As someone who’s worked extensively as an integrator of supply chain systems, I can attest that businesses continue to struggle with this make-or-break challenge of data interoperability. Without a doubt, our supply chain industry with its countless systems, have more digital disconnects and data silos than other industries. The silver lining is that there are emerging technologies like AI that can help us overcome these obstacles. As a result, we can have seamless, secure, and high-velocity data exchanges.

In this article, I’ll take a closer look at the ongoing struggles with supply chain interoperability. Further, I’ll tell you why current data interoperability solutions are falling short. Most importantly, I’ll share with you new approaches using emerging technologies like AI that can help supply chains share information seamlessly. By leveraging these innovative solutions, businesses can unlock new levels of supply chain visibility, agility, and resilience.

1. The Digital Connect Interoperability Problem with Supply Chains. 

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

Supply chains have a unique problem – their data is spread across countless systems and geographical areas. Moreover, this data is owned by many stakeholders either inclined to not share their data or do not have the knowhow to share their data effectively. As a result, we have data disconnects, information chaos, and analytics that are not aligned with decision-makers. Indeed, this is a real tragedy. This is because supply chains not only need this data, but they need it accurate, complete, understandable, and timely.

For more details on these data disconnects, see my article, Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation.

2. Current Interoperability Solutions Not Working. 

To sum it up, there is a large gap between the interoperability needed by supply chains versus what they actually get. What’s more, this digital divide is not getting better because supply chain organizations keep on using the same data integrations solutions over and over again. Without a doubt, traditional interoperability solutions, such as proprietary digital portals and expensive data integration service providers, have significant limitations. Let’s look closer at these traditional data interoperability solutions that fail to unify supply chain data and make it actionable.

a. IT Integration: Slow, Expensive, Brittle.

With this type of interoperability solution, the integrator devises a customized data map to join disparate data sources together. Typically these types of implementations can last many weeks or months. Moreover, it is too common that the data transmitted through these customized interfaces are not particularly actionable or understood by the receiving system. Worse, these custom data maps frequently break and are hard to repair or update. To some degree Standards Development Organizations (SDO) help to minimize customized data interfaces, but for the pragmatic IT integrator, new, standard data interfaces are “just another standard” to charge for.

b. Proprietary Digital Hubs: The Cycle of Ascendancy and Obsolescence.

Another common integration approach is to rely on digital portals or platforms like ERPs, online marketplaces, or digital exchanges. However, these solutions have significant drawbacks. First, they require all supply chain partners to join the proprietary network, which isn’t always feasible. Moreover, these exclusive portals often increase subscription fees over time or are soon abandoned in favor of newer, “better, cheaper” platforms funded by new venture capital. For more on the past failures of these digital hubs, see these links – Real-Time Transportation Visibility Platforms (RTTvP), Digital Freight Matching (DFM) platforms, and procurement hubs.

3. New Approaches to Make Shared Data Seamless: Understandable, Secure, and High Velocity.

Emerging technologies like artificial intelligence (AI), machine learning (ML), knowledge graphs, and digital identity tech are poised to revolutionize current data interoperability practices. Indeed, these technologies, especially AI, can streamline integration setups, and accelerate data standards development through automation and self-learning. What’s more, these new innovations can enhance shared understanding of business data via knowledge graphs, and increase trust between data-sharing systems and devices. By leveraging these innovations, businesses can achieve more efficient, secure, and high-velocity data integrations. To list, below are emerging technologies that will make a difference. 

a. Artificial Intelligence to Streamline Data Integration Tasks. 

For instance, AI and AI agents can automate data discovery, mapping, data quality improvements, and metadata management to name a few. Also, Computer Vision AI can streamline data integration flows using image-based messaging and translation. 

b. Machine Learning (ML) to Accelerate Data Standard Development. 

Here, ML can accelerate the task of classifying and predicting events. Also, AI can analyze large data sets to help identify new additions to data models and standards.

c. Knowledge Graph Tech that Links Data and Enables Shared Meaning.

For example, this tech can help with automated fact checks, increase contextual understanding, and link related entities to name a few. As a result, shared data between systems is better understood by all stakeholders. 

Digital Identity In Logistics And What To Know – The Best Security, Scary Risks

d. Trusted Interoperability Leveraging Digital Identity Tech. 

For example, this tech equips businesses to have positive control of their data, enables regulation compliance, provides protection against bad actors, and empowers businesses to securely leverage new tech such as AI agents and IoT devices.

For more on the promise of digital identity solutions, see my article, Digital Identity In Logistics And What To Know – The Best Security, Scary Risks.

e. Advanced Analytics that Streamlines Information Gathering and Synthesis. 

Instead of massive data dumps and duplication of data that clogs up data pipelines, AI and traditional analytics can help us to only share targeted relevant information. For more insights on this, see my article, Targeted, Relevant Information Is The Only Way To Make Timely, Informed Decisions – Here’s How!

Targeted, Relevant Information Is The Only Way To Make Timely, Informed Decisions – Here’s How!

More Data Interoperability References.

Lastly, for more information on emerging technologies that can improve supply chain data interoperability, here are more references:

• Data Interoperability Tech: It Is More Than Integrations – It’s Understandable, Secure, And High Velocity Technology.
• Data Interoperability For Supply Chains: The Best Ways To Unlock Your Digital Assets And Empower Innovation
• The Way To Better Supply Chain Analytics: Overcome Data Interoperability With Intelligent Tracking Status
• A Less Painful Way To Unlock Total Landed Cost Insights By First Fixing The Massive Disconnects In Supply Chain Data

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

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

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.