A practical guide to identifying what’s actually going wrong – and where to look first.
You know something isn’t right. Maybe orders are shipping late more often than they used to. Perhaps stock-outs have become a regular occurrence, or your warehouse team is constantly firefighting. The symptoms are clear enough – but pinning down the cause is more complicated.
Supply chain problems rarely announce themselves in an obvious way. A delivery failure might stem from a forecasting issue three months earlier. Rising costs might trace back to a supplier relationship that deteriorated quietly over time. Often the challenge isn’t fixing issues, it’s working out what’s actually causing the problem in the first place.
This guide offers a practical framework for diagnosing supply chain issues: where to look, what questions to ask, and how to use data to move from symptoms to root causes.
Start with the Symptoms, Not the Suspected Cause
When something goes wrong, it’s tempting to jump to conclusions. Late deliveries? Must be a transport problem. Stock-outs? Probably forecasting. But early assumptions can send you down the wrong path entirely.
Before investigating causes, get clear on what you’re actually observing:
What specific outcomes are failing?
Be precise. “Customer service is poor” is too vague. “OTIF has dropped from 94% to 87% over six months” gives you something to work with.
When did it start? Gradual decline suggests systemic issues. Sudden drops often point to a specific event or change.
Where in the chain is it happening? Is the problem at goods-in, in storage, at pick and pack, or in final delivery?
Who or what is affected? Are all customers equally affected, or is it specific to a channel? All products, or certain categories?
This initial scoping prevents the common mistake of solving the wrong problem with considerable effort and expense.
Follow the Flow
Supply chains are sequences. Problems that manifest at one stage often originate elsewhere. A useful diagnostic approach is to trace the flow – both forwards and backwards from where the symptom appears.
If deliveries are late: Work backwards. Were orders dispatched on time? If not, was picking delayed? If so, was stock available? If stock was available, was labour the constraint? Each question narrows the focus.
If stock-outs are increasing: Trace the replenishment cycle. Are purchase orders being raised in time? Are suppliers delivering to schedule? Is demand outpacing forecasts? Is safety stock set appropriately?
If costs are rising unexpectedly: Map your cost-to-serve. Where specifically are costs increasing? Transport? Warehousing labour? Returns processing? Holding costs from excess inventory?
Simple Root Cause Techniques
Once you’ve located where a problem seems to be occurring, you need to understand why. Two straightforward techniques can help.
The 5 Whys
This method is exactly what it sounds like: asking “why” repeatedly until you reach a root cause. The number five is a guide rather than a rule.
Example:
- Why are customers receiving incomplete orders? Because items are being marked as picked but aren’t in the box.
- Why aren’t items in the box? Because pickers are scanning barcodes but not physically picking the product.
- Why are they scanning without picking? Because the pick locations are congested and they’re under time pressure.
- Why are locations congested? Because replenishment is happening during picking hours.
- Why is replenishment scheduled during picking? Because it was never reviewed after the shift pattern changed.
The surface problem (incomplete orders) traces back to a scheduling decision. Fixing the symptom without understanding this would likely fail.
Pareto Analysis
Not all problems contribute equally to poor outcomes. Pareto analysis, the principle that roughly 80% of effects come from 20% of causes, helps you prioritise.
If you’re experiencing high return rates, for instance, break down returns by reason code. You might find that three categories account for most of the volume. Focus there first.
If delivery performance is suffering, analyse failures by carrier, region, or product type. Patterns will emerge. Perhaps one carrier is responsible for a disproportionate share of late deliveries, or a particular product category is consistently problematic to pick.
Pareto thinking prevents the scatter-gun approach of trying to fix everything at once. It directs effort where it will have the greatest impact.
Let the Data Guide You
Gut instinct has its place, but data transforms diagnosis from guesswork into evidence. Most supply chains generate far more data than gets used – the challenge is knowing which data to interrogate.
Order Patterns and Variability
Understanding demand variability is fundamental. High variability (unpredictable spikes and troughs) makes planning harder and increases the likelihood of both stock-outs and excess inventory.
Look at your order history by SKU, by channel, and by time period. Where is variability highest? Is it genuinely random, or are there patterns (promotions, seasonality, customer behaviour) that could be anticipated?
If your planning team is constantly surprised by demand, that’s a diagnostic finding in itself.
OTIF Trending
On-Time, In-Full is often treated as a single number, but it’s more useful when decomposed. Track the “on-time” and “in-full” components separately. Are you failing on timing, completeness, or both?
Trend OTIF over time, by customer, by product category, and by order type. A steady 92% average might mask that one major customer is at 85% and dropping. Aggregated metrics hide problems; segmented data reveals them.
Inventory Turns and Ageing
Slow-moving and obsolete stock ties up working capital and clutters warehouses. Calculate inventory turns by category and compare to benchmarks. Which products are turning too slowly? Which are turning so fast that you’re constantly at risk of stock-out?
Ageing analysis shows how long stock has been sitting. If significant inventory is more than six months old, that’s a symptom worth investigating. Why was it bought? Why isn’t it selling? What happens if this continues?
Cost-to-Serve Breakdown
Many businesses know their total supply chain cost but not where it accumulates. A cost-to-serve analysis breaks down costs by activity: procurement, inbound logistics, warehousing, outbound transport, returns.
Often, a small number of cost drivers account for the majority of spend. Identifying these creates focus. If outbound transport is 40% of your supply chain cost and rising, that’s where diagnostic effort should concentrate.
Common Diagnostic Blind Spots
Some problem areas are frequently overlooked in initial investigations.
Data quality: Poor data leads to poor decisions. If your inventory system says you have stock but the warehouse can’t find it, no amount of clever analysis will help. Before blaming processes, check whether the information those processes rely on is accurate.
Interfaces between functions: Problems often occur at handoffs, between sales and operations, between warehouse and transport, between your systems and suppliers. These are natural places for miscommunication and delay.
Historic decisions left unreviewed: Safety stock levels set three years ago. Supplier lead times that haven’t been updated since pre-pandemic. Routing rules based on a distribution network that has since changed. Legacy assumptions embedded in systems and processes can quietly cause ongoing problems.
The human element: Sometimes processes are fine on paper but break down in practice. Talk to the people doing the work. Warehouse operatives, planners, customer service teams – they often know exactly where the problems are, even if they haven’t been asked.
When to Bring in External Help
Not every problem requires outside expertise. If the issue is well-defined and the solution is clear, your team can likely handle it.
But some situations benefit from a fresh perspective:
- You’re too close to see it: internal teams can become blind to issues they’ve worked around for years. An outsider notices what you’ve stopped noticing.
- The problem spans multiple functions: cross-functional issues are politically difficult to diagnose internally. Each team naturally defends its own processes.
- You lack the analytical capability: extracting insights from complex data requires skills and tools that not every business has in-house.
- You’ve tried and haven’t found the answer: if internal investigations have stalled, a different approach might be needed.
The goal isn’t to outsource thinking, but to accelerate the path to understanding. A good external partner will work with your team, not around them, and leave you with a clear path to get back on track.
Building Diagnostic Capability
The best supply chains don’t wait for problems to become crises. They build ongoing diagnostic capability: regular reviews, meaningful metrics, and a culture that asks “why” rather than just “what.”
This doesn’t require sophisticated tools. It requires curiosity, access to relevant data, and the discipline to look beyond symptoms to causes. Over time, this capability becomes a competitive advantage, catching issues early before they compound.
If you’re facing a supply chain challenge you can’t quite pin down, and want to explore how data-led diagnosis could help, get in touch for a conversation.