https://store-images.s-microsoft.com/image/apps.10958.257b0e28-1117-4c93-bd3e-1c941a5153ed.c10981f3-9c3d-4087-b3d8-0a12a383a46c.5aa9e346-4863-43f7-910b-af3343dc5381

SMART Shelf Recognition

yayıncı: SMART business LLC

Neural network for effective merchandising

Neural network for effective merchandising to monitor goods on the shelf layout according to planogram. Solution improves merchandising and replenishment operations for retailers and CPG.

SMART Shelf Recognition is using Computer Vision on-shelf data and Advanced Analytics scenarios to improve category performance, in-store on-shelf field data collection automation and brings the understanding of the competitor's marketing mix.

What it gives?

Each manufacturer of goods has its own standards for laying on the shelves. The correctness of the calculation of the goods depends on their sales, and monitoring this process is an important business task.

Objective control of the shelf. Manual audit of outlets brings mistakes due to inattention, lack of time, incompetence and bad mood of the performer.

Reduced audit time: in the market from 50 to 20 minutes, in traditional retail from 5 to 2.5 minutes. The speed of the audit depends only on the speed of photography.

Objective data not only by SKU, but also for the shelf share. Thus, competitive goods of other categories do not impact the result, even if they stand side by side.

How can technology be used to solve business problems?

It can be presented in the form of a mobile application installed on a merchandiser smartphone, or as a program on a computer in the office of the manufacturer, where real-time analytics is displayed at point of sale, where the layouts with products are correctly or incorrectly designed. You can also configure notifications that will be sent to a user-friendly application if the camcorder has "spotted" an incorrect layout at the point of sale.

Bir bakışta

https://store-images.s-microsoft.com/image/apps.42375.257b0e28-1117-4c93-bd3e-1c941a5153ed.c10981f3-9c3d-4087-b3d8-0a12a383a46c.efa9ee5a-4f5c-4681-8310-237c97825713