Internet of Transport: Insights from G7’s Julian Ma on the Future of Logistics

October 25, 2017

 

 

The term IoT has been around for decades - so long that it has become overhyped, thrown into every context ranging from optimized energy grids to wi-fi enabled diapers.  In a recent interview, G7’s Julian Ma brings the focus into logistics by distilling the Internet of Transport into its three progressive phases.  

 

States Ma, the first stage in IoT for tech companies, and OEMs alike, is data acquisition.  While having devices embedded into life routines may not directly benefit the customer, the information is critical for research and development.  For G7, data acquisition comes not only through the G7 Smart device, but also through supplementary sensors.  Such sensors begin at the front of the vehicle, tracking driver behavior, road conditions, and traffic constraints.  In the back, sensors may detect cargo features, e.g. temperature and shock-impact, as well as external vehicle conditions, such as tire tread and roll stability.

 

The second stage addresses data extrapolation.  By extensively combing through the data, G7 identifies patterns and creates definitions of high-risk scenarios.  This type of data interpretation was traditionally conducted by fleet managers, however today’s computer systems allow for more extensive calculations.  One key factor is digitizing preexisting experience from legacy fleet managers so that it can be factored into future scenarios.

 

The third stage in IoT progress is closing the feedback loop and making predictions.  This final stage is the most critical as it integrates artificial intelligence into the daily decision-making process.  After fleets execute recommended actions, the results are captured and brought back into the system for subsequent iterations.

 

G7’s end goal for the third stage is to optimize in three critical areas: timeliness, safety, and cost.  In the past, G7 has focused on capturing fleet activity and providing this data back to the fleets, themselves.  However, most recently, with G7 Powered by Argus, G7 has made broad steps into intelligent decision-making.  G7 Powered by Argus not only takes collective information across fleets to make the most accurate predictions in delivery times but also allows for complete re-routing.  Instead of asking the system, “how long?”, fleet managers would tell the system, “make it fast.”

 

Another key area in IoT is “management by exception”.  Rather than track where all vehicles are at all time, system users have the base assumption all vehicles are where they should be.  Managers are only notified when vehicles are not on track to meet their expected targets.  This is factored into a risk profile.  By considering whether issues are caused by driver, vehicle, route, or even customer demand, fleets can then preempt complications and make the best possible adjustments.

 

In the future, G7 plans to layer complementary data sources which serve to refine its predictions. One key aspect would be the dimension of human analysis: knowing which drivers are most suited for which business areas and identify areas of training to solve problems: picking the low-hanging fruit.  By pursuing this direction, G7 ties its growth directly to improvements to its customers and is betting squarely on the technology to facilitate said improvements.

 

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