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 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.

Effective shipment visibility is crucial for optimizing supply chain operations. From the initial planning stages to the final profit analysis, tracking every move ensures that all stakeholders have the necessary information to make informed decisions. Without this visibility, companies risk inefficiencies, delays, and increased costs. Indeed, it is essential for supply chain professionals to be both proactive and have the insights to respond quickly to disruptions, optimize costs, enhance customer satisfaction, and gain constructive feedback to remain competitive.

So, let’s look at why supply chain stakeholders have disjointed views of shipment visibility. To do this, we need to examine how supply chain stakeholders have different views of shipment visibility. Indeed, the major reason for this is that we rely on many disparate enterprise systems that separate our shipping data into distinct silos. For instance, these systems disconnect our shipping data between products, shipments, cost types, service performance, and unplanned events.

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

Achieving visibility within supply chains is challenging due to poor data fluency. This is evident even in basic shipment tracking, where questions like “Where’s my stuff?” and “What’s your ETA?” often receive inadequate answers.  There are three primary reasons for this. 

Obstacles to Achieving Shipment Visibility

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

• Lack access to the right data
• No clear linkage between disjointed data sources
• We fail to understand the data we do have

What’s worse, we continue to pour our resources into more and more advanced AI and analytics tech. Now, advanced tech can help solve our data access issues, but it does not do well at unifying shipping data and making it understandable. As a result, many businesses continue to struggle operating within functional silos with myopic views of the supply chain. To elaborate, this includes their lack of visibility across siloed departments such as shipping, warehousing, order management, third-party logistics providers, and accounting. For a more detailed discussion, see my article, The Data Interoperability Challenge: It’s The Need For Tech Standards, Compliance, Security, Massive Resources, And Be Understandable.

b. Six Types of Shipment Visibility with Disjointed Perspectives and Unintended Consequences.

Indeed, there are many types of visibility that we need from our shipping data spanning across products, shipments, cost types, service performance, and unplanned events. However, because of our disjointed data and myopic perspectives, our shipment visibility is distorted, leading to lost opportunities, inefficient operations, and unintended consequences. To better understand these visibility issues, let’s examine the challenges and information gaps in each of the following six type of shipment visibility.

Data Disconnects with Different Types of Shipment Visibility

1) Transportation Visibility.

This type of visibility focuses on finding “stuff.” Specifically, it wants to know where shipments are and their Estimated Time of Arrival (ETA). It is disconnected from products or shipping costs.

2) Capacity Visibility.

This visibility focuses on critical paths, identifying choke points and eliminating them. It is not looking at individual products and shipments. Also, when stakeholder limit this type of visibility to a functional silo, it creates an myopic viewpoint where siloed department’s take actions that result in unintended consequences in other departments.

3) Shipment Data Analytics Visibility.

This visibility focuses on aggregated outcomes. If analysts use out-of-date metrics not aligned with corporate goals or if their data is incomplete, their analyses will lead to false positives and unintended consequences.

4) Rates Visibility.

This visibility focuses on costs. Again, this can lead to a myoptic viewpoint when the costs are not directly linked to service performance, customer satisfaction, or other non-cost factors.

5) Supply Chain Planning Visibility.

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

This visibility focuses on preparing for future operations. In many cases, planners are disconnected from real-world decision-makers, changing operational conditions, and lack information across the supply chain. As a result, planners provide recommendations that cannot be trusted (this includes not trusting AI). 

6) Strategic Supply Chain Visibility.

This visibility focuses on innovating processes. This can include improving or introducing new products or services to assure future success. Again, any information distortion leads to lost opportunities, inefficient future 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.

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

So, supply chains continue to have significant shipment visibility problems despite the best AI and analytics tools. Indeed, we need a way to link all shipping data together, including data from forecasting, shipment booking, the transport of goods, and final settlement. Basically, supply chains need shipment visibility from planning to profit. One way to do this is to use a Transport Load ID that links all shipping data from planning through execution to final settlement, and even post-shipment analysis. Specifically, the shipper (or their representative) creates this Load ID when they anticipate a need to ship their products. As a result, this provides a method to link all shipping data back to the originating Load ID.

Indeed, a Load ID provides shipping data linkage across various supply chain dimensions, including products, shipments, cost types, service performance, and unplanned events. Today, most supply chains do not use this Load ID concept. As a result their shipping data is fragmented, scattered across many systems. For instance, supply chains stakeholders will associate some of their shipping data with a carrier’s tracking number and they will link other shipping data to a reference numbers such as purchase orders, invoice numbers, and many other fragmented identifiers. 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.

2. A Connected Shipment Visibility Framework: Unified Shipping Data, One Source of Truth.

So, there is definitely a need for unified shipping data. Without this one, unifying source of truth, supply chains are practically helpless to leverage the latest AI and advanced analytics. Only with a one data framework to enable Connected Shipment Visibility, can we empower supply chains to see with clarity. Hence with actionable Insights, they can seize opportunities, improve operations, and avoid unintended consequences. Moreover, decision-makers can act both confidently and rapidly with connected shipping information. Indeed, a unified data framework using Transport Load Ids provides unparalleled visibility across the supply chain from planning to profit.

As an example of a digital shipping data framework, the ASTM International F49 committee is developing specifications for an Intelligent Shipping Data Framework for the goods movement process (GMP). Now, this data framework includes the key phases within the goods movement process, but also includes a forecasting and post-analytical phase. A key linking component of this data framework is a Transport Unit Identifier (TUID). This critical digital ID unifies all shipping data for a shipment load from planning through execution and financial settlement to post-analysis. Below, I’ll describe each phase of this Intelligent Shipping Data Framework. Most importantly, I’ll identify how this unified digital framework provides for new shipment visibility analytics opportunities. Also, see diagram below.

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

Phase 1- Forecast Visibility: Long-Range Planning and Optimization Using Aggregated Shipping Requirements.

This 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 and identify resources and assets needed. This phase does not focus on single shipments. Example shipping data analytics activities include:

Example Activities During Shipping Forecast Visibility Phase

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

Load ID Forecasting Opportunity. In the past, most supply chain planners were stymied with integrating shipping data into their forecasting processes. Now with the use of Load IDs to link historical shipping data together, planners can have a greater visibility of transportation requirements in terms of granularity as well as cause and effect. Specifically with the use of Load Ids, planners can see the relationships between products, shipments, transportation cost types, service performance, and unplanned events. To illustrate, planners could use Load Ids to link the forecast changes in the mix of fresh product orders and what impacts are likely to occur in regard to reefer trailers requirements.

Phase 2 – Planning Shipment Loads: Gain Visibility of Rates and Capacity to Book the Best Transport.

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

Example Activities During Planning Shipment Load Phase

• A supplier has a requirement for a transportation carrier to transport 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.

Load ID Shipment Booking Opportunity. Today, especially for certain types of transportation like Truckload and Less-Than-Truckload (LTL), the rating and booking process is chaotic. As a result, stakeholders struggle to determine what is a real “load” and whether it was booked. This is because 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. This chaos also further clouds shipment visibility where fraudulent carriers will steal shipments. With a Load ID, both rates and shipment requirements have traceability and become manageable. Moreover, gaining visibility over the booking process provides greater visibility for managing shipping operations and reconciling financial settlements.

Phase 3 – Execution Movement: Achieving Shipment Visibility and Managing Exceptions.

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 status events include:

Example Activities During Execution Movement Phase

• Carrier has picked up a shipment of automotive parts and is currently in transit to the dealership.
• Carrier has delivered a shipment of medical supplies where the destination hospital has signed for the shipment.

Load ID Movement Execution Opportunity. In the past, most supply chains have used carrier-generated tracking numbers to track shipments. This works to some extent except for intermodal and international trade where there may be many types of carriers involved as well as stakeholders. Additionally, the carrier tracking number in many cases does not link to product and financial data that is associated with the shipment. Thus, a Load ID enables an unified view of the shipping data to include product and financial details. For example, a shipping department would have visibility of critical parts needed by a car dealer, and proactively prioritize rerouting the shipment when disruptions occur to assure on-time delivery.

Phase 4 – Settlement: Reconcile Contract Rates Against Load Movement Performance.

This phase normally begins after delivery with 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.

Load ID Shipment Settlement Opportunity. Today, most supply chains use the carrier’s invoice as the reference for processing shipping data associated with the shipment. The trouble is that this process falls apart when there are many shipping transactions such as invoice adjustments, disputes, partial payments, credits, refunds, duties, and miscellaneous fees to name a few. As a result, this settlement phase is labor intensive. A Load ID that unifies all this shipping data together would enable stakeholders to have a unified view of the data and make better decisions. Moreover, supply chains would have more opportunities to automate these disjointed processes.

Phase 5 – Post-Analysis: Use Shipment Visibility Feedback to Diagnose, Gain Insights, and Improve Operations.

As with the Forecast phase, this phase focuses on shipment data in aggregate, and is not focused on single shipments except for deriving “lessons-learned” insights. This phase encompasses all post-shipment activities involving shipping data. The goal 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. Example shipping data analytics activities include:

Example Activities During Post Analysis Phase

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

Load ID Post-Analysis Opportunity. Today, post-shipment analytics requires much transportation data experience and is plagued by inaccurate, incomplete, and disjointed data. This is especially true when analysts attempt to look at both financial and service performance data for insights. Additionally, diagnostic analytics is challenging for more complex shipping activities such as intermodal and international. Moreover, it is a real issue to compare shipping data between different carriers and service providers. Lastly, it is challenging to link actual shipping activity to what was planned. With a Load ID to tie this shipping data together, diagnostic analysis becomes much easier. Additionally, there are more opportunities to leverage advanced analytics, AI, and even automation to yield better, rapid Insights.

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.

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.

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.
• 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

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