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Achieving Logistics Interoperability: The Best Way to Breakthrough The Tangle Of Dumb Data Integrations

Nowadays, logistics organizations know that their data holds the key to incredible insights and a competitive edge. However, many struggle to harness this power due their data being scattered across many systems, linked by a labyrinth of data integrations. Worse, even if this data gets transmitted to another system, the meaning of the data often gets lost in translation, resulting in “dumb” data that is practically useless. As supply chains move forward with digitalization, there is indeed a better data integration approach than just continuing to expand this tangled mess of data connections. This better data integration approach is called semantic interoperability.

In this article, I’ll explain what semantic interoperability is and why it is the best approach for businesses to exchange meaningful data across their supply chains. Further, I’ll detail the benefits of semantic interoperability and how other industries and disciplines are advancing it. Also, I have four recommendations on how best for logistics and standards organizations to move forward with achieving semantic interoperability. This includes the importance of knowledgeable business leaders leading these interoperability efforts. Further, I’ll highlight how we can leverage emerging tech such as AI and knowledge graphs to accelerate the implementation of semantic interoperability in supply chains.

1. Semantic Interoperability May Be the Answer, But What Is It?

achieving semantic interoperability

Most supply chain leaders may not be familiar with the term semantic interoperability, but most are familiar with data integration. Both these terms are similar in that both are associated with the transfer of data between different systems. The difference is that semantic interoperability is more specific about the end result of data transfer. Namely, that both systems have a shared understanding of the data transmitted. See discussion below about the importance of supply chains achieving semantic interoperability versus just transferring data between systems.

a. Semantic Interoperability Is Similar to Data Integration. 

Semantic interoperability is closely akin to data integration that currently consumes a lot of IT resources for most businesses. In fact, according to a survey on IT budgets from Digibee, more than half of IT organizations are spending as much as 25% of their time on data integration initiatives. In other words, these organizations are spending a significant amount of their budget attempting to achieve data interoperability between business systems. Even though IT data integration is resource intensive, many business leaders do not know what it entails. For instance, what is the difference between uploading a data file via Dropbox and what IT is doing on all these costly data integration projects?

b. Semantic Interoperability Defined. 

So, the question is why does IT “data interoperability” cost so much? Indeed, the reasons for these skyrocketing costs are much more than just the expense of transmitting the data. Positively, data interoperability is a lot more than just the transfer of data. In addition to facilitating data transfer, IT integrators are creating solutions that support meaningful data interchange between systems. The key word is “meaningful”, and specifically, meaningful data. Another term for this is semantics. So, the whole purpose of IT data Integration is to achieve semantic interoperability. Below is a definition for semantic interoperability:

“ability of computer systems and organizations to exchange data with unambiguous, shared meaning”


c. More Trade and Government Organizations Are Recognizing that Semantic Interoperability  Is the Way for Seamless Data Flows.

Positively, many trade and government member organizations such as ICAO and the EU are keenly promoting semantic interoperability. This is because they understand the importance of helping their members succeed by fostering collaboration and cooperation amongst their systems. Indeed, with the ever increasing number of enterprise business systems, organizations are seeking more efficient ways for their IT systems to exchange meaningful data between each other. For example, the EU’s European Interoperability Framework (EIF) is conducting extensive work to enable semantic interoperability. Click here to see what the EIF is doing to advance data interoperability within the Europe Union’s public administrations.

Also, see Wikipedia for a more detailed discussion on semantic interoperability.

2. What Would Happen If We Achieved 100% Semantic Interoperability in Our Supply Chains?

Imagine a world where all supply chains systems acted in concert, seamlessly exchanging data. Indeed, each link in the supply chain would have near-perfect information (total visibility) to act with optimal efficiency. Better yet all logistics planners and operators could fully leverage data-driven applications such as AI and data analytics. In fact, there would be no more miscommunications, less guessing, and minimum exceptions that today ripple across supply chains causing disruption, mayhem, and ultimately dissatisfied customers. To list, below is just a sampling of the many benefits of achieving semantic interoperability within supply chains.

The Many Ways Supply Chains Benefit From Semantic Interoperability
  • Able To Leverage Emerging Tech Such As AI and IoT
  • Enhanced Collaboration Among Partners
  • Improved Decision-Making and Forecasting
  • Increased Operational Efficiency
  • Reduced Lead Times and Downtime
  • Better Inventory Management and Optimization
  • Enhanced Customer Satisfaction and Loyalty
  • Greater Visibility Across the Supply Chain
  • Faster Decision-Making and Response Time
  • Increased Adaptability to Market Changes
  • Reduced Costs and Improved Efficiency
  • Better Risk Management and Mitigation
  • Enhanced Customer Services and Reliability
  • Facilitated Regulatory Compliance and Sustainability

The bottom line is supply chain data interoperability enables supply chain staff and systems to take action with quality supply chain data that is highly accurate, complete, timely, and meaningful. Indeed, semantic interoperability results in supply chain excellence and unleashes innovation. For a more detailed discussion on the challenges and benefits of data interoperability, see my article, Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation.

3. Examples of Where Other Disciplines Like Healthcare, IoT, and Language Translation Are Advancing Toward Semantic Interoperability.

It is not just supply chains that are grappling with semantic interoperability. In fact, interoperability is critical for most industries and disciplines to include healthcare, defense, language translation, and Internet of Things (IoT) to name a few. What’s more, many of these disciplines are well on their way in this transformative process toward semantic interoperability. The question is, do these disciplines hold a blueprint that the logistics industry can emulate to drive innovation within supply chains? To help answer this question, below are some examples of semantic interoperability initiatives on-going in other industries and disciplines.

“And here lies the core semantic interoperability problem: How can the inherent meaning of a piece of data be preserved across different domains, without needing a human understand and ensure correct translation?”


a. Healthcare. 

Healthcare semantic interoperability allows healthcare systems to share patient data that is both secure and meaningful. As a result, this improves diagnosis and treatment outcomes. In the areas of healthcare there are many ongoing semantic interoperability initiatives. For instance, the US National Institutes of Health is leveraging ISO/IEC 11179 Metadata Registry to build a common semantic model. Also, the U.S. Office of the National Coordinator for Health Information Technology (ONC) is building what they call The United States Core Data for Interoperability (USCDI). This is a standardized set of health data classes and data elements for nationwide, interoperable health information exchange.

b. Department of Defense.

DoD has a program called Department of Defense Architecture Framework (DODAF). Within this program, DoD is building logical data models to achieve semantic interoperability. As DODAF describes it “… the emphasis is on the identification and description of the information in a semantic form (what it means) and why it is of interest (who uses it).”

c. Language Translation Software. 

Traditionally, language translation has relied on statistical machine translation (SMT) approaches. Basically, these approaches automatically map sentences in one human language (for example, French) into another human language (such as English). Now, starting with Google Translate efforts back in 2016, the focus is now on neural machine translation (NMT). This AI-based tool provides improved quality, fluency, and context preservation. See Adriano Raiano’s article, Modern Technology And The Future Of Language Translation, for an excellent overview of the evolution of language translation software.

d. Internet of Things (IoT).  

IoT vendors and standards groups are advancing semantic interoperability to enable devices to communicate and work together intelligently. For instance, see Erricsson’s Niklas Widell blog post, What is semantic interoperability in IoT and why is it important?. According to Circutor, IoT interoperability is a top trend in the IoT industry.

So, there are many industries and disciplines that are both committed and having success with achieving semantic interoperability. Now, what about supply chains?

4. Four Things Need to Happen For Supply Chains to Achieve Semantic Interoperability.

For the supply chain industry to truly succeed with achieving semantic interoperability, several things need to happen. First, business leaders need to get involved. This includes both supporting semantic interoperability efforts and helping to provide clarity to supply chain data terms we use today. Indeed, data is now a strategic asset and tool for businesses to leverage to achieve both efficiency and improve service levels.

“… we need to stop building a “Tower of Babel” with the proliferation of more proprietary data interfaces.”

Further, it is critical that business leadership help cut the ambiguity out of logistics data. This will go a long way in advancing the meaningful exchange of data within the supply chains. Positively, this is a business-led effort that brings clarity and mutual understanding to supply chain data terms. This is not something that IT or technology does well. 

Additionally, businesses need to make it a priority on their next data integration project to first seek to use existing data standards versus establishing another proprietary data interface. Indeed, we need to stop building a “Tower of Babel” with the proliferation of more proprietary data interfaces. Lastly, there are opportunities to streamline the process of achieving semantic interoperability using emerging tech such as AI and knowledge graphs.

See below for the specifics on my four recommendations on what the supply chain industry need to achieve semantic interoperability.

a. First, Businesses Need to Adopt a Data-Centric Mindset.

1) Supply Chain Data Is Fragmented With No Single Source of Truth (SSOT).

Indeed, most businesses are challenged today to leverage the vast amount of data within each of their business systems. This is because their data is fragmented, locked in individual software application silos. Thus, most supply chains only have rudimentary data interoperability between their systems and external systems. Worse, their data is duplicated, inaccurate, incomplete and out-of-date with no data repository offering a Single Source of Truth (SSOT). 

2) Data Is Either Not Shared or Loses Meaning During Data Transmission.

Further, the data that does get exchanged between systems in many cases loses much of its meaning during the data exchange. For example, a data transmission may contain a date field, but what type of date does it represent? – shipped, delivered, created, transmitted, loaded on the truck, etc. Indeed, this is not so much a technical problem, but a problem where a business person needs to dig into the data and provide direction to eliminate ambiguity.

3) Need To Shift Our Mindset From Software-Centric to Data-Centric.

A major cause of our data interoperability problem is that we have gotten too software centric where we think that just another software update will solve all our problems. As a result, we continue to think that data is just a by-product of software applications. 

This application-centric mindset needs to change. Indeed, what business leaders need to do is adopt a data-centric mindset where data is treated as a most valuable and permanent asset. Adopting a data-centric mindset enables businesses to start thinking of data as something to nurture and leverage as a competitive advantage. Hence, a data-centric approach leads to businesses investing in data structure and networks, training personnel to become data-savvy, and fostering a culture where data is shared and leveraged across departments. 

By shifting the focus to the intrinsic value of data, businesses can build a strong foundation that supports true semantic interoperability. For a more detailed discussion on developing a data-centric mindset, see my article, A Data Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation.

b. Second, Need Logistics Organizations to Partner With Standards Development Organizations (SDO) to Advanced Semantic Interoperability.

At its essence, supply chains are focused on the movement of goods and the financing that supports logistics. Thus, the primary components of supply chain data consist of shipments (moment) and products (goods) as well as the FinTech transactions and terms that support logistics financing. 

1) Data Gets Transmitted, But Loses Meaning In Translation.

Now over the years, businesses have found many ways to transfer data. For more details, see my article, The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos.

The data ambiguity challenge lies with semantic interoperability – transmitting data that retains its shared meaning. Take for example a carrier that provides an electronic invoice in either a comma-separated values (csv) or electronic data interchange (EDI) format for convenient download. If the shipper’s IT department then fails to properly translate the invoice data into their system, issues arise. This miscommunication might stem from inadequate data transfer documentation from the carrier. On the other hand, the shipper may have misunderstood the documentation. Additionally, it might result from poor collaboration between the shipper’s IT and business groups while mapping the data to their system. Consequently, semantic interoperability remains limited or non-existent.

2) Standards Development Organizations (SDO) Help Provide Shared Meaning to Data.

So, most businesses can transfer data, but what can we do better to advance data interoperability? One of the best ways to do this is for businesses to better support standards development organizations (SDO). Just think, standards development is how railroads in the U.S. now use the same gauge of railroad tracks. As a result, railroads can seamlessly transport railroad cars over each other’s tracks. Hence with supply chain semantic interoperability, we need to do the same and support the advancement of standards, data standards. Basically, these standards provide a metadata framework that describes and gives information about supply chain data. Below are examples of metadata frameworks that provide meaning to supply chain data and related financial data terms.   

3) The Need for Logistics Organizations to Partner With Standards Development Organizations.

In summary, I recommend that supply chain leaders get involved with supporting standards development organizations (SDO) that help advance semantic interoperability in the logistics industry. This is not just something left to IT. Data is a business asset. Developing and adopting a supply chain metadata framework requires collaboration across the industry. It is supply chain leaders who need to agree on common terms, definitions, and taxonomies to ensure that data retains its significance regardless of where it travels. For example of the benefits of logistics organizations benefiting by partnering with standards development organizations, see my article, Unifying Shipping Data Using Transport Load IDs: Here Are 10 Ways It Can Unlock Analytics And Empower Logistics Tech.

c. Third, Need to Move Away From Proprietary Data Interfaces.

Proprietary data interfaces are an expedient method to transfer data, but usually the benefits are short-lived. In many cases with a proprietary interface, the data will lose meaning in translation and it is difficult to reuse for analytics. Worse, proprietary data formats overtime lock data into silos. Further, to make updates to these custom data interfaces, requires expensive IT resources with long lead times. See below for more details and the compelling reasons to move away from proprietary data interfaces.

1) Proprietary Data Interfaces Is Currently the Norm for Exchanging Supply Chain Data.

For decades, logistics organizations have implemented proprietary system-to-system data Interfaces. Especially for supply chains, this has created a tangled web of information flows and data silos. Worse, over the years many large logistics organizations and electronic marketplace have encouraged their customers to adopt their proprietary data interfaces and APIs to create a seamless online community. At best, this has created short-term benefits, but in the long-term these proprietary data networks become obsolete, very expensive, or go out of business. The end result – more proprietary data interfaces and data silos.

At the same time 100% semantic interoperability is not going to happen overnight. See my article, The Digital Supply Chain Challenge: Is A High Tech 3PL The Best Way? on using a 3rd party logistics provider (3PL) for digitalizing your supply chain versus doing it yourself.

2) Complex Network of Supply Chain Systems, Technology, and Data Requirements.

Further, supply chains are complex, having a diverse range of technologies, hardware, and software platforms used by different stakeholders. What’s more, few industries have to deal with so many systems, numerous stakeholders, and massive amounts of data. Worse, shipment and inventory status updates in supply chain operations are by their nature fleeting adding even more challenges for decision-makers.  Moreover, integrating these disparate systems is a monstrous task that requires considerable time, effort, and resources. 

3) We Have Created a Data Interoperability Nightmare.

The end result is that we have created a data interoperability nightmare compounded by our continued use of proprietary data interfaces. Undeniably, this untenable situation does not make for a common framework for communications to exchange meaningful, actionable data between our supply chain systems.

Consequently, if supply chains continue this practice of adding more proprietary interfaces, we will just continue this data interoperability nightmare. This quagmire needs to stop. It is preventing supply chains from making innovative transformations to increase efficiencies and improve services. To list, below are examples of the challenges we create every time we add a new proprietary interface to this data interoperability nightmare.

Examples Of Poor Supply Chain Data Interoperability
  • Inconsistent Data Formats.
  • Lack Of Standardized Codes.
  • Limited Data Sharing Capabilities.
  • Manual Data Entry And Reconciliation.
  • Lack Of Real-Time Visibility.
  • Incomplete Or Inaccurate Data.
  • Non-Standardized Naming Conventions.
  • Lack of Data Validation Checks.
  • Incompatible Data Integration Tools.
  • Proprietary System Lock-In.
  • Ineffective Digital Identity Solution.

For a more detailed discussion of these data interoperability challenges, see my article, Why Is Data Interoperability Such A Nightmare For Supply Chains?

d. Forth, Leverage AI and Knowledge-Centric Tech to Enable Data Standards to Learn, Evolve, and Expand.

To maintain a dynamic metadata standards framework, supply chains can harness powerful emerging tech such as knowledge graphs and artificial intelligence (AI). Knowledge graphs tech enables the robust interconnection of data. Due to its graph nature, it provides context and understanding to data. Coupled with AI, these knowledge graphs can analyze patterns, predict trends, and reveal insights that were not previously discernible. By leveraging these emerging technologies, a standardized metadata framework becomes a living system that learns and evolves within the supply chain community. Additionally, these technologies can easily expand to incorporate new sources of data and relationships, ensuring continuous improvement and relevance. 

See below on how logistics organizations conduct data integration today and how emerging technology can help improve both data integration implementation and advance semantic interoperability.

1) How Is Supply Chain Data Made Understandable Today?

For establishing any data interface between two systems, IT integrators refer to a data specification document. For the most part, these data specifications are communicated through online documentation such as a PDF document or a web page. This is generally true for both proprietary data specifications and standards development organizations (SDO) specifications. For instance,  a commercial Software as a Service (SaaS) platform will normally provide proprietary “data dictionaries” for both their database data elements and for associated application programming interfaces (API).  These specifications usually include a description of each data element and example use cases. 

SDOs also follow the same practice except their specs are not proprietary and open for any organization to follow. For example, GS1 General Specifications Standard provides universally accepted data specification for barcodes that are used to identify products and track packages. Indeed, these barcode data specifications are now used within most supply chains.

So, today supply chain data is made understandable by either proprietary or SDO documentation. However, to move away from the tangled web of proprietary data Interfaces, supply chain leaders will need to work better with SDOs such as ISO, GS1, W3C, and ATSM International. This includes participation by business leaders.

However, there are also emerging technologies that can both help SDOs and supply chain organizations achieve semantic interoperability. For instance, AI can help businesses streamline data integration efforts as well as help SDOs make their standards more understandable and adaptable to implement. Also, emerging technology such as knowledge graphs can provide SDOs a better way to both document and evolve standards. See below for more information and reference on emerging tech to help better achieve semantic interoperability.

2) AI / Machine Learning (ML) Opportunities to Help Achieve Semantic Interoperability.

Data Integration Automation Support. AI and ML offers several opportunities in the area of traditional data integration tasks. Basically, AI can automate and streamline many labor-intensive tasks. Example use cases include data discovery, mapping, data quality improvements, data transformation, and metadata management to name a few. Possibly in the near future, AI agents could both follow SDO-based data standards and autonomously establish new system-to-system interfaces. However, before autonomous data integration can occur we need to mature our data standards and eliminate the ambiguity that exists in much of our data today. For a more detail discussion on AI data integration opportunities, see AIMultiple’s article, Machine Learning in Data Integration: 8 Challenges & Use Cases.

Embedded Learning Capability to Rapidly Mature Data Models. Embedding a learning capability into our data standards also presents an AI opportunity to enhance semantic interoperability. Specifically, Machine Learning (ML) technology excels in classifying and predicting events. We could, therefore, apply ML to statistically analyze large datasets to identify new additions for data models and standards. Essentially, this automated learning process would rapidly unearth new insights by examining vast amounts of data. This approach promises significant labor savings and a faster pace in maturing data models. For a more detailed discussion on leveraging AI in advancing semantic interoperability, see IEC’s white paper, Semantic interoperability: challenges in the digital transformation age.

3) Knowledge Graph Opportunities to Help Achieve Semantic Interoperability. 

Knowledge graphs, while not a new concept and pre-dating computers, only gained widespread use in this century, especially by search engines like Google. With the rise of AI and faster computing capabilities, software developers are now increasingly utilizing knowledge graphs to provide context and meaning to data.

Especially for data interoperability, knowledge graph technologies are powerful, flexible tools. Specifically, graph tech defines relationships and contexts between data elements, storing those relationships themselves in a graph as data. Indeed, knowledge graph tech is well suited for supporting and documenting semantic interoperability in support of both standards development and for implementation. For instance with supply chains, knowledge graph tech can support anything from smart contracts, international ecommerce, and the Internet of Things (IoT). Coupled with AI, knowledge graphs will greatly facilitate the exchange of meaningful data using autonomous AI agents.

For more information on knowledge graph technology see Kevin Doubleday’s article, Semantic Interoperability: Exchanging Data with Meaning and my article, Knowledge Graph Tech: Enabling A More Discerning Perspective For AI. Also, see Pierre Levy’s blog posting, Semantic Interoperability and the Future of AI for an example of how we can implement knowledge graph technologies to further advance semantic interoperability.


In the past, our supply chains were transformed by standardizing bar codes and ocean containers to achieve better interoperability. Now to fully leverage data-driven tech like AI, we need to achieve interoperability with our supply chain data. In this article I offer a blueprint with four recommendations on how best for logistics and standards organizations to move forward with achieving semantic interoperability. This included the importance of knowledgeable business leaders working with standards development organizations (SDO) to lead these interoperability efforts. Additionally, it is time to move away from proprietary data interfaces. Further, I highlighted how we can leverage emerging tech such as AI and knowledge graphs to accelerate the implementation of semantic interoperability in supply chains.

OK, semantic interoperability will not happen overnight, but it will happen over time to transform logistics operations. Just think what the standardization of bar codes and ocean containers did for the industry in the past.

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

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