AI and machine learning aren’t just for self-driving cars, smart speakers, and pilotless jets – they can make seemingly mundane tasks like inventory management quicker and easier as well. What might a potential solution for the Polish market look like? Let’s take a look!
Are you ready for a robot to help you find the materials for your next home improvement project? You should be, because robots, machine learning, and AI are already teaming up to change the way stores manage inventory and improve the way we shop. A simple scan of the news on any given day shows us that machine learning and AI are already being used to make critical medical decisions, pilot autonomous vehicles, and programmatically recognize faces. Media sites depend on machine learning to provide song or movie recommendations, and marketers use it to learn more about purchasing behavior. Now, it’s set to change the way retailers think about inventory management.
How can AI and machine learning revolutionize inventory management?
It’s already happening – two of America’s best-known big box retailers are using intelligent robots to manage their inventory. Hardware giant Lowe’s has introduced its “LoweBot” in 11 stores throughout the San Francisco Bay Area – customers operate these autonomous retail robots by talking to them or using the touchscreen to find items inside the store or ask questions. The LoweBot not only helps customers, but also creates real-time data by using computer vision and machine learning to scan inventory and look for patterns in product or price discrepancies. Meanwhile, electronics retailer Best Buy and Par Systems have teamed up to introduce Chloe, an automated machine that not only retrieves products from shelves but also tracks shopping patterns that are analyzed and used to refresh inventory.
The ITMAGINATION difference
At ITMAGINATION, we’re getting a head start on bringing the latest inventory management technologies to the Polish market. Our team has come up with an innovation that’s aimed at the tangible business needs of one of our clients, a leading media house and retail products chain. The client struggles with misplacement of their products – customers put books away in the wrong places, which forces workers to spend an inordinate amount of time looking for them and putting them back. On top of that, customers often order books online that employees have problems locating. Our team prepared a solution architecture idea featuring a machine learning algorithm that uses convolutional neural networks, images of shelves, planograms, and product catalogs to detect and identify misplaced products on the shelves, saving time and making products easier to identify.
Stock it. Find it. Done.