How to Reduce Picking Errors in a Warehouse: A Root Cause Analysis Case Study

This case study shows how a high-volume UK pharmaceutical warehouse reduced picking errors and improved picking accuracy using Root Cause Analysis (RCA) — without introducing new technology or automation.

The Problem: Picking Errors Were Consistent — Not Random

Most discussions about how to reduce picking errors focus on technology. Better scanners. Automation. Voice picking. System upgrades.

What gets overlooked is that many warehouse picking errors are not system failures — they are the result of process gaps, behaviour patterns, and environmental issues.

In this operation, error rates were not catastrophic. Service levels were acceptable. Nothing appeared fundamentally broken.

But when we analysed the data more closely, a pattern emerged. Picking errors were repeating within the same product ranges, concentrated among specific operators, and more frequent during certain shifts.

This distinction matters.

Consistent warehouse picking errors mean the problem is knowable. And if it is knowable, it is fixable.

Why Most Warehouse Error Reduction Efforts Fail

The default response to warehouse picking errors is usually one of two things: retrain everyone, or add additional checks.

Neither approach improves warehouse picking accuracy in a meaningful or sustainable way.

Retraining without specificity wastes time. Additional checks slow the pick operation and often just move the error downstream — catching it later rather than preventing it.

If you want to reduce picking errors and improve warehouse performance, you need to understand exactly why errors are happening — not just where.

What We Changed: Introducing Root Cause Analysis

The turning point came when we introduced Root Cause Analysis (RCA) into the pick operation.

Instead of asking "What went wrong?" we started asking "Why did this happen — and what allowed it to happen?"

Every picking error was analysed using two simple but powerful methods:

• Fishbone (Ishikawa) diagrams — to map all contributing factors across categories
• 5 Whys analysis — to follow each factor to its true root cause

This shifted the operation from reactive problem-solving to structured warehouse process improvement.

Using Fishbone Diagrams to Understand Warehouse Picking Errors

Each picking error was broken down across five contributing categories:

This immediately changed how errors were understood. Most picking errors were not caused by a single mistake — they were the result of multiple small weaknesses aligning.

For example, a wrong-item pick might involve:

• Similar product packaging (Materials)
• Poor location labelling (Environment)
• Rushed confirmation behaviour (People)

Without structured RCA, this would be labelled as "operator error." In reality, it was a system-level warehouse efficiency issue — and fixing only one of those three factors would leave the others in place to generate the same error again.

Applying 5 Whys to Improve Picking Accuracy

Once errors were categorised, we applied 5 Whys to identify the true root cause. Here is a real example from the operation:

This changes everything. The solution is no longer "be more careful." It becomes: improve slotting rules to separate lookalike products.

That is how warehouse picking accuracy actually improves — not through pressure, but through targeted changes to the conditions that generate errors.

A Critical Insight: Shelf Capacity vs System Capacity Misalignment

One of the most impactful findings from the Root Cause Analysis had nothing to do with operator behaviour at all.

The consequences of this misalignment were significant:

• Overfilled locations increased the chance of picking the wrong item due to poor product separation
• Visual clarity at the pick face was reduced, making identification harder
• Replenishment became inconsistent, compounding the layout problem over time

The corrective actions were straightforward:

• Align system capacity settings with actual physical shelf capacity
• Adjust fill limits to maintain clear product separation at the pick face
• Standardise replenishment practices to prevent overfilling recurring

This single category of fix improved picking accuracy in affected zones without adding labour, technology, or process complexity. It is the kind of improvement that only becomes visible through structured RCA — not through briefings or additional checking.

Building Actions From Root Causes — Not Symptoms

Before introducing RCA, corrective actions were generic:

• Retrain staff
• Remind teams to be more careful

After RCA, actions became targeted and directly traceable to a verified cause:

Each action was linked to a specific, verified root cause. That is the difference between reactive management and structured warehouse continuous improvement.

Patterns Emerged Quickly

Once Root Cause Analysis was applied consistently, patterns became visible within weeks:

• Specific zones generated recurring picking errors — pointing to environmental or layout root causes
• Certain SKUs appeared repeatedly in error records — pointing to product presentation or slotting issues
• Behavioural patterns increased picking error risk under time pressure — pointing to process and coaching gaps

This allowed the operation to focus on high-impact changes instead of applying broad, ineffective fixes across the whole warehouse.

What Actually Reduced Picking Errors

The improvement did not come from a single change. It came from combining:

No new systems were introduced. This was purely process-driven warehouse efficiency improvement.

Tools That Supported the Process

The improvement was not technology-led — but a few tools made the process faster and more consistent:

Three Things to Do This Week

If you want to reduce warehouse picking errors using Root Cause Analysis, start here:

1. Categorise your picking errors

   Stop treating all picking errors as the same problem. Pull your last 30 days of error data and split it into wrong item, wrong quantity, missed item and location errors. The category with the highest share tells you where to focus — and which type of root cause you are most likely dealing with.
2. Identify your top three error locations

   If errors cluster in specific zones, they are not random — they are structural. Map your errors to locations and walk the highest-error zones before doing anything else. Check labelling, product adjacency, lighting and fill levels. The environment often explains what operator-level data cannot.
3. Apply 5 Whys to one recurring error

   Take your most frequent picking error type and follow it through five levels of "why." Keep going until you reach a process, layout or system-level cause — not an operator-level one. That is where the fix lives. None of this requires new tools or budget. It requires structured thinking applied to data you already have.

Picking Accuracy Improves Through Analysis, Not Pressure

Picking accuracy does not improve through pressure, reminders, or technology alone.

It improves through clear analysis, structured thinking, and disciplined follow-through on what the Root Cause Analysis reveals.

Most warehouses already have the data required to reduce picking errors. Very few apply RCA consistently enough to turn that data into meaningful warehouse process improvement.

That is where the difference is made.