Driving Warehouse Management Innovation With Machine Learning

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What Is Machine Learning?

Machine learning is a form of artificial intelligence based on teaching neural networks to identify and adapt to known patterns on the fly.

Looking deeper into the core operations of warehousing, machine learning can apply to facility-wide production standards, optimize for specific metrics, and ensure a continuously improving user experience. 

How Does Machine Learning Tie Into Warehouse Management Systems?

Warehouses are dynamic environments. Machine learning software combines new and incoming information with pre-existing training to further hone pattern-based predictions including picking and routing info, customer demand, storage usage, and similar data sets to ensure all shipments are fulfilled on time. 

Training neural networks with information from workers and historical data can identify ways in which workers can improve. While training machines can initially be resource-intensive, it is often less costly than hiring new human workers.

In the long-term, machine learning can lower operational costs because training can be replicated on machines in a way that is not possible with humans who must be trained individually and on an ongoing basis. 

Warehouse management systems (WMS) are an excellent application for machine learning because consumer demand changes rapidly, causing overstocking and product scarcity in warehouses around the world. Overstocking is a particular concern because warehouse space availability is at 7%, the lowest since 2000.

Machine learning can not only predict these changes on the data side but can also accelerate the pace of adaptation on the production side. Production-side machine learning encompasses storage optimization, picking routes, and even picking. Other facilities are evaluating machine learning and machine vision applications in the docking and loading/unloading carrier shipments. Machines can also be used to optimize shipment aggregation and sorting processes as the technology matures.

How Does Machine Learning Improve Productivity?

Productivity metrics vary between businesses — some may favor order fulfillment over accuracy or vice versa, as well as a wide array of other potential indicators of productivity. Machine learning enables neural networks to break down whatever productivity expectations a business utilizes, recognize patterns, and enhance performance over time. 

Common productivity metrics include:

• Time to fulfillment
• Fulfillment accuracy
• Shipment utilization (fullness of carrier containers)
• Idle time

Since their purchase of Kiva, Amazon has saved more than $22 million in each facility where Kiva robots are utilized for order fulfillment. 

Other companies who reported success with machine learning and warehouse automation include Merck, which achieved a 50% reduction in product carrying costs, and RRD, who helps other companies adapt to the artificial intelligence landscape.  Ken O’Brien, Executive Vice President and Chief Information Officer of RRD, “What sets us apart is our ability to take these technologies — from artificial intelligence to business process automation to machine learning — and demonstrate meaningful business results that improve our own internal processes and do the same for our clients.”

How Can Businesses Implement Machine Learning In WMS?

As recently as 2018, nearly 35% of warehouses hadn’t implemented a WMS. Of the businesses that have already transitioned to include a WMS, machine learning can be implemented in workflows ranging from supply requisition planning through carrier shipment loading. 

Order Management And Supply Requisition

• Machine learning can predict consumer demand patterns. This knowledge allows businesses to adapt order volumes in real-time based on these predictions, preventing costly under- or overstocking. It can also adapt to whatever demand patterns are most relevant to your business. 

Warehouse Optimization

• Aggregating product requires in-depth knowledge of current consumer demand — predictions from artificial intelligence resources keep these predictions on point.
• Warehouses are often optimized — both in terms of cargo placement and in pathing — to keep picking efficient. Machine learning keeps these optimizations up-to-date at all times.

Order Fulfillment

• Machines can choose optimal pathing for picking speed and accurate order fulfillment.
• Shortest path optimization ensures quick order fulfillment, while storage optimization and consistency keep return rates low.

Shipping and Loading

• Cargo loads can be optimized for maximum space usage in carrier vehicles.

Sorting and Aggregation

• Divvying large batches of supplies into smaller portions can mean higher handling difficulty at the sorting facility due to issues getting the right amounts of products to the correct locations. Using machine learning, sorting can be tuned based on current or previous demand such as order management and requisitions. More accurate quantities of goods can then be sent from sorting facilities to distribution centers, lowering handling difficulty.
• Aggregation can package shipments based on their final destination to reduce the number of times packages have to be split apart for final delivery.

Machine learning is a valuable tool for expanding and optimizing warehouse management operations, especially when combined with other technology like IoT connectivity and an effective warehouse management system.

Image Credit: Zapp2Photo / Shutterstock

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