“ 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 60 kg 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 60 kg 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 . 


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 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 60 kg 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 60 kg 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 .
### Output: 