Picture this: a shipment moves through the supply chain, its journey chronicled not in lines of code or proprietary shipment status interfaces, but in a series of photographs. As packages move from warehouse to destination, AI-powered cameras capture the shipment’s route in a series of photographs, instantly analyzing each image to reveal location, condition, and progress.  This isn’t science fiction—it’s the transformative potential of computer vision AI to enable picture-perfect supply chain visibility. By turning real-world visuals into actionable insights, this technology sidesteps complex data integration hurdles. At the same time, it delivers reliability and unprecedented clarity into logistics operations. The result? A supply chain you can literally see.

In this article, I’ll look at the tech challenges we still continue to have with supply chain visibility and the merits of using emerging computer vision AI. Indeed, it is truly amazing that data integration projects and services can consume up to 25% of IT budgets. Specifically, I’ll look at current data interoperability constraints, how computer vision AI tech may bypass many of these issues, and potential image-based shipment status solutions.

1. The Data Interoperability Challenges that Continue to Impair Supply Chain Visibility.

Despite the promise of real-time visibility, supply chains continue to face data integration and interoperability challenges. Indeed, organizations struggle to achieve seamless integration due to legacy systems and disparate data formats. Moreover, they have to deal with data privacy and access issues. Further, these issues are compounded by the overwhelming volume of data that flows across supply chains. These challenges create visibility gaps, slow down decision-making, and increase risk. Further, companies find that traditional data integration approaches like API connectivity and data warehouses demand significant resources to implement and maintain.

However, the biggest obstacle for data exchange and interoperability is not a technical or a financial challenge. On the contrary, the number one problem is that the data that we send does not get understood by the intended recipients. Specifically, transmissions are not correctly interpreted when the data crosses system or organizational boundaries. Indeed, these digital messages often get lost in translation. As a result, the data we rely on for shipment visibility is disjointed and yields little insights. Further, in many cases, it is stowed away in proprietary data silos that no one can access. To summarize, below are the major data Interoperability obstacles preventing total supply chain visibility.

Five Data Interoperability Constraints Impeding Supply Chain Visibility

The Data Interoperability Challenge: It’s The Need For Tech Standards, Compliance, Security, Massive Resources, And Be Understandable

• Adhering to Tech Standards: The Lack Of Standards Frustrates Data Interoperability.
• Complying with Regulations: Obeying Data Protection Laws and Corporate Policies.
• Securing Access to Data: The Puzzling Challenge to Verify, Authorize, and Authenticate.
• Massive Tech Costs: The High Costs of IT Data Interoperability and Integration Projects.
• Make the Data Sent Understood: The Absence of Shared Knowledge Across Systems and Organizations to Correctly Interpret Data.

For a more detailed examination of these constraints, read my article, The Data Interoperability Challenge: It’s The Need For Tech Standards, Compliance, Security, Massive Resources, And Be Understandable.

So, these data Interoperability challenges continue to impede supply chain achieving end-to-end visibility. However with the advent of computer vision AI technology, there may be a way to side-steps many of these data Interoperability issues. Most importantly, computer vision AI tech may have a better way to transfer meaningful shipment status data messages across the supply chain. Moreover, computer vision tech holds the promise of both simplifying and reducing the integration costs of shipment status data exchange.  

2. Can We Use Computer Vision AI Tech to Bypass Data Translation and Interoperability Issues?

Surprisingly, computer vision AI offers a workaround, and possibly an ultimate solution to these data interoperability challenges discussed above. In fact, this emerging technology can streamline interoperability by providing “picture-perfect” supply chain visibility.  Specifically by leveraging visual data – images and videos – computer vision algorithms are able to extract insights and meaning. Most importantly, shipment status based on images provides an authoritative source of exactly what is happening. Also, a computer vision AI solution is much less reliant on complex data integration practices. Specifically, an imaged-based shipment status message is less dependent on detailed data formats, data dictionaries, and comprehensive business glossaries for achieving semantic understanding. 

Indeed, computer vision AI can automatically detect objects, track movements, and identify patterns within visual images. Thus it provides a better avenue for gaining visibility into supply chain operations. Also, computer vision systems can easily integrate with other tech to help track shipments in the supply chain. See below, for examples of where logistics organizations can use image-based shipment status to gain visibility across the supply chain. Further, this end-to-end visibility from order processing through final delivery.

Examples of Computer Vision AI Tech Integration to Support Supply Chain Visibility

• Optical Character Recognition (OCR). Here, cameras read text characters off of labels or shipping containers to verify a shipment’s arrival or departure .  
• Machine Learning (ML). For example, computer vision AI software can detect defects in a product or a damaged shipping box. It can do this by comparing the actual image to how it should look.
• Augmented Reality (AR) and Global Positioning System (GPS) Tech. In this case, a delivery driver wearing AR glasses could get assistance with routing, verifying delivery location, and taking a proof-of-delivery (POD) picture.

What’s more, computer vision systems are getting smarter as AI / ML continues to mature and learn. For more on what computer vision AI can do for supply chains, see my article, Computer Vision AI: The Unlimited Ways To Use This Awesome Tech To Empower Supply Chains.

3. How Would Supply Chain Visibility Work If We Used Shipment Status Pictures and Computer Vision AI to Figure Out What Happened?

So, let’s imagine a scenario where shipment status is communicated not through EDI messages or API updates, but through photographs. In this scenario, a carrier takes a picture of a package at each scan point. Next, computer vision AI analyzes these images to determine the shipment’s location, condition, and progress in real-time. The end result is supply chain operators can then share this visual data stream with logistics partners and stakeholders. Further, the supply chain has an unambiguous, authoritative view of a shipment’s status. Also, by bypassing traditional data integration hurdles, this image-based tech both simplifies interoperability and enables remarkably picture-perfect supply chain visibility.

Below, I’ll provide some examples of computer vision AI tech enabling a more reliable, cost effective way to gain supply chain visibility and track shipments. Also, I’ll identify what else is needed for the supply chain industry to fully leverage computer vision AI for end-to-end supply chain visibility.

a. Examples of How Computer Vision AI Can Identify a Shipping Event: Shipped, Intransit, Delivery, Exceptions. 

See below for specific examples of where computer vision AI has the capability to generate shipment status across the supply chain. This includes shipping, receiving, intransit, delivery, and shipment exceptions. 

1) Shipping / Receiving: Validate what is Shipped or Received at the Warehouse.

In this example, a forklift operator loads pallets of goods onto a truck. As the truck is loaded, a computer vision AI camera at the dock door tracks the pallets. Further, the system can confirm the shipment matches the expected items and quantities. Also in another example, a distribution center can use the same AI system during the receiving process. For instance, it can verify receiving packages against the expected shipment, flagging any discrepancies. In both cases, the AI system uses OCR and barcode scanning tech to “read” the image or video. 

Also, to minimize supply chain data integration, the imaging system at the dock door does not necessarily need to “translate” the image locally. Indeed, the imaging system at the shipment’s location can instead directly transmit images and GPS locations to an overall supply chain visibility system or partner systems. Then these other systems could use an AI computer vision module to “translate” the image into meta-data in the form of a shipment status event. For an example of a standalone computer vision AI system for dock-door use cases, See Kargo‘s solution. Here, they offer imaging towers installed on either side of the loading dock doors.

2) Intransit Events: Verify Shipments Arrived or Departed No Matter What the Mode of Transportation.

In the case of intransit, supply chains and their logistics partners can place computer vision AI cameras and sensors at key locations along transportation lanes. For instance with rail, the local operation places cameras in train terminals to capture images of rail cars. Thus, these cameras confirm a shipment’s arrival or departure. Also, this use case holds true for trucks carrying goods that pass through a toll booth or receiving gate. Here the cameras take snapshots of the truck and its license plate, tracking the shipment’s movement. This can even work for ocean ports where cameras take pictures of ships arriving at an assigned berth. Also, cameras can take snapshots of cranes loading and unloading containers. 

Again in the case of intransit events, a standalone AI system can “translate” the image or, better yet, the local operator can transmit the image with GPS locations to supply chain partners. Then partner systems can use AI image recognition to convert the image to metadata and then into a shipment status event. This in turn, results in a reliable shipment status event for each stakeholder. Additionally, this image exchange approach simplifies costly data integration. 

3) Parcel Delivery: Certify that the Package Was Delivered to the Right Place, On-Time.

For example, An ecommerce delivery driver drops off a package at a customer’s doorstep. Here, a parcel carrier has the computer vision AI integrated into the driver’s handheld device or even AR glasses. Then the system can automatically confirm that the driver delivered the package to the correct location. This can also work with AI-powered parcel delivery lockers or mail rooms. Here, the computer vision tech verifies the package was placed in the correct locker and records the time of delivery.

4) Shipping Exceptions: Determine that Exceptions Have Occurred or About to Occur.

As an example of shipping exceptions, computer vision AI monitors video footage of a warehouse receiving dock. In this case, it looks for damages to boxes or products. If detected, it sends an alert to operations for action. Another example is where cameras at a port take a picture of a shipment that is damaged or tampered with. Also, a local operation could use computer vision AI with geo-fencing to generate an alert if a shipment left a restricted area.

Again, there are two ways that the computer vision AI system could work in generating shipment exception messages. First, a standalone AI system triggers a shipment exception alert. Also, a second option is where the on-site system could just transmit the image with the GPS location to stakeholders’ visibility systems. Then, their visibility systems could “translate” the image and GPS location into a shipment status exception event. Also, another benefit of these AI systems is that supply chains could use these images with GPS and local timestamps to estimate if a shipment is on-time or delayed.

b. What Else is Needed to Fully Leverage Computer Vision AI to Track Shipments and Reduce Integration Costs?

So, a computer vision AI system has the capability to translate images and videos into shipment status events. Indeed, this capability offers unlimited potential to dramatically cut data interoperability costs. However, the question is what do we need to achieve “picture-perfect” supply chain visibility? 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

• Camera System to Take Images or Video. This part of the system would capture the raw image and key metadata such as tracking ID, GPS location, and local timedatestamp.
• A Standardized Supply Chain Visibility (SCV) Image-Based Interface. Tracking locations would use this standardized image-based shipment status interface to transmit the raw tracking image and key metadata.
• Cloud-Based AI Image Interpreter Tracking Module. An AI interpreter would then convert the raw image and metadata into a shipment status update to share with tracking and visibility systems.

For a more detailed description of an image-based shipment status solution, see my article, How to Make End-to-End Shipment Tracking the Best Using Computer Vision AI.

Conclusion.

Despite all the recent technology advancements, it is amazing that there is still so much work left for us to do to achieve total supply chain visibility. In fact, it is unbelievable the magnitude of data integration challenges we face. Worse, the integration costs continue to increase where it is estimated that data integration can now take up to 25% of an IT budget for a typical organization. However, there is hope to simplify data interoperability by leveraging computer vision AI tech.

So, in this article I have provided examples of how image-based shipment status has the potential to provide total supply chain visibility. Also, I have identified what is needed for us to fully leverage this tech to track shipments and reduce integration costs. Indeed, by translating the physical world into actionable insights, computer vision AI is able to bypass traditional data integration challenges and costs. Moreover, it will provide a remarkably clear and reliable view of supply chain operations. 

How to Make End-to-End Shipment Tracking the Best Using Computer Vision AI

More References.

• Computer Vision Logistics Use Cases: A Guide. This article by Roboflow provides several logistics use cases and the basics on how computer vision AI works.
• Object Tracking in Computer Vision: An In-Depth Exploration and Practical Guide. Great article from BASIC.AI on the difference between object detection and tracking as well how this tech can integrate with other technologies.
• OCR and AI in Cargo Tracking: Real-Time Visibility. This article by eNest identifies OCR and computer vision use cases for cargo tracking and automation.
• Revolutionizing Label Reading with AI. This article by KARGO provides details on computer vision AI tech within the logistics industry.
• Computer Vision AI: The Unlimited Ways To Use This Awesome Tech To Empower Supply Chains. This article provides more information on what computer vision AI can do for the supply chain, not just supply chain visibility.
• How to Make End-to-End Shipment Tracking the Best Using Computer Vision AI. This article provides a detailed description on what is needed in an image-based shipment status solution that minimizes data interoperability issues.

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