Magnit launches goods availability assessment system

The Company has developed a digital solution to assess and monitor availability of products in stores. The automatic system identifies and performs detailed analysis of delivery-related failures along the entire chain, from the manufacturer to the shelf. Rapid communication of problems directly to stores allows quickly eliminating the lack of products on the shelf, and accurate analysis of the causes of violations helps to avoid such situations in the future.

Krasnodar, Russia (November 5, 2020): With the help of Big Data and Machine Learning, the system analyzes deviations using an extensive library containing data about sales and assortment. Today, it considers around 100 possible reasons for the unavailability of the product on the shelf, and this list is constantly updated.

When the system was piloted, sales in 100 stores chosen for testing have increased by 1.5%–2%, while sales of promo products, which the Company is also working on in a separate stream, have increased by almost 17% in Magnit Cosmetic stores and by 5% in Convenience stores. The effect is achieved by combining the efforts of store personnel, specialists from the headquarters, and manufacturers’ merchandisers to ensure constant availability of goods on the shelves.

One of the most important steps is resolving problems directly in the stores to ensure that customers can always find products they need in sufficient quantity. This directly affects the turnover and sales density. Using a special smartphone app, Magnit informs administrators and sales personnel about problems with availability. The primary focus is on replenishing the shelves with high-demand products. Store personnel resolves the identified problem and register the results of the work done in the app. Today, this staff notification system is already in place in 700 Magnit stores.

Similar notifications are sent to suppliers ‘merchandisers. Besides the information about the absence of goods on the shelf and the risk of virtual stock, specialists have access to information about the current assortment of a particular store, presence of an item on the stock balance, price tags, promo period, and location of products on the sales floor, which enables them to work effectively. The goods availability assessment system is a logical continuation of the work with suppliers that the Company started several years ago. Magnit strives to ensure maximum transparency for its partners and offers them to participate in monitoring availability of their products. Currently, the project involves 50 major manufacturers and about 2,000 merchandisers and covers 10,000 stores and almost 40% of the entire assortment, including Fresh and Ultra-Fresh products.

Rollout of the project across all Magnit stores will happen in stages and take around one year.

“Previously, our departments had to work with different on-shelf availability assessment systems, but the data there did not always correlate with one another and serve the purposes of analysis. Thanks to the new tool, we have combined all information and tools used to process it into a single informational system that can be accessed by any department working in that field, and the information will be the same at all stages. By rolling out this tool to all 21,000 stores, we will be able to recoup lost sales that we suffer today due to the limitations of our on-shelf availability assessments,”—said Ruslan Ismailov, Magnit Deputy CEO and Retail Chain Director.

“Store employees have their work scheduled to the minute. The tool we have developed allows them not only to understand the problems with the product along the entire chain, from the manufacturer to the shelf, but also to reduce the time spent on finding problematic positions. Previously, our personnel could not see the reasons behind declining sales for specific SKUs, which in turn led to growing shrinkage and lost sales. Today, Big Data and Machine Learning can help improve the quality of the offer for the customer. We have already done a lot to set up the system and involve relevant departments and major manufacturers in the process. From this point on, we will work on developing systems based on ML algorithms, rolling out across the entire store network, increasing the number of partners using the solution, as well as developing and implementing additional tools, including image recognition,”—noted Fabian Schaefer, Director of Analytics and Data Management at Magnit.