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. Moreover, 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

“[Computer Vision AI] has the potential to revolutionize supply chain management through reliable, end-to-end shipment tracking.”

2. To Streamline Data Interoperability for End-to-End Shipment Visibility, We Need to Go Beyond Standalone Computer Vision AI Solutions.

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

Currently, most computer vision AI systems supporting supply chain operations are localized, providing single-task functions within a warehouse, distribution center, or shipping dock. In many cases, these Vision AI systems do capture shipment images, but the data has only single-use utility, isolated within proprietary data silos. As a result, shipment data is not available for broader supply chain visibility and integration. Hence, most shipment visibility systems continue to use proprietary data formats and translation methodologies developed in the 1970s to share shipment status. As a result the supply chain industry continues to have major interoperability problems, using bulky, error-prone, data-translations, resulting in limited, disjointed shipment visibility.

However, there’s a simple solution, using image-based tracking, that sidesteps most of these Interoperability problems. Namely, supply chains can leverage the inherent understandability of shipment images themselves. Just think of the possibilities when a shipment image is paired with only essential contextual data such as GPS location, tracking IDs, and timestamps. Indeed, an image-based shipment status message becomes much more universally comprehensible than current proprietary shipment status data formats. Hence, this image-based approach of transmitting shipment statuses avoids 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. Also, see graphical depiction below.

5-step Image-Based Shipment Tracking Solution.

• 1st Step – Capture Tracking Event Image. First, localized camera system captures image or video of tracking event.
• 2nd Step – Transmit Event Image. Next, the local system transmits image-based shipment status event data to a centralized AI machine vision translator.
• 3rd Step – AI Translator Interprets Image. In this step, the AI Machine Vision Translator interprets tracking image and metadata.
• 4th Step – AI Translator Shares Shipment Status Messages. Here, the AI Machine Vision System shares shipment status messages with stakeholder systems.
• 5th Step – End-to-End Visibility. As a result, stakeholders have end-to-end visibility of trusted, picture-perfect shipment statuses.

For a more detailed breakout of this image-based solution with examples, see my article, AI Machine Vision Fundamentals You Need to Know For Implementing an Innovative 5-Step Shipment Tracking Solution.

AI Machine Vision Fundamentals You Need to Know For Implementing an Innovative 5-Step Shipment Tracking Solution

“… an image-based shipment status message becomes much more universally comprehensible than current proprietary shipment status data formats.”

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

So, to effectively implement an image-based tracking solution and overcome past data interoperability issues, a shipment visibility provider must integrate all three core computer vision AI components discussed previously. Namely, the visibility solution needs image capture, an image-based shipment status interface, and an AI interpreter tracking app. Also, key design considerations for this solution include linking images to unique tracking IDs, standardizing image-based metadata, and developing an AI interpreter tracking module to translate visual data into clear shipment status messages. See below for a detailed discussion of these design consideration to implement a picture-perfect shipment visibility solution.

a. Link the Image to an ID for Tracking.

First, besides capturing a shipment image, a tracking system needs to associate the image with a tracking ID. Without a doubt, this is a fundamental capability for any type of shipment visibility system in order for it 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.

Options to Link Image to a Tracking ID of Load ID.

• Traditional Methods: Use Bar Code or RFID. Computer vision AI can leverage traditional bar code tracking by reading labels with pre-assigned tracking IDs, associating the captured images to the ID. Also, image-based tracking solutions can integrate IoT devices like passive RFID to associate an ID to a shipment image.
• Use Tracking ID Embedded in Image. Also, another method is to embed tracking IDs directly into the shipment image. In this case, the computer vision system can leverage Optical Character Recognition (OCR) and symbol recognition to automatically extract tracking identifiers. Thus, the system can extract tracking IDs from traditional labels, bar codes, symbols, large print, or even handwritten text. With proper camera placement as well as with specialty camera such as 3D cameras, this capability creates a “digital thread”, linking all subsequent images to a unique ID throughout a shipment’s journey.

b. Include a Small Set of Standardized Shipment Status Metadata.

With an image-based tracking solution, a shipment status event message only needs a small set of meta-data to accompany the shipment image. Indeed, there is no need to use bulky, proprietary data files containing dozens of data elements. Hence, an image-based tracking solution sidestepping most data Interoperability issues. In fact, an image-based tracking solution only needs a few data elements to accompany a tracking image. See below.

Minimum Data Elements Needed for Image-Based Shipment Status Message.

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

• tracking image
• tracking ID
• timedatestamp
• location ID

Moreover, the local image-capture system could embed all these data elements with the shipment status image. Hence, only the image would need to be transmitted to capture the shipment event.

Without a doubt, image-based tracking can revolutionize shipment visibility by minimizing data elements, effectively bypassing the extensive data integration challenges posed by traditional methods. Moreover, this simplicity streamlines interoperability, similar to what happened with the introduction of bar code decades ago. Indeed, an image-based tracking solution stands in stark contrast to current systems that struggle with countless free-form, text-based shipment statuses from various carriers that often leading to confusion and complex translation efforts. For more information on the current challenges with 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.

What’s more there are already emerging data standards to support image-based tracking solutions. 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. Today, many supply chains are already implementing computer vision AI solutions in their warehouses and shipping locations. Hence, implementing an image-based, shipment visibility solution only needs a broader perspective to gain visibility across the supply chain. 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. In fact, if we just translated shipment images into shipment status messages at the local level, we would just be repeating what we do today with proprietary tracking systems. Hence, by leveraging computer vision AI tech, supply chains can now enjoy reliable, picture-perfect shipment status while avoiding redundant data translations and integration work.

“… key design considerations for [an image-based tracking] solution include linking images to unique tracking IDs, standardizing image-based metadata, and developing an AI interpreter tracking module to translate visual data into clear shipment status messages.”

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. For more information on “Picture-Perfect” visibility, see my article, Picture-Perfect Tech for Supply Chain Visibility: The Use Of Emerging Computer Vision AI To Find Out What Happened

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

Need help with an innovative solution to make your supply chain systems work together? I’m Randy McClure, and I’ve spent many years solving data interoperability and visibility problems. As a supply chain tech advisor, I’ve implemented hundreds of successful pilot projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in proof-of-concept and pilot projects for emerging technologies. If you’re ready to modernize your data infrastructure or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

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