As areas around the world began to adapt to the pandemic, like millions of others, my family and I had to search for alternate ways to shop for and purchase necessities. While shopping online is easier than ever – even in urban areas like where we live – delivery of an order is just now becoming less complicated and more reliable. As more people were quarantined in their homes, delivery may have slowed for a few days, but most retailers were able to stay open and deliver orders.
That is a remarkable achievement. While demand was spiking to unprecedented levels, warehouses that were fulfilling orders suddenly had to operate with fewer people working farther apart to maintain social distancing.
I give credit not only to the teams executing online operations but also to a less-noticed piece of the puzzle: processors and software that enable warehouse robots to identify patterns and learn continuously from the activity taking place around them. These robots collaborated with their human partners to sort and send orders from warehouses to every corner of the planet.
The use of machine learning in warehouses and factories has been on the rise for a few years, but the pandemic has been a wake-up call. Among the lessons: Consumer demands are changing faster than production lines across many industries are capable of handling. Some companies shut down production to help contain infection risks for employees working close to each other on assembly lines. Without workers to assemble products, some operations came to a halt.
But other companies had a better experience. Businesses that invested in unmanned robots guided by machine-learning algorithms were able to react creatively, swiftly and productively. In the warehousing and distribution sector, for example, companies that depended on humans to drive forklifts were sidelined, while those using unmanned robots “driven" by machine learning algorithms kept warehouses humming.
Machine learning is a form of artificial intelligence designed to recognize patterns in enormous amounts of data generated by electronic images, video, text and speech. Algorithms identify patterns and turn them into rules that guide robots to make intelligent, safe, secure and autonomous decisions, such as where to insert the right rivet in the right place at the right tension on an assembly line. Or algorithms can guide a fleet of warehouse robots to receive and store products, choreograph order fulfillment, optimize inventory, and deliver goods on a more continuous basis. It’s also the same technology that is enabling more autonomy in our automobiles.
Those capabilities are made possible with the combination of processors, software and specialized algorithms.
In my role leading processors strategy and products for our company, I continually monitor trends in the market and talk regularly to our customers. Here are three insights I’ve gained about the role that machine learning will play in the way we work and meet customer needs:
The right investments can help you prepare for the future
As businesses look to the future, they should consider investing in machine learning tools that can anticipate challenges before they arise. For example, predictive maintenance can help businesses monitor and interpret data from sensor networks and detect when equipment might fail so they can proactively schedule maintenance repairs and avoid costly downtime. Networks of sensors and processors can be used for predictive maintenance in factories, building automation, smart homes, automotive and vehicle battery management systems, and other applications. Regardless of your industry, making an investment in digital transformation can help companies continue operations and be agile to changing circumstances.
Machine learning can help optimize retail operations
Machine learning is making an impact far beyond the factory or the warehouse floor. Look at grocery stores, for example. While there aren’t many robots in the aisles when you buy a loaf of bread today, retailers are beginning to test the waters. In some stores, robots monitor the shelves, connect to cloud-based inventory-management systems and notify employees when items are out of stock, in the wrong location or priced incorrectly. They can identify a spill and even clean it up. One example is a grocery store chain in China that uses robots as shopping carts. An autonomous cart follows a shopper – avoiding other people and objects – and scans items as they are placed in it.
Robots can make filling orders more efficient
In areas such as inventory management, machine learning algorithms can take into account customer demand for a particular product to guide an unmanned robot to store the goods on shelves closest to the receiving docks, where the products are ready for pickup and delivery to end users. When orders come in, the unmanned robot instantly knows where the item resides in the warehouse and the shortest and safest route to move it for pickup.
These advancements are not novelties. Software and a new generation of processors are making it easier to get started with machine learning and robotics. In some cases, the robot system with machine learning technologies can pay for itself just one year after installation. The key to making machine learning and robotics more mainstream is developing affordable, practical innovation.
Through machine learning, robots are being transformed from science fiction to science. They are able to adapt quickly to change, reduce costs and improve the customer experience. Manufacturers and logistics companies that fail to adapt to change and build more agile systems will fall further behind those that embrace the technology.