Warehouses are perfect for testing the effectiveness of complicated technology like robots and drones because they provide a controlled environment. That is also one of the reasons the warehousing industry is actively dabbling with artificial intelligence (A.I.) and automation.
A company can reduce labor-intensive tasks such as repetitious manual labor, data entry, and analysis as part of an automation initiative. But how exactly can you use A.I. to enhance efficiency and safety in the workplace?
What Is Warehouse Automation?
As the name suggests, warehouse automation automates processes through software and technology such as robotics and sensors. Existing tools, such as inventory management software, are used in conjunction with these advancements.
It is the autonomous transportation of goods into, within, and out of facilities to customers with the least amount of human intervention. For instance, a warehouse employee might put large products onto an unmanned mobility robot.
The inventory is moved from one end of the building to the shipping area by a robot. The movement of the product is recorded by the software to keep all records up to date.
How Does It Work?
This ideal process ensures that your premises’ business-critical procedures satisfy customer demand. It all starts with a warehouse management system (WMS). The WMS automates manual procedures, data collection, inventory control, and data analysis.
These systems work in tandem with other tools to organize and automate processes across a variety of business and supply chain operations.
With this, A.I. will make addressing warehouse optimization concerns easier and less expensive for facilities of all sizes.
Note that warehouse automation doesn’t necessarily require robots or physical equipment. In many cases, it essentially refers to the employment of software to perform tasks that would otherwise be performed manually.
5 Uses of A.I. In Warehouses
For warehouse operating concerns that historically needed expensive and high maintenance expert systems, A.I. is the modern solution. There are many real-world applications for it, and five of the best ones are listed below.
1) Workforce Planning
To ensure that orders are delivered on time and that staffing issues are avoided, optimal labor allocation is critical. Good decisions come from reliable facts and predictions, which are typically dependent on each manager’s unique skill and experience.
Machine learning is used to forecast labor demand and work timelines to enhance results and promote safety. An A.I. system can also create simulations to discover the optimum way to finish the job, eliminating delays and maximizing labor efficiency.
2) Back-Office Automation
Back-office operations can be boosted by using WMS networks with digital process automation technologies. Corporations can isolate applications that cause productivity to decline.
If your marketing and customer service teams require manual data input and extraction across various systems, it may be time for an upgrade. NetSuite implementations can be used to get better customer service functions, sales, and barcoding.
3) In-House Travel Adjustments
Because warehouse employees spend so much of their time traveling within the facility, reducing travel time and obstacles are critical to increasing productivity. Travel is reduced by robots. Plus, A.I. can be applied in places where robotics alone is insufficient.
Large volumes of process data are used by A.I. and machine learning devices to learn how to manage objectives and save commute time. Their smart order batching and pick sequencing features make this possible. This made same-day deliveries possible for companies.
4) Performance Monitoring
Engineered Labor Standards (ELS)-based labor management systems have been around for a long time. However, its data collection process can be too demanding for manual work. With the use of algorithms, A.I. can help with that.
Algorithms learn on correct performance data received from inside activity, taking different factors into account. Its variables include work area, the product, quantity, destination, and more. When changes happen, the models adapt, and the expected results become more accurate.
5) Product Slotting
Product slotting has an impact on labor efficiency, safety, and consistency, but it’s not easy to get it right. That’s why it’s a multi-objective optimization concern as well as a combinatorial optimization issue.
Conventional slotting systems need a lot of engineering work, laborious data updates, and warehouse mapping. Based on activity-level data gathered in the facility, automated apps can understand the spatial attributes and trip distance estimations needed for a slotting strategy.
