An IoT/IoE enabled architecture framework for precision on shelf availability: Enhancing proactive shopper experience

In today´s customer centric world, enabling shopper experience is a very important factor in maintaining customer loyalty. On shelf availability (OSA) of the product that the customer is looking for is a key metric in ensuring shopper experience. Current manual approaches to enabling OSA have gaps and might fall short of customer expectations and impact bottom line of retailers. We propose an automated Internet of Things/Internet of Everything (IoT/IoE) enabled model for ensuring OSA. The use of sensors and big data analytics helps process data from various information sources to generate information about out-of-stock products on the shelf and notifies the store associates to take actions to stock up the products on the shelves. The precision level of such approaches could vary and hence we look at multiple sensor configurations that can be used in the retail segment to address the OSA needs. In addition, we also propose an algorithm for enabling OSA and an integrated architecture to precisely identify and predict trends towards out-of-stock situations. The algorithm also emphasizes on multi stage cross verification to reduce false alarms. Such a model is capable of improving operational efficiencies, reducing operational cost and improving shopper experience.