Every supplier issue goes into the same bucket: "problems." The delivery was late. The quality was wrong. The price was disputed. They're all issues, all tracked the same way, all counting equally toward some vague sense of supplier trouble.

This undifferentiated approach wastes information. The patterns that would reveal systemic problems, operational improvements, and supplier management priorities are invisible when everything is just "an issue."

Why Categories Matter

Imagine you could see that 60% of your supplier issues are related to incorrect invoicing. That's not a supplier performance problem—it's a process problem, probably on your side. Your contract data is wrong, or your PO system has errors, or your specifications are ambiguous about pricing.

Now imagine you could see that one specific supplier generates more quality issues than all others combined, even accounting for their higher volume. That's a supplier capability problem that deserves targeted attention—perhaps quality audits, process improvement support, or ultimately supplier change.

These insights are invisible without categorisation. When every issue is just "an issue," you can't distinguish systemic process problems from individual supplier failures, or see whether issues cluster in ways that reveal root causes.

Designing the Category Structure

Effective categorisation requires thought about what distinctions are meaningful for your operations. Generic categories that don't map to your business create data that's hard to interpret and action.

Start with broad categories that capture fundamental issue types. Delivery issues—timing, quantity, location. Quality issues—defects, specification failures, packaging problems. Commercial issues—pricing, invoicing, payment terms. Administrative issues—documentation, communication, responsiveness.

Within these broad categories, sub-categories add precision. Delivery might include late delivery, early delivery, partial delivery, wrong location. Quality might include manufacturing defects, damage in transit, specification mismatch, documentation errors. The right level of detail depends on volume and how finely you need to diagnose problems.

Resist the temptation to create too many categories. Every category you add is a decision someone has to make when logging issues. Too many options create confusion and inconsistency. Start simpler and add categories only when you have evidence they'd be useful.

The Root Cause Dimension

Beyond what type of issue occurred, categorisation can capture why it occurred—or at least where responsibility lies.

Was the issue caused by the supplier's actions, or by your own? A delivery delay because the supplier missed their schedule is different from a delivery delay because you changed the specification at the last minute. Both are "late delivery," but they imply different responses.

This root cause categorisation requires some judgment at time of logging, and that judgment may be contested. The supplier might disagree about who's responsible. But even imperfect categorisation provides useful signal.

Over time, patterns in root cause data reveal improvement opportunities. If a high proportion of issues are "caused by our specification changes," maybe your specification management process needs work. If most quality issues trace to "supplier manufacturing error," that's a clear supplier capability gap.

Capturing Severity

Not all issues are equal. A minor paperwork discrepancy and a production-stopping quality failure are both "issues," but they warrant completely different levels of attention and response.

Severity categorisation helps prioritise response and enables meaningful pattern analysis. High-severity issues demand immediate attention and senior escalation. Low-severity issues can follow normal processes.

Define severity in terms of impact, not just inconvenience. A truly high-severity issue is one that stops production, risks safety, creates major financial exposure, or threatens customer relationships. Reserve high severity for genuine emergencies rather than letting severity inflation make the categorisation meaningless.

When analysing patterns, weight by severity. One high-severity issue may matter more than ten low-severity issues. A supplier with many small problems might be less concerning than one with few but catastrophic failures.

Making Categorisation Consistent

The value of categorisation depends on consistency. If different people categorise similar issues differently, pattern analysis becomes unreliable.

Clear definitions help. For each category, document what belongs and what doesn't, with examples. When edge cases arise, make decisions and add them to the guidance.

Training matters, especially when issue logging is distributed across many people. A brief orientation on categorisation choices prevents drift and inconsistency.

Quality checks can catch problems. Periodic review of categorisation patterns—are categories being used as intended?—identifies where retraining or definition clarification is needed.

Accept some imperfection. Perfect categorisation is impossible, and striving for it creates administrative burden that defeats the purpose. Good enough categorisation that's consistently applied is more valuable than perfect categorisation that's burdensome and inconsistently followed.

Analysing Category Patterns

With consistent categorisation in place, analysis becomes possible. Several perspectives are typically valuable.

Category distribution shows where issues concentrate. If 70% of issues are commercial rather than quality or delivery, that's a signal about where improvement efforts should focus.

Category trends reveal whether specific problem types are increasing or decreasing. Quality issues trending up might indicate supplier capacity problems or material issues. Commercial issues trending down might reflect recent process improvements.

Category by supplier identifies which suppliers have particular problem types. Supplier A might have delivery issues; Supplier B might have quality issues. Different problems suggest different interventions.

Category by internal area can be revealing. Do issues concentrate with particular sites, business units, or categories? The pattern might indicate process problems in specific areas rather than supplier problems overall.

From Analysis to Action

Categorised data is only valuable if it drives decisions. Several mechanisms help turn insight into action.

Regular reporting surfaces patterns to people who can act on them. Monthly or quarterly summaries of issue categories, trends, and outliers should reach supplier managers, category leads, and senior stakeholders.

Threshold triggers can escalate automatically. If a supplier exceeds normal levels of a particular issue category, the system flags for investigation. The analysis happens continuously rather than waiting for periodic review.

Root cause reviews for high-category-concentration situations drive improvement. If quality issues dominate, a quality-focused improvement initiative makes sense. The categories guide where to apply resources.

Supplier scorecards should incorporate category analysis. Overall issue counts matter, but category breakdown adds nuance. A supplier with many minor administrative issues is different from one with fewer but more serious quality problems.

The Continuous Learning Loop

Categorisation is itself a learning opportunity. Which categories prove useful? Which are rarely used or consistently confused? How could the structure better capture the issues you actually face?

Review the category structure periodically—perhaps annually—and adjust based on experience. Add categories that would provide useful distinction. Remove or merge categories that aren't serving their purpose. The structure should evolve with your operations.

This evolution should be documented and communicated. When categories change, everyone logging issues needs to understand the new structure. Historical data may need remapping for trend analysis to remain meaningful.

The goal is not a perfect permanent structure, but a useful evolving structure that provides insight into supplier issues and guides improvement. Categorisation is a tool, not an end in itself. Its value is measured in the improvements it enables.