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Health crisis: what impact on supply management in points of sale?


The health crisis is accelerating the transformation of physical points of sale. With the development of e-commerce, consumers can buy anything, anywhere, anytime. E-commerce pure players can offer hundreds of products in a single product category. The multiplication of choices induced by this model translates into a drop in productivity of the physical store space and an increase in markdowns at the end of the season to sell unsold products.

Traditional retailers must transform to move closer to the ideal model, i.e. a single channel with physical and digital contact points; and one of the levers, in addition to omnichannel, is the reconfiguration of their store network. This reconfiguration has a significant impact on the way they build their product offering.

The health crisis is accelerating the resizing and differentiation of stores

The adaptation of point of sale networks to the health crisis takes several forms, depending on the brand. The first consequence is the acceleration of the closure or the divestiture of the less profitable stores. Another trend is the downsizing or resizing of certain stores by reducing the surface area: the breadth of the resized store’s offer must then be reduced and adjusted to the store’s specific clientele.

We are also observing more and more a pooling of points of sale with other brands marketing complementary products or services: a simple corner or a complete department of the store is dedicated to another brand, which makes it possible to increase the flow of customers in the store and improve store profitability.

Another consequence is that retailers are developing plans to relocate stores to be in closer proximity to consumers, given the accelerated growth of teleworking and the tendency of customers to reduce long distances for their purchases. Brands that were historically located in shopping centers far from city centers are developing a network of physical stores in areas of higher density, which again necessitates reducing the offer to take into account the reduction in the sales area.

Finally, some brands are creating new concepts derived from the main concept in order to broaden and renew the target of customers. This could be, for example, a move upmarket, an extension of the range or, on the contrary, a specialization within the usual offer.

Optimizing the construction of the product offer is becoming more and more necessary

This acceleration of the reconfiguration of the store network reinforces the need to optimize the differentiation of the offer to take into account this increasingly complex segmentation. Assortments need to be even more localized and consumer-centric. It is more complex to differentiate the offer by store, when these have very different formats and characteristics, than to manage a listing that is generally common to all the points of sale.

Artificial intelligence makes it possible to respond better to these new challenges. The algorithms can identify product attributes explaining differences in performance between products and thus recommend the ideal range according to the store segment. These attributes are for example the price level of the product, its shape, its material, its color, its brand, its season, its life cycle, the categories of customers to which it is addressed, etc. The aim is to differentiate the assortment locally according to consumer expectations.

To do this, the store segmentation needs to be finer. Retailers today group stores based on a combination of a limited number of attributes such as volume and location. We can now take into account many more criteria, and in particular those linked to the specific expectations of customers, to cross-reference them with the characteristics of the products.

Space and presentation constraints must also be taken into account: the minimum presentation which makes the offer credible, but also the obligation to offer the heart of the offer, that is to say the products that correspond to the brand’s image and which must be available in all stores; or on the contrary regional specificities. It is therefore a matter of finding the best compromise between a truly localized offer and a set of objectives and constraints. This complexity requires more planning effort than only large data analysis and algorithms using artificial intelligence can help manage.

On the other hand, as we can see, the management of the offer is based on a lot of internal data: for example the characteristics of the products defined in the PLM or the data on customers, coming from loyalty systems; or external, social networks, competitor price surveys, trends by product category from market research. Only a certain degree of automation can control all of this data. The exploitation of these new sources of internal and external data considerably improves the retailer’s understanding of customer needs and allows better detection of the forecast performance of products and the ideal assortment by store.

This automation also allows faster decision-making during the process of building and updating the offer. Repetitive activities are processed by the system, which not only allows for a faster decision, but also a focus on exceptions, and a better quality of judgment.

On the other hand, the current situation – the health crisis – underscores the importance of being able to cope with the uncertainties of forecasting and planning. For example, you have to have the flexibility to quickly adapt the offer to the new context. For this, it is necessary to have real-time visibility on the effective performance of the offer to make quick decisions such as adding or removing certain items in certain stores.

Optimizing the offer has an immediate impact on the retailer’s performance

From a better localized offer, it is also easier to predict the depth of the offer, that is to say the purchase commitment that will be crucial for the financial performance of the retailer. The product’s life cycle, the stores assigned to it, its seasonality curve, its forecast performance, its minimum presentation, data which are defined in the assortment plan, allow better forecasting of the quantities sold and thus the quantities purchased.

Improving the construction of the offer and its adequacy with consumers is therefore decisive in the overall performance of retailers: increase in sales because we choose the best performing products, decrease in lost sales because we have enough purchased, better inventory flow and reduction in markdown to dispose of unsold products thanks to a better match of the offer to consumers.

For all of these reasons, supply management is one of the current priorities of many retailers, the health crisis having served as an accelerator.


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