AI ain’t ready for prime time in the supply chain

During the past few years, the buzz in the supply chain has been directed to […]

During the past few years, the buzz in the supply chain has been directed to the benefits of artificial intelligence in developing new solutions to internal taskings. Overall, the concept of the AI approach has driven cost reductions, better processes and better products.

The issue remains how to slay the elephant in the room in the execution of contracted tasks from the carriers, or overall logistics providers from the carriers to streamline lines from point of origin to include the last mile or delivery. The same issues are not native to just the steamship lines. Other modes of transportation have the same issues of a single set of eyes or bots to find a solution.

The saying “AI ain’t ready for prime time yet” revolves around the lack of integration between carriers, port operators, airlines, and truckers. Of all the aspects of movement, fragmentation abounds between each portion of the process with very little digital access or coordination available in a single platform. If each portion of the process does not function properly it causes delays and can challenge the cost of the supply chain.

However, AI can highlight issues and develop solutions for the next step toward the final mile. But, the execution of logistics tasks keep users from having a complete operating system. The customs clearance process within the government around the world can be included as part of that fragmentation jungle.

Amazon makes it work

What are some of the issues slowing down the development of AI in logistics?

There have been a few retailers that have developed their own internal AI supply chain platforms. The best-known success has been Amazon. Their contribution to the supply chain was to create their own carriers, ship charter, airline, trucker, and fulfillment centers.  They closed as much fragmentation as possible allowing the code writers to build their platform and make the decisions needed to bring real life to AI. It allowed them by controlling the logic to take all the customer sales and forecast the best mode of transportation and logistics with overnight deliveries.

Overcoming barriers

The biggest barrier to having the new technology operate as a single platform is that AI is based on an open source of data collection. Why is this a barrier? Simply due to the competitive nature of each carrier within the carrier’s network of operations or routes.

Why would a competing carrier allow its proprietary pricing and services to be shared with everyone? While most shippers in the internal market sign for space so that the carrier’s trade lanes can be better applied knowing how much equipment and revenue can be derived from their customer’s agreed service contracts.

Again, if open-source of data is available why would anyone allow the information to be public? Somehow data standards or guard rails must be developed to act as a street cop that regulates the data exchange.

During the early days of EDI, ANSI was established to do just that. Once established, the barriers to entry mostly became capital to develop, find a beta source to figure out the bugs then sell it to customers. Many third-party providers used their systems to close the loop of information technology. The industry then used the power of information to bundle the services together and control the full revenue chain. Through the theory and development of the digital experience, supply chain became reality not a Christmas list of expectations.

Currently, across the spectrum of AI services, the industry reached $200 billion in revenues or investment. The AI industry is projected to be around $500 billion. However, researching the value of AI cannot be completely defined as revenue, capital investment, or acquisition of contributing developers such as ChatGBT.

There are some good small to medium providers in the logistics market that can and do have specific logistics clients that provide excellent information. Having said that, the big players in data software the likes of SAP and IBM are aggressively jumping into the AI world of developing logistics software. Yet, until the carriers acquire more competition or form alliances with competitors up to the last mile the use of AI most likely not be ready for prime time for at least three to five years.

All sources of digital usage have to have a revenue stream to make money. Like most search engines they use AI to run the bot agenda to find the answer to the inquiry. Ok, the information comes back quickly. However, advertisers start sending you ads based on your inquiry. That information is sold and now you start seeing ads for products. A new revenue line for someone.

While logistics AI does not work that way, it has to be harnessed so that the inquiry is not genetic but specific and guarded in nature to keep services and rates in closed-source bases An additional barrier to entry is the carriers’ spot market pricing to their customers. Again, who will allow their almost daily changing rates to be found in an open-source platform? Clearly, AI ain’t ready for prime time.

Steve Knepp is a 38-year veteran in the global supply chain and logistics industry. His experiences span all sectors of the industry and modes of operation. He is as well experienced in the government sector designing and applying cargo movements in war zones. He is retired and residing in Knoxville, TN, and be reached at .(JavaScript must be enabled to view this email address).


The original article can be found at: Supply Chain Management Review