Logistics Data Standards: Challenges and Benefits
Just a few short decades ago, it was nearly unimaginable that we could ever turn vast amounts of data into actionable information for logistics providers around the globe. But fast forward to today, and data has become the cornerstone of any modern logistics operation. Improved operational efficiency, last-mile and real-time route optimization, strategic network and capacity planning, customer service improvement and more product innovation are just a few of the major benefits now easily produced by a data-driven business.
There are both pros and cons that come with this access to massive quantities of information: While this data has the potential to inspire exciting business transformation, it’s often more difficult for logistics organizations to sort through it to discern what’s useful and what’s not. If your company has piles of data in various formats and systems which aren’t being used to create actionable insights, its storage will only become useful when a data-driven solution is implemented later on. This is why many organizations are starting to rely on industry data standards, which dictate how data should be recorded, stored, and shared. Standards may vary by industry, but the governing principle is that any crucial information must be exchanged in a common format that makes collaborating and extracting insights simple and straightforward.
Unfortunately, across the industry, logistics data standards have yet to become a common practice. The lack of industry data standards complicates the exchange of information, which in turn limits innovation by providing an incomplete picture of issues that may be affecting logistics companies. For example, maritime freight shippers have cited a lack of common data standards as a major industry problem, making it more difficult to conduct business between carriers. Container shippers have also recognized the need for standardization in their industry. Let’s take a deeper look into the challenges this issue brings, and how creating widely accepted standards will benefit the industry as a whole.
Logistics data is ripe for standardization: After all, most logistics organizations are completing the same processes — they just operate, store, and use that information in different ways. Creating a common format for arrival timestamps, for instance, would reduce the amount of additional work that needs to be carried out. Adding uniform data definitions would also make it easier to remove the paper from the typical logistics workflow, greatly improving accuracy and eliminating environmental waste. These universally agreed-on data standards would also usher in stronger cybersecurity — such as the security mandate put in place to help vessel owners mitigate the increasing risk of a cyberattack on their ships. Continued measures like these would make it possible for all parties involved to operate using secure information transfer methods in an era when data security is a bigger issue than ever.
When it comes to providing logistics companies with high-quality analytics, standardized data is a crucial component. Only by creating clean data can analytics tools make sense of it in a useful way — for example, by cleaning up an organization’s data and standardizing it, Transmetrics can automatically extract important information from transportation systems, calculate resource optimization, deliver better insights, and make stronger predictions for logistics providers.
Transmetrics’ standard data model contains information about pieces, orders, consolidations, trips, trip legs, and so on. This data model tends to be very precise in terms of representing what is actually happening with the shipments in reality. By further cleansing and enriching customers’ data with AI and machine learning, the solution can demonstrate the complete detailed information for every shipment, such as the weight, the volume, the dimensions, the density, whether the shipment is stackable or not, and other properties that the logistics companies might need. As a result, Transmetrics is able to bring customers’ data to a high-quality standard, which then serves as a solid basis for further predictive optimization.
Data Standards are Industry Standards
Let’s take a look at a great example of an industry embracing common data standards. The banking industry developed the Society for Worldwide Interbank Financial Telecommunication (SWIFT) standard, which makes it possible for banks around the world to execute transactions between customers of different banking systems, no matter their location. Although the global freight industry still lags behind others when it comes to establishing a set of common industry standards, it’s clear that the container shipping sector is paving the new road for data standards.
One container shipping solution that is rising to the data standards challenge is the Digital Container Shipping Association (DSCA), a nonprofit working to develop technology and security standards related to data exchange through the shipping industry. Representing more than 70% of the industry, the DSCA is supported by major logistics companies like Maersk, MSC, and ONE. DSCA seeks to reduce risk and inefficiency by introducing common information technology standards of digitalization and interoperability, and its support by the industry leaders illustrates the significance of the organization and its mission.
Plenty of progress has already been made: The nonprofit created its first Industry Blueprint in 2019, consisting of recommended state standards for the processes used in container shipping which can be used as a baseline standard for digitalization and standardization initiatives in shipments, equipment, and vessels. In January 2020, further advances in the DSCA mission were made when the committee released comprehensive track-and-trace standards for the global container shipping industry. The new standards will aid carriers, shippers and third parties in their efforts to enable easier cross-carrier shipment tracking.
Another solution that could help nudge the logistics field forward is blockchain. To define it as simply as possible, this technology powers highly secure, traceable ledgers to record and account for crucial information. However, organizations must adhere to specific data standards offered by The Blockchain in Transport Alliance (BiTA). By partnering with logistics giants like FedEx, Schneider, UPS, and Anheuser-Busch, BiTA seeks to connect providers throughout the logistics industry with secure data solutions. Yet, to successfully implement blockchain within their processes, logistics companies must first face their internal data issues. By cleaning up their data and setting up for blockchain success, logistics companies could also open their operations up to implement other powerful technologies, including artificial intelligence (AI) and Internet-of-Things (IoT) devices.
In an industry driven largely by efficiency and speed, a lack of guidelines for data standards is a resource-draining blind spot. Of course, logistics is hardly alone in this: It’s estimated that data scientists spend up to 80% of their time finding, cleaning, and organizing data. This wasted time of sifting through information that could be cataloged and organized in an efficient, useful matter, leaves just 20% of a data scientist’s typical routine to produce the valuable insights they’re expected to deliver. As mentioned earlier, data without insights is essentially futile — so companies must take the time to clean and transform their data so they can allow their data scientists or third parties to spend their time on what really matters: discovering the patterns and systems that will make the business run as quickly and efficiently as possible.
Adopting a Path Forward
Despite the best efforts of data standardization organizations, a great deal of work remains to be done. The Australian Logistics Council, for instance, continues to call for improved supply chain visibility and the adoption of Global Data Standards to improve collaboration between logistics providers. But fissures between standardization groups have led to slow adoption and questions about the technology’s viability in the freight industry, leading some to believe the industry faces a bumpy road on its way to common standards.
Fortunately, because data standardization already plays a significant role in nearly every other major industry, it seems inevitable that logistics providers will eventually agree on a set of universal data standards. The opportunities presented by AI, blockchain, and IoT are simply too valuable to pass up, and while the path to widespread adoption may be challenging, it’s certainly a problem worth solving in order to deliver next-generation insights and performance.
Once companies have good quality standardized data, it unlocks all kinds of opportunities for predictive optimization in logistics to achieve much higher levels of operational efficiency. With that in mind, Logistics providers seeking greater efficiency and collaboration should continue taking small steps of their own to embrace data standards and build an industry more powerful than ever before.