Top Digital Transformation Trends in the Logistics Industry

Top Digital Transformation Trends in the Logistics Industry
Image from Pexels: Logistics

Technology is one of the biggest factors and contributors that transform logistics and supply chain operations. Yasir Shamim, an Executive at PureVPN, bares that over the past years and in the coming years, the use of advanced mobile technologies or enterprise mobility will be one of the big trends in logistics.

She adds that mobile and cloud technologies are evolving fast, with the logistics industry witnessing a complete overhaul and digital transformation. Enterprises are increasingly embracing advanced mobile devices and prefer to empower logistics workforces with technologies that ensure improved productivity, efficiency, and communication. In hindsight, advanced mobile technologies like the internet of things (IoTs) are even increasingly gaining significance in logistics.

As a way to further understand the implications of digital technologies in the logistics industry, we’ve asked our friend, Trey Willis, the Chief Technology Officer of CTSI-Global, about his opinions in the subject, as well as what he thinks are the top trends that’ll shape its future. Here are his thoughts:

1. Artificial Intelligence (AI) and Machine Learning

Business Intelligence (BI) has been around for a while and Artificial Intelligence (AI) is sometimes regarded as merely theoretical. But in the logistics industry, and with my team, in particular, we’re trying to put AI and machine learning into play in real life.

Massive amounts of data require more advanced data analytics to put companies (and logistics leaders in those companies) in the best possible decision-making scenario. A couple of examples of the way we’re seeing AI and machine learning emerge in the BI space are 1) optimizing processes for cost control and 2) becoming more proactive in improving a shipper’s customer experience.

The idea is to begin by setting some boundaries for artificial intelligence, but not programming fixed answers. That lets us take a proactive approach rather than just seeing the data in preconceived ways that require biased human actors to make all the decisions in a given scenario.

The risk of traditional business analytics is the data can simply validate or shape our already formed hypotheses. Machine learning, on the other hand, and AI can help us to make truer/cleaner decisions that are less about supporting prescribed notions and more about business decisions having actual support from data science.

The added benefit is speed and time. Machine learning means correct answers are provided quicker and with greater clarity because a person doesn’t have to identify, test, and prove out their existing thesis.

2. Real-Time Visibility and Data Visualization

In the logistics space that’s especially emerging around event management—using more real-time integration to present a constant flow of information. Instead of information being batched every hour or twice a day, we’re moving across the board toward active status messages, shipment messages, and notifications, and publishing all of that in a real-time dashboard for instant visibility.

That information, because it’s real-time, then more effectively feeds into an AI engine. The operative question is, ‘what can we learn if we have the data really quickly and available to machine learning at the same time as it is human actors?’

On the ground examples include seeing routes and fleet statuses on a real-time basis, warehouse information, trucks waiting to leave the dock, are the trucks taking an extra five minutes each time before leaving the dock? That’s actionable real-time intelligence.

There’s a tremendous opportunity and potential for getting up close and personal with the data in a live format as opposed to tomorrow or in two weeks.

3. The Internet of Things (IoT)

What IoT is bringing to the logistics industry is the hyper-connectivity concept among devices—for example, sensors. Take for example temperature sensors that make sure vaccines stay at 94 degrees or colder, which send a status signal to a home base and also alert a driver to pull over and address faults that affect the efficiency and quality of the delivered product (perhaps obtain dry ice).

IoT is starting to allow for more conscious, smarter decisions. Manufacturers and shippers will be more highly connected to their products along the supply chain. The industry will also press on and integrate that into larger concepts of digital transformation around the supply chain.

4. Data Transmission and Transformation

I think we’ll move away from legacy data transmission and transformation techniques. To put a fine point on it, we’ll stop using FTP, AS2, EDI, etc.

As an industry, we’ll plug into more highly connected techniques like web services (HTTPS) and API types of interfaces. EDI, for example, has been around for 45 years or so. There’s not a huge value in staying with that stuff when compared to any other standardized interface. The industry is going to align with processes used more broadly and currently outside of the logistics field.

We’re going to see more direct application connections—especially for major Enterprise Resource Planning (ERP) tools like SAP’s ERP—meaning app to app integration pushing out legacy data transformation and transformation techniques.

Author Profile

Amanda Thomas
Amanda Thomas
A professional auto-engineer woman, Amanda has worked with the development and design team of automotive companies to employ sophisticated technology in a reliable and user-friendly method in the vehicle’s navigation, alarm, control, and fuel usages.
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