Data analytics for logistics and transportation
How is data analytics used in the transportation industry?
The transportation and logistics industry are faced with many specific challenges. For example, road conditions, weather conditions, access difficulties during the last mile of delivery and the presence of the customer on site are all factors that greatly influence the price of transport. Thanks to Big data it is possible to have a 360° view on the totality of the supply chain parameters.
Leveraging transportation company data with big data can help solve the problem of inefficiency when delivering goods on the last mile. In a typical supply chain, approximately 28% of the delivery price of a package depends on the last mile of delivery. With Big Data it is possible to better predict the delivery means adapted to this last mile. It is also possible to know the number of floors that the delivery workers must climb, know if and when the recipient is home and can also report any damage they may have caused to the package.
The traveling salesman algorithm has been used for a long time to optimize routes and delivery times. Unfortunately, the more pickup and drop-off points there are the more computational resources are required. Thanks to Big data and the machine learning that feeds off its information it is now possible to integrate road quality, changes in fuel prices as well as other difficult to predict variables into transportation cost calculations.
Delivering sensitive, fragile products or products with hazardous contents is an eternal headache for transportation companies. It requires special pick-up and drop-off conditions that often result in undesirable additional costs. This is especially true for perishable goods. More and more carriers are equipping their refrigerated trucks with IoT sensors to monitor the quality of the products being transported. Larger companies are even using blockchain to create smart contracts that link contract compliance to cold chain compliance. All of this is greatly simplified by big data.
Big data allows for the complete automation of warehouse management as well as the entire supply chain. Market leaders like Amazon could not operate without these data science related technologies. The number of movements in the supply chain is so large that it becomes impossible to rely on human operators. Big data mining modules can directly control robots in warehouses allowing for global supervision when the transportation company’s data lake is tapped into for real-time insight into the entire supply chain.
How can big data transform the transportation industry?
Even though Big Data has been utilized the transportation and logistics industry for quite some time, but can now go even further in its use cases. The following 7 not so frequently identified use can that can provide a definite competitive advantage to companies in the sector.
- Reuse data
- A clear view of the weather
- Vehicle fleets
- Business forecasts
- Industrial performance
- Web Site Data
- Social media information
Reuse data from already operational systems in a more rational way
Transportation companies can take advantage of the vast data resources already collected by their pre-existing operational systems.
They can use it with machine learning algorithms to boost their business.
LOAMICS-Total Suite, a key asset for logistics
LOAMICS-Total Suite has strengths that are unmatched by the competition. Processing is performed on a dynamic basis with a high level of interoperability. This results in a high ROI in terms of time and tool TCO. LOAMICS-DataLake is immediately accessible and unlimited. The integration of our modules is fast and non-intrusive to your existing cloud and transportation industry applications. The price of the Suite is fixed, it is not based on the number of connected users and the amount of data collected, stored, or processed. Finally, IT team training is accessible and fast. Our team is very attentive of specific transportation industry use cases. Let us provide you with personalized answers to your problems in the shortest possible time.
