In the fast-paced world of logistics, seamless supply chains are the holy grail. Imagine a world where data flows effortlessly between systems, where every stakeholder speaks the same language, and where decisions are made with crystal-clear insights. This isn’t just a pipe dream; it’s the promise of semantic data interoperability. Indeed, semantic interoperability is more than traditional IT data integration, with their custom data interfaces between application-centric data silos. No, semantic interoperability is focused on the meaningful exchange of data that is both understandable and actionable across the supply chain. 

In this article, I’ll offer a four step process for logistics organizations to take to move from their current tangled web of custom data integrations to a seamless supply chain. To achieve this semantic interoperability, organizations will need to shift their mindset from application-centric to data-centric. Next, supply chains will need to do better at leverage emerging data standards to help them advance toward meaningful data exchanges. At the same time, they will need to move away from costly proprietary data interfaces that are both fragile and lock data in rigid application silos. Lastly, supply chains need to leverage emerging tech such as AI, knowledge graphs, and digital identity tech to further empower semantic interoperability within their organizations.  

The Data Interoperability Challenge Within Supply Chains.

Before getting into these four-steps toward achieving interoperability, let’s look at the current state of our digital supply chains. Namely, they are complex ecosystems with numerous players, each using their own systems and data formats. Moreover, this fragmentation leads to inefficiencies, delays, and errors. The challenge lies in getting all these systems to communicate seamlessly. This is the purpose of semantic data interoperability. It assures that the data sent is understood. As an example of these misunderstandings, a simple misinterpretation of a product code or delivery date can lead to delays, stockouts, or overstocking. For a more detailed explanation of semantic interoperability, see my article, This Is What Semantic Interoperability Is: It’s The Best Last Chance For Seamless Supply Chains.

Four Steps to Attain Semantic Interoperability for Seamless Supply Chain Operations.

Achieving semantic interoperability is the key to unlocking seamless supply chain operations. By following these four strategic steps that I’ll describe below, logistics organizations can break down data silos, optimize information flows, and leverage advanced technologies. Each step builds on the previous one, creating a robust framework that ensures actionable data flows smoothly and efficiently across the entire supply chain. Let’s look at these steps in detail to see how they can transform your operations.

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

The journey to seamless supply chains begins with a shift in mindset. Over the decades with the advent of business computing, organizations focused on leveraging enterprise software applications. Though successful, we have taken on an application-centric mindset where monolith software applications generate their data and pour their digital by-products into their data silos. Now, things are changing in that we are seeing that we can derive insights from all this data. Indeed, we are beginning to realize that data is a strategic asset to make informed decisions. This is why it is time to take on a data-centric mindset. Below, I’ll elaborate on our current supply chain data woes and how to start adopting a data-centric mindset.   

a. 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 within individual software application silos. Thus, most supply chains are not “seamless” because they only have rudimentary data interoperability between their systems and external systems. Worse, their data is duplicated, inaccurate, incomplete and out-of-date. As a result, there is no Single Source of Truth (SSOT) that they can trust. 

b. The Sorry State of Data Integration: Data Is Difficult to Share and Loses Meaning During Data Transmission.

Moreover, data integration initiatives often have long lead times and for many supply chain organizations these initiatives can consume over 20% of an IT budget with minimal returns. To top it off, many times data can lose its meaning during its transmission from one system to another. For example, a date field in a digital exchange interface might be undefined. As a result, the receiving system is unsure if the date data element represents shipped, delivered, created, or associated with some other action. Again, this highlights the need for organizations to stop treating data as a by-product and to focus on generating valuable data that is both meaningful and actionable.

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

A major cause of our data interoperability problem is that we’ve become too software-centric, believing that another software update will solve all our issues. This application-centric mindset needs to change. Specifically, supply chains need to adopt a data-centric approach, treating data as a valuable and permanent asset. By doing so, they can start nurturing and leveraging data as a competitive advantage. This includes investing in data structure and networks, training personnel to become data-savvy, and fostering a culture of data sharing and utilization across departments. 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..

A Data Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation

2. Second, Logistics Organizations Must Leverage Standards Development Organizations (SDOs) to Advance Semantic Interoperability.

Collaboration is key to achieving semantic interoperability. In fact, this is the primary reason that Standards Development Organizations (SDO) exist. For instance, SDOs have helped supply chains to adopt bar codes, implement standardized data communications, standardize payment methods, and even enabled physical interoperability such as standardized ocean containers. Hence for these reasons, logistics organizations should also partner with SDOs to develop and adopt industry-wide data standards. Indeed, by working together, we can create a common language that simplifies data exchange and enhances operational efficiency. Below, I’ll elaborate on why supply chain organizations should collaborate with SDOs.

a. Data Gets Transmitted, But Loses Meaning In Translation.

Now over the years, businesses have found many ways to transfer data. Indeed, the real problem in most cases is with data ambiguity and not a technical data integration issue. Indeed, this is the semantic interoperability challenge. Namely, we need to transmit data that retains its 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. In this case, semantic interoperability (e.g. meaningful data transfer) does not occur for three reasons.

Reasons Data Loses Meaning During Data Transfer

• Sender Not Providing Data Definitions. In this case, the carrier’s IT department did not  provide adequate data transfer documentation. The documentation needed would include both a technical data dictionary and business glossary definitions. 
• Receiver Technical Issue. The shipper’s IT department fails to follow the carrier’s data dictionary and technical implementation spec. Hence, the data interface does not get implemented or the data is garbled when imported. This is purely a data integration problem, but it affects the meaningful transfer of data.
• Receiver Misunderstands Data. Lastly, the shipper may have misunderstood the carrier’s data interface documentation, specifically the business definitions. Thus, data inadvertently gets mistranslated, making the data not understandable. 

Hence, for these reasons many data integrations result in “what is sent is not understood”. So, in many cases successful technical data integrations do not achieve semantic interoperability within supply chains. Indeed, supply chains could alleviate many of these data communications misunderstandings if organizations could follow commonly accepted data interoperability standards and practices.

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

So, while most businesses can transfer data, how can we achieve true data interoperability where we can consistently transfer meaningful data? One effective approach is for businesses to better support Standards Development Organizations (SDOs). Consider how standardization enabled U.S. railroads to use uniform track gauges or how standardized ocean containers revolutionized international trade. Similarly, with supply chain semantic data interoperability, we need to support the advancement of data standards. Below are examples of SDO initiatives that facilitate the meaningful exchange of supply chain data and financial digital trends.

Examples of Standards Development Organizations (SDO) and Trends

• GS1. GS1 offers many interoperability standards such as GS1 General Specifications Standard for bar codes and Global Shipment Identification Number (GS1 GSIN) for shipment visibility.
• ASTM F49. ASTM F49 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 shipment status with their partners. 
• FinTech Trends. For examples of emerging financial technology (FinTech) trends, see my articles, 14 New EFT Payment Technology Trends In The FinTech Industry and Freight Payment Terms: A Painful Money Game, Its Purpose, Is There A Better Way?

Also, SDOs are more than just developing technical data standards such as data dictionaries. They are also working on defining business glossaries which are critical for achieving semantic data interoperability. Indeed, business glossaries are needed to provide clear definitions to enable both business systems and their organizations to successfully communicate and be understood. For more on the critical role that business glossaries have on achieving semantic interoperability, see my article, Feeble Business Glossaries: Know It’s The Greatest Killer Of Supply Chain Collaboration And Data Interoperability

Feeble Business Glossaries: Know It’s The Greatest Killer Of Supply Chain Collaboration And Data Interoperability

c. 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. This is not just something left to IT. Data is a business asset. Developing and adopting a supply chain data interoperability framework requires collaboration across the industry. Indeed, 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 is shared or transmitted. For example of the advantages 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.

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

3. 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. Moreover, the quality of these custom data interfaces are questionable, where many either have very proprietary content or the data itself has little meaning for its intended recipient. Worse, proprietary data formats overtime lock data into customized data 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.

a. Proprietary Data Interfaces Are Both Application-Centric and the Norm for Exchanging Supply Chain Data.

For decades, logistics organizations have implemented proprietary system-to-system data interfaces. As a result, we have created a tangled web of information flows and data silos. Moreover, large logistics firms and numerous electronic marketplaces have often encouraged customers to adopt their proprietary data interfaces and APIs, ostensibly to create a “seamless” online community. While this has provided short-term benefits, it has also led to long-term issues as these proprietary networks become obsolete, raise their service fees, or they go out of business. Ultimately this has led to more and more data silos and proprietary interfaces.

b. We Are Living a Data Integration Nightmare: The Many Challenges to Untangle this Proprietary Mess.

The end result is that we have created a data integration nightmare compounded by our continued use of proprietary data interfaces. Indeed, this quagmire needs to stop. It is preventing supply chains from innovating that would increase efficiencies and improve their 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

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

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

4. Fourth, Leverage AI, Knowledge-Centric, and Digital Identity Technologies to Empower Semantic Interoperability.

In this digital era, business leaders are realizing the necessity of true data interoperability. The old ways of data silos locked within proprietary enterprise systems simply won’t cut it. Further, cobbling together these systems with more custom-built data interfaces is not the answer. Indeed, traditional data integration efforts are a slow-moving technical process that demands significant know-how. Further, digital supply chains increasingly have more requirements to have users, devices, systems, and even AI agents access their networks. The question to answer is can emerging technology help us to achieve true data interoperability in this increasingly complex modern age? The answer is yes, there are emerging technologies that can advance semantic data interoperability in supply chains. This includes:

Emerging Tech to Advance Semantic Data Interoperability

Data Interoperability Tech: It Is More Than Integrations – It’s Understandable, Secure, And High Velocity Technology

• Artificial Intelligence (AI) / Machine Learning (ML). Holds great promise to streamline data interface setups and help in the development of self-learning data models.
• Knowledge Graph Tech. Here, organizations can use this tech to place data within context and define relations. Thus, it can greatly enhance semantic interoperability and infuse data with meaning.
• Digital Identity Tech. This is needed to have trusted interoperability with supply chain partners, users, systems, devices, and even AI agents. Also, it is needed to protect against bad actors such as hackers.

For a detailed discussion on how emerging technologies can help supply chains achieve true data interoperability, see my article, Data Interoperability Tech: It Is More Than Integrations – It’s Understandable, Secure, And High Velocity Technology.

Conclusion.

In this article, I have offered a four step process for logistics organizations to move from their current tangled web of custom data integrations to a seamless supply chain. This four step process includes:

• Step 1 – Adopt a Data-Centric Mindset. Stop being application-centric and treat your data as a strategic asset. 
• Step 2 – Leverage Standards Development Organizations (SDO). Stop reinventing and developing costly, customizing data specifications. 
• Step 3 – Move Away from Proprietary Data Interfaces. These customized data integrations are both fragile and lock your data in rigid application silos. 
• Step 4 – Leverage Emerging Tech. This includes AI, knowledge graphs, and digital identity tech to further empower semantic interoperability. 

So, let’s start advancing toward the promise of semantic data interoperability. This is what we need – meaningful data exchange that is understandable and actionable across the supply chain. For more information on achieving supply chain semantic interoperability, see my article, Achieving Logistics Interoperability: The Best Way to Breakthrough The Tangle Of Dumb Data Integrations.

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

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.

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