Several years ago, while I was serving as a store manager at one of Metro’s largest regional wholesale centers, I encountered a significant case of stock management.
At Metro, due to the scale of operations, the store structure included department managers responsible for various product categories. These managers were incentivized with performance bonuses linked to Key Performance Indicators (KPIs), one of which among others was the shrinkage level.
During my initial weeks at the store, I observed an alarmingly high level of losses in the Dairy/Delicatessen department, primarily due to wastage. This wastage was disproportionately higher—almost tenfold—compared to the other five stores I had previously worked for. As a result, I placed the department under my daily control.
To address the issue, we adjusted our forecasts, implemented product return options, and dismissed some suppliers’ merchandisers. These measures brought the department’s losses closer to the acceptable range for a regional store. Despite these improvements, the department still exhibited an unusually high stock value, suggesting that some issues were still there.
My team and I overlooked a critical indicator: we failed to detect the irregularities in stock reporting. For instance, an item, a butter, showing 200 kilograms in 250-gram packages remained in stock for weeks without raising red flags.
My team and I overlooked a critical indicator: we failed to detect the irregularities in stock reporting. For instance, an item, a butter, showing 200 kilograms in 250-gram packages remained in stock for weeks without raising red flags. Given Metro’s role as a wholesaler to independent traders, such stock levels per SKU were theoretically possible, and the sales dates were fresh, which did not arouse suspicion based on the standard reports.
A few months later, during the annual stocktaking, we flagged this SKU for a second recount due to its high stock value. This recount was conducted not by the Dairy department staff but by a floor manager, who confirmed the stock was missing. In the next days, we re-counted several products, uncovering clear cases where the department manager was aware of the stock loss or wastage but failed to report it accurately. While some write-offs occurred weekly, they were insufficient to reflect the true stock levels.
This situation highlighted a common issue: intentionally maintaining inaccurate stock records to create a “foggy” picture, driven by the fear of reporting losses and the desire to secure performance bonuses. The discrepancy fell through the gaps of our existing management reporting system. We monitored “articles without sales”, “old stock”, “negative stock”, and “low stock” articles, but the department manager managed to stay in-between those control cycles. The extensive assortment range of Metro stores (over 30,000 SKUs) made it nearly impossible to identify such issues through manual checks alone.
At that time, I dreamed abount a tool capable of identifying stock inconsistencies, but in the mid-2000s, AI-driven stock analysis solutions were not available.
At that time, I dreamed abount a tool capable of identifying stock inconsistencies, but in the mid-2000s, AI-driven stock analysis solutions were not available. Otherwise, we would detect the issue much faster. With minimum efforts. With the same management people, most of those were not “analytical” type of persons. Without “pen&paper technology”. Fully mobile. Even without human interventions – automatic stock adjustments.
In conclusion, I highly recommend exploring Akuret Solutions, a cutting-edge tool dedicated to routine stock management. Akuret Solutions offers capabilities that could have significantly streamlined our processes and improved accuracy.
Hope this helps,
Pavel Ryukhov
Paul Ryukhov
CEO/COO Ledo Supermarket
