Design Thinking for Innovation  University of Virginia  Darden School of Business    Reflection – Mind Mapping    Challenge:  Connecting time for passengers at airports has always been a topic for optimization by airlines and airports  alike. The main idea is to minimize connecting time at airports because this means flights with shortest  transit times may get preference on travelers’ pick list when browsing for air travel itineraries. However,  it is a balancing act to minimize the connecting time at airports. There’s a whole chain of network partners  affecting this determined time, such as Air Traffic Management and Airport Operators whose priorities  are to ensure safe and economic flight operations. There’s also the commercial aspect of airlines buying  “landing slot times”, one-hour time brackets for landing and takeoff. All of this mix and much more  complications such as lack of data and data sharing among partners made the Minimum Connecting Time  something close to an intuitive judgement.  With the advent of Big Data and Cloud technologies, it was decided to give the Connecting Time another  go. This time, the challenge was first to determine the “actual connecting time” in terminals, predict,  based on machine learning, the future connecting time, and then attempt to optimize by looking at all  findings to come up with a minimum connecting time.    Selection:  Mind map was chosen because of its intuitive approach engaging various stakeholders to find data sources  and mines to tackle this challenge. One needs to keep in mind that up to that point, such an undertaking  was not attempted on this scale, there was always limited bilateral work between airports and airlines,  but this was truly a wholistic and global approach. The project team figured out that in order to get the  best ideas out of people, they needed to be a same physical space to allow interactions and represent a  whole array of sectors, fields of study and expertise – a mosaic of stakeholders (including avid travelers).  The workshop participants were grouped in teams to discuss about the potential sources of data and  approaches that would help provide insights on solving the challenge.    Application:  Prior to coming to the point of the mind mapping, the project team investigated the “what is” but asking  SMEs, passengers, academics (mathematicians, OR), and big data experts. The project team used  customer diaries, empathy maps and visualization. The common denominator between all those  stakeholders where that they were themselves travelers and ended up sympathizing with this challenge  on a personal basis rather than a research problem. The project team then placed all those collected  artifacts and data on a white wall and asked the workshop participants to cluster leads that could help  unravel the challenge. It was interactive and there was lots of room for discussions and debates,  eventually the project team got a whole work cut out for them sorting through the post-it notes and trying  to make sense of the solution taking shape.    Insight:  All previous failed attempts to tackle this challenge on a global scale assumed that airports and airlines  needed to share data and this data collection effort was proven to be too much of a hassle. However, the  project team along with the workshop participants discovered, that, by using passenger transactional data  the data science team could extrapolate the needed information. This data resides in an industry

organization and was virtually untapped. The passenger/flight data along with data from open source  platform would make it possible to start a data lake (collecting, cleansing and storing) and apply data  science techniques to provide prediction.    Approach:  Mind Mapping is a great way to gain insights, ideas and out-of-the box feedback. I would employ this again  thin the future because it gets people talking to each other, brainstorming and challenging their  assumptions when they see the big picture and listen to their value chain neighbors. In the example I  provided, Mind Mapping worked effectively, however the undertaken scope by the project team was  enormous and could have easily been split up to phases or teams. It was a massive global challenge that  is still in its infancy stage but is set to revolutionize the experience of travel passengers. Also, it means  huge gains for airlines, cost savings for airports and potential reduction in carbon emissions. Mind Map  allowed the project team to figure out a back-door to data, bypassing the exhausting data collection effort,  therefore it made the work-in-progress to solve this challenge possible.


Design Thinking for Innovation University of Virginia Darden School of Business Reflection – Mind Mapping Challenge : Connecting time for passengers at airports has always been a topic for optimization by airlines and airports alike . The main idea is to minimize connecting time at airports because this means flights with shortest transit times may get preference on travelers ’ pick list when browsing for air travel itineraries . However , it is a balancing act to minimize the connecting time at airports . There ’s a whole chain of network partners affecting this determined time , such as Air Traffic Management and Airport Operators whose priorities are to ensure safe and economic flight operations . There ’s also the commercial aspect of airlines buying “ landing slot times ” , one - hour time brackets for landing and takeoff . All of this mix and much more complications such as lack of data and data sharing among partners made the Minimum Connecting Time something close to an intuitive judgement . With the advent of Big Data and Cloud technologies , it was decided to give the Connecting Time another go . This time , the challenge was first to determine the “ actual connecting time ” in terminals , predict , based on machine learning , the future connecting time , and then attempt to optimize by looking at all findings to come up with a minimum connecting time . Selection : Mind map was chosen because of its intuitive approach engaging various stakeholders to find data sources and mines to tackle this challenge . One needs to keep in mind that up to that point , such an undertaking was not attempted on this scale , there was always limited bilateral work between airports and airlines , but this was truly a wholistic and global approach . The project team figured out that in order to get the best ideas out of people , they needed to be a same physical space to allow interactions and represent a whole array of sectors , fields of study and expertise – a mosaic of stakeholders ( including avid travelers ) . The workshop participants were grouped in teams to discuss about the potential sources of data and approaches that would help provide insights on solving the challenge . Application : Prior to coming to the point of the mind mapping , the project team investigated the “ what is ” but asking SMEs , passengers , academics ( mathematicians , OR ) , and big data experts . The project team used customer diaries , empathy maps and visualization . The common denominator between all those stakeholders where that they were themselves travelers and ended up sympathizing with this challenge on a personal basis rather than a research problem . The project team then placed all those collected artifacts and data on a white wall and asked the workshop participants to cluster leads that could help unravel the challenge . It was interactive and there was lots of room for discussions and debates , eventually the project team got a whole work cut out for them sorting through the post - it notes and trying to make sense of the solution taking shape . Insight : All previous failed attempts to tackle this challenge on a global scale assumed that airports and airlines needed to share data and this data collection effort was proven to be too much of a hassle . However , the project team along with the workshop participants discovered , that , by using passenger transactional data the data science team could extrapolate the needed information . This data resides in an industry organization and was virtually untapped . The passenger / flight data along with data from open source platform would make it possible to start a data lake ( collecting , cleansing and storing ) and apply data science techniques to provide prediction . Approach : Mind Mapping is a great way to gain insights , ideas and out - of - the box feedback . I would employ this again thin the future because it gets people talking to each other , brainstorming and challenging their assumptions when they see the big picture and listen to their value chain neighbors . In the example I provided , Mind Mapping worked effectively , however the undertaken scope by the project team was enormous and could have easily been split up to phases or teams . It was a massive global challenge that is still in its infancy stage but is set to revolutionize the experience of travel passengers . Also , it means huge gains for airlines , cost savings for airports and potential reduction in carbon emissions . Mind Map allowed the project team to figure out a back - door to data , bypassing the exhausting data collection effort , therefore it made the work - in - progress to solve this challenge possible .