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02_Guided_Analytics

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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.

Used extensions & nodes

Created with KNIME Analytics Platform version 3.6.0
  • KNIME Core Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 3.6.0

  • KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 3.6.0

  • KNIME Image Processing Trusted extension

    University of Konstanz / KNIME

    Version 1.7.0

  • KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 3.6.0

  • KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 3.6.0

  • KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 3.6.0

  • KNIME SVG Support Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 3.6.0

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