Disjointed data distorts shipment visibility in supply chains, creating a cascade of issues and stifling initiative. For instance, customer service is blind to transportation disruptions, leading to poor customer experiences. Further, transportation carriers are unaware of the products they transport. Thus, they are not proactive when product priorities change. Moreover, supply chain operations routinely take actions resulting in unintended consequences, while planners lack the full picture. Lastly, finance only discovers budget overruns weeks later. What’s more, these visibility gaps not only cause mishaps but also prevent supply chains from seizing opportunities, optimizing performance, leveraging tech innovations, and staying competitive.

The root of the problem? Supply chains have disconnected, incomplete, and duplicated shipping data, Thus, there is no single source of truth (SSOT) that unifies shipment visibility. In this article, I’ll examine why we can’t leverage our shipping data to track every activity, every move. Moreover, this visibility problem is more than just a “Where’s My Stuff?” type problem. In fact this problem extends to many types of shipment visibility from initial planning through final payment and post-shipping analysis. The good news is there is a promising digital framework for shipment visibility. Indeed, this Intelligent Shipping Data Framework has the missing link to unify shipping data and establish one source of truth.

1. Our Shipment Visibility Problem: Why We Can’t Track Every Move from Planning to Profit.

Without a doubt, having effective shipment visibility is paramount for optimizing supply chain operations. Yet many companies do not track every shipment-related activity from planning to profit. There are three reasons for this. First, we have a pervasive data interoperability issue where countless systems fail to “speak” the same language when exchanging data. Second, routinely business stakeholders provide vague visibility requirements, leading IT to provide the data that is easily available, but not what is needed. Lastly, there is an absence of a unifying shipment identifier to connect all shipping data across its entire life cycle from planning, execution, settlement, and post-analysis. So, let’s look at these three shipment visibility challenges in detail.

a. Shipment Visibility Disjointed Due to a Lack of Data Fluency.

Achieving true visibility within supply chains is profoundly challenging due to what I term “poor data fluency.” This deficiency means that essential data is either not transferred at all, arrives incomplete, or, even when transmitted, its meaning is not mutually understood across different systems and stakeholders. This fundamental breakdown is starkly evident when even basic shipment visibility questions, such as “Where’s my stuff?” or “What’s your ETA?”, frequently go unanswered or receive digital responses that are far from satisfactory. To list, there are three primary reasons for this interoperability issue.

Data Interoperability Obstacles to Achieving Shipment Visibility

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

1. Lack access to the right data
2. No clear linkage between disjointed data sources
3. We fail to understand the data we do have

What’s worse is that despite significant investments in advanced AI and analytics, many businesses continue to gain limited insights from their shipping data. While these technologies can improve data access, they often fall short in effectively linking and making sense of disparate shipping data. Consequently, organizations remain trapped in functional silos, leading to a myopic views of the supply chain. This fragmentation is evident as vital, unlinked data is spread across numerous departments like shipping, warehousing, order management, third-party logistics providers, and accounting. For a deeper dive into this critical data interoperability challenge, I recommend my article, The Data Interoperability Challenge: It’s The Need For Tech Standards, Compliance, Security, Massive Resources, And Be Understandable.

“… we have a pervasive data interoperability issue where countless systems fail to “speak” the same language when exchanging data.”

b. Seven Types of Shipment Visibility that Businesses Fail to Define and IT Seldom Delivers.

Also, there are many types of visibility that we need from our shipping data. First and most obvious we need basic shipment tracking. However, supply chains also need more diverse forms of shipping data visibility. This includes shipping data visibility to products within containers, shipping rates, service performance, available capacity, unplanned events, and to support operational planning, among others. The trouble is that these visibility requirements are often poorly defined by business. Thus, too often IT departments will provide the data that is available, not what is needed. Compounding this issue, the necessary shipping data for these varied visibility requirements is typically difficult to access and is disjointed.

To fully grasp these shipping visibility information gaps, let’s examine each type.

Data Disconnects with Different Types of Shipment Visibility

1. Transportation Visibility: Find My Stuff. In this case, the focus is on where shipments are and their Estimated Time of Arrival (ETA). Routinely, this visibility is disconnected from products or shipping costs.
2. Capacity Visibility: Identify Choke Points in the Supply Chain. With this visibility you are not looking at individual shipments. The challenge is that the data needed is siloed across many functions and systems. As a result, decision-makers frequently have only myopic visibility where each department takes actions that result in unintended consequences.
3. Shipment Data Analytics Visibility: Measure Performance. If metrics are not aligned with corporate goals or if data is incomplete, analyses lead to false positives and unintended consequences.
4. Rates Visibility: Manage Transportation Spend. This visibility focuses on costs. Again, this can lead to a myoptic viewpoint, not considering service, customer, or other non-cost factors.
5. Supply Chain Planning Visibility: Prepare for Future Operations. In many cases, planners are disconnected from real-world decision-makers and work with “stale” data.
6. Supply Chain Operational Visibility: Proactively Manage Current Operations. Here, operational data is routinely out-of-date. Worse, shipping exceptions information is not available to avert negative outcomes.
7. Strategic Supply Chain Visibility: Innovate And Optimize Processes. For instance, this visibility provides insights for improving or introducing new products and services to assure future success. Again, any information distortion leads to lost opportunities, inefficient future operations, and unintended consequences.

The bottom line is that because of our disjointed shipping data and myopic perspectives, our supply chains lack visibility. This leads to lost opportunities, inefficient operations, and unintended consequences. For more detailed discussion on the many forms of supply chain visibility, see my article, Surprisingly Supply Chain Visibility Has Many Forms: See Which One Is Best To Be Your Business’ First Focus.

“… routinely business stakeholders provide vague visibility requirements, leading IT to provide the data that is easily available, but not what is needed.”

Surprisingly Supply Chain Visibility Has Many Forms: See Which One Is Best To Be Your Business’ First Focus

c. The Missing Link Between Products, Shipments, Cost Types, Service Performance, and Unplanned Events.

Better Shipping Data Analytics Results: Use Of Load IDs To Achieve The Best Efficiency, Visibility, And Financials

Despite the best AI and analytics tools, supply chains continue to lack visibility, grappling with disjointed shipping data. This underscores the urgent need to link all shipping data from forecasting, booking, and transport through to final settlement and post-analysis—essentially, from planning to profit.

One solution is a Transport Load ID (TUID), created by the shipper when they identify the need to transport goods. Indeed, this can serve as the crucial unifying element, linking all shipping data across its entire life cycle. Moreover, this Load ID provides essential data linkage across various supply chain dimensions, including products, shipments, costs, service performance, and unplanned events. This directly addresses the fragmentation that currently forces stakeholders to rely on disparate identifiers like carrier tracking numbers, purchase orders, and invoice numbers. For more information on Transport Load IDs, see my article, Better Shipping Data Analytics Results: Use Of Load IDs To Achieve The Best Efficiency, Visibility, And Financials.

“… there is an absence of a unifying shipment identifier to connect all shipping data across its entire life cycle from planning, execution, settlement, and post-analysis.”

2. Complete Shipment Visibility Leveraging An Intelligent Shipping Data Framework for Unified Shipping Data, One Source of Truth.

So, there is definitely a need for us to defrag our shipping data. What’s required is a data framework that unifies shipping data. Indeed, supply chains need a multi-faceted, 360-degree view over their shipping data’s life cycle. This would enable a single source of truth starting with shipment forecasting through execution to post-analysis. For instance, the ASTM International F49 committee is developing specifications for an Intelligent Shipping Data Framework for the goods movement process (GMP). Moreover, this framework includes a Transport Unit Identifier (TUID), previously mentioned, that acts as a shipment load ID. As a result, supply chains can unify their shipping data through its entire life cycle. See diagram below for details.

Goods Movement Process (GMP) and Shipment Analytics “Bookend” Phases

An Intelligent Shipping Data Framework – 5 Phases

Below are more details of each phase of an Intelligent Shipping Data Framework.

1. Forecast: Plan & Optimize for Future Using a 360-Degree View of Shipping Data. This phase involves leveraging historical and current supply chain data to predict future shipping activity and identify necessary resources. For example, planners might forecast an increased need for refrigerated transport due to a rise in fresh produce shipments.
2. Planning: Identify Requirements and Create Load IDs for Shipment Tender. This phase begins with a specific transport requirement for a load and concludes when an approved carrier picks up the shipment. For optimal traceability, the shipper assigns a unique Load ID for each ship requirement. An example activity during this phase could be a supplier listing a shipment of electronics on a freight exchange platform.
3. Execution: Proactively Manage for Exceptions and Leverage Shipping Data visibility. During this phase, transportation services are actively performed, from carrier pickup to final delivery. In this case, the focus is on managing exceptions using comprehensive shipping data. For instance, a car dealership could use a Load ID to track a critical part, not just a shipment, allowing them to proactively act if a shipment exception occurs.
4. Settlement: Reconcile for Payment Using Collated Shipping Data. This phase occurs after delivery and encompasses all administrative tasks required to process payment and resolve any outstanding issues. An example is a freight auditor processing a carrier invoice against contract rates and delivery performance using collated shipping data linked by a Load ID.
5. Post-Analysis: Use Integrated Shipping Data to Diagnose, Gain Insights, and Improve. Lastly, this phase focuses on analyzing aggregated and individual shipment data to diagnose systemic issues, gain operational and financial insights, and improve future operations. For example, an analytics team might review a past quarter’s on-time delivery rates to identify root causes.

“… there is definitely a need for us to defrag our shipping data.”

Conclusion.

In this article, I first examined the wide-ranging issue that supply chains have with shipment visibility. Specifically, we are not able to leverage our shipping data to track every activity, every move, from planning to profit. Moreover, this visibility problem is more than just a “Where’s My Stuff?” type problem. Specifically, we have many types of shipment visibility problems from initial transportation planning through final payment and post-shipping analysis. However, there is hope. This is where I have identified a promising digital framework for shipment visibility. This Intelligent Shipping Data Framework provides the missing link to unify shipping data and establish one source of truth across the supply chain.

More Shipment Visibility References.

• ASTM F49 committee and their work on the Goods Movement Process, see Standard Terminology for Goods Movement Process (GMP) Precise Foundational Definitions.
• Also, for more information on disjointed shipping data, see my article, Poor Shipping Data – Here Are The 4 Reasons Impeding High Tech Visibility And Actionable Analytics.
• For more use cases by unifying shipping data, see my article, A Less Painful Way To Unlock Total Landed Cost Insights By First Fixing The Massive Disconnects In Supply Chain Data.
• For more discussion on intelligent tracking, see my article, The Way To Better Supply Chain Analytics: Overcome Data Interoperability With Intelligent Tracking Status.
• GS1’s Electronic Product Code Information Services (EPCIS). This document lays the groundwork in specifying how to embed intelligence in event statuses. In particular defining events into 4 aspects: What, Where, When, Why (and How).

The Way To Better Supply Chain Analytics: Overcome Data Interoperability With Intelligent Tracking Status

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 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 operational 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.

For more from SC Tech Insights, see the latest articles on interoperability, data analysis, and shipping.