Machine Learning Powers Smarter Warehouses

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 and recognize patterns, regularities, and interdependencies to achieve greater productivity.

Here are five key warehouse functions that can benefit from machine learning:

  1. More accurate inventory – Experts say that many companies have excessive inventory costs 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 fifty times faster than manual processes. This results in 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 the bar code or use RFID sensors to relay the inventory count back to the ERP system. In 2016, Walmart reportedly began using drones to manage inventory in their warehouses, and today, Amazon deploys 200,000 robots in many its warehouses around the world.
  2. 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 movements required to fulfill orders, are minimized, so there are fewer opportunities to make mistakes or damage goods. In addition, workflows are defined for picking multiple orders simultaneously. At some warehouses, including market leaders Ocado, Walmart, and Alibaba, all the picking is done by robots powered by machine learning.
  3. Improved quality control. Order picking is the last touch point between a company’s 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 past returns and customer complaints to identify items that have higher than normal error rates. To enhance 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.
  4. Faster, more reliable deliveries. Speedy deliveries are an integral part of customer satisfaction. Late deliveries can result in returns, poor product and/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 necessary, and catch errors early. In addition, input from historical ERP delivery data, weather conditions, delays at ports, or even employee strikes, can be scanned to predict risks in delivery times.
  5. 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 data from ERP systems. 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, 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 vast inventories, a broad product range, where each product requires different storage locations, picking procedures, and handling needs. Machine learning, however, has 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. Today, data scientists require in-depth knowledge of warehouse 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 tremendous potential and the added functionalities that have already been realized, machine learning can become an essential building block for the smarter warehouse of the future.

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