I’ve seen it too often where 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. This is because they just have access to compartmentalized data, especially shipping data. Hence, they make decisions in isolation, with consequences that are both costly and, frankly, avoidable. For instance, think of a shipping department that repeatedly sends packages to the wrong address, oblivious to recurring address correction fees. Or engineers, stranded on-site, waiting for a critical part, with no idea which shipment holds their golden ticket. 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 clear: we must master our shipping data, from forecast through execution to post-analysis. In this digital age, fragmented data is a strategic liability. The good news? A complete view of the truth can unlock unparalleled operational efficiency, significantly reduces costs, and transforms logistics into a formidable strategic asset. Indeed, only through connected shipping data can we empower our supply chains with clarity and a single source of truth. In this article, I will introduce you to an Intelligent Shipping Data Framework – a blueprint for gaining visibility across the entire shipping data life cycle, delivering truly actionable insights. Let’s get started.

“In this digital age, fragmented data is a strategic liability.”

Overview: An Intelligent, Multifaceted Framework for Shipping Data from Planning Through Post-Analysis.

Let’s begin with an overview of an Intelligent Shipping Data Framework and its critical necessity. From my perspective, supply chain decision-makers today gain few insights from fragmented shipping data trapped within countless planning, operational, and financial systems. What is needed is an Intelligent Shipping Data Framework designed to unify this shipment information. This type of data framework would provide all decision-makers with a multi-faceted, 360-degree view of shipping information. Moreover, it establishes a single source of truth across a shipment’s entire life cycle. This includes planning through execution to post-analysis. Ultimately, this unlocks the full potential of shipment analytics. Most importantly, this data framework enables decision-makers to proactively avoid unintended consequences caused by our current fragmented view of goods movement.

“… decision-makers today gain few insights from fragmented shipping data trapped within countless planning, operational, and financial systems.”

As a prime example of an Intelligent Shipping Data Framework, the ASTM International F49 committee is developing specifications for a Goods Movement Process (GMP). This comprehensive framework of a shipment’s life cycle uniquely extends beyond just execution, starting with a shipping forecast phase and ending with a post-analytical phase. A core component of this framework is the Transport Unit Identifier (TUID), a critical digital ID that unifies all shipping data for a shipment load across its entire digital life cycle. In the remainder of this article, I’ll detail each of this framework’s phases. Moreover, I’ll offer examples and recommendations on how to leverage TUID-driven analytics throughout this Intelligent Shipping Data Framework. See diagram below.

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

1st Phase – Forecast: Plan & Optimize for Future Using a 360-Degree View of Shipping Data.

This first phase leverages past shipment historical data as well as internal and external supply chain data. The focus of this phase is to forecast future shipping activity as well as identify resources and assets needed. More often than not, this phase does not focus on single shipments, but more on aggregated shipping data. Also, during this phase, planning teams systematically analyze trends, seasonality, and relevant external factors. Additionally, this foundational phase is not just about looking ahead. Indeed, the Forecast Phase is about constructing a resilient analytical framework that anticipates challenges and capitalizes on opportunities for decision-makers. Examples of shipping data analytics activities in this phase include:

Example Analytical Activities During Shipping Forecast Visibility Phase

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

• 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.

Traditionally, planners have relied on aggregated historical shipping data for forecasts, but struggle to link shipment details to products, purchase orders, and financials. As discussed previously, if supply chains started assigning a Transport Unit ID (TUID) to each load, they could connect all shipment data together, providing shipment-level granularity for better analytics. This 360-degree view of data allows planners to connect products, shipments, costs, performance, and events.

To illustrate, planners for a cold chain logistics operations could use Load Ids 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. Click here for more information on how a Transport Unit Load ID (TUID) can be created and used.

“… the Forecast Phase is about constructing a resilient analytical framework that anticipates challenges and capitalizes on opportunities for decision-makers.”

2nd Phase – Planning: Identify Requirements and Create Load IDs for Shipment Tender.

This second phase of an Intelligent 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

• 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.

Again, this is where I recommend that shippers use a Load ID. This enables all stakeholders to gain visibility over the shipment booking process. Also, with shippers assigning a Load ID during this phase, all stakeholders have both traceability and a unified view of shipping data through each phase of its digital life cycle.

“Today, the process of load planning, rating and tendering is chaotic.”

3rd Phase – Execution: Proactively Manage for Exceptions and Leverage Shipping Data visibility.

This phase is where the 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.

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

Today, the norm for tracking shipments is to use a tracking number issued by the transportation carrier. While useful for simple shipments, it proves inadequate for complex shipping operations. Primary examples of this include intermodal and international trade involving multiple carriers and stakeholders. Also, a major drawback of these carrier tracking numbers is their inability to link to crucial product and financial data associated with the shipment. Again, I recommend that all stakeholders adopt a Load ID to gain comprehensive shipment visibility. This creates a unified view of shipping data, integrating product and financial details. As a result, the use of a shipment Load ID transforms chaotic, disruption-prone operations into proactively informed and adaptable ones.

For example, a car dealership could track critical parts using a Load ID tied to a shipment’s product contents and not just use a carrier’s tracking number. Then, should a shipment exception occur, all stakeholders 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 more on how to implement intelligent shipment tracking, see my article, The Way To Better Supply Chain Analytics: Overcome Data Interoperability With Intelligent Tracking Status.

“… the use of a shipment Load ID transforms chaotic, disruption-prone operations into proactively informed and adaptable ones.”

4th Phase – Settlement: Reconcile for Payment Using Collated Shipping Data.

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 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.

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, empowering stakeholders to make informed decisions and 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.

“The bottom line – freight bill audit today is a preposterous administrative challenge, yielding few insights and dubious cost savings.”

5th Phase – Post-Analysis: Use Integrated Shipping Data to Diagnose, Gain Insights, and Improve.

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

As with the Forecast Phase, this phase focuses primarily on shipment data in aggregate. However, this phase can also include analyses on a single shipment to diagnose systemic issues and glean “lessons-learned” insights. The goal of this post-analysis phase is to gain both operational and financial insights from the shipment data to make forward-looking decisions. Most of the activities in this phase are focused on shipment analytics. For instance, analysts conduct post-diagnostics and trend analysis 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.

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

Today, post-shipment analytics requires much 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 takes much transportation data expertise to compare shipping data between different carriers and service providers. Further, tasks such as calculating Total Landed Costs are excruciating at best. Lastly, it is challenging to link actual shipping activity to what was planned.

However, there is hope with streamlining shipping post-analytics and significantly increasing insights for optimizing supply chains and shipping operations. This is where Load IDs can tie shipping data together, making analytics both easier and enabling the use of advanced AI tools for rapid, actionable Insights.

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 Intelligent Shipping Data Framework as discussed provides the missing link to unify shipping data and establish one source of truth across the supply chain.

“… an Intelligent 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 Intelligent 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).
• Transport Load ID Use Cases. Unifying Shipping Data Using Transport Load IDs: Here Are 10 Ways It Can Unlock Analytics And Empower Logistics Tech
• Multi-Hop Reasoning. Multi-Hop Reasoning For Supply Chains: This Is The Way To Make Better Decisions And Avoid Unintended Consequences
• Shipment Visibility Using an Intelligent Shipping Data Framework. 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

Need help with an innovative solution to make your supply chain analytics actionable? I’m Randy McClure, and I’ve spent many years solving data analytics 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 launching new analytics-based strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your analytics 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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