Supply chain visibility isn’t just a luxury—it’s essential for effective logistics operations. Despite massive industry investments in complex shipment tracking solutions, achieving true end-to-end visibility remains elusive. IT departments currently spend up to 25% of their IT budgets on data integration projects and services, yet serious visibility gaps and trust issues persist. But there’s promising news: emerging computer vision AI technology offers an innovative path forward. Specifically, this type of AI tech has the capability to generate image-based shipment statuses that bypasses most traditional data integration hurdles. Moreover, it can deliver more reliable, picture-perfect shipment tracking across the entire supply chain.

So, the question is what do we need to achieve “picture-perfect” supply chain visibility? In this article, I’ll offer a compelling solution. First, I’ll look at the key computer vision AI components needed to develop a superior end-to-end shipment tracking solution. This includes the camera system, data interfaces, and a cloud-based AI image interpreter software that converts image data into shipment status messages. Lastly, I’ll examine design considerations to drastically reduce data interoperability issues using image-based shipment tracking solutions.

1. Computer Vision AI Is the Most Awesome Way To Solve Shipment Tracking Integration Issues.

Indeed, computer vision AI is expanding beyond traditional applications such as robotics, self-driving vehicles, and facial recognition. Even in today’s supply chains, it is a powerful tracking tool for fraud prevention and asset monitoring. Further as I will explain in this article, it has the potential to revolutionize supply chain management through reliable, end-to-end shipment tracking. While computer vision AI might sound intimidating, its basic framework is surprisingly simple. See below:

Computer Vision AI – Basic Components

• Imaging System. Consists of a camera or imaging device that captures visual information.
• AI Software. Leverages machine learning (ML) algorithms to interpret and analyze images. As a result depending on the use case, the AI derives digital information from the images. For instance, the AI can translate these images into digital output that classifies and describes the object or what is happening. Hence, this tech essentially enables computers to “see” and understand the world around them.

For instance, a computer vision AI system located at a warehouse dock can instantly scan a pallet’s barcode, QR code, and characters on labels, automatically registering a shipment without manual data entry. Also, a computer vision AI system can track assets outside of the warehouse such as tracking vehicles to include capturing licence plate information. Indeed, computer vision AI has unlimited potential for end-to-end shipment visibility and many more logistics functions. For more information on current computer vision AI capabilities, see my article, Computer Vision AI Use Cases for Supply Chains and DHL’s Computer Vision Trend Overview. 

Computer Vision AI: The Unlimited Ways To Use This Awesome Tech To Empower Supply Chains

2. To Streamline Data Interoperability for End-to-End Shipment Visibility We Need a New Type of Computer Vision AI Solution.

Currently, most computer vision AI systems operate as a standalone system. For instance, a business may deploy these AI systems with cameras at individual locations like warehouses. While this approach serves local needs, these computer vision AI solutions need some adjustments to truly revolutionize shipment tracking across the broader supply chain. The core problem lies in how each AI system converts images into its own proprietary digital format. As a result, these systems end up just creating a new data silo as well as another integration translation problem when it comes to achieving shipment visibility.

However, there’s a simple solution for image-based tracking that sidesteps most of these Interoperability problems. Namely, supply chains can leverage the inherent understandability of the images themselves and save the trouble of sharing bulky, error-prone data transmissions. Just think of the possibilities when a shipment image is paired with only essential contextual data such as GPS coordinates, tracking IDs, and timestamps. Indeed, a shipment status message becomes much more universally comprehensible than current proprietary data formats. Hence, this image-based approach of transmitting shipment statuses sidesteps most data interoperability hurdles that plague other tracking solutions. 

So, the next question to ask is what is needed for end-to-end shipment tracking using computer vision AI? See below for a breakout of key components and how it would work. 

The Three Components of a Computer Vision AI Solution for Image-Based Shipment Visibility

a. Camera System to Take Images or Video. 

Here, supply chains can use an existing camera system or a special-purpose system at each tracking point. In some cases, computer vision AI may need a specific type of image or video (ex. 2d or 3d). Hence in this situation, they would need a special purpose camera. For examples of how cameras can capture shipment status images, see this article on Capturing Shipment Status Pictures to Work with Computer Vision AI. This article provides specific shipment status event examples such as shipped, in transit, delivered, and shipment exceptions.

b. Supply Chain Visibility (SCV) Image-Based Interface. 

Also, supply chain organizations need a method to transmit the image with key metadata such as GPS location, DateTimeStamp, and Tracking ID. Ideally, organizations would use a universally accepted data standard developed by a Standards Development Organization (SDO). 

c. AI Image Interpreter Tracking Module. 

Here, you need a computer vision AI interpreter module that analyzes the imaging data. Also, it would translate the image-based data into a shipment status event transaction for use by visibility systems. Ideally, each visibility system would use this simple AI Interpreter tracking module. At the same time, multiple tech vendors could also offer this AI interpreter tracking app or a supply chain organization could develop the AI app themselves. The key is that these AI interpreter tracking apps would use a standardized supply chain image-based interface that is universally accepted as discussed above.

To summarize, this computer vision AI approach precludes unnecessary data translation when the image itself already provides most of the shipment status information. Consequently, image-based shipment status messages can provide a perfect picture of a shipment’s journey that has both a simple data format and provides reliable information. For instance, this image-based shipment tracking solution would consist of a 5-step process as depicted below. The operational steps to obtain shipment visibility would include 1) capture image; 2) transmit image-based shipment status; 3) AI interprets image data; 4) shipment status message exchanged; 5) target systems have end-to-end shipment visibility.

3. Advice to Drastically Reduce Data Interoperability Issues Leveraging Image-based Shipment Tracking.

So to implement an image-based tracking solution, a couple things are needed in order not to repeat the data interoperability failures of the past. As discussed, computer vision AI tech is available today. However, to provide end-to-end shipment visibility, the solution provider would first need to implement all three components of the computer vision AI solution as I outlined above. This includes the image capture, the image-based shipment status interface, and the AI interpreter tracking app. In addition, the software developer needs to design a solution that minimizes data interoperability issues. These design considerations include:

Key Capabilities to Minimize Data Interoperability Issues for Image-based Shipment Tracking

• Link the shipment status image to a unique tracking ID
• Include a small set of standardized metadata in the shipment status image transmission.
• Develop a cloud-based AI interpreter tracking module that translates incoming image data into shipment status transactions for visibility systems such as TMS or other tracking systems. 

Below is a more detailed discussion of these key capabilities when implementing an image-based tracking solution that minimizes data Interoperability issues.

a. Options to Link the Image to a Tracking ID or Load ID.

Besides capturing and having AI interpret the image, a system needs to associate the image with a tracking ID. This is of course obvious as all shipment visibility systems are designed to track unique objects. What is different with image-based tracking compared to other tracking systems is that there are more options to link a tracking ID to the package or container. To detail, see below.

1) Use Tracking ID Embedded in Image. 

For most image-based tracking scenarios, a preferred option is to embed the tracking ID in the image itself. Indeed, this is a unique option versus traditional tracking methodologies. This is because computer vision systems have the exceptional capabilities of using Optical Character Recognition (OCR) or identifying unique symbols visible on objects. Thus, these systems can automatically extract tracking numbers or load reference IDs from labels, barcodes, or even handwritten text. Further, this image-based capability can go one step further. Specifically, the AI can interpret unique symbols displayed on a container much like what AI does with facial recognition. 

Also to better recognize embedded tracking IDs, an operation can better position cameras and use specialty cameras such as 3D cameras. Further, shippers could modify some of their practices such as printing the tracking ID in larger letters on shipment containers and not obscuring the printed text.

For an example use case, let’s look at when a truck arrives at a facility to deliver a shipment. Here, the system can snap photos from multiple angles, instantly recognize the container number or tracking ID, and links all subsequent images to this unique identifier. Think of it as creating a digital thread that connects every visual touch point throughout the shipment’s journey.

2) Use Traditional Methods of Assigning a Tracking ID to a Shipment Image.

Decades ago with the advent of business computers, supply chains have and continue to track shipments using barcode labels. So with the use of computer vision AI to track shipments, this type of tech can also read the barcodes. Thus, the local image capture system would assign a tracking ID to the image as part of the image’s metadata much as done today with traditional tracking systems. Also, an image-based tracking solution can use Internet of Things (Iot) devices such as passive RFID. The key thing is the solution provider needs to link the unique tracking ID to the image.

b. Minimize Data Mapping Issues by Limiting the Number of Data Elements Transmitted with Shipment Status Images.

Another piece of advice is less is more when it comes to data transmission and translation. Indeed, an image-based tracking solution transmits fewer data elements, thus sidestepping most data Interoperability issues. For instance, computer vision tech can capture essential label and document details like barcodes, RMA numbers, addresses, and order numbers. Better yet, a superior image-based tracking solution would have the key data elements embedded in the image. Below is a more detailed discussion about minimizing data mapping issues.

1) Minimum Data Elements Needed for Image-Based Shipment Status.

First, for most shipment tracking use cases, physical tracking locations only need to transmit four key elements with each image: 

• tracking image
• tracking ID
• timedatestamp
• location ID

In fact, the local operations would only have to transmit the image, and not the metadata, if the tracking ID, timedatestamp, and location ID data were embedded within the image. 

2) Minimum Data Elements Side Steps Most Data Integration Challenges.

Indeed, an image-based tracking solution is like sending a postcard instead of a novel – you get the essential message across without the bulking data transmissions and data mapping challenges. So, no more having to handle the countless data formats, data dictionaries, and business glossaries that IT integrators use today. Without a doubt, a typical data integration effort without an image-based tracking solution gets bogged down in complex data integration and translation tasks. In fact, this is what makes image-based shipment status revolutionary. Undeniably, this type of AI image-based tracking solution would radically change the industry much like barcodes did decades ago. 

3) Example of the Magnitude of Traditional Data Integration Efforts Needed to Achieve Shipment Visibility Today.

Indeed, simple image-based metadata as detailed above is so much simpler compared to what tracking systems have to deal with today. For example, UPS has over a thousand different shipment statuses formatted in free-form text. Further, this is made more complicated because UPS can transmit these custom, text-based statuses in different languages besides English. Worse, the receivers of these text-based shipment statuses in many cases do not have a clear idea of what these status messages mean. Again, this is just one transportation carrier. For more information on the current state of custom-built shipment status, see my article, Custom-Built Shipment Statuses: Digital Supply Chains Can Do Better And Need A Reckoning To Eliminate This Insidious Habit.

Custom-Built Shipment Statuses: Digital Supply Chains Can Do Better And Need A Reckoning To Eliminate This Insidious Habit

4) Data Standards Already in Place for Tracking ID, TimeDataStamp, and Location ID.

What makes this image-based tracking solution easy to implement is that there are already accepted data standards for key image metadata. This includes data elements like tracking ID, timedatestamp, and location ID. For example, see what Standards Development Organizations (SDO) like the ASTM F49 Digital Information in the Supply Chain group is doing to advance supply chain visibility standards. Also, there are well established metadata standards for images, such as Google’s proprietary Image Metadata in Google Images.

c. Use a Cloud-based Computer Vision AI Module to Interpret and Translate Images Into Reliable Shipment Status Updates.

Indeed, computer vision AI tech offers a simpler approach to shipment tracking by eliminating complex data integration between systems. Further, only limited adjustments are needed for an image-based tracking solution to eliminate most of the usual data integration challenges. Specifically, rather than deploying standalone computer vision systems that process images at each physical shipment location, it’s more efficient to have AI interpret the raw images directly at the cloud level. Without a doubt, converting images into proprietary data formats at the local level only creates unnecessary data translation efforts later. By leveraging computer vision AI tech, supply chains will enjoy reliable, picture-perfect shipment status while avoiding redundant data translations and integration work.

Conclusion.

The supply chain industry’s search for total shipment visibility and a Single Source of Truth (SSOT) has long seemed out of reach. However, computer vision AI is a new shipment visibility tool, offering an innovative approach that sidesteps traditional data integration challenges. By capturing “picture-perfect” visual data, companies can now track shipments with unprecedented accuracy across their entire logistics network. This image-based approach doesn’t just solve visibility gaps – it streamlines how we monitor and verify shipment status, setting a new standard for supply chain transparency.

Picture-Perfect Tech for Supply Chain Visibility: The Use Of Emerging Computer Vision AI To Find Out What Happened

For more from SC Tech Insights, see the latest articles on Data, Interoperability, and Shipping.