August 21, 2023

Daka reduced
overstock by 47%

The Challenge

Daka’s traditional replenishment system caused stockouts in some stores, excessive inventory in others, leading to broken size arcs, lost revenue, and an unsatisfactory customer experience. 

The Solution 

Daka utilises WAIR’s AI Replenisher to forecast sales at the SKU level for its 18 physical stores, allowing the implementation of customised replenishment strategies. The ERP system automatically adjusts the minimum and maximum stocking limits based on customer demand trends and sales performance.

The Results 

Traditional vs AI Replenisher

Forecast accuracy:    49% vs. 96%
Overprediction:           51% vs 4%
Underprediction:        0% vs. 0%

Amount increase of SKUs rightfully predicted: 299.000

About Daka 

Daka is a popular sports retailer in the Netherlands. It operates 18 stores located across the country and has an online shop. Daka offers the most comprehensive and best selection of sports and lifestyle articles. Daka sells 636 thousand products of the relevant seasons a year with 8k different styles and 35k SKUs, providing a variety of options to ensure shoppers can always find what they need, to enjoy their sports even more.

The Challenge

Consequently, the central warehouse experienced stockouts, unable to replenish empty stores, while other stores had excessive inventory. Leading to mid term stockout in season. This necessitated the redistribution of inventory, resulting in a costly and time-consuming process

The Solution 

Daka implemented the AI Replenisher, equipped with a fashion retail-specific Deep Learning model, to accurately predict and address the seasonal influences on their sales. This forecast is made with WAIR’s Deep Learning model. It considers external data sources and retailer-specific information such as sales data, product details, local demand, and individual store performance, to make SKU-level sales predictions. This allows Daka to allocate stock based on revenue potential and replenish their physical stores which are heavily influenced by seasonality accordingly.

By continuously learning from customer behavior, the model suggests incremental replenishment adjustments that collectively result in significant improvements to sell-through rates, reduction of overstock, and revenue growth.

The AI Replenisher seamlessly integrates with a company’s ERP software, allowing teams to harness the full capabilities of WAIR directly from their familiar ERP interface. 

Daka  successfully implemented the AI Replenisher within their Microsoft Dynamics 365 system, utilizing ACA Fashion Software’s XPRT solution, in a quick time frame of just 7 days.

Now, the ERP receives intelligent sales predictions, which are then utilised to automatically adapt the minimum and maximum stocking limits. Daka’s merchandisers have the flexibility to override recommendations, establish business rules, and switch between algorithmic and manual control for any store, category, or SKU based on their specific requirements.

The Results

With the AI Replenisher Daka can now implement a replenishment strategy that significantly increases the full-price sell-through rate for all products. What’s more, this optimisation process is completed much faster compared to their previous method of only focusing on the high and low performers.

Daka reserves 18% of their total stock for replenishment purposes. By significantly enhancing forecast accuracy from 49% to 96% and drastically reducing overpredictions from 50% to 4%, Daka has made an enormous improvement.

Daka now expects to accurately forecast the sales of 610.000 SKUs with the AI Replenisher, which is an incredible improvement compared to the previous correctly replenished 312.000 SKUs.

Traditional replenisher



Over prediction


under prediction

The AI Replenisher



Over prediction


under prediction

50% to 4%

Reduced overstock

49% to 96%

Forecast replenishment accuracy

0% to 0%

Understock - meaning zero risk when implemented

About WAIR

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.

Calculation sheet 

Below some additional calculations on the added value of the AI Replenisher at Shoeby. It’s important to note that these calculations make certain assumptions (conservatively estimated), as there are numerous other variables that can impact its performance.

For instance: 

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) 49,1% 50,90% 0,05
AI Replenisher 96% 3,5% 0,43%
SKU Sales (new season & collections) Forecast Accuracy Overprediction Underprediction
Traditional replenishment (Min/Max) 312.586 323.986 318
AI Replenisher 611.669 22.482 2739
Improvement in % 196%
Improvement in SKU 299.084 -301.504 2.420
What happens with only +1% 2.991
Sell-through improvement 0,5%
Original Price (incl VAT) € 57,02
Below the line profit - EBT (incl VAT)* € 170.537
Reduced SKUs sent from Warehouse to Store -10% € -30.150
Picking costs (Warehouse) per SKU € 0,10 € -3.015
Transportation Costs per SKU € 0,19 € -5.729
Distribution Savings** € -8.744
Total € 179.281

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.

We are more than happy to explain more about these calculations and our case studies please feel free to contact us

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