Streamlining warehouse processes: stock accuracy first

Stock accuracy describes how many of the checked bin locations hold exactly the quantity the system reports. What matters is the view per location and per item. A report on the total value of the warehouse is misleading, because surpluses and shortages cancel each other out: two items each off by forty units in opposite directions add up to zero in the value balance and still produce two orders that cannot be shipped. Anyone introducing the metric therefore first defines what counts as a match and which tolerance applies to bulk goods, cut-to-length material or small parts.

The consequences of poor stock records rarely show up in the warehouse itself, but in sales and purchasing. Promised goods are not there, the line is shipped later, freight is paid twice. Purchasing reorders because coverage was calculated on a wrong figure, or it does not reorder because the system shows stock that nobody can find. In between sits search time: staff walk the aisles, ask colleagues, open pallets. That time appears in no statistic, because it passes as ordinary warehouse work.

In wholesale and retail, inventories tie up a considerable share of current assets (Statistisches Bundesamt). That alone justifies looking at data quality before any other measure: as long as stock figures are wrong, a metrics report on turnover frequency or days of coverage only creates apparent certainty. The order is: count first, remove causes, then optimise.

Many warehouses that have grown over the years run on experience rather than a location system. Long-serving staff know where things are and find goods faster than any list. That knowledge is valuable and at the same time a risk: it cannot be transferred, cannot be analysed and is unavailable during holidays or sickness. New staff need weeks before they can pick on their own, and seasonal helpers remain permanently dependent on supervision.

A location system starts with a unique, clearly legible label per bin: aisle, rack, level, position — for example A-04-12. It matters that the label belongs to the location and not to the item, so that it survives changes in the range. Items with high pick frequency belong at grab height and close to the packing bench, rarely needed items may sit far back and high up. This allocation by pick frequency shortens walking distances more than any change to the picking sequence.

The transition does not have to happen overnight. A practical start is with the highest-turnover items: these receive fixed locations, are labelled and stored in the system, while the rest stays unchanged for the time being. After each counting round further items follow. For companies with several sites or an attached carrier it pays to choose one consistent location logic from the start — in logistics operations this is the basis for any later analysis across sites.

Picking absorbs the largest share of working time in many warehouses. Three adjustments work reliably here: ordering the lines along the walking route rather than by item number, combining several small orders into one round, and confirming each line at the moment of the pick. The first two save distance, the third keeps stock figures current and makes shortages visible while they can still be clarified.

What matters is how deviations are handled the moment they occur. If a picker finds only four instead of six units at the location, there are two paths: the difference is silently accepted and the order passed on short, or the device records the actual quantity, creates a replenishment task and flags the location for a recount. Only the second path produces information. The first produces a stock figure that still shows six units although the location is empty.

The short dialogue shows the principle: the deviation is not clicked away but translated into two follow-up tasks — replenishment and recount. Over a few weeks this produces a list of locations where differences occur repeatedly. That list is more useful than any overall rate, because it names the place of the problem: a poorly labelled bin, two similar items side by side, or a packaging unit stored incorrectly in the system.

Instead of one large count in December, many companies do better with small, regular rounds. The basic idea is to classify the range by value and pick frequency: A items are counted several times a year, B items once or twice, C items rarely. This is complemented by event-driven counts — when a location runs empty on paper, when a deviation occurs during picking, or when an item has seen no movement for a long time.

The organisational advantage is considerable: a round of thirty to fifty locations can be completed in the first hour of a shift without stopping operations (project experience). Counting is done by people who know the area, the device specifies the location, and the entry is made without showing the expected figure. That last point is essential: anyone who sees the expected number confirms it more often than they count it.

Such a report needs no additional software, only two tables from the existing inventory management system. Two views are worth having: the largest individual deviations and the locations that stand out across several rounds. The second group points to structural causes, the first usually to isolated posting errors. Going through both lists monthly gives a sound picture after one quarter — considerably earlier than with an annual count.

The order of the measures matters more than their scale. Starting with the purchase of devices often digitises a workflow whose content has never been settled. The reverse route works better: measure first, then set the organisational rules, then add technology. The following steps can be carried out alongside daily business in most companies, provided one person is responsible and a fixed weekly slot is reserved for it.

After these three months there is usually enough material to decide on further steps: replenishment control by reorder point, connecting the parcel carrier, batch or serial number tracking. Which step pays off follows from the measured deviations rather than from a general recommendation. A structured process analysis sorts the findings, quantifies the effort per measure and makes the sequence traceable.

What emerges applies beyond the warehouse: stock accuracy is not a state that is established once, but the result of rules that are followed in daily business. A company that posts every movement where it happens needs a major correction far less often — and can rely on the figures that sales and purchasing work with.