Big Data and Route Planning: How They Work Together

“Every company has big data in its future and every company will eventually be in the data business” - Thomas H. Davenport

The above statement instills the clarity of what the future is going to run on - technology and big data. 

Therefore, a niche such as software logistical management that depends on data analytics should progress swiftly towards processing big data. But do you know the biggest fleet management segment that gets impacted by big data? Route planning! Why? Let’s learn in this article! 

How is Big Data Related to Route Planning? 

Route planning heavily relies on forecasts and external factors. Weather and traffic forecasts influence the entire supply chain through effective route planning. After all, regardless of the short distance of a route, landslides aren’t ideal situations to land your vehicle at, right? 

There is only so much that real-time data can do to help forecast logistical parameters. It is big data that allows managers to see the bigger picture and make accurate route budgets and ETA forecasts.

The Interrelation of Route Planning and Big Data 

An interesting thing to note about big data is that it relies on the process that it is meant to aid in the future. Confused? Let us explain! 

Big data is generated when vehicles with integrated sensors collect data over an extended period. It is when this information is collected and analyzed that the resulting big data creates better understanding of external factors. 

Here are a few resources from where route planning aiding big data can be collected:

• GPS or sim-based tracking data 

• Financial business forecasts 

• Social media updates regarding roads, and traffic 

• Further traffic and weather data from forecast systems 

• Vehicle diagnostics 

• Driver behaviour data 

• Operational data from the organization 

An Example of How They Come Together 

Big data is simply a collection of data from various sources that is later applied on a single process. Therefore, when big data is collected through sources such as weather forecasts and operational data, it can be analyzed to create optimum routes. 

Big data is used to analyze the possibilities between the starting and ending point and through machine learning, it is able to decipher one or two routes that have the least traffic and weather problems and will cost the least amount of time and fuel to the company.