whole idea can then be tested in real time if approved by the railway  authorities, if it is cost efficient, ticket can be made cheaper, not too much loss of man power,  significant reduction in waiting time, less crowd inside the train and finally customers are happier  with the whole concept.

Insight and approach    The most important insight our teams gained throughout the project was thinking with one clear  perception in mind i.e. to step into the shoes of our customers and to understand the need for a change  from their point of view. This helped us to develop our plan at each step (by selecting the learning launch  tool) and mostly to gain the trust of our customers who then happily agreed to participate in the  experimental trials of a new ticketing approach which will lead to reduce waiting time at each stop and as  a consequence of which, will lead to less crowd in trains.    We then brainstormed some questions in each of our teams (along with railway authorities), in order to  test the idea in real life scenario and came to the conclusion that, as of now, learning launch is the best  tool that can be applied in the design development of the project. One alternative tool, which we think,  can be applied and tested is Mind mapping tool which is in development and now only few insight can be  shared. The objective as defined in module 3, is to look for patterns and insight in large quantity of data  collected to explore What is criteria of the design process before jumping to What if criteria? The data  collection process is the same as defined in learning launch process. Thus by implementing Mind mapping  tool (for What is), the next criteria can be establish for the what if phase of the design thinking process  which can lead to the last two stages of the process that are What wows? and what works (it involves  learning launch). Hence, as learning launch was applied directly as a tool for our case, and if it’s find  success, this time, if it’s appropriate the process can be started by Mind mapping tool (1st phase of the  design process) to move next to 2nd and 3rd phase, and to check if they are really needed to carry forward  to the final 4th stage.                 *This reflection is set in the time 6 years ago (2018), when the author used to live in Mumbai

