There is an increasing trend of companies turning to artificial intelligence (AI) and machine learning to try to gain an edge in a highly competitive world. In the inventory management field, industry leaders have been hard at work developing new AI and machine learning technologies for the last 10 years. Major companies are now using machine learning to optimise business processes in revolutionary ways, especially inventory management which is expected to lead the way of smart automation over the next 10 years.
Taking a Leaf from American Retailers
In America, two large retail companies have implemented machine learning into part of their inventory management systems. In 2016, Lowe introduced LoweBots, 5-foot bilingual retail robots that help customers find items that they are looking for. LoweBot feature voice recognition functions, as well as a searchable computer displays. LoweBot are autonomous retail robots that not only help customers with inquiries, but they also create real-time data using machine learning to scan inventory and look for themes and trends in products and even price discrepancies. This has enabled Lowe to provide customers with the convenience and efficiency of the robot, while employees are left with more time to consult with customers on creative and more difficult projects.
Walmart has also implemented machine learning behind the scenes. Walmart is testing proprietary drones in its large warehouses to improve inventory management. Manually checking inventory and performing routine inventory stock takes is significantly time consuming. It can even take up to a month for employees and is typically prone to errors, but the same task can be completed in 24 hours using sophisticated drones that fly through the warehouse, scan items, and check for misplaced items.
Everything is becoming more connected, making it easier to collect and share data, This acts as a catalyst in machine learning, allowing potentially everything to be measured. By using advanced analytics tools encompassing machine learning we are able to see how things work, how things interact with every other part of an operation.
This data can be collected on inventory stock – from the place of origin, transit routes, times when it is scanned or its location and status are reported using radio frequency tags, coming together in a centralised system. This ability to create a machine learning model has the potential to predict the operation based on the data associated with it. For example, this could dictate probabilities of inventory stock arriving on time or disruption in operations that could result in delays. Where disruptions are found, remedial action can be taken ahead of customer inconvenience, who are more than likely to be appreciative of an email apology when a shipment is likely to be delayed, rather than simply to be kept waiting.
Picking, packing and shipping are all time and resource-intensive processes that affect inventory management and can dramatically impact on a business’s bottom line. These are complex processes that are often dependent on outside forces, such as suppliers, service providers and even weather. These variables make getting it right even more difficult. This is why many people who use inventory management systems are keen adopters of Big Data and machine learning analytics technology. Creating efficiencies in complex systems which involve multiple, often compartmentalised processes is an area where this technology excels. The ability of machine learning is creating savings and efficiencies that are only going to be explored further.