Example Reflection – Learning Launch

Challenge & Selection

At the moment I work for a Danish company specialising in food quality analysis at  the global scale. The company is building both hardware- sample analysis  instruments and software. The high rate of instruments utilisation, are crucial to  global food producers. Hence monitoring and preventing any downtime of  instruments can help to avoid big losses in production capacity.

The challenge is to find out if we have enough data points to accurately predict  potential instrument downtime in order to give an early warning. The design thinking  methodology can quickly address this issue focusing on customers needs. The  process will follow specific steps that will help us better understand the current  situation and to uncover what are the underlying needs of customers when trying to  solve the problem of instruments downtime predictions.

Application

After collecting the data to assess “What is the problem” from as broad perspective  as possible, we would like to examine what is the “As-Is” situation. We could do that  by visiting some customers and observing how they use our instruments and  conducting interviews. The question we could ask could be related to e.g what do  customers do when there is an actual downtime? How often this occurs? Are those  incidents reported? How quickly do we send a technician to fix the issue? Who is  accountable for the downtime if standard operating procedures (SOP) are not  followed?  We could then combine that empirical data with other data points to  formulate our hypothesis that downtime of our instruments (or any production critical  instruments) can have a direct impact on revenue losses, hence following SOP is  critical.

By developing mind-mapping in collaboration with customers we could address what  are the SOP for each interviewed company and what each instrument’s operator is  doing to follow it. That will help us to address if there are any gaps in the process. It  will also help us to generate new ideas on what data points we can collect, either  internally or by asking customers to submit their operating practice. Knowing that will  make is easier for us to find out if those data points are sufficient enough to correctly  predict potential instrument downtime and perhaps also to address the root cause of  it.

That is where we’ll create a testable prototype to address our assumption. This is the  learning launch. The purpose is to learn not to introduce a new product yet. The

learning launch is based on an idea which has to be suitable for a specific person/s,  that has a specific need. The idea should meet that need and provide better solution  than the current alternative. In this case, the idea is to develop a service, e.g an app  or data dashboard that is suitable to our customers who are looking for a quick  notification about potential downtime as an alternative to the current process which is  reactive rather than proactive, resulting in slowed down or event stopped production.

Insight

Since this is a real project, planned to be tested the next month, I can not yet say  much about the insight that I’ve gained from applying the learning launch tool.  Needless to say, I’m very excited about implementing the methods, especially that  the company I work for is very conservative and usually, customer tests are taking  months to complete. To run such a test in a short amount of time and get relevant  insides through this method and this structured approach is something very new to  this company.

Approach

I believe that regardless of the results from this learning launch I will be positive  continuing with design thinking. Perhaps in order to get even quicker hypothesis  validation I will use google design sprint and then conduct more detailed  anthropological research after the hypothesis will be quickly tested.


Example Reflection – Learning Launch Challenge & Selection At the moment I work for a Danish company specialising in food quality analysis at the global scale . The company is building both hardware- sample analysis instruments and software . The high rate of instruments utilisation , are crucial to global food producers . Hence monitoring and preventing any downtime of instruments can help to avoid big losses in production capacity . The challenge is to find out if we have enough data points to accurately predict potential instrument downtime in order to give an early warning . The design thinking methodology can quickly address this issue focusing on customers needs . The process will follow specific steps that will help us better understand the current situation and to uncover what are the underlying needs of customers when trying to solve the problem of instruments downtime predictions . Application After collecting the data to assess “ What is the problem ” from as broad perspective as possible , we would like to examine what is the “ As - Is ” situation . We could do that by visiting some customers and observing how they use our instruments and conducting interviews . The question we could ask could be related to e.g what do customers do when there is an actual downtime ? How often this occurs ? Are those incidents reported ? How quickly do we send a technician to fix the issue ? Who is accountable for the downtime if standard operating procedures ( SOP ) are not followed ? We could then combine that empirical data with other data points to formulate our hypothesis that downtime of our instruments ( or any production critical instruments ) can have a direct impact on revenue losses , hence following SOP is critical . By developing mind - mapping in collaboration with customers we could address what are the SOP for each interviewed company and what each instrument ’s operator is doing to follow it . That will help us to address if there are any gaps in the process . It will also help us to generate new ideas on what data points we can collect , either internally or by asking customers to submit their operating practice . Knowing that will make is easier for us to find out if those data points are sufficient enough to correctly predict potential instrument downtime and perhaps also to address the root cause of it . That is where we ’ll create a testable prototype to address our assumption . This is the learning launch . The purpose is to learn not to introduce a new product yet . The learning launch is based on an idea which has to be suitable for a specific person / s , that has a specific need . The idea should meet that need and provide better solution than the current alternative . In this case , the idea is to develop a service , e.g an app or data dashboard that is suitable to our customers who are looking for a quick notification about potential downtime as an alternative to the current process which is reactive rather than proactive , resulting in slowed down or event stopped production . Insight Since this is a real project , planned to be tested the next month , I can not yet say much about the insight that I ’ve gained from applying the learning launch tool . Needless to say , I ’m very excited about implementing the methods , especially that the company I work for is very conservative and usually , customer tests are taking months to complete . To run such a test in a short amount of time and get relevant insides through this method and this structured approach is something very new to this company . Approach I believe that regardless of the results from this learning launch I will be positive continuing with design thinking . Perhaps in order to get even quicker hypothesis validation I will use google design sprint and then conduct more detailed anthropological research after the hypothesis will be quickly tested .