Quanovo | Data Technologies | Liverpool

Transportation & Logistics

"We’re using big data to drive smarter and the idea is an extension of that to other things."

David Barnes, Chief Information Officer at UPS

Transportation & Logistics


Logistics experts DHL produced a white paper in 2016 that suggested that the majority of companies have mountains of data regarding supply chain processes but perhaps do not make the best use of that data. Already containers and packages are labelled with bar codes or RFID codes for tracking but with the growth of the Internet of Things (IoT) it can be assumed that this quantity of data is only going to grow.

One of the areas in this industry that is set to benefit from Data Science is Predictive network and capacity planning where optimal usage of resources gives logistics providers a competitive advantage. With the correct application of Big Data techniques improvement of the reliability of planning and level of detail can be found, giving logistics providers the ability to perfectly match demand and available resources.

Route optimisation is another great example of how Algorithms can be applied to create business value. Having a real-time optimisation solution set up can create optimal delivery plans based on traffic data, sensor data from shipment items and even availability information about the recipient. It is also possible to factor in fuel usage and costs into such an algorithm to provide cost effective routes as well. When managing a fleet it may be required to re-plan routes throughout the day, using data analytics that run in real time this can be achieved in a seamless fashion.

Predictive maintenance is another feature of Data Science that can be applied to this industry. Managing a fleet of vehicles that require maintenance can be optimised using predictive modelling to with sensor data to create dynamic maintenance schedules. Doing this can often reduce the impact on the transportation and logistics network when a vehicle is having maintenance done on it and can help eliminate the uncertainty surrounding when maintenance will be required. Static preventative maintenance schedules are often more costly and disruptive than they need to be.

Optimise Route Planning

Utalise Predictive Maintenance

Improved Capacity Planning

Leverage IoT Data More Effectively


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