Despite the impressive array of high-tech tools and advanced supply chain analytics at our disposal, logistics organizations still struggle to gain total visibility of their shipments. The root of the problem? Bad shipment data. Just consider the fragmented state of shipment data – it’s often dispersed across many systems, inaccurate, incomplete, outdated, and duplicated. And, even if a company had 100% perfect shipment data with up-to-date shipment status, sharing this treasure trove effectively with other systems remains a challenge. What we really need is a common data framework for supply chain systems to generate intelligent tracking statuses. This is what will “ensure what is sent is what is understood” to achieve total supply chain visibility.

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In this article, I’ll discuss how data interoperability issues impact supply chain analytics and shipment visibility. Mainly, it’s a problem with the shipment data. We depend too much on various custom-built, proprietary tracking systems, which often provide unclear and incomplete updates on shipments. I’ll also explore a solution to this problem – an intelligent tracking data framework. This data framework will help solve these shipment data interoperability problems by enabling different systems to speak the same language. As a result,we can get better shipment updates. Hence, overall supply chain analytics will improve because the data will be consistent and interoperable.

1. Supply Chain Analytics Falls Short Due to Lack of Shipment Data Interoperability.

Despite the potential of supply chain analytics, its effectiveness is often undermined by the fragmented, ambiguous nature of shipment data and tracking statuses. This disjointed situation leads to shipment data interoperability issues. Hence, supply chains are hampered in gaining a comprehensive view of their operations, leading to misinformed decisions and inefficiencies. Indeed, this lack of data interoperability between organizations makes it difficult to establish a common digital framework for communication. Surprisingly, most supply chain managers do not fully realize how big of a problem shipment data interoperability is. To detail, below are 11 reasons why shipment data interoperability is such a problem for supply chain analytics and decision-making.

11 Reasons Why We Have Poor Shipment Data Interoperability

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

1. Inconsistent Data Formats. Here, one uses csv format, another uses XML; data elements are not consistent.
2. Lack Of Standardized Codes. In this case, one uses UPC codes, another uses EAN codes, another proprietary codes.
3. Limited Data Sharing Capabilities. Indeed, this is because there is a lot of front-end work to integrate systems.
4. Manual Data Entry And Reconciliation. Here, humans make data entry errors or systems use freeform text that are not intelligible to other systems.
5. Lack Of Real-Time Visibility. This is because data is in silos, duplicated, and not up-to-date.
6. Incomplete Or Inaccurate Data. A case of Garbage In, Garbage Out (GIGO) – ambiguous data, missing data, and so on.
7. Non-Standardized Naming Conventions. Here, one company refers to a product as “Widget A”, another refers to it as “Product 123”.
8. Lack of Data Validation Checks. As a result, data is imported with errors, then corrupted data is shared and duplicated within other systems.
9. Incompatible Data Integration Tools. In this case, one company uses APIs, another uses file-based data transfer.
10. Proprietary System Lock-In. Here, it is difficult to migrate or upgrade legacy systems; proprietary system-to-system data integrations are difficult to untangle.
11. Ineffective Digital Identity Solution. Lastly, systems either lacks usability, data security, or both  due to inadequate user verification, authorization, and authentication.

For more detailed discussion on these data interoperability issues, see Why Is Data Interoperability Such A Nightmare For Supply Chains?

2. Ambiguous, Customized Tracking Statuses Are Holding Back Supply Chain Analytics and Better Decision-Making.

Over the years, supply chains have created countless customized tracking status interfaces. These customized data integrations have created a labyrinth of data flows, making digital supply chains neither seamless nor very insightful. As a result, data interoperability issues are now the norm, resulting in poor operational visibility for most supply chains. Surprisingly, new tech like AI won’t resolve these interoperability issues. This is because this is primarily a business problem where the data lacks the operational clarity to communicate meaningful information to the intended recipients. Moreover, this challenge is worsening as we increase functionality, automation, and global expansion of our supply chains.

For a detailed discussion on this subject to include examples, see my article, Custom-Built Shipment Statuses: Digital Supply Chains Can Do Better And Need A Reckoning To Eliminate This Insidious Habit.

3. Intelligent Tracking Statuses Enables Both Total Visibility and Better Shipment Analytics.

Imagine a world where every shipment status, from “Shipped” to “Delivered,” is universally recognized. This data interoperability would revolutionize shipment analytics. Furthermore, if all supply chain partners could seamlessly exchange intelligent tracking status, shipping operations would become faster, response times quicker, and proactive supply chain actions the norm. Indeed, these scenarios demonstrate how intelligent tracking statuses can strengthen analytics and enhance decision-making. Thus, this ensures timely delivery of goods to their intended destinations as well as improving shipment analytics from forecasting through post-shipment analytics. So, what steps can we take to enable our systems to generate intelligent tracking statuses?

a. First, Need to Unify Shipment Data Using a Universal Load ID.

If we really want to solve the current disjointed state of shipment statuses, we need to first start with unifying shipment data. Currently, data for even one shipment is often scattered across the supply chain within various departments and organizations. Further, the shipment data is tied to a multitude of reference numbers such as purchase order, customer reference number, tracking numbers, invoice numbers and so on. Worse, the fragmented nature of shipment data makes it difficult to establish a Single Source of Truth (SSOT). 

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

So because this data is not unified, supply chain systems and organizations do not have a real picture of the current status of shipments. Imagine the immense benefits supply chains could reap by tapping into unified shipment data and total visibility. Plus, with access to unified shipment data, they could truly leverage advanced, data-driven technologies such as data analytics, AI, and decision intelligence to achieve remarkable results!

This is where the concept of using a Universal Load ID, if implemented, can unify all our shipping data. This load ID is more than just a carrier’s tracking number, it is a load ID that a shipper or their representative can create to unify shipment data to include planning data, shipment status, and financials. For more details about this Universal Load ID concept as well as use cases, see my article, Better Shipping Data Analytics Results: Use Of Load IDs To Achieve The Best Efficiency, Visibility, And Financials.

b. A Need for Intelligent Tracking: Shipment Statuses That Provide Meaningful Insights Are What Enables Total Shipment Visibility.

Now, unifying shipment data around a single reference number such as a Universal Load ID is the first step in gaining total shipment visibility. The next step is the ability to easily and effectively transmit intelligent tracking updates across the supply chain to the receivers and subscribers of a shipment’s status. Specifically, these shipment status recipients can include end-customers, internal supply chain systems, 3rd party partners, government agencies, and other systems. Indeed, it is critical that these recipients of shipment statuses have an understanding of the data sent. Thus, they can make the best decisions and act upon good information. So what we need is a common data framework for systems to generate intelligent, meaningful tracking statuses.

Poor Shipping Data Analytics – Here Are The 4 Reasons Impeding High Tech Visibility And Optimization

As discussed previously, shipment status updates today are increasingly unintelligible, Hence, we are challenged both to leverage cutting-edge technology and meet the needs of our modern supply chains. What we have is a serious data interoperability problem. The European Interoperability Framework (EIF) calls this a semantic interoperability problem. Their definition of semantic interoperability is as follows:

“Ensuring what is sent is what is understood”

European Commission – EIF

So to sum up this shipping data interoperability problem, we need both total visibility of the data as well as assure that the data is timely and understandable. Hence, this calls for a way to unify shipping data such as the use of a Universal Load ID. Further, there is a need to make sure that all supply chain stakeholders can receive shipment status that is understandable, and thus, actionable. This calls for an intelligent tracking data framework that I will discuss next.

4. An Intelligent Tracking Data Framework.

One answer to this problem is to start transitioning from these proprietary, custom-built data integrations that are based more on tradition and shortsighted expediency than on foresight. Undeniably, what we need is a common, simple way to transmit intelligent tracking statuses to supply chain partners and end-customers. As an example, let’s explore what the ASTM International F49 committee, a standards development organization (SDO), is doing to increase data interoperability with the Goods Movement Process (GMP).

What ASTM F49 is doing is defining a non-proprietary, intelligent tracking data framework to achieve true data interoperability. This interoperability framework will enable supply chain stakeholders to share intelligent shipping status with their partners. What’s more this data framework is not just for goods movement execution, but empowers organizations to leverage normalized shipment data for forecasting as well as for post-shipment analytics.

Also, ASTM F49 is segmenting shipping data activities within this intelligent tracking data framework into five phases: Forecast, Planning, Execution, Reconciliation, and Research / Analysis. Within these shipping-related phases there are many shipment data-related activities, events, and milestones. To get a better understanding of this intelligent tracking data framework, see the picket fence analogy below:

This diagram provides a visual representation of the various phases of ASTM F49’ intelligent tracking data framework in terms of the phases (fence panels), the milestones (fence posts), and shipping activities (fence pickets). Below is a description of this intelligent tracking data framework’s phases, milestones, and example shipping activities that can occur within the data framework.

Intelligent Tracking Data Framework

a. Forecast: ID Future Aggregate Shipping Requirements and Resources Needed.

This phase focuses on shipment data in aggregate, and is not focused on single shipments. It encompasses all the activities prior to generating specific requirements to ship goods, identified as a transport unit. 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. Milestones during this phase include Predict and Project. Example shipping data analytics activities include:

• 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.

b. Planning: Goods Movement Process Begins.

This phase starts with a specific shipment requirement, 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:

• 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.
• The wholesaler officially designates a carrier to transport construction materials to a building site. This includes all details confirmed with both parties and the carrier is scheduled to pick up the load.

c. Execution: The Physical Movement of Goods. 

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 milestones for this phase are En-Route and Delivered. Example shipping status events include:

• 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.

d. Settlement / Reconciliation: Administrative and Financial Actions Occur Associated With the Movement of Goods. 

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 Archived. Example shipping status events include:

• 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.

e. Research / Analysis: Use Shipment Data for Analytics.

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 shipment 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. Milestones included in this phase are Structured and Anonymized. Example shipping data analytics activities include:

• 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.

Also, for another graphical depiction of this intelligent tracking data framework, see diagram below that illustrates the 5 phases: Goods Movement Process – Planning, Execution, and Reconciliation and Shipment Analytics “Bookend” Phases: Forecast and Analysis. 

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

For more information on ASTM F49 committee and their work on the Goods Movement Process, see Standard Terminology for Goods Movement Process (GMP) Precise Foundational Definitions. Also, see ASTM F49’s New Terminology for GMP Bookend Phases: Forecast Phase and Research/Analysis Phase for more information on the supply chain analytics phases of the goods movement process. 

Additionally, an European Union NextGenerationEU funded organization, Basic Data Infrastructure, has a great white paper on this subject, Developing Semantics for Supply Chain, Transport and Logistics. This white paper provides a detailed discussion on semantics and event-driven thinking in developing a common language for defining supply chain events. Also, this paper elaborates on an excellent data interoperability approach that is event-driven and links data / entities to the event. Further, great discussion on GS1’s Electronic Product Code Information Services (EPCIS) laying the groundwork in specifying how to embed intelligence in event statuses. In particular defining events into 4 aspects: What, Where, When, Why (and How). 

5. The Advantages of Intelligent Tracking Interoperability For Supply Chain Event Management.

Now, the benefits of implementing an intelligent tracking data framework are far-reaching. For one, it enables intelligent tracking that instills both confidence and understandability in the shipment status data that supply chain organizations transmits. Thus, this enables comprehensive shipment visibility allowing companies to respond proactively to any disruptions.

Also, this intelligent tracking data framework fosters a collaborative ecosystem where all supply chain partners can align their strategies and operations. Further, data accuracy is enhanced, reducing the risk of errors, discrepancies, and uncertainty. Ultimately, data interoperability paves the way for advanced analytics and automation. Hence, setting the stage for a more resilient, agile, and situational aware supply chain that can adapt to rapidly changing situations and the ever-evolving demands of the market.

Below are 12 advantages for adopting an intelligent tracking data framework for your supply chain systems.

Advantages of Intelligent Tracking

1. Enhances Visibility for Customers and Across the Entire Supply Chain. Tracking interoperability enables a clear view of movement throughout the entire supply chain.
2. Improves Accuracy and Reduced Errors. Interoperable tracking systems minimize ambiguity and maximize trust that statuses are correct.
3. Increases Carrier and 3rd Party Accountability. Clear tracking of goods creates an accountable environment, where each participant’s actions are transparent.
4. Facilitation of Compliance and Reporting. Intelligent tracking statuses simplify compliance with regulations by providing accurate information and confidence in reporting.
5. Better Customer Experience by Delivering Accurate Status to Reduce Delays. Intelligent tracking status provides all stakeholders confidence and foresight to avoid mishaps.
6. Streamlines the Recall Process for Faster Response. Tracking interoperability speeds up the identification and retrieval of defective products during recalls.
7. Increases Efficiency with Automated Data Capture. Intelligent, standardized tracking enables fully automated information sharing that is actionable..
8. Streamlines Coordination Across Multiple Stakeholders. Intelligent tracking statuses ensure that all stakeholders have access to the same information, facilitating coordinated actions.
9. Bolsters Security and Reduces Fraud Risks. Reliable tracking strengthens security measures and reduces the risk of fraud within the supply chain.
10. Better Data for Logistics Planning. Comprehensive and consistent tracking data improves the quality of analytics, leading to more accurate demand forecasting, asset allocation, and network planning.
11. Leveraging Data Analytics for Optimizing Operations. Rich tracking data, when analyzed, can uncover trends and insights for both strategic supply chain decisions and improving operations..
12. Better Analytics to Reduce Costs and Improve Financials. With accurate, detailed shipment data, analyst can uncover hidden cost, non-value charges, and strengthen negotiation positions with vendors.

More References:

For more information on intelligent tracking and supply chain event management. See CTSI-Global’s Boost Shipment Efficiency with Enhanced Supply Chain Event Management and ZAPOJ’s Critical Event Management Tool for the Supply Chain Management. These two articles focused on proprietary intelligent tracking systems, but do highlight the principles of intelligent tracking.

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

Also, see more of my articles on supply chain visibility and unifying shipping data for better decision-making: Surprisingly Supply Chain Visibility Has Many Forms: See Which One Is Best To Be Your Business’ First Focus and Unifying Shipping Data Using Transport Load IDs: Here Are 10 Ways It Can Unlock Analytics And Empower Logistics Tech.

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