I’ve watched supply chains pour millions into visibility platforms, control towers, BI dashboards, and real-time tracking tools — only to discover the “insights” they need are still lost in translation. The culprit isn’t the technology. It’s the decades-old habit of every carrier, forwarder, and platform inventing its own custom-built shipment status codes, dictionaries, and message formats. The result is a supply chain where what’s sent isn’t understood, where every new integration is a slow, costly translation project, and where “visibility” is a reconciliation exercise rather than a state of clarity. This insidious practice is the root of today’s visibility gaps — and it has stubbornly outlived every “next-generation” platform that promised to fix it.

The good news: there’s a path from this situational confusion to operational clarity, and it doesn’t require waiting on a global data standard that may never arrive. In what follows, I’ll show how these custom-built statuses quietly sabotage the visibility you’re paying for, how supply chains can move toward genuine interoperability where what’s transmitted is simply understood — and why data integration alone isn’t the finish line. Beyond it lies computer-vision AI tracking: the layer that turns picture-perfect signals into unprecedented insights and, more importantly, into high-velocity action. If you’ve ever stared at a status board that told you everything and explained nothing, read on.

1. Custom-Built Shipment Statuses: The Root Cause of Supply Chain Visibility Gaps

The root cause of most supply chain visibility gaps isn’t a lack of data — it’s a lack of shared meaning. Over the years, thousands of logistics organizations have developed their own custom shipment status data dictionaries, codes, and business glossaries, each transmitting a proprietary brand of shipment status message. For example, when a carrier sends “Status 42” and the manufacturer’s system expects “shipped”, someone has to build and maintain a translation layer to bridge the gap. Multiply that across the dozens of partners in a typical supply chain, and you get a labyrinth of fragile, error-prone data integrations that break the moment a partner changes a field or adds a new code. 

Worse, the deeper problem is the absence of mutually agreed-upon business definitions — without them, even perfect data integration can’t deliver operational clarity. To illustrate, see below for all the possible statuses that the term “shipped” could actually represent within a typical supply chain.

What “Shipped” Really Means: Five Different Shipment Statuses

• Carrier in Possession. The carrier has picked up the shipment and it’s in transit. This is the most common interpretation — but not necessarily what actually happened.
• Label Printed. The shipper printed a shipping label and placed it on the package. Many systems generate a “shipped” status from this label-printed event alone.
• Ready for Pickup. The shipment is on the shipping dock, waiting for the carrier to pick it up. From a customer’s perspective, this is not “shipped.”
• Loaded on a Trailer. The shipment is on a trailer in the dockyard, awaiting carrier pickup. This event is also routinely reported as “shipped.”
• Absolutely Nothing. I’ve seen cases where a user accidentally enters an erroneous tracking number like “123” into a tracking web page — and the page still returns a status of “shipped.”

“… a labyrinth of fragile, error-prone data integrations that break the moment a partner changes a field or adds a new code.”

2. Accelerating Visibility Demands Require More Than Just Haphazard Shipment Status Updates

Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation

Proprietary, custom-built shipment status interfaces are nothing new. These disjointed interfaces started with EDI in the 1970s, and their inherent deficiencies have not gotten any better, despite using APIs, XML, etc. In fact, it’s worse now because supply chains need to digitally integrate with more systems, each exchanging its own custom-built brand of shipment status messages. Moreover, customers now expect fast, proactive, reliable, tailored, and data-driven visibility — and they expect it on-demand and on their own terms. The gap between what operations demand and what legacy shipment status feeds deliver is widening every quarter, driven by e-commerce expectations, multi-modal complexity, and the rise of exception-driven logistics where minutes matter. 

To illustrate, let’s look at international and intermodal shipping. They require precise shipment status updates due to the need for visibility across numerous events, including operational, administrative, and financial activities. However, these complex operations often suffer from incomplete, inaccurate, and delayed tracking updates. As a result, this negatively impacts stakeholders and hobbles supply chains from achieving their basic function: getting the right product to the right place at the right time. Hence, this lack of data interoperability is a significant challenge, making it impossible for supply chains to operate smoothly.

To find out more about data interoperability, its benefits, and ways to improve it, see my article,  Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation.

“… customers now expect fast, proactive, reliable, tailored, and data-driven visibility — and they expect it on-demand and on their own terms.”

3. The Move Toward Seamless Data Interoperability: What Is Transmitted Is Understood

The move toward seamless data interoperability starts with a simple principle: what is transmitted must be understood. However, despite the increased automation in supply chains and the expectation of real-time, precise shipment updates, we still struggle with exchanging actionable information between our systems. Bottom line – our expanding number of supply chain data interfaces still consist of disjointed, free-form text strings, where ambiguous information exchanges are the norm. Below are the two major reasons we do not have seamless information flows in our supply chains.

a. Supply Chains Need Common Business Glossaries With Clear Operational Definitions.

As discussed previously, supply chains lack common business definitions for shipment status. For instance, major parcel carriers like  FedEx, UPS, and USPS have hundreds of unique definitions, many of which are undefined or lack clear, measurable meaning (see links for specifics). Moreover, this issue is compounded by thousands of logistics organizations developing their own custom shipment status data dictionaries and business glossaries. Hence, the lack of mutually agreed upon business definitions is a major reason we do not have seamless information flows in our supply chains. For a more detailed discussion on this topic, see my article, Poor Operational Definitions Impede Supply Chain Tech Adoption: Now Is the Time For A Big Change.

Poor Operational Definitions Impede Supply Chain Tech Adoption: Now Is the Time For A Big Change

b. The Need for Data Interoperability: The Way to Breakaway from the Tangle of Custom-Built Shipment Statuses.

Semantic Digital Interoperability: This Is The Ultimate Way To Make Supply Chains Seamless

Besides supply chain organizations agreeing on a common set of business terms, it is time for our industry to move past custom-built data integrations. What is needed is true data interoperability where “what is sent, is understood.” Below are four steps organizations can take to make their supply chain seamless with 100% shipment visibility.

• Adopt a Data-Centric Mindset. This is the first step to improve the quality of data. Here, supply chain organizations must shift their mindset from application-centric to data-centric. The key is to stop treating data as a by-product, and start using it as a strategic asset.
• Leverage Standards Development Organizations (SDOs). Without a doubt, supply chains need to do better at leveraging emerging and established data standards. This will assure advancement toward meaningful data exchanges. We successfully leverage SDOs for implementing bar code solutions; why not data exchange?
• Move Away From Proprietary “Dumb” Data Interfaces. Without a doubt, we need to move away from costly proprietary data interfaces that are both fragile and lock our precious supply chain data to rigid application silos and proprietary digital exchanges.
• Leverage Emerging Digital Technologies and Methodologies. Unquestionably, supply chains need to leverage emerging tech such as AI, knowledge graphs, and digital identity tech to further enable semantic interoperability within their organizations.

For a much more detailed explanation of these four steps to achieve supply chain interoperability, see my article, Semantic Digital Interoperability: This Is The Ultimate Way To Make Supply Chains Seamless.

“Besides supply chain organizations agreeing on a common set of business terms, it is time for our industry to move past custom-built data integrations. What is needed is true data interoperability where ‘what is sent, is understood‘.”

4. Beyond Data Integration: Computer Vision AI Tracking That Streamlines Insights and Drives Action

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

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. This is where AI — specifically computer vision AI tracking — changes the equation. Instead of transmitting proprietary status codes that require translation, a vision-based approach transmits an image: a shipment photo paired with minimal context like a GPS location, tracking ID, and timestamp. From there, AI can interpret the shipment status. Without a doubt, humans and now AI can “see” the status of a shipment better by looking at an image than by parsing any proprietary status code. Without a doubt, a photo of a pallet on a loading dock means the same thing to a carrier in Singapore and a shipper in Chicago.

The beauty of computer vision AI is that it sidesteps most of the interoperability hurdles that plague traditional shipment tracking. This is because it reduces dependence on complex data formats, dictionaries, and business glossaries. Computer vision AI turns raw images into actionable insights — identifying what was shipped, confirming it arrived intact, and flagging exceptions before they escalate. Best of all, a status-based image brings trust and eliminates miscommunication. With that confidence, supply chains can automate shipment visibility workflows, triggering downstream processes without a human stitching the steps together. That’s not just visibility. That’s operational clarity and efficiency. For more on computer vision AI tracking, see my article, How to Make End-to-End Shipment Tracking the Best Using Computer Vision AI.

“Instead of transmitting proprietary status codes that require translation, a vision-based approach transmits an image: a shipment photo paired with minimal context like a GPS location, tracking ID, and timestamp.”

More References

• Data Readiness: The Definitive Guide to Data Readiness: Why Every Enterprise Must Evolve in the Age of AI
• Computer Vision AI Tracking: Picture-Perfect Tech for Supply Chain Visibility: The Use Of Emerging Computer Vision AI To Find Out What Happened
• Shipping Data Life Cycle: Mastering The Shipping Data Life Cycle: The Way To A Complete View Of The Truth
• Shipment Visibility: The Best Shipment Visibility: One Source Of Truth Framework For Better Planning, Execution, Post-Analysis

The Best Shipment Visibility: One Source Of Truth Framework For Better Planning, Execution, Post-Analysis

For more from SC Tech Insights, see our latest articles on AI, Data Readiness, and Information Technology.

Need help with an innovative solution to make your supply chain data ready? I’m Randy McClure, and I’ve spent many years solving data readiness challenges to help decision-makers gain better, faster insights and for organizations to leverage data-intensive technologies. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in pilot projects and program management 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.