Store layout and product placement has always been a key aspect for retailers for increasing product sales. Managing product placement of over hundreds of products is a challenging task performed using realograms and planograms. Previously, creating realograms was a difficult task often requiring manual activities. With image recognition the only manual input to the process are the photos taken of the products themselves and of the store shelves. Deep Convolutional Neural Networks automatically recognize the products and their visibility to the customer, helping achieve an increased sales revenue. The decision maker can use the results to generate realograms. A potential added benefit of the solution, if repeated periodically, is the improved shelf stock management. The neural network can learn when a product is in danger of falling out of stock and can raise the necessary alerts to commence a stock refill. This business case demonstrates the product recognition capabilities of a machine learning model.
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Created with KNIME Analytics Platform version 3.6.0
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