Methods for automating in the warehouse through software and material handling equipment—from tools such as predictive analytics to autonomous vehicles—are evolving fast with the adoption of artificial intelligence (AI) and its subset machine learning.
The integration of AI into warehouse management systems (WMS), labor management systems and other planning software bring efficiencies, allowing warehouse managers to further optimize operations and improve their decision-making, supported by intelligent systems.
The drivers of these trends? In addition to the obvious operational efficiencies from greater levels of automation and AI-powered tools in the warehouse, shippers and 3PLs need to offset dependence on manual labor in the face of rising wages and labor shortages.
In fact, according to the 2024 28th Annual 3PL Study, a report that surveys 3PLs and shippers worldwide, just over 50 percent of 3PL and shipper respondents said they are either currently researching or implementing new technology/automation to offset talent shortages. Another sizeable group reports no plans or are unsure about undertaking such initiatives. Others may have also recently implemented new technologies.
Chances are good that AI is playing a role or will be soon play a role in these new technology initiatives. Research by Modern Materials Handling publication and its research arm Peerless Research Group in the 2023 Material Handing Technology Study found that only about 16 percent of the companies surveyed have started using AI. But greater numbers of companies are either currently evaluating AI technology or plan to evaluate it within two years. Another 22 percent plan to evaluate AI at a future date. Even larger numbers said they either need to know more about AI or have no plans at present to use AI. Separately, about 20 percent of those surveyed are already using big data and IoT technology.
AGVs and AMRs reduce labor
When it comes to material handling equipment, automated guided vehicles (AGVs) operate on fixed routes and stop for obstacles. These automated, autonomous vehicles stay on their routes using markers such as magnetic strips or laser paths. AGVs can be integrated into a WMS to allow the vehicle to receive tasks and communicate with warehouse operations. AI and the incorporation of machine learning algorithms into “smart AGVs” leverage large amounts of data and training models so vehicles can handle more complex tasks, continuously improving decision-making.
Other uses for AI in the warehouse include helping managers optimize picking routes and streamline packing. By analyzing order data, AI can determine the most efficient picking routes, whether made by autonomous mobile robots (AMRs) or human workers, ultimately reducing human travel time and boosting productivity. AMRs can be designed to work in collaboration with warehouse workers or operate independently to actually pick products from shelves, thus automating the task entirely.
Both AGVs and AMRs increase efficiency and productivity, offering reliability for repetitive tasks, and serve to eliminate the physical labor of transporting materials, freeing up warehouse associates for more complex tasks.
Another tool in the warehouse toolbox is predictive analytics, which uses big data and AI algorithms to analyze historical data and predict future trends in demand, inventory levels, or warehouse staffing needs, among other supply chain dynamics. Examples include predicting future outcomes such as potential bottlenecks or maintenance needs on a piece of equipment. This helps warehouses optimize inventory levels, anticipate customer needs, improve the customer experience, and reduce stockouts, among other benefits.
Worley Warehousing is not alone in its quest to examine AI’s role in automation. Our new Worley Technology Park is our innovation lab where we are test piloting technologies such as AI to determine their potential application in our operations.
Here are a couple of ways that Worley Warehousing is already making use of warehouse data and algorithms to optimize operations and make better decisions.
Task interleaving optimizes resources
Task interleaving optimizes the storage and retrieval of products in the warehouse. Task Interleaving is an existing algorithm that is already built into our Worley WMS Work Manager logic. The Worley WMS reads our warehouse storage bays like a map on a real-time basis to look for opportunities that reduce dead-head time while the lift truck is performing its chief task.
Real-time task interleaving optimizes labor in the warehouse to make more efficient use of resources. In the case of a simple task where a driver on a forklift is picking pallets from storage to load on an outbound truck, the driver may receive an interleave message. The system makes a request to the driver to go to a nearby receiving door to unload a unit, take back the product and receive it into a nearby bay location where warehouse associates are picking product for the outbound order.
Always measuring productivity
Our proprietary WMS also tracks productivity (units per man hour) on a real-time basis. Using radio frequency technology, our forklifts time-stamp every move we make, whether unloading a truck or sweeping out a trailer. We track all activities to determine our fully loaded productivity measurements. These measurements are accumulated into a variety of reports that are used daily but also archived for future data analysis.
It’s clear that what’s possible today in warehouse automation is rapidly advancing due to the adoption of AI, machine learning and other technologies. It’s up to shippers and 3PLS to make the best use of it after evaluating which types of automation and technology make sense to use, based on looking at the benefit to their operations, return on investment, and customers.