A new pricing system uses deep learning algorithms and real-time data to price produce and reduce food waste.
We have seen many solutions that tackle the issue of food waste, such as grocery stores introducing new pricing systems to ensure products close to expiring will sell. One example of this is a real-time pricing solution that uses radio frequency identification (RFID), electronic shelf labelling, and a dynamic pricing engine to offer cheaper prices. Another example is an automated discount rack that reduces prices for expiring products both online and in store. A new solution, called RapidMathematix, also aims to reduce food waste using deep learning algorithms and machine vision.
RapidMathematix provides automated retail pricing for fresh produce, changing the prices of produce depending on freshness, market conditions and competition. This ensures that customers get their money’s worth of what they pay and that stores are able to reduce food waste by offering discounts. Data collected from various sources about freshness, location, product and demand level is processed by RapidMathematix’s algorithms to offer the most accurate prices. Additionally, the system is connected to electronic shelf labels, enabling it to calculate and recommend prices in real time.
IoT devices are also integrated into the RapidMathematix system and are used to gather information from inside the retail store. For example, information is collected from products, the shop floor, shelves and customer devices. The data offered by the system can also be used to negotiate prices with vendors, beat competitor prices, and give users more control over their pricing decisions.