Transportation and logistics companies have long adapted to market disruptions from technologies such as automated guided vehicles and point of sale scanners. However, T&L firms have lagged behind the broader business world in addressing the implications of big data.
Challenging the Status Quo
Consumer-facing industries, from social media to grocery chains, were quick to adopt big data technologies and analytics. By utilising data, companies can gain insights on customer interests to then improve client experiences. Companies can derive value from their own data and/or purchase it from other sources. In fact, many make the bulk of their profits from repackaging and selling this high-level information to third parties fit.
Many medium-sized T&L firms (10-500M) having long-established contracts and have become complacent, despite industry innovations. However, customers are already seeking out consultants and advisors, internally and externally, to drive down cost. Their recommendations often identify quick savings through renegotiated or competing logistics contracts. Firms that aren’t fully leveraging their data and exploring differentiated data services are already on an eroding path.
Leveraging Data in T&L
The logistics industry is information dependent, generating massive amounts of data. Most companies continually monitor their trucks and truck sub-systems for fuel efficiency, performance, stress, and current and future repair needs.
Warehouses similarly create and gather large amounts of data around their customers, inventory and SKUs, including contents and their movement.
Externally, data from truck telematics can continually feed information to parts and maintenance suppliers, so that they can more efficiently service the trucks, increasing their profits and reducing costs for the trucking company. Warehouse data might be valuable to the suppliers of the warehouse’s customers and to the customers of those customers. This type of information has the potential to improve efficiency up and down the supply chain – making just-in-time supply systems less risky, automating other functions, and optimising the customer experience.
Data is also valuable to the insurance industry. With roughly 70% of all freight in the United States carried by trucks, insurance coverage on truck fleets and for goods/loss is important for both T&L firms and insurance groups. Most insurance companies use data to determine policy limits and deductibles. Transportation companies can provide information about accident history, speeding violations, and condition of vehicle fleet to help drive down insurance premiums.
New Industry Standards: The Threat of Emerging Players
What has historically been a fairly stable sector is now seeing an increase of new competition. These entrants are unique in that most are technology companies, rather than traditional logistics groups. They use big data to anticipate just-in-time changes such as gas prices, route changes, and weather issues. Without data monitoring and subsequent adjustments, these newcomers can hurt an incumbent firm’s profit margins before it even knows it’s in trouble.
Unlike traditional firms, these data driven logistics brokers offer more than just long-term relationship benefits. They run real-time portals, utilise instant freight booking, publicise driver performance ratings, optimise routes, and automate cost structures (TL vs. LTL, volume vs. weight, etc.).
These data exchange marketplaces exist to securely transfer data directly between market participants, increasing agile performance, and bring together a range of data buyers and sellers, increasing sales potential. They are also able to circumvent complex system differences between parties and act as an information filter, sorting data and aligning it to user needs.
Starting Points and Roadblocks to Expanding Data Platforms
Companies seeking to build out their logistics data management should define a long-term strategy of how they will build proprietary data assets, create new offerings, and participate in data ecosystems.
To start, internal data monetisation leads to better efficiencies throughout the organisation, leveraging cost reduction and increasing productivity – all of which translates to operational profit. This includes the tracking of routes, validating driver idle time, determining optimal routes based on past history, tracking real-time traffic data, and appropriating trigger points based on the arrival of a shipping container to port. However, making the most of this data will require external monetisation by creating a new incremental revenue stream that goes beyond capturing and selling raw data. A solid first step is to reach out to existing business relationships and build out those channels, providing data to suppliers and vendors.
One major challenge in a B2B environment is the structuring of useful data. Companies capture it, but do not know how to package it for use by others. First, data must be formatted and standardised to be easily shared on platforms that enable near real-time data exchanges. Companies also need to consider whether their contracts need to be restructured if they have access to another company’s data, to ensure that it can be ethically and legally used (SKUs vs. Driver Performance).
The Road Ahead
While data transformations can be challenging, it enables business optimisation in an increasingly competitive and innovative industry. With the right skills and a comprehensive strategy in place, companies can better analyse data to augment their spend and working capital utilisation.
As digital economies continue to change and disrupt the logistics and transportation industry, firms must start to rationalise and analyse their data before they are passed by competitors. Digital capabilities are no longer just nice to have, they are the key to survival.