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OFM was using a traditional replenishment system to keep the initial distribution intact without adapting stock levels based on demand.
OFM transformed their stock management with WAIR’s AI Replenisher, leveraging accurate sales forecasts and seamless ERP integration to strategically allocate stock, improve sell-through rates, and achieve significant growth.
Traditional vs AI Replenisher
Forecast accuracy: Â Â 66% vs. 95%
Overprediction:Â Â Â Â Â Â 34% vs 5%
Underprediction:Â Â Â Â 0% vs. 0%Amount
Amount increase of SKUs rightfully predicted: 299.000
OFM was using a traditional replenishment system that was not efficient in maximising sell-through rates. The system set stock levels (minimum and maximum) based on the initial distribution of products and maintained those levels throughout the season. However, this system did not consider how well items performed in different stores. This led to a stock push strategy while a pull strategy based on demand was preferred.
 As a result, the central warehouse was often out of stock for certain products and many stores there was clear overstock or understock. This led to the need to redistribute inventory, which proved to be a costly and time-consuming process. This situation always causes increased expenses for packaging, handling, and transportation. Moreover, when items are constantly
The SolutionÂ
Based on a simulation of OFM’s retail operations, OFM revolutionised their replenishment process by adopting WAIR’s AI Replenisher, a cutting-edge solution that optimises revenue through efficient stock replenishment. By leveraging WAIR’s proprietary deep learning model designed specifically for fashion retail, OFM receives accurate sales forecasts to restock their physical stores. The AI model takes into account both external data resources and retailer specific data like sales data, product information, local demand, and individual store performance, enabling SKU-level sales predictions. This empowers OFM to strategically allocate stock based on revenue potential, ensuring their physical stores are replenished in accordance with demand.
Through constant analysis of customer behaviour, the model consistently recommends incremental replenishment adjustments. These cumulative adjustments have a substantial impact, leading to notable improvements in sell-through rates untill sales, a decrease in both overstock and understock, and significant revenue growth.
The AI Replenisher seamlessly integrates with a company’s ERP software, providing teams with convenient access to WAIR’s complete functionalities directly from their familiar ERP interface.Â
OFM successfully incorporated the AI Replenisher into their Microsoft Dynamics 365 system using ACA Fashion Software’s XPRT solution.
Via the implementation, the ERP system receives intelligent sales predictions, enabling automatic adjustments to the minimum and maximum stocking limits. OFM’s merchandisers have the flexibility to override recommendations, establish customised business rules, and seamlessly switch between algorithmic and manual control for specific stores, categories, or SKUs, catering to their unique requirements.
With the AI Replenisher, OFM transitioned from a standard rule-based replenishment process to implementing unique replenishment actions per SKU/store.
Over prediction
under prediction
Over prediction
under prediction
WAIR is an Amsterdam based startup founded in 2019 with the idea that top-shelf technologies should be accessible to retailers of all sizes. With a team of retail experts and machine learning engineers working side-by-side, WAIR designs solutions that allow businesses to add automated intelligence without breaking processes or platforms and with low investment and high ROI.
1% increase in sell-through rate for full price is direct added revenue and margin.
The initial 1% (as showcased below) serves as a baseline figure. However, considering the demonstrated improvement in accuracy and the absence of any decrease in underprediction, this represents only the minimum expected outcome. The potential increase in the sell-through rate percentage can range anywhere from 1% to 20%.
| Calculation sheet | Forecast Accuracy | Overprediction | Underprediction |
|---|---|---|---|
| Traditional replenishment (Min/Max) | 65,96% | 33,90% | 0,14% |
| AI Replenisher | 95,3% | 6,82% | 0,3% |
| SKU Sales (new season & collections) | Forecast Accuracy | Overprediction | Underprediction |
| Traditional replenishment (Min/Max) | 212.364 | 109.144 | 451 |
| AI Replenisher | 306.956 | 14.037 | 966 |
| Improvement in % | 145% | ||
| Improvement in SKU | 94.592 | -95.107 | 515 |
| What happens with only +1% | 946 | ||
| Sell-through improvement | 0,3% | ||
| Original Price (incl VAT) | € 98,22 | ||
| Below the line profit - EBT (incl VAT)* | € 92.908 | ||
| Reduced SKUs sent from Warehouse to Store | -10% | -9.511 | |
| Picking costs (Warehouse) per SKU | € 0,10 | € -951 | |
| Transportation Costs per SKU | € 0,19 | € -1.807 | |
| Distribution Savings** | € -2.758 | ||
| Total | €95.666 |
Additional Revenue /Margin (incl VAT)* – This is the expected additional revenue from the AI Replenisher. Distribution Savings** – Assuming 10% fewer overpredicted SKUs, which will save on distribution costs. Added benefits like: FTE automation, instore efficiency, customer experience improvement are not taken
At WAIR, we consider the 1% as the minimum benchmark due to the ever-changing dynamics of the fashion retail industry, making it challenging to precisely quantify our contributions. Our approach is to underpromise and overdeliver, ensuring we exceed expectations in our efforts.
