“Learning Launch to Convince Headquarters of Regional Solution Development”

Challenge

At a large multinational company with headquarters located away from our region, convincing

high-level management to make big investments in research and development can be very challenging.  Even so, if the company wants to stay competitive and lead innovation in the region where Agriculture is  the most promising field, a solid work of customer investigation and validation is necessary. For our  region, assertiveness when choosing in which products to invest in first is key to gain trust from  headquarters so we can convince them that our region should be the global competence center in  agriculture. That said, engineers have developed a new concept for a grain-harvesting machine, one that  allows the machine to use the wasted hit from the engine as energy to reduce the humidity level from  grains during harvesting. With this in mind, our team faced two major challenges before starting the  project. The first challenge was to figure out how much extra money farmers are willing to pay for such  a solution plus the optimal percentage level of grain-humidity reduction. The second challenge was to  prove the perceived value to OEM companies, as they are our main customers.

Selection

In order to overcome the challenges mentioned above, our team decided to use one of the

most important design thinking tools, the learning launch. This process involves coming up with key  customer execution and defensibility assumptions to figure out what customer need is and whether or  not they are willing to pay for it.  Getting out of the building and talking to customers very quickly is key.  “An experimental learning launch is a search for the truth. The purpose of a learning launch is not to  prove you are smart. The purpose of a learning launch is not to prove you have a good idea. The  purpose of a learning launch is to get the truth and then the truth either confirms or dis-confirms your  assumptions”, Stefan Kraus - Learning Launch Tool week 4. The first thing we did was to create a set of key  hypothesis related to specific group of customers, farmers and OEM companies. Hypothesis number one  was that farmers are willing to pay an additional of $50.000 if the system dries 3% of humidity from  grains during harvesting. Hypothesis number two was that OEM companies are willing to implement the  system on their new machines if we confirmed that 25 of the largest farmers in the region are willing to  pay the additional $50.000 for the 1% humidity reduction.

Application

To test our hypothesis, we chose to run a pilot at the largest agriculture trade-show in the

region. Because the fair unites both of our key stakeholders in one place, farmers and OEM, it was a  great opportunity to observe their reactions and allow room for conversation. We built a fake screen  demonstrator that simulated the screen of a harvesting machine while doing its job. There, our  stakeholders could observe all the usual information such as the amount of grain harvested by hectare,  the speed, the time, oil consumption, soil conditions, and other relevant information. It is important to  note that average price for a 60kg sack of soybean or corn with the humidity level of 18% is $30. After  harvesting, the grains are stored in large silos and remain there for a certain period until the humidity  level is reduced to approximately 15%. This means that farmers are not able to sell their grains until the  optimal percentage level is reached. Thus, leading to high logistics and operational costs. Our value  proposition was to reduce the 3% humidity level during harvesting, which will lead to higher operational  efficiency, reduce storage costs and most importantly provide our customers with a competitive  advantage as they can sell their grains directly to their end-customers after harvesting. Now, let’s go  back to the trade show. Our fake screen demonstrator allowed farmers to simulate the percentage of  grain humidity reduction for every 60kg sack in comparison to the additional price they were willing to  pay for it (1% reduction equal $25.000, 2%reduction equals $35.000 and 3%redution equals $50.000).  Equally important, was that the OEM were present and observed how farmers reacted when the fake  screen demonstrator simulated the percentage of grain humidity that was being reduced. Seeing that,

our negotiation power increased with the OEM. As a result, over 200 farmers tested the fake screen  demonstrators and more than 10 OEM passed by our booth to see the experiment running. After  analyzing all the data collected during the trade show, our team concluded that the most rated option  was of 2 % humidity reduction for $35.000. Even though this sounds promising, our team is unsure  about the technical feasibility of the solution and if we can price it under $50.000. However, that is a  separate problem we will have to solve. The main goal of the trade-show was achieved, validation or  invalidation of customer´s desirability and willingness to pay for the solution.

Insight

What I found most interesting about using the learning launch tool is that all of our key

hypothesis were tested in a very effective and meaningful way. Bringing our stakeholders together at  the tradeshow and allowing room for conversation and observation increased trust and empowered our  negotiation strategy. Afterwards when we set down to with OEM to discuss the research and  development opportunities, everyone spoke the same language and referred to the tradeshow as a  successful approach. Additionally, our team wrote down the most relevant quotations from farmers  during the trade show, quotes such as” oww, if I can reduce 2% humidity during harvesting, my logistics  costs will decrease by 25%, I will easily pay an additional $35.000 for the system.” Thus, with the OEM  on board, convincing headquarters about developing the solution in the region became more feasible.  All things considered, running experiments to test and validate or invalidate hypothesis is one of the  most important phases of design thinking and lean startups methodologies. For this reason, no business  idea survives the first contact with the customers because the truth is out there and we are responsible  for seeking it.

Approach

On balance, the launch tool was extremely effective and appropriate for the challenge we had

to overcome. However, we are still very far away from developing the system, what we tested was the  value proposition and not the user interface and easiness of use of the product. A different approach  will be required before the solution goes into development. I would use a mind-mapping approach to  get everyone on the same page and gain a common understanding about how the design and usability  of the solution should look like. In design thinking we use the mind mapping process to capture and  represent the minds of our customers. “We involve our colleagues in order to develop a common mind  among them regarding the current reality of a design challenge. Through mind mapping, we extract  meaning from data in a way that lets patterns and insights emerge” Jessica Alvarez, - Mind Mapping Tool  Week 3. In the long run, one must be capable of properly identifying the moments and phases of a  project to best select the tools that must be used. I am confident that for this specific challenge the  learning launch tool was the best option; but at the same time I recognize that for the other phases that  are to come different tools such as visualization and mind mapping are more appropriated.

