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Why logistics managers must use data to optimise transport operations efficiently

Data-driven decision making is the key to the successful optimisation of transport operations. Additionally, other teams such as warehousing and sales benefit from it too. But how can logistics managers use transport data to improve the performance of different teams? Find out more in this article.

Every day, thousands of pallets are moved across the UK. Manufacturers, logistics operators, retailers and others are under constant pressure to select the best routes for their recurrent and spot shipments. Most of these companies rely on a variety of different partners to deal with this challenge. This means they typically work with low-tech solutions to handle their daily operations such as physical PoDs, and high-volume correspondence like calls or emails. The result of this is that, besides being inefficient, it prevents logistics teams from gathering aggregated data about routes and transport operations.

The problem is that, in today’s world of data science, AI and machine learning, data is invaluable. In fact, 90% of logistics companies state that data and analytics will be the key to their business success in the next five years. Without information on KPIs such as, for example, on-time rate, success rate or standstills, it’s impossible to optimise transport operations to improve efficiency. So how can manufacturers, distributors, retailers and logistics companies improve their transport operations with the help of data? Read on.

How To Use Data To Improve Transport Operations

Leverage Tracking Technology To Collect Data

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Routing optimisation and tracking are amongst the most important fleet management features for logistics professionals. Source: Deloitte

Of course, the first step to using data for the optimisation of transport operations is to create a supply chain that enables the gathering of useful data. This data could include peak transport hours and places, routes with most pallets transported and the most efficient truck types. In order to be able to collect this information, firms need to install the proper track and trace technology in their logistics networks. Furthermore, when working with third parties, they should make sure that they’re selecting transport providers and 3PLs that utilise tracking technology for their supply, trucks and operations too.

Analyse Data To Receive Actionable Insights

Simply gathering data on its own won’t generate any competitive advantages unless you translate that data into meaningful information that facilitates decision-making based on facts. To avoid being faced with an overwhelming collection of numbers and figures, data analytics and algorithms will need to be implemented. When actioning these data analytics processes for logistics needs, the focus should be on optimising operations efficiency, reducing logistics costs and shortening delivery times.

Share Transport Data Across Different Teams

In order to truly benefit from transport data analytics, companies need to implement technological solutions that allow them to share valuable insights with specific teams and managers to help them improve decision-making. A great way to do this is by using a digital platform that enables different teams to access actionable and relevant data through reports and dashboards that are tailor-made for business users and decision-makers.

How Different Teams Can Benefit

Logistic Managers

By collecting data about factors such as order pick-up points, routes and drop-offs and sharing valuable insights across different departments, a number of different departmental teams can optimise their performance:


The operations team can use data to reduce standstills and prevent bottlenecks by anticipating and planning for peak hours. This thanks to gaining visibility on where pallets are located and heading to at any given moment. Additionally, data regarding routes and pallet tracking could also improve the return routes of trucks. These improvements could mean effectively incorporating client returns, and receiving pallets in a more streamlined, functional way, thus avoiding the phenomenon of empty returns. As a result, transport time and costs can be reduced significantly as resource capacity utilisation and truck uptime are vastly improved.


The logistics department can leverage transport data to enable driver performance scoring based on on-time rates, driving behaviour and other factors. In fact, a survey by Deloitte reveals that 77% of logistics experts believe that the ability to monitor driving behaviour is highly-important. The main reason for this is that they can compare driver profiles and pick the individuals that are the most reliable. Moreover, information about driver habits also allows logistics teams to reduce fuel, maintenance and insurance expenditure within their transport operations.



Warehousing teams benefit from access to transport data too, since this data could help them to group loads in the warehouse in order to optimise truck capacity utilisation and route selection. In the future, the combination of data, analytics and automation will even make it possible to implement automatic loading and unloading systems that can adjust journeys according to real-time information.


It’s not just the supply chain-related teams that benefit from transport data analytics. Even sales teams could reap rewards from access to data, since they can use insights and statistics in their commercial pitches to demonstrate the added value of their services. For instance, they can demonstrate how their data-driven decision-making guarantees client orders will arrive on time and without any damages. Furthermore, since the optimisation of transport operations reduces costs, sales teams will also benefit from being able to offer lower delivery prices.

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