I’ve seen it too often: planners, operators, and finance teams working in silos, armed with advanced tech but only a fragmented view of the truth. Because their data, especially their shipping data, is trapped in disjointed systems, critical decisions are made in isolation. This leads to costly, avoidable consequences. For example, a shipping department repeatedly uses the wrong address for a customer, oblivious to the root cause of countless address correction fees incurred for the same address. Or, in another case, engineers stranded on-site, waiting for a “golden ticket” part hidden inside an untraceable container. Without a doubt, these aren’t just anecdotes; they’re symptoms of a fragmented data landscape that demands our immediate attention.

The bottom line is it’s time to fix our disjointed shipping data. Without a doubt, we must master our supply chain’s entire shipping data life cycle, from forecast to delivery to settlement to post-analysis. In this digital age, fragmented data is a weakness; a complete view of the truth is a formidable strategic asset. By connecting these disparate data points with a “golden thread”, we have a data framework that empowers our supply chains with the clarity of a single source of truth. In this article, I’ll introduce you to an Interlinked Shipping Data Framework—a blueprint for gaining visibility across the entire shipping data life cycle, delivering truly actionable insights. So, let’s get started.

The Golden Thread: A Unified Framework for Shipping Data from Planning to Post-Analysis.

From my perspective, current supply chain decision-making is hobbled by fragmented data trapped in incompatible planning, operational, and financial silos. In fact, we aren’t just losing efficiency; what we have is no Single Source of the Truth (SSOT) to make informed decisions. Moreover, we do not have a unified view of our supply chains to collaborate effectively. What is needed is a data framework that links our shipping data together, a “golden thread” across all stages of shipping from initial forecast through execution to post-analysis. By architecting a framework for our supply chain’s shipping data life cycle, we move beyond basic reports and establish a single source of truth for better decision-making and collaboration.

To place a name on this unifying data structure, I’m calling it an Interlinked Shipping Data Framework. An excellent candidate for this framework is ASTM International F49 committee‘s Goods Movement Process (GMP). This comprehensive data framework is designed to encompass a shipment’s entire data life cycle and all events associated with a shipment load. This not only includes the movement of goods during the execution phase, but also starts with a forecast phase and ends with a post-analytical phase. Most importantly, this framework has a “golden thread” linking the shipping data together called a Transport Unit Identifier (TUID). This shipper-generated ID unifies all shipping data for a shipment load across all events within its life cycle. See diagram below.

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

In the remainder of this article, I’ll detail each of the Interlinked Shipping Data Framework’s phases. Moreover, I’ll offer examples and recommendations on how to leverage TUID-based analytics and shipment visibility throughout the shipping data’s life cycle.

1st Phase – The Forecast: Building Confidence with Unified Data at the Shipment-Level Detail.

In this first phase of the shipping data life cycle, planners use historical shipment data to forecast future activity and identify necessary assets. Traditionally, planners have had a hidden impediment that has hobbled supply chain forecasting. Namely, they have had to rely on aggregated shipping data rather than shipment-level detail. This lack of granularity stems from a historic struggle to link individual shipments to specific products and financial transactions. This is where the Transport Unit ID (TUID) – “the Golden Thread” – solves this problem by anchoring historical planning data at the shipment level. To understand how an Interlinked Shipping Data Framework using TUIDs transforms the forecast process, let’s examine some specific examples of analytical activities performed during the Forecasting Phase.

Example Analytical Activities During the Shipping Forecast Phase

• Planners forecast that their supply chain will need more refrigerated transport due to a seasonal increase in fresh produce shipping.
• Planners estimate the need for additional warehouse space to accommodate an upcoming holiday season’s surge in ecommerce orders.

Supply Chain Planning: Data Analytics Advice That Will Result In A Better Way

As with these forecasting examples above, planners traditionally have not been able to effectively dig into shipment details; forecast confidence levels suffer. However, when planners leverage a unified, detailed view of shipments, products, and financials, the confidence level of their forecasts increases significantly. Moreover, planners become more adaptable, rapidly updating their forecasts as new information is available. Without a doubt, this granularity allows for more sophisticated scenario modeling, especially when powered by AI. Instead of relying on static reports, they can provide the on-demand analytics required for timely, high-velocity decision-making. Ultimately, shipment-level detail data transforms supply chain forecasting from a best-guess exercise into a precise, adaptive capability.

To illustrate, planners for a cold chain logistics operations could use historical TUID-linked data to make better recommendations based on the mix of fresh product orders and what impacts are likely to occur in regard to reefer trailers requirements. Moreover, as new information becomes available about future operations, planners and their forecasting systems can rapidly model scenarios and provide up-to-date forecasts to decision-makers. For more background information on supply chain planning and data analytics, see my article, Supply Chain Planning.

“… when planners leverage a unified view of shipments, products, and financials, the confidence level of their forecasts increases significantly. Moreover, planners become more adaptable, rapidly updating their forecasts …”

2nd Phase – Planning: Defining Shipment Requirements and Creating ID for Load Tender.

This second phase of an Interlinked Shipping Data Framework starts with a specific shipment requirement for a transport unit, a load, and ends with the approved carrier picking up the load. The key milestones during this phase are Posted, Pre-Booked, and Booked. Example shipping events include:

Example Activities During Planning Shipment Load Phase

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

• A supplier has a transport requirement for a transportation carrier to deliver a shipment of electronics to a retail store. They list it on a freight exchange platform and await carrier bids.
• A preferred carrier tentatively agrees to transport a new line of fashion apparel to various outlets, pending final confirmation of details.

Today, the process of load planning, rating and tendering is chaotic. This is especially true for Truckload and Less-Than-Truckload (LTL). For instance, shippers and brokers will post their “load” on many transportation load boards.Then there is no follow-up on what did or did not happen with the load. As a result, stakeholders struggle to determine what is a real “load” and whether it was booked. This chaos also further clouds shipment visibility. Worse, it enables bad actors through fraud to steal shipments.

Without a doubt, shippers can eliminate chaos during their load planning phase by using an Interlinked Shipping Data Framework. Now, the shipper or their representative assigns a unique Transport Unit Identifier (TUID) – the “Golden Thread” – to the load requirement. This ID not only identifies the shipment, it is the unifying ID for all associated shipping data points, from purchase orders, and BOLs, to tracking numbers and invoices. By unifying these disparate links, the TUID provides all stakeholders with total traceability and a single, continuous view through every phase of the shipping life cycle. For more information on this topic, see my article, Better Shipping Data Analytics Results: Use Of Load IDs To Achieve The Best Efficiency, Visibility, And Financials.

“the shipper … assigns a unique Transport Unit Identifier (TUID) – the “Golden Thread” – to the load requirement … the unifying ID for all associated shipping data points, from purchase orders, and BOLs, to tracking numbers and invoices.”

3rd Phase – Execution: Proactively Manage Events and Exceptions Using Unified Shipping Data.

This phase is where transportation services are executed. This begins with the carrier picking up the cargo, and ends with the final delivery of the shipment by the delivering carrier. The key milestone for this phase is Delivered. Example shipping events include:

Example Activities During Execution Movement Phase

• The carrier picks up a shipment of automotive parts and is currently in transit to the dealership.
• The carrier delivers a shipment of medical supplies and the destination hospital signs for the shipment.

Today, the norm for tracking shipments is to use a tracking number issued by the transportation carrier. While useful for simple shipment tracking, it proves inadequate for managing complex shipping operations such as intermodal transportation and international shipping. Moreover, a carrier-generated shipment ID is inadequate for tracking the entirety of supply chain events. This is because a tracking number alone only has limited linkage to financial and product-level information. This is where a shipper-generated TUID and a standardized Goods Movement Process (GMP) enables decision-makers to proactively manage exception events and gain total supply chain visibility. So, let’s look at the advantages of using an Interlinked Shipping Data Framework during this execution phase.

a. Single Source of Truth (SSOT) for Managing Shipment-Level Events.

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

First, a carrier-generated tracking number cannot provide a Single Source of Truth. This is because it only answers “Where’s my stuff?”, lacking the information to answer questions like “What’s the impact on my profit?”. Without a doubt, carriers operate with a fragmented view of the supply chain, lacking critical product-level details and financial data. Worse, their disjointed systems often generate one-dimensional statuses that are as ambiguous as they are incomplete. To stay competitive, shippers must move beyond these limitations. The superior alternative is a Transport Unit Identifier (TUID) – the “Golden Thread” – generated by the shipper. Indeed, the TUID is the missing link between products, shipments, costs, service performance and unplanned events, enabling a single, searchable reality.

For example of the power of TUID, let’s take a car dealership that is tracking critical parts. In this scenario, their supply chain systems could use a Load ID (TUID) tied to a shipment’s product contents and not just use a carrier’s tracking number. Then, should a shipment exception occur, all stakeholders, not just the car dealership, could act with clarity. In this case, the dealership could rapidly facilitate and prioritize rerouting of the shipment containing the critical part. Or, they could reach out to alternative suppliers. For a more detailed discussion on a SSOT and a better way to managing shipment-level events, see my article, The Best Shipment Visibility: One Source Of Truth Framework For Better Planning, Execution, Post-Analysis.

b. Interlinked Shipment Tracking to Unify Supply Chain Analytics and Event Management.

Today, it is the norm for supply chains to have disjointed shipping data spread across their many systems, creating a real interoperability nightmare for logistics organizations. An Interlinked Shipping Data Framework is the best way to solve these data interoperability issues that are shackling both supply chain management and analytics. Moreover, by providing a unifying structure for shipping data, we can minimize ambiguous, customized tracking status messages. To truly achieve interlinked tracking that unifies both supply chain management and analytics we need the following:

Interlinked Shipment Tracking Requirements

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

• Intelligent Tracking Status. Today, most shipment status messages exchanged between supply chain systems are custom-built and proprietary. As a result, setting up new data interfaces such as APIs are time-intensive requiring very experienced IT professionals. Worse, in many cases, the data transmitted is neither understandable, or actionable. This no longer needs to be the case, if supply chains use the TUID and the Goods Movement Process’s (GMP) standard shipment statuses. For more on how to do this and TUID benefits, see my article, The Way To Better Supply Chain Analytics.
• Clarity in Business Terminology. Indeed, an Interlinked Shipping Data Framework also requires definitive business glossaries, enabling supply chains to share mutually understandable information. For example, the term, “delivery window”, is frequently misunderstood between a shipper and a carrier where the shipper expects a precise 2-hour slot, while the carrier thinks it means a 4-hour period. What is needed are mutually agreed upon definitions that are measurable that both machines and humans understand. This is where good business glossaries come in. The challenge is that today’s Supply Chain Industry has countless, overlapping business glossaries, lacking clear definitions. For more on this topic, see my article, Supply Chain Business Communications Need Clarity: This Is What is Hobbling New Tech Innovations.

Supply Chain Business Communications Need Clarity: This Is What is Hobbling New Tech Innovations

“… a shipper-generated TUID and a standardized Goods Movement Process (GMP) enables decision-makers to proactively manage exception events and gain total supply chain visibility.”

4th Phase – Settlement: Streamline Freight Audit and Cost Allocations.

This phase of the shipping data life cycle normally begins after the shipment delivery. It includes all the administrative activities required to issue a payment, allocate costs, and close any unresolved tasks associated with the shipment. Key milestones during this phase are Invoiced and Settlement Complete (Archived). Example shipping activities include:

Example Activities During Settlement Phase

• The carrier issues an invoice to the shipper for a completed delivery of designer furniture to a showroom.
• A shipper makes payment for a bulk grain shipment.

From a financial reconciliation perspective, disjointed shipping data during the settlement phase is the cause of two major pain points: auditing freight bills and allocating costs. An Interlinked Shipping Data Framework can streamline both of these supply chain problems.

a. Freight Bill Audit & Pay Streamlined.

The Most Strange Way Freight Bills Are Processed And Its Data Insights Lost

Today’s many audit & pay processes rely solely on the carrier’s invoice ID as the primary reference for settlement. However, this auditing methodology quickly breaks down when faced with numerous shipment-related transactions. For instance, these transactional anomalies can include invoice adjustments, service failures, disputes, partial payments, and late fees. As these types of settlement exceptions are quite common, the audit process is routinely slow and labor-intensive. The bottom line – freight bill audit today is a preposterous administrative challenge, yielding few insights and dubious cost savings. For a more detailed discussion on the challenges with freight bill audit & pay, click here.

On the other hand, there is hope to streamline freight bill auditing. One way is to implement a Load ID scheme to unify all shipping data, unlocking opportunities to automate even the most complex processes. For example, Load IDs help to seamlessly collate critical settlement information. This includes shipping data like proof of delivery, precise transit times, and any deviations from transportation service provided or contract rates differences.

b. Efficient Cost Allocation Leading to Better Profit Visibility.

A Less Painful Way To Unlock Total Landed Cost Insights By First Fixing The Massive Disconnects In Supply Chain Data

Without a doubt, allocating costs, especially transportation costs, is practically impossible except in the most simple circumstances. For example, your CEO asks for the Total Landed Cost on your best-selling product. Simple question, right? Yet your team scrambles through spreadsheets, shipping records, and financial systems only to piece together a rough estimate weeks later. Also, for most businesses, transportation costs are usually 6 to 8 percent of revenue. Hence, most businesses have a need to use GL-coding to allocate these costs to a profit center within their accounting system.

Again, allocating transportation costs is a difficult undertaking due to the disjointed nature of shipping data. In fact, to do it right, companies would need to engage a very expensive transport expert to properly allocate these costs. On the other hand, an Interlinked Shipping Data Framework would simplify this process of allocating transportation costs. With this framework, shipping data is automatically linked to products, shipments, cost types, procurement contracts and unplanned events. For more information on streamlining cost allocation challenges such as calculating Total Landed Costs, see my article, A Less Painful Way To Unlock Total Landed Cost Insights By First Fixing The Massive Disconnects In Supply Chain Data.

“… disjointed shipping data … are the cause of two major pain points: auditing freight bills and allocating costs. An Interlinked Shipping Data Framework can streamline both …”

5th Phase – Post-Analysis: Effectively Diagnose to Gain Insights and Improve.

The Truth About Diagnostic Analytics: A Forgotten Way To Better Business Performance

As with the Forecast Phase, traditionally the Post-Analytics Phase has focused on shipment data in aggregate. However, this phase also needs analyses at the shipment level to diagnose systemic issues and glean “lessons-learned” insights. Overall, the goal of this post-analysis phase is to gain feedback in the form of insights to make forward-looking decisions. More specifically, analysts perform post-diagnostics and trend analyses against the shipping data to identify opportunities to improve both future operations and financials. For more on diagnostic analytics, click here. Example shipping data analytics activities include:

Example Activities During Post Analysis Phase

• The analytics team reviews the on-time delivery rates and condition of goods for the past quarter to identify patterns or root causes for delays or damages.
• Analysts study fuel cost fluctuations and their impact on shipping expenses over the past year to identify opportunities for cost-saving negotiations with carriers.

Multi-Hop Reasoning For Supply Chains: This Is The Way To Make Better Decisions And Avoid Unintended Consequences

Today, post-shipment analytics requires a lot of transportation data experience. Moreover, the shipping data is routinely inaccurate, incomplete, and disjointed. This is especially true when analysts attempt to look at both financial and service performance data for insights. Additionally, diagnostic analytics is challenging when the focus is on complex shipping activities such as intermodal and international. Also, it is particularly challenging to compare shipping data between different transportation service providers and determine the root cause of shipment exceptions.

As with other phases of managing shipments, the underlying problem with shipment post-analysis is the fragmented nature of shipping data. This is because it is scattered across multiple systems and stakeholders. Again, this is where an Interlinked Shipping Data Framework with the TUID-based load IDs streamlines analytical processes and significantly increases insights for optimizing supply chains. Moreover, with this interlinked data framework supply chains can leverage emerging technologies like AI and Multi-Hop Reasoning to supercharge post-analysis. For more on how Multi-Hop Reasoning works for supply chains, click here.

Final Thoughts.

The bottom line is clear: we must master our shipping data, from forecast through execution to post-analysis. In this digital age, fragmented data is a strategic liability. Today, our supply chain’s planners, operators, and finance teams, despite leveraging advanced information technologies, are consistently working with only a fragmented view of the truth. However, there is hope. This is where I have identified a promising digital framework to gain complete visibility over your supply chain’s shipping data life cycle. Without a doubt, using an Interlinked Shipping Data Framework as discussed provides the missing link to unify shipping data and establish one source of truth across the supply chain.

“… an Interlinked Shipping Data Framework … the missing link to unify shipping data and establish one source of truth across the supply chain.”

Unifying Shipping Data Using Transport Load IDs: Here Are 10 Ways It Can Unlock Analytics And Empower Logistics Tech

More References on an Interlinked Shipping Data Framework and Transport Unit ID (TUID).

• ASTM F49 committee and their work on the Goods Movement Process, see Standard Terminology for Goods Movement Process (GMP) Precise Foundational Definitions.
• 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).
• Use Cases for a Transport Load ID. Unifying Shipping Data Using Transport Load IDs: Here Are 10 Ways It Can Unlock Analytics And Empower Logistics Tech
• Data Interoperability is needed to share data. See my article, Logistics Data Interoperability: Advice To Make It Understandable, Usable, Secure
• Data Readiness: Be Data Ready: It’s About Relevant Information — Targeted and Timely — for the Best Business Decisions
• AI Vision Tracking, one more way to streamline interoperability. See my article, AI Machine Vision Fundamentals You Need to Know For Implementing an Innovative 5-Step Shipment Tracking Solution
• Government helping with Interoperability. See my article, Securing Data Interoperability In The Supply Chain: Can The Government Make For Better Results?
• Intermodal Transportation, especially, needs a better data framework. See my article, Intermodal Transportation Requires A Breakthrough To Overcome Its Interoperability Problems

Intermodal Transportation Requires A Breakthrough To Overcome Its Interoperability Problems

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.

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