BIG DATA AND LOGISTICS ANALYTICS

 

BIG DATA and LOGISTIC ANALYTICS

 

Introduction:

Big data in logistics refers to the collection, processing, and analysis of complex datasets related to logistics management operations. You can use sensors, GPS devices, RFID tags, and enterprise resource planning (ERP) systems.

What is Big Data?

Big Data is a collection of data that is huge in volume yet growing exponentially with time. It is data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also data but with huge size.

Uses of big data in logistics

The following areas are excellent examples of how big data can be used to revolutionize the way logistics companies operate:

 

1.    Route optimization:

Big data and analytics tools in the logistics sector use weather data, shipment data, traffic situations, and delivery sequences to help you determine when it’s time to go. You can also assign the shortest route possible for delivery, saving a huge chunk of money that could have been channeled to fuel and other expenditures.

For big data to be successfully implemented in route optimization, data involved throughout the delivery process should be noted. These include:

  • The frequency in which customers order goods.
  • Number of vehicles available for delivery
  • Distance between the pick-up and delivery points
  • Areas with the most and the least orders

 

 

2.    Optimization of the last-mile processes:

Last-mile optimization is one of the areas in logistics operations that benefit hugely from big data. By analyzing data collected from various sources, logistics companies are better positioned to change and improve internal processes and control external factors in near real time. This increases transparency in delivery processes and improves customer satisfaction.

3.    Tracking the transportation of goods

Through GPS devices, RFID tags, and bar codes, big data analytics technology can capture real-time traffic data, making it easier for logistics managers to schedule deliveries conveniently. These technologies can also send automated notifications to receiving facility managers when the delivery is within a mile of its destination, giving them ample time to plan ahead and avoid unpleasant surprises.

Conclusion:

Big data has come a long way, but its utility is only now beginning. With regard to data processing and storage, elastic scalability provided by cloud computing has broadened its application. Graph databases are also developing into significant big data assets. To increase performance, offer flexibility, and promote agility, they are used to arrange chaotic and complex data elements in accordance with their relationships.

References:

https://addepto.com/blog/10-use-cases-of-big-data-in-logistics/

https://www.guru99.com/what-is-big-data.html

https://www.google.com/search?q=big+data+and+logistics+analytics&oq=&aqs=chrome.2.35i39i362l8.973880390j0j15&sourceid=chrome&ie=UTF-8

 

Narsima Ahmed

@INTERNATIONAL SCHOOL OF MANAGEMENT EXCELLENCE

Intern @Hunnarvi Technologies under guidance of Nanobi data and analytics pvt ltd.

Views are personal.

#Big data #logistics analytics #nanobi #hunnarvi #ISME

 

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