The abilities of Artificial Intelligence are far reaching. We see ourselves surrounded by voice recognition software like Siri or Alexa, self driving cars being used by Uber and autonomous robots being used. The warehouse industry, like the factories around the world, has seen a lot of automation in the last few years. In a competitive industry such as this, AI can have a strong impact on improving the inventory accuracy by using machine learning, performance by using robots and efficiency in following orders by using speech recognition. Machine Learning can be used in decision making process. A huge amount of data can be collected on a daily basis about items ordered more often, inventory levels and shipping. Machine learning can learn from this data to predict when and which item to re-stock and how often. Robots can be used to perform tasks like loading and unloading a pallet, where to bring certain items, who to report to, how to maneuver without bumping into other robots or humans. We have witnessed a lot of advancement in this section, which you'll read in a bit. Speech recognition software can be used to train the machines in the warehouse to follow employees' orders with minimal effort, or as is commonly known, a hands free experience.
Key Performance Indicators When you employ a person at your organisation, you evaluate his or her performance under a given set of criteria. Similarly, any AI application you choose, has to be driven by performance metrics that are commonly called Key Performance Indicators (KPIs). In warehouse management, these KPIs could be anything ranging from productivity, order accuracy, inventory accuracy to fulfillment time etc. Productivity: Warehouses that do not use picking methods guided by a system experience a high difference in the productivity of most and least productive order picker. In this case, machine learning can be deployed to analyze the bar code of items and order frequency against the route the most productive pickers took and come up with the best possible sorting order of items that will reduce the travel distance of pickers while avoiding congestion. Efficiency: The location of high velocity products (products that get ordered too often) should be optimized to minimize congestion while improving picking efficiency. When the number of SKUs (products) is too high, it can be difficult to optimize their location according to given criteria. In this case, software can be used to help operators slot the SKUs in the optimum positions. Aggregating data for KPIs to fare well is a challenge as data from various warehouse locations has to be input into the system: data cleaning and data integration is a task in itself.
Real World Examples But enough of technical information. What are some of the latest class apart automated warehouse systems? Ocado, a British online-only supermarket, designs highly automated warehouses. In their older warehouses, groceries are unpacked in a linear manner. Deliveries are unpacked onto crates, crates are placed on conveyor belts which carry them to the shelves where human operators pack the items per order. The newer way has become efficient space wise. Crates are now stacked in piles, upto 17 boxes high! Items not related to each other can be placed adjacent to each other, the position is decided algorithmically - rarer purchases are placed on the bottom. These crates can be grabbed by the bots and taken to a new location or dropped down a vertical chute to a picking station. Humans are involved in the packing and unpacking while robots maneuver around the warehouse 24 hours a day. Here's a sneak peek into an Ocado warehouse:
Another example is Alibaba's smart warehousing system where robots do 70% of the work. They are capable of carrying up to 500 kilograms! They're equipped with special sensors that help them avoid colliding into each other. On running out of battery, they carry themselves to the charging station and a mere 5 minute charge can power them for around 4 hours!
End Notes Automation is the heart of innovation in every industry. With the steadily growing e-commerce sector, faster and more flexible systems will be required. Masses of available data can be used by AI algorithms to analyze the processes and suggest solutions to optimise them.