Every industry is taking advantage of analytics. Analytics play a vital role in transport and logistics industry for engagement and conversions. The use of analytics in transport industry has completely changed the landscape of working methods.

Let’s take a view of the role of analytics in the transport and logistics.

Logistics and Transport Industry Overview

• Postal Services: Annual growth is -.3%, total worth is $69 billion.
• Courier:    Annual Growth is 1.3%; total worth is $82 billion.
• Air Freight:    2 billion dollars of import and 14 billion dollars of export

The above figures states about the dollar value of this industry. Even one of the famous brands in the cargo shipped around 16 billion tons kilometer freight in 2014. It’s an enormous weight. The transport industry carries a lot weights as well as returns on investment for the Transportation and Logistics Suppliers as stated in the above figures.

Nevertheless, it is also a striking fact that air cargo is one of the fast and furious ways for transporting articles from storehouse to the customer. Its effectiveness is vital to the global economic success. Moreover, the supply chain requirements need to meet quickly because the competition is fast and furious.

 In this way, cargo carriers require making the best use of technology. Exercising best technology will assure the whole the supply chain of air cargo flexible, adaptable and connected.  Though, for proper optimization of the entire process need excellent decision making as well as quality analytics. Therefore, the ones who can go through full data providers and develop action plans are more likely to win in the long run. 

Logistics and Transport Industry Analysis

Along earning a great amount of revenues, this industry is also facing several problems like data access, delivery issues, data disconnection etc. Transportation & Logistics Suppliers that are operating within the cargo world needs collaboration with their counterparts to access data easily and share common problems and issues such as delay in delivery; it can be easily handled through a logistic alliance.

Another problem that is encountered by transport industry is data disconnection across various phases in freight shipments. Because data is stored in different structures and formats that restricts all the stakeholders to access it.  Thus it is difficult to get the clear picture of what is going on. Hence it is hard to take full advantage of analytics. Therefore, it is essential to bring the entire data into the cloud because it will link application (like silos) and provide information to all the involved parties in the real state of time.

Challenge: Using Analytics in Logistics and Transport Industry

Data Integration: As mentioned above, data collection is quite tough because data is pooled in various software houses and external databases that make it hard for one to view the entire picture of information.

Indeed, during project analysis, 80% time has been spent in dealing with data issues. Boosting efficiency needs processes of data integration and improvement in collaboration among all stakeholders.

On other side, business users also need to employ data connectors to tap into external systems. Because the quantity of data collected may seem enormous, however, data profiling can facilitate in pinpointing routine trouble points. In this way, important issues are:-   

• How to get better service levels and enhance capacity to meet the demand?
• The impact of disruptive technologies in the air freight industry, for instance, 100 ton of air shipment, etc.

Customer Analytics: Logistics and Transport Industry

Descriptive analytics can aid to highlight your customer base by developing the detailed customer profile. These profiles will depend on the data of industry demographics, buying patterns, shipping history, location, size, etc. A particular marketing campaign can be developed based on the data of customer profile. It will help in customer segmentation as well as forecasting sales and consumer behavior.

Analytics can also help to minimize transportation cost and maximize revenue by forecasting demand while meeting any limitations of capacity. The model of analytics would include several predictors like show up behavior, seasonal influence, buying pattern and cargo capacity availability on the monthly, weekly and daily basis. The results of analytics should be consistently updated with real-time results. In this way, a business plan can be able to adopt changes through adjusting schedules of flights or prices that are charged during different periods of demand. Consequently, it would help in maximizing revenue and enhance customer satisfaction.     

Conclusion

Companies need to think about strategic alliances and work in joint ventures to take the maximum advantage of analytics and data. The purpose is to help each other in accessing rich data of the customer for better marketing and sales planning.

The additional benefit is to support each other in adopting data-driven technologies like IIOT which will also help in certain applications include shipments tracking in real-time, delivery to last-mile, route optimization, forecasted asset maintenance, optimization of warehouse capacity, etc.