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Replenishment
Fashion

Finding balance: optimizing collection width and depth in fashion

The limited space in stores presents several challenges from the perspective of those responsible for allocation and replenishment in the fashion industry. One of them is finding the optimal balance between the depth and width of collections. A balance that will provide a diverse offering attractive to customers while avoiding individual pieces of products on shelves and racks. Is finding the golden mean possible in this field?

Glossary

In this document, the width of the collection is understood as the number of products (model-colors) available within a given product category. Example: the spring-summer clothing collection in a store includes 10 models of women’s blazers, and within each of them, 3 color variants are offered. This means that the width of this collection is 30 products. 

The depth of the collection is understood here as the number of units of a given product (model-color) in all sizes combined. Example: in the store, there are woolen women’s blazers in gray (one specific model in one color): 1 unit in size S, 2 in size M, 2 in size L, and 1 in size XL. This means that the depth of this collection is 6 units of the product, on average 1.5 units per size in which this gray blazer is available. 

A wide collection as a magnet for customers

It seems that among most retailers, there is a belief that the collection should be as wide as possible. This stems from the desire to offer customers the most diverse and rich assortment possible, as it allows for satisfactory sales. What lies at the heart of such an approach? 

Firstly, it’s about ensuring that customers visiting physical or online stores feel a large selection of products. On the other hand, retailers try to cater to the tastes of as many customers as possible, and the likelihood usually increases with the number of different products offered. 

Additionally, by proposing many complementary products in the assortment, retailers increase the chances of increasing the value of the shopping basket. Visual aspects and merchandising policy are also important—shelves and racks filled with diverse products attract more attention than a monotonous display, which may be perceived as poor and unattractive. 

Shallow collection and lost sales

The problem with this approach arises when combining a wide commercial assortment with limited store space. Often in practice, this means that a wide collection is also a shallow collection. There are many products available in stores, but in individual units.  

Another issue is the approach to the available sizes of each product in the store: should we ensure availability of the full size range at the expense of the depth of the collection, or do we accept the lack of products in extremely small and extremely large sizes, allowing for greater depth of typical sizes? 

Maintaining individual units of a given model-color in a particular size can result in periodic product shortages, thus leading to lost sales. Therefore, it is crucial to find an answer to the question of the proper proportion between the width and depth of the collection. Moreover, ideally, this proportion should be determined for each product but also for each store to reflect the individual character of the local market. 

Collection planning based on expert knowledge

Experts dealing with the depth and width of collections plan store assortments based on their own knowledge, experience, and insights from historical sales analysis. This often happens a year before the goods are introduced for sale, further complicating the task. On the other hand, decisions regarding store assortments affect the availability of goods in stores.  

If the allocation of a new collection is inadequate to consumer demand, it may result in lost sales and/or the need for inter-store transfers, which are always costly and time-consuming. Moreover, there is a risk of creating a snowball effect — mistakes made in the allocation stage can disrupt the replenishment process, thus increasing their impact on the profitability of businesses. 

However, experts do not have to act alone in their efforts. They are supported by various IT systems that allow for drawing conclusions from data and automating processes. One such tool is Occubee. 

How do we do it in Occubee?

In Occubee, we gather and analyze sales data and many other variables using machine learning models and probabilistic models. We compare historical sales with information on the availability of a specific product in a specific store, which allows us to fully understand the sales characteristics throughout the season. 

As a result, we generate insights that allow us to determine the sales potential of a given product category or product in a specific store. This, in turn, facilitates decision-making for experts regarding the optimal width and depth of the collection. 

At the same time, as part of servicing allocation and replenishment processes, we address the merchandising policy assumptions of a given retail network. We do this, among other things, by including recommendations for commissioning “must-have” products that must be available in the store to ensure the required width and visual consistency of the collection. 

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