I’ve seen firsthand how the complexity of modern supply chains can turn data interoperability into a seemingly insurmountable challenge. In particular for supply chains, this is an acute problem of complexity because of the diverse range of technologies, countless stakeholders, and vast amounts of data associated with logistics operations. What’s more, supply chain systems have differing data standards, data protection requirements, and the need for continuous updates. As a result, supply chain managers are left grappling with a data interoperability nightmare. Worse, this digital integration maze threatens to undermine every effort to leverage tech to streamline operations and improve services.
In this article, I’ll share 12 data-related reasons why the supply chain industry cannot just use tech alone to conquer the data interoperability swamp.
1. Inconsistent Data Formats, Dictionaries, and Glossaries for Seamless Data Interoperability.
For example of inconsistent data formats, a company might receive shipment data in CSV format from one supplier, while another supplier sends it in XML format. As a result, this requires manual conversion or complex data translations to integrate the data. Worse, data dictionaries and business glossaries lack clarity. This is because they do not convey a common understanding for each data element, nor business terms.
For more insights on the interoperability challenges with data dictionaries and business glossaries, see my article, Feeble Business Glossaries: Know It’s The Greatest Killer Of Supply Chain Collaboration And Data Interoperability.
2. Lack Of Standardized Product and Shipment Data Codes.
For instance, one company uses Universal Product Code (UPC) codes while another uses International Article Number (IAN) codes. As a result, this makes it challenging to match and reconcile data across systems.
3. Legacy Enterprise Apps Not Designed for Data Interoperability.
Most enterprise supply systems such as TMS, WMS, and OMS are application-centric. This is because these systems were designed as transactional-based systems to update databases for a specific function. Hence, the data itself is considered as a byproduct of the “system”. As a result, new data interface setups are extremely challenging. Consequently, the real interoperability problem is that many enterprise systems are not data-centric, they are application-centric.
For example, an older enterprise resource planning (ERP) system might not be able to exchange data seamlessly with newer cloud-based logistics platforms. As a result, this leads to delays and errors in data exchange. For more insights on how a data-centric mindset facilitates data interoperability, see my article, A Data Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation.
4. Manual Data Entry And Disjointed Reconciliation Between Systems.
For example, it is quite common for a warehouse worker to manually enter shipment details into multiple systems. As a result, this increases the risk of data errors and delays.
5. Shipping Data Not Unified Across Functional or Multiple Logistics Stakeholders.
It is a normal practice for supply chain professionals and their systems to tie shipment data to many reference IDs. This includes multiple tracking numbers, purchase orders, carrier invoice number, and customer reference numbers. As a result, stakeholders do not have an unified view of shipment loads across the supply chain. For instance, the accounting department is challenged to pay a carrier’s freight invoice because they can’t match the purchase order with the shipment number. In another case, the shipper loses visibility to their shipment load because the original tracking number is not used by the final delivery carrier.
For more insights on unifying shipping data, see my article, Unifying Shipping Data Using Transport Load IDs: Here Are 10 Ways It Can Unlock Analytics And Empower Logistics Tech.
6. Incomplete Or Inaccurate Data.
To illustrate, if a supplier fails to update product specifications in their system, it can lead to incorrect information being shared across the supply chain. In another example, missing or incorrect shipment information such as needed for customs clearance can lead to delays and increased costs.
7. Duplicate Data, No Single Source of Truth (SSOT).
For example, multiple systems might contain different variations of shipment data. As a result, this makes it challenging to determine the accurate status. To illustrate, it is a common practice for a Business Intelligence (BI) reporting system to extract copies of data from source systems like WMS, OMS, or TMS. In these cases, analysts may need to modify data using an Extract, Load, Transform (ELT) process or through manual manipulation. Over time, this data gets duplicated, modified, and summarized for different uses, leading to multiple variations.
For more ideas on minimizing duplicate data and achieving a Single Source of Truth (SSOT), see my article, Data-Centric Advice to Reduce IT Complexity and Make Tech Remarkably More Useful.
8. Non-Standardized Naming Conventions and Definitions.
Non-standardized naming conventions can lead to data integration challenges. For example, one company will label a product as “Widget A” while another company refers to it as “Product 123”. As a result, this leads to miscommunication and errors. This also holds true with ambiguous supply chain terms. For instance, let’s take the business term, “delivery window” as an example. Here the shipper expects a precise 2-hour slot, while the carrier thinks it means a 4-hour period. Worse, the Delivery Window data element, “1000 hrs”, is shared between systems. As a result, this lack of specificity and ambiguity will cause disputes, possible fines, and tension at the very least.
For more insights on how poor business communications adversely affects interoperability, see my article, Supply Chain Business Communications Need Clarity: This Is What is Hobbling New Tech Innovations.
9. Lack of Data Validation Checks.
The absence of data validation checks can result in inaccurate data. For instance, if a system does not validate the accuracy and completeness of incoming data, it can result in the propagation of errors throughout the supply chain.
10. Incompatible Data Interoperability and Integration Tools.
Incompatible integration tools can hinder data interoperability. For example, if one company uses an API-based integration tool while another uses a file-based integration tool, then data either does not get transferred or manual workarounds are implemented. As a result, systems do not exchange critical data or data is corrupted as a result of manipulation, either through manual efforts or highly customized automation. For more information on data integration tools and methods, see my article, The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos.
11. Proprietary System and Customized Data Interface Lock-In.
Proprietary systems and interfaces inhibit data interoperability, delaying or preventing new system integrations. For example, a shipping system has a proprietary data interface. Thus, it makes it difficult for a shipper to onboard new parcel carriers. This is because shippers can’t print shipping labels for any new carriers that are not compatible with the proprietary shipping system. Worse, the shipper hires a programmer to develop a custom data Interface to solve the problem; a month later it breaks because of a system update!
For more insights on avoiding proprietary system lock-ins, see my article, Achieving Logistics Interoperability: The Best Way to Breakthrough The Tangle Of Dumb Data Integrations.
12. Ineffective Digital Identity Solution.
With any digital system, you have to have an effective digital identity system. Specifically, these systems are used to verify new users, authorize what they can access, and authenticate users when they access systems. The challenge comes in balancing between having secure systems and usability. For more details on digital identity, see my article, Digital Identity In Logistics And What To Know – The Best Security, Scary Risks.
Final Thoughts.
In this article, I’ve identified the top 12 reasons why technology alone is insufficient to achieve seamless supply chain interoperability. This is because complex supply chains, with their diverse technologies, numerous stakeholders, and vast data volumes, require more than just technical solutions. My advice to overcome these supply chain interoperability challenges is for us to first undergo a fundamental shift in our business mindset. Specifically, this includes achieving clarity in communicating our business terminologies and prioritizing data over software applications. By doing so, organizations can unlock true data interoperability, effectively leverage digital technologies, and drive more efficient, informed decision-making across the supply chain. For more insights, on solving data interoperability in your supply chain, see references below.
“Interoperability enables us to seamlessly move data, and more importantly insight, between various systems.”
Amy Waldron, Google Cloud
More References.
- For more insights on data interoperability and its solutions, see my article, Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation.
- Data interoperability is critical for supply chain analytics and shipment tracking. For more information, see my article, The Way To Better Supply Chain Analytics: Overcome Data Interoperability With Intelligent Tracking Status.
- Data interoperability is not so much a problem for IT to solve as a need for a change of business mindset from being application-centric to data-centric. To help implement a data-centric business strategy, see my article, A Data-Centric Business Strategy Checklist: The Way To Energize A Digital Enterprise To Be More Agile, Bold, And Simplified.
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.
For more from SC Tech Insights, see the latest articles on Interoperability and Information Technology.
Greetings! As a supply chain tech advisor with 30+ years of hands-on experience, I take great pleasure in providing actionable insights and solutions to logistics leaders. My focus is to drive transformation within the logistics industry by leveraging emerging LogTech, applying data-centric solutions, and increasing interoperability within supply chains. I have a wide range of experience to include successfully leading the development of 100s of innovative software solutions across supply chains and delivering business intelligence (BI) solutions to 1,000s of shippers. Click here for more info.