Real Example of Reducing Warehouse Errors at a Company

At eight in the morning, the warehouse is already operational, but the first complaint arrives: the customer ordered two pieces, but only received one. Meanwhile, sales see that the system shows stock, but according to the warehouse, the product is already out of stock. At the end of the day, someone reviews the picking lists again, corrects the invoice, and tries to find out where the process went wrong. real example of warehouse error reduction doesn't necessarily start with the introduction of a new system. Often it begins with finally taking a precise look at what happens during an order's journey.

For a growing commercial enterprise, this situation was no longer a one-time inconvenience. Orders from the webshop, B2B, and phone were all directed to the same warehouse, but not in the same manner. Pickers worked from printed lists, inventory movements were recorded partly in an enterprise resource planning system and partly in spreadsheets, and urgent orders were communicated to each other via messages.

The error rate didn't seem catastrophic yet. However, several dozen corrective shipments, credits, and customer reconciliations accumulated monthly. A bigger issue was that no one could say for sure where the errors originated and which change would bring actual results.

What did a warehouse error really mean?

The term 'warehouse error' is too broad. If all discrepancies are put into one category, fixing them becomes guesswork. Initially, management suspected mainly incorrect product dispatch. However, detailed investigation revealed several distinct problems.

Sometimes the picker took a product from the wrong shelf. Other times the correct product was placed in the box, but in the wrong quantity. There were also cases where the system inventory was inaccurate even before picking, so the order could only be fulfilled with a substitute or delay. From a complaint perspective, these are all errors, but their causes and handling are not the same.

For four weeks, the company recorded every discrepancy according to the same criteria: order type, product group, warehouse zone, shift, error type, discovery location, and necessary rework. This was not to evaluate individual employees. The goal was to make the process visible.

A significant pattern emerged after the first week. Most errors didn't start at picking but earlier: when order information from multiple channels, in different formats, was added to the warehouse list.

Real example of warehouse error reduction: uncovering the process

During the analysis, the question wasn't who made the mistake, but how many times the same information had to be passed on, interpreted, or re-recorded for an order. This difference is crucial.

Webshop orders automatically entered the administrative system, but the picking list for the warehouse was only generated from a morning export. Separate lists were made for orders arriving in the morning. Urgent orders were often communicated via chat messages, and then someone manually added them to the printed document.

Some B2B orders were manually entered by salespeople. If the customer used their own item number, product identification was often done based on experience. With a similarly named product stored nearby, even a small misunderstanding was enough for incorrect picking.

Inventory inaccuracies also had more than one cause. Some products were handled in broken packages, but unit conversion didn't occur the same way in all systems. For example, a carton appeared as a carton in procurement, as a piece in the webshop, and sometimes in both units in warehouse records. The discrepancy didn't arise on inventory day but built up continuously.

It became clear that encouraging faster work wouldn't solve the problem. Colleagues were already managing many exceptions mentally. The process made them the link between information, while they should have been focusing on the accurate physical movement of goods.

Not every fix is a technological project

Several changes followed from the identified problems, but they didn't all happen at once and not solely as system developments. First, they standardized what counted as an urgent order, who was authorized to request it, and how the request reached the warehouse. Chat messages ceased to be an official ordering channel.

Then they organized the product master data necessary for warehouse work: clear names, barcodes, storage locations, packaging units, and substitution rules. This is a less visible task than introducing a new application, but it directly reduces situations where an employee has to guess.

They also reviewed the storage logic. Products often ordered together and with similar packaging were too close to each other. Rearranging a few shelf locations, clearly readable location identifiers, and a separate return zone already reduced the chance of mix-ups.

Only then did the improvement of system connections come to the agenda. The goal wasn't an 'automated warehouse' but to ensure that order status, inventory movement, and picking tasks were based on the same current information. The warehouse shouldn't have to work from morning exports and manually supplemented lists during the day.

Control points should be where errors are still cheap

The company introduced a simple but consistent control principle. The system required product identification at picking, and a second control point was applied before packaging for higher-value or frequently mistaken product groups.

It wasn't practical to fully review every order twice. That would have tied up significant capacity and created a new bottleneck with higher traffic. Instead, the control was shaped by risk: more attention was given to higher-value products, items existing in multiple similar versions, and orders picked from new or temporary storage locations.

This approach also provides an important managerial lesson. Error reduction doesn't mean placing every process under maximum control. The goal is for the right control to work at the right point and not unnecessarily slow down the entire operation.

What did the numbers change?

Initially, the company recorded some discrepancy in about 3.6 percent of dispatched orders. This included incorrect products, incorrect quantities, post-modifications due to stock shortages, and documentation errors. Three months after the process changes, this rate dropped to 1.4 percent.

The number is favorable on its own, but it wasn't the only result. The time spent investigating complaints significantly shortened because the order, picking, and packaging status became more traceable. There were also fewer daily interruptions: the warehouse manager had to immediately resolve a situation arising from missing or ambiguous information less often.

The biggest change was felt by the employees. The performance pressure didn't increase, but the number of decisions they had to make based on incomplete data, routine, or individual memory decreased. This predictability is also valuable in training: a new colleague doesn't become effective solely by learning who to ask.

What to consider before choosing a solution?

If discrepancies regularly occur in a warehouse, it's worth first understanding the cost and path of errors. It's not just about returns or credits, but also customer service time, reshipping, inventory planning distortion, managerial interruptions, and customer trust.

It's also worth examining how much information the warehouse receives from other systems and how much of it needs to be manually rewritten or interpreted. Where the same item number, quantity, or status appears in multiple places, discrepancies will eventually arise. A new system may not be immediately necessary. Perhaps appointing a data steward, clearer ordering rules, or a well-defined system connection will solve the problem.

Lasting improvement begins when a warehouse error appears not as human negligence but as an operational process signal. Every recurring discrepancy reveals something about how information flows and where the system demands too much from people. It's worth understanding this before quickly choosing new technology.