David Greenlees, Managing Director, Priority Software UK presents five key warehouse functions that can benefit from machine learning
As part of its warehouse management functionality, ERP systems typically track information about products as they’re picked, packed, and shipped, so that orders can be filled faster and more accurately. However, real-time data can now be collected and analyzed using smart sensors, cloud technology, and self-learning algorithms to generate new insights. Powered by machine learning, ERP systems can power smart warehouses where systems learn to recognize patterns, irregularities, and interdependencies to achieve greater productivity.
Here are five key warehouse functions that can benefit from machine learning:
More accurate inventory
Many companies have excessive inventory costs simply because they don’t know what they have in stock or where it’s located. Drone scanning, for example, powered by machine learning, can count inventory 50 times faster than manual processes. This means greater accuracy, time and manpower savings, fewer safety risks, and minimizes system downtime. Deep learning technology enables drones to recognize images based on a network of learning layers to visually inspect product labels, photograph barcodes or use RFID sensors to relay inventory count back to the ERP system. In 2016, Walmart reportedly began using drones to manage inventory in their warehouses, and today, Amazon deploys some 200,000 robots in its warehouses around the world.
Quicker order fulfillment
Machine learning models use input from ERP systems to design more efficient warehouses. Product location in a warehouse is determined based on buying frequency and products that are typically purchased together. Using machine learning, the number of steps required to fulfill orders are minimized, so there are fewer opportunities for errors or to damage goods, and workflows are defined for picking multiple orders simultaneously. At some warehouses, including global giants, Walmart and Alibaba, all the picking is done by robots – and it’s all powered by machine learning.
Improved quality control
Order picking is the last touch point between the warehouse and the customer, be it the end-user or distributor. It’s here that mistakes can harm a company’s reputation. Machine learning algorithms are used to analyze previous returns and customer complaints to identify items that have higher than normal error rates. To improve quality control, continuous error analysis, backed by machine learning, can be used to place products in different locations, re-tag for faster identification, or classify using colors instead of numbers to reduce picker fatigue. An ERP system powered by machine learning can also be used to warn pickers about items that often get mixed up, and require special attention.
Faster, more reliable deliveries
Speedy deliveries are an integral part of customer satisfaction. Late deliveries can result in returns, poor product or vendor reviews and disgruntled customers. Machine learning can analyze fleet performance to optimize distribution channels, and ensure that goods are delivered on time, while sensors can track the exact location of ships, containers, and trucks to adjust routes if needed, and catch errors early on. Even input from historical ERP delivery data, such as weather conditions, delays at ports, or even employee strikes, can be scanned to predict risks or delays in delivery times.
Better managed demand shifts
Machine learning algorithms can forecast actual demand to ensure that warehouse personnel and processes are equipped to handle bursts of orders based on historical ERP data. Shifts in demand can be caused by busy periods, such as Christmas or Black Friday, or seasonality, the demand for specific products, such as lawn furniture or snow blowers. Residential heating and air conditioning provider, Lennox, for example, use machine learning to sift through hundreds of thousands of product files to identify “clusters” of those with similar seasonality profiles to enhance their marketing and sales.
Machine learning provides valuable insights for warehouses that handle tremendous inventories – extensive product range, where each product requires different storage locations, picking procedures, and even handling. Machine learning, however, does have its limitations, as the technology is still evolving. Drones, for example, although growing in popularity, can only see what’s in front of a pallet – they can’t see inside a bin. To fully optimize today’s warehouse, data scientists require in-depth knowledge of its workflows to build effective machine learning models and interpret their results. In parallel, ERP systems need to be open, flexible and scalable to become the mediator for the tsunami of data that is ingested, analyzed, and shared.
Based on its enormous potential and added functionalities, many of which have already been realized, machine learning can become a key building block for tomorrow’s smart (and smarter) warehouse.