Paul R Salmon FCILT, FSCM, FCMI
Introduction
Codification is one of those processes that is often invisible to the uninitiated, yet utterly indispensable to military and industrial supply chains. Within NATO, codification underpins interoperability. It assigns items their NATO Stock Numbers (NSNs), ensures consistency across systems, and prevents duplication. Without codification, supply chains would fragment into costly, siloed pools of incompatible data.
For decades, codification has been a painstaking, rules-driven task handled by specialist codifiers. These experts rely on deep knowledge of the NATO Codification System (NCS), technical drawings, supplier specifications, and a labyrinth of classification rules. It is slow, resource-intensive, and vulnerable to human error. And yet, as operations grow faster and more complex, codification has never been more critical.
This is where artificial intelligence enters the picture. AI is not here to replace the codifier; it is here to amplify their effectiveness. By automating repetitive tasks, flagging anomalies, and learning from historical data, AI has the potential to transform codification from a bottleneck into a force multiplier.
This article explores how AI can be applied to codification, the benefits it could bring, the risks that must be managed, and the strategic impact on NATO and UK Defence logistics.
Why Codification Matters
At its simplest, codification is the process of uniquely identifying and describing an item of supply so that it can be managed, stored, and requisitioned consistently across a supply chain. In NATO terms, it means assigning an NSN – a 13-digit identifier that becomes the lingua franca for logisticians across nations.
Codification ensures:
Standardisation: A bolt described in the same way in Germany as it is in the UK. Interoperability: Allies can share parts with confidence that they are referring to the same item. Data Integrity: Inventory systems are not clogged with duplicates or incorrect descriptions. Readiness: Forces can obtain and use items quickly, without ambiguity.
The problem is that codification is slow. Introducing a new platform into service may require tens of thousands of items to be codified. Traditional codification involves reading technical manuals, extracting data, classifying items into NATO supply classes, and assigning reference numbers. A skilled codifier may take 30–60 minutes per item. Multiply that by tens of thousands, and codification quickly becomes a bottleneck in procurement and operational readiness.
The Case for AI
Artificial intelligence offers a way to scale codification. Using machine learning, natural language processing, and anomaly detection, AI systems can process technical data faster and more consistently than humans, while flagging exceptions for expert review.
The role of AI here is not to make final codification decisions. It is to assist the codifier, accelerating routine tasks and highlighting where expert judgement is most needed.
In short: AI won’t replace the codifier, but the codifier using AI will replace the one who doesn’t.
Applications of AI in Codification
1. Automated Classification
AI models can be trained to recognise patterns in technical data sheets, drawings, and supplier catalogues. For example, given a set of dimensions, materials, and part functions, AI can suggest the most likely NATO Supply Class and even a preliminary item description.
This transforms codification from a manual classification exercise into a collaborative process where the human codifier validates and adjusts AI recommendations rather than starting from scratch.
2. Duplicate Detection
Duplicate NSNs are a common problem. Multiple entries for what is essentially the same item inflate inventory costs, confuse procurement, and waste storage space. AI excels at pattern recognition. It can compare descriptions, dimensions, and supplier data at scale, flagging likely duplicates for consolidation.
3. Error Identification
AI can act as a quality gate. By comparing input data against known ranges, units of measure, or dimensional constraints, AI can flag anomalies (e.g., a bolt listed as 20 metres long). This reduces rework and improves the overall reliability of codification data.
4. Predictive Codification
Using historical codification data, AI can “learn” what kinds of items typically fall into which classes and descriptions. When a new item arrives, AI can suggest a likely codification outcome before human review. This is particularly valuable in rapid procurement during operations, where speed is essential.
5. Multilingual Processing
Codification often involves supplier data from multiple languages. AI translation models, fine-tuned for technical terminology, can translate supplier specifications into a common codification language, reducing reliance on scarce specialist linguists.
6. Continuous Learning
Every time a codifier accepts, modifies, or rejects an AI suggestion, the system learns. Over time, accuracy improves. This creates a virtuous cycle where human expertise and machine efficiency reinforce each other.
Defence Use Cases
Accelerating Platform Introduction
When new aircraft, ships, or vehicles enter service, thousands of items must be codified before they can be supported. AI could reduce codification times from months to weeks, getting platforms operational sooner.
Supporting Urgent Operational Requirements
In a crisis, forces often procure items locally in-theatre. Rapid codification is essential to integrate these items into logistic systems. AI could provide instant suggested codifications, giving logisticians a head start.
Interoperability Across NATO
Nations codify differently. AI tools trained on multi-national data could harmonise codification practices, reducing friction in coalition operations.
Benefits of AI in Codification
Speed: Codification timelines could be halved, dramatically improving operational readiness. Consistency: AI reduces variability between codifiers, ensuring greater standardisation. Cost Efficiency: By cutting duplication and error, AI reduces inventory and support costs. Scalability: AI can handle surges in codification workload without the need for proportionate increases in human resources. Strategic Advantage: Faster, cleaner codification means better interoperability across NATO forces, enhancing coalition effectiveness.
Risks and Challenges
AI in codification is not without its risks:
Data Quality: AI is only as good as the training data. Legacy codification data may be inconsistent or incomplete. Human Oversight: Codification decisions carry legal and contractual implications. Humans must remain accountable. Governance: Clear frameworks are required to ensure AI recommendations comply with NATO Codification System rules. Transparency: Codifiers and commanders must understand how AI reaches its conclusions. “Black box” models are unacceptable in Defence. Change Management: Codifiers may be sceptical of AI. Adoption will require training, reassurance, and demonstration of tangible benefits.
Lessons from Industry
Commercial supply chains are already applying AI to product master data, classification, and cataloguing. Large manufacturers and retailers use AI to:
Merge supplier catalogues without duplication. Classify products automatically into taxonomies. Detect anomalies in item descriptions.
Defence can learn from these approaches, while recognising that NATO’s regulatory environment and security context add unique complexity.
The Strategic Impact
Codification is often overlooked in discussions of Defence modernisation. Yet it is the hidden plumbing that keeps the system flowing.
By applying AI, Defence can:
Reduce delays in bringing capability into service. Ensure greater consistency across coalition partners. Free codification experts to focus on complex edge cases rather than routine classification. Improve the quality of item master data that underpins everything from forecasting to obsolescence management.
AI becomes a force multiplier: not replacing human expertise, but magnifying its impact across the codification enterprise.
Conclusion
In a world of contested logistics, rapid procurement, and coalition operations, codification can no longer be a bottleneck. Artificial intelligence offers a pathway to faster, more reliable, and more interoperable codification.
The opportunity is clear: AI will not replace the codifier, but the codifier using AI will deliver better, faster, and cheaper outcomes than ever before.
Codification may never be glamorous. But in the shadows of supply chains, AI can transform it into a strategic enabler – one that strengthens readiness, reduces cost, and ensures NATO forces speak the same logistic language when it matters most.
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