Challenge

In South-East Asia, especially in the Indian subcontinent, the economy is largely dependent on  agriculture. It’s been this way since ancient times because of the abundance of fertile land and  weather pattern. Though in recent times the economy significantly moved towards  manufacturing and heavy industry. But the rural economy is largely dependent on agriculture.  Bangladesh is a small riverine country in this region. The country has a subtropical monsoon  climate and the majority of its population is living in rural areas. Dairying is a good source of  income to small and marginal farmers. The feeds required for milk production can be met from  their limited land resources as most of the milch animals are ruminants and the majority of their  food can be derived from forages, coarse roughages, and byproducts not utilized by human  beings, without incurring much additional cost. The demand for milk and milk products is  increasing because of the rapid increase in population, the spread of education, and growing  nutrition awareness. But the inefficient and age-old farming methodology is the root cause of  millions of tons of annual deficiency of milk in our country. So I and my team, which consist of  four other members wanted to capitalize on this opportunity. We wanted to incorporate modern  technology in dairy farming for efficiency and we also wanted to deliver it to the rural farmers in  a very affordable rate. So after research, existing data analysis we came up with an integrated  idea and three major prototypes to test. A health tracker-a wearable IoT device for the cow,  which monitors the health and predicts heat. An RFID Tracker- an ear clip-on tag to track a cow's  location and to store breed information. And a digital app for a one-stop digital platform for  training, knowledge, and access to resources. And now in this module, I want to apply the  “Learning Launch” design tool to verify the primary assumptions about the prototypes.    Selection

A learning launch is an experiment conducted in the marketplace quickly and inexpensively. It  forms a bridge between customer co-creation and commercial rollout. As the previous steps like  idea generation and prototyping are already done in this case learning launch is the perfect tool  to capitalize at this point in time. As we need to verify our assumptions through real-time data  and customer feedback. And it is required to get the answer to the questions like 1.what do we  know? 2. What do we not know? And 3. What we need to know. And as the hypothesis is done  and the key assumptions are surfaced, now only real-time testing in the market place can prove  the worth of the idea.

Application

Since this is a learning launch and not a pilot, it is important to plan for it to end. Set concrete  limits on key variables, such as time, geography, number of customers, features, and partner  firms. So for our first learning launch, we focused on the structure of the existing market and  current constraints in it. We delivered one hundred pieces of health tracker and RFID tracker to  three different dairy firms in three different regions of the country. Selecting three different

regions is necessary to bring diversity in the test. And we also provide the farmers our digital app  and primary training to get along with it. We delivered the product for a fraction of the  production cost and the testing period was scheduled to be one month long. During this one  month, the dairy farmers will use the technology properly. And after the month of usage, we  hoped that we would find enough data to verify our assumption. If the assumptions are verified  then we could go to the mass production and marketing stage. Or if otherwise happened we can  go to the drawing board and run our second learning launch in some time.

Insight

Our main assumption was that this tools will help the farmers to become more organized than  ever before. So the efficiency will increase and with efficiency total production and profit margin  will also increase. The wearable health tracker will constantly update the temperature of the  animals in the mobile app of the farmer. And immediate action can be taken if there is any sick  animal in the firm. And the RFID tracker will help the farmers to store the relative necessary  information more conveniently. And the final outcome was largely positive. In three firms  production was increased moderately and sick animals were identified and treated much faster  than usual time. And the overall customer feedback was positive. But as usual there was some  isolated cases of device malfunction and disintegration due to extreme environment. As we used  leather strap for health tracker, in some case the leather was disintegrated due to exposure in  constant moisture. In this regard, we need to go back to the design board and do some  modification in the product. But otherwise, it was a very successful experiment.

Approach

The learning launch is when your growth project first makes contact with reality. As our team has  already gone through several design thinking activities/tools and are at this stage in the timeline,  the learning launch is the most appropriate tool. So there is no confusion in the choice of tool.  But to successfully implement the learning launch tool we have to beware of four important  things, confirmation bias, quantity of data, quality of data, and group thinking. In these case  quality of was good and quantity was sufficient. But as it was a startup and the all the team  members are from the same field so there might be some confirmation bias due to lack of  diversity in the group. And that should be modified in the long run.

