In recent years, organisations have focused their efforts on platforms that help them capture and store large volumes of information. Presently those efforts are focused on drawing value from data by applying advanced analytical techniques, and the transport and logistics sector is no exception. The real value of data in the transport sector has the potential to completely change business operations in all regards, from the commercial space to operations and customer service.

Data in the transport sector

Companies in the transport sector dedicated to both passenger and freight transports rely on multiple systems that span fleet management, logistics operations, route planning, customers and HR. To improve the efficiency of these business processes, companies must invest in solutions that incorporate advanced analytical techniques. It allows a rapid response to the changing needs of logistics operations, optimising fuel consumption, the use of the fleet or personnel and improving the productivity of available resources.

Applying Advanced Data Analytics

It is necessary to utilise Machine Learning and Predictive Analytics solutions to improve customer service, compliance levels of demand and increase the efficiency of the logistics chain. These solutions allow predicting what may happen in the future, determining how to anticipate the demand for an activity or future trends. This information is of great value for the tactical and strategic planning of logistics operations. Additionally, Prescriptive Analytics, a discipline that includes operational research techniques that help to choose the best option. Based on the value provided by predicting future scenarios and using historical data to identify the best course of action to achieve profitability and service level objectives, minimising costs.

Today, companies in the transport and logistics sector are supporting their decisions with Artificial Intelligence solutions, but this has yet to be applied in the commercial side of the business.

From Reluctance to Digital Transformation

Currently, companies in the logistics sector are immersed in a profound process of transformation due to the emergence of new technologies. These changes have led to a drastic transformation in demand for services.

However, sales forces in T&L companies are relying on outdated processes which limit insights into customer preferences and opportunities for growth. Given analytics’ ability to facilitate transformation, companies in the logistics sector are under enormous pressure to adapt to technology. Given the new challenges that arise, the search for competitive advantages focuses on new trends such as the robotisation of warehouses, the automation of transport or the optimisation of routes.

This technology, adapted to the logistics sector, will have its most significant impact on customer service, response time, optimisation of times and routes, predictive maintenance, chain supply efficiency and operating margins. Embracing analytics will open up opportunities that companies in the logistics and transport sector must take advantage of for commercial growth.

Advanced Analytics in T&L companies

To be able to perform the services with the quality expected by the end consumer while also remaining competitive and profitable, T&L companies must make use of existing data, embed analytics within daily routines, and also make analytics accessible for the sales teams.

Advanced analytical techniques and Artificial Intelligence helps optimise and automate logistics processes to achieve greater efficiency. This proves most beneficial in the forecasting of demand and planning, transport, maintenance of assets of the supply chain, and customer service. The extensive use of data increases the need to apply a smarter solution to manage complex data, and this is where Artificial Intelligence can be of benefit, in particular with:

1. Demand forecasting

Predictive Analytics can help determine demand that a business can expect in the future. The projection of demand can be used to create optimal schedules and advance the shipments of customers, increasing efficiency in logistics and supply chain processes.

2. Smart planning

Using different techniques of Advanced Data Analytics, and demand forecasting, T&L companies can create optimal and automated schedules both for the distribution routes, timetables, shifts and tasks of the workers. This centralises all the information to manage assets more directly, and with a holistisoc vision of all the operations of the company.

3. Tracking of shipments

Thanks to advanced image recognition systems, you can track the status of shipments at any time in an intuitive way. Advanced image recognition uses the most advanced techniques of Artificial Intelligence, such as Deep Learning, to identify, track and coordinate shipments at any time.

4. Autonomous transportation

Autonomous driving vehicles and advanced technology in transportation and consequently in the logistical sector. Technology can save delivery drivers valuable time, automatically parking while they deliver or pick up shipments at a specific location.

Companies like Amazon are already working on developing their autonomous vehicles to distribute packages through cities, and thus be able to add to the time and cost savings that this technology supposes.

5. Optimisation of transport routes

By combining different techniques of Advanced Analytics, real-time information such as the most optimal route to follow can increase efficiency. Taking into account numerous variables such as the type of vehicle, course, the traffic agglomerations, constraints, etc., and looking for the quickest and best route that entails lower fuel costs.

Large companies in the logistics sector already incorporate this solution to optimise their deliveries and avoid pre-established routes that don't take the means of transport nor real-time traffic into consideration.

6. Maximisation of fleet availability

Through Predictive Maintenance techniques, you can maximise the availability of the fleet and the useful life of vehicles and assets in the supply chain.

Using models of Machine Learning, optimised schedules of maintenance tasks and systems are created to anticipate any possible breakdown of the equipment, as well as maximise all available resources.

7. Automation and robotisation of warehouses

Implementing robotics in supply chains is another way in which new technologies can be applied in logistics. Robots can be responsible for moving and packaging in the warehouse, achieving an increase in effectiveness and productivity.

DHL has already begun testing collaboration robots in selected warehouses to transform supply chains.

Key Takeaways

The application of data analytics in transport and logistics companies will undoubtedly allow more efficient management of processes. If T&L companies start to incorporate these technologies into all aspects of business operations, the supply chain will be more profitable alongside better experiences for the customer.

To understand how Inside Info can help transport & logistics companies use data to its fullest potential check out our information sheet or give us a call.