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Replenishment
Fashion

Margin optimization in the fashion industry: challenges and strategies for managing product surpluses

A significant challenge for fashion industry retailers arises when certain stores are burdened with surplus products that aren't selling, while demand for the same products exceeds expectations in other stores. During the season, customers may find themselves unable to purchase the products they came for, due to inadequate stock alignment with the actual sales potential of specific products in specific stores.

This mismatch leads to various consequences, among the most severe of which – besides lost sales – is the inability to sell a product at its initial price, resulting in margin loss. How can this be prevented? 

Inter-store and store-warehouse transfers

Overstocking some stores and understocking others often stems from an imperfect allocation of products at the beginning of the season. This problem is addressed through inter-store transfers and transfers along the store-warehouse line. The intention is to move the product to another store or sales channel (e.g., online store) where the likelihood of its sale is higher. However, this scenario has several negative implications. 

Firstly, transfers are time-consuming. Packing, transportation, and unpacking of goods typically take 2-4 days, or even longer if any part of the process falls on a weekend. During this time, the product is unavailable to customers, increasing the risk of lost sales. 

Secondly, store employees are involved in carrying out this process, reducing their time for customer service. In the long term, when such situations occur frequently, it can lead to worsened customer experiences and a weakening of their loyalty to the brand, which is already challenging to build in current market conditions. 

Thirdly, transfers incur significant costs, especially in terms of logistics operational costs. Even if shipments are outsourced and contractors offer competitive rates, each transport reduces the product margin. It’s worth noting that inter-store transfers are also made in response to out-of-stocks in the warehouse. This particularly applies to the most popular products, where demand exceeds supply and they quickly disappear from shelves. Due to long lead times, products often cannot be reordered during the season. Therefore, to replenish the assortment and exposure in selected stores, transfers based on sales results in individual stores are used. 

Expert dilemmas 

Experts responsible for inter-store transfers face a difficult challenge. On the one hand, transfers seem necessary as they increase the chances of selling goods. On the other hand, they are associated with risk. What if the observed demand changes, and the product in another store also remains unsold? There is no shortage of situations where products circulate between stores, losing margin and incurring additional costs, thus reducing business profitability. 

Moreover, the analysis of transfers, i.e., finding answers to questions such as “which product, from which store, and to which store (or sales channel) should be redirected?” is time-consuming and must be performed frequently, often several times within one week. This consumes valuable time of experts who could otherwise use it to manage exceptions or difficult cases that require their specialized skills and experience. 

Sales and outlets 

Overstock in stores and warehouses is even more concerning as the end of the season approaches. The threat of being left with a large inventory of products for which there will soon be no demand, and which will be out of fashion in the same season next year, can be a nightmare for those responsible for replenishment. 

Traditionally, retailers resort to solutions such as product sales. Price reductions sometimes reach 70%, which unequivocally results in significant margin loss. Alternatively, surplus products are sent to outlets – where discounts can reach levels of 80-90%. Each of these scenarios represents a loss for the company. 

Advanced data analysis and margin protection 

At Occubee, we aim to ensure the increase of product sales at the initial price. We support retailers in reducing overstocks and limiting the number while increasing the efficiency of inter-store transfers. 

Both in the case of overstocks and transfers, the saying “prevention is better than cure” holds true. Allocation, followed by replenishment, is crucial. In the fashion industry, both processes follow a different logic, which we can reflect in our system. Each of these processes can be optimized using advanced data analysis and artificial intelligence. 

Sales forecasts generated by Occubee at a high level of detail – for a specific product in a specific store – serve as the starting point. For retailers with dozens of stores and tens of thousands of SKUs, this task alone is impossible to achieve manually. In the next step, based on forecasts and additional information (e.g., store inventory, goods in transit, merchandise assortment), the system creates store demands, and then picking orders (sometimes also called warehouse orders), which allow products to be redistributed to stores according to their individual sales potential. This ensures that stock in each store matches the demand identified in the local market. This, in turn, leads to a reduction in overstocks without sacrificing sales. 

Better planned initial stocking of stores, followed by their replenishment, reduces the need for costly inter-store transfers. This does not mean their complete elimination, but it significantly reduces their scale. Additionally, we aim to increase the efficiency of transfers. To achieve this, we utilize recommendation algorithms, the results of which are presented in the form of automatically generated reports. These reports contain suggestions for products to be transferred, along with information on which products from which store to which store are worth redirecting. Thanks to them, experts in a given fashion network save time on analysis, only verifying and accepting or modifying proposals generated by Occubee. 

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