With more customers settling nicely into the hybrid shopping mode, the onus has fallen on eCommerce merchants to fulfill orders accurately. With rapid advancements in technology, automated order-picking systems have emerged as a solution to meet this challenge, revolutionizing the way goods are retrieved and prepared for shipping.
But what is AI-driven order picking, really? The answer to this lies in how AI is used to optimize the entire picking process from start to finish. Artificial Intelligence runs on algorithms to process and correctly slot volumes of data across inventory, levels, order volume and worker productivity. Not only that, AI-order picking even identifies potential bottlenecks in the system and recommends adjustments to increase efficiency. Think of it as the Jarvis to your Ironman, i.e. having a super-smart assistant who's always one step ahead and constantly looking for ways to make things better.
If you're looking to improve the efficiency and accuracy of your order-picking process, AI-driven systems are the way to go!
What is Automated order picking?
Using technology and equipment to automatically locate and pack products for order fulfillment is known as automated order picking.There are various kinds of automated order-picking systems available, ranging from straightforward conveyor belts to sophisticated robotic systems. If you've ever wondered how your online orders are completed so swiftly and precisely, it's because e-commerce, retail, and logistics use automated order picking to boost speed and precision.
AI-powered systems are becoming increasingly popular in optimizing the order-picking process. These systems utilize advanced algorithms and machine-learning techniques to streamline the process of order fulfillment. To understand how this works, consider the following example:
An online retailer processes thousands of daily orders, and employs an AI-powered system to streamline the order-picking process. The system uses data on order trends, product availability, and inventory levels to predict future demand. This enables the retailer to prepare for high-demand products and ensure they have enough stock to fulfill orders. With this information, the AI system generates efficient picking routes for warehouse workers, which reduces the time and effort needed to complete each order. Moreover, the system employs computer vision technology to identify products in the warehouse, allowing workers to locate each item quickly and efficiently.
Overall, the AI-powered system enables the retailer to fulfill orders more efficiently and quickly than it would have been possible had it been through traditional methods. By optimizing the order-picking process, the retailer can reduce costs, improve customer satisfaction, and increase revenue.
According to a report by Grand View Research, the global automated order-picking market size was valued at USD 6.24 billion in 2020 and is expected to grow at a CAGR of 9.9% from 2021 to 2028. This figure underlines the expectation for faster, efficient and accurate order processing in the e-commerce and logistics industries. eCommerce businesses that use offline stores to process online orders would particularly benefit from an eCommerce system that speeds up warehouse functions, making it easier for staff to correctly liquidate inventory, locate the right products and match them to orders received.
Some examples of companies that use AI-powered order-picking systems include Amazon, Alibaba, and DHL, all of which have reported an average of 43% improvements in order processing times and accuracy.
Methods employed for order picking
In a previous post, we had elaborated on a few strategies that companies use to handle order picking, which will be reiterated here:
With this technique, the warehouse is divided into various zones, and each picker is given a certain zone from which to select items. Once the picker is done picking, the items are transferred to a central place for consolidation. The picker will only pick objects from their designated zone. Large warehouses with high volume orders benefit greatly from zone picking because it cuts down on travel time for choosing things.
With this approach, the picker will collect all the products needed for several orders in one trip around the warehouse after being given a list of orders to fill. This technique reduces the amount of time spent moving around the warehouse, making it effective for warehouses with a high volume of small orders.
Using this approach, orders are grouped into waves and picked in bunches throughout the course of the day. Each wave will have a set of orders to complete, and the picker will make one journey across the warehouse to gather everything needed for those orders. Wave picking balances the workload throughout the day, making it perfect for warehouses that receive both small and large orders.
This technique uses a conveyor system to transport goods to pickers positioned along the conveyor line at various places. Each picker is in charge of selecting particular products from the conveyor and handing them off to the picker after them. This technique cuts down on the amount of time pickers must spend traveling and is useful for orders with big volume.
AI enhances the effectiveness of all these order picking methods. In order to cut down on the number of visits that employees must make, it can analyze data to predict which items are likely to be ordered together and arrange them accordingly. This also makes the routing of personnel and equipment more efficient, cutting down on travel time and increasing production. Furthermore, AI-powered systems may keep track of the picking procedure in real-time and notify management of any problems that require attention.
Amazon is a prime example of a company that has invested heavily in AI-powered order picking. One of their most innovative solutions is the use of the "SmartPac" system. This uses machine learning algorithms to analyze customer orders and determine the most efficient packing solution for each one. By considering factors such as product size, weight, and fragility, the system can recommend the optimal box size and packing materials, thereby reducing waste and improving efficiency.
How is AI helping in order picking?
Artificial Intelligence (AI) has become an indispensable tool in the realm of order fulfillment, particularly in the domain of order picking. With the aid of sophisticated algorithms and cutting-edge machine learning techniques, AI is facilitating significant enhancements in the areas of process efficiency, accuracy, and speed for companies that incorporate it into their operations.
Here are some of the ways that AI is helping to improve order picking:
Order picking is a critical step in the fulfillment process that directly affects customer satisfaction and the company's ability to meet demand. It involves retrieving and assembling items from the inventory to fulfill a customer's order.
If a product is out of stock, the order picker will not be able to retrieve it, and the order will be delayed or even canceled, leading to lost sales and customer dissatisfaction. Therefore, accurate demand predictions are essential for ensuring that the right products are available when customers place orders, which makes the order picking process more efficient and improves fulfillment times.
To avoid this situation, demand predictions to anticipate which products will be in demand. By analyzing past order data and market trends, companies can adjust inventory levels accordingly, ensuring that the right products are available when customers place orders. This improves fulfillment times and customer satisfaction while also maximizing profits for the company.
Optimizing warehouse layout
Inefficient warehouse layouts can lead to workers spending a lot of time walking back and forth to pick up orders, resulting in longer processing times, higher labor costs, and decreased productivity. AI can help by analyzing various factors such as order volume, product size, and worker traffic patterns to create the most efficient layout for the warehouse.
Optimizing the layout of the warehouse can help to reduce travel time for workers and increase the efficiency of order picking. This could involve placing frequently-picked items near the packing stations or organizing products based on their size and weight to reduce the effort needed for picking and transporting.
Routing and scheduling
One of the biggest challenges that companies face in the delivery of goods and services is the efficient routing and scheduling of orders. Inefficient routing and scheduling can lead to longer delivery times, higher transportation costs, and a negative impact on customer satisfaction.
AI can optimize the routing and scheduling of orders by analyzing data on delivery locations, traffic patterns, worker availability, and other factors such as weather conditions and road closures. This can help to recommend the best routes for workers to take to complete deliveries in the shortest amount of time possible, reducing travel time and costs while improving customer satisfaction. On the Easyecom platform, you can make use of priority, pincode and distance-based routing to optimize delivery routes and shorten delivery timelines. Through automation, you can halve down the man hours and double the delivery efficiency and accuracy, thereby keeping your customers happy! That’s what it comes down to, right?
In traditional order-picking processes, there is often a lack of real-time monitoring which can lead to delays in order fulfillment, errors in picking items, and overall inefficiencies.
Real-time monitoring using AI can involve various techniques such as computer vision, sensor technologies, and machine learning algorithms. These technologies enable businesses to monitor the order-picking process in real time, providing managers with instant alerts and notifications of any issues.
For example, computer vision can be used to identify items that have been incorrectly picked or placed in the wrong bin. This information can then be immediately communicated to the picker and their supervisor, allowing them to quickly correct the mistake. Similarly, sensor technologies can be used to monitor the movement of goods within the warehouse, enabling managers to identify bottlenecks in the order-picking process and make necessary adjustments.
Overall, AI is helping companies to streamline their order-picking processes, reduce errors, and improve customer service. As AI technology continues to evolve, we can expect to see even more advanced systems that further optimize order fulfillment processes.
How manual and AI-driven order picking differ
Future of AI in Order picking
AI is set to transform the future of order picking by improving accuracy, increasing productivity, and enabling real-time tracking and monitoring of inventory and order status. As AI-powered systems become more advanced, we can expect to see the following developments:
By using AI-driven systems, order-picking accuracy can be enhanced through the analysis of patterns in customer behavior, inventory levels, and product demand. This leads to better identification of the products needed to fulfill an order, reducing the likelihood of incorrect or incomplete shipments.
Streamlined order fulfillment
AI algorithms can analyze data from multiple sources in real-time to optimize the order picking process. This can reduce errors, minimize delays, and increase the accuracy of order fulfillment. With AI-powered systems, warehouses can operate at maximum efficiency, resulting in increased output and faster delivery times.
Advanced robotics such as autonomous mobile robots (AMRs) and drones can revolutionize the order-picking process by efficiently navigating through the warehouse and picking orders without human intervention. This technology can improve accuracy, reduce labor costs, and enhance overall productivity in the order picking process.
Real-time tracking is a crucial aspect of AI-based order picking. It allows companies to track and monitor inventory levels, order status, and shipment information in real-time, giving them valuable insights into their operations. By leveraging this information, companies can make informed decisions and respond to changes quickly, improving their efficiency and productivity.
Predictive analytics uses AI algorithms to analyze large volumes of data, enabling companies to anticipate future demand patterns and optimize their inventory management strategies. This results in significant cost savings and improved productivity as companies can avoid overstocking or stockouts.
Integration with IoT
The integration of AI with IoT in order picking enables companies to monitor and track inventory levels, detect any equipment failures, and optimize the performance of their warehouses. This results in more efficient and accurate order picking, reducing the risk of errors and delays.
D2C brands, B2C and B2B sellers that use automated order picking can quickly analyze customer data and adjust their order fulfillment processes to meet individual customer preferences. This customization can lead to more satisfied customers who are more likely to return and recommend the company to others.
As eCommerce companies continue to adopt AI-driven order-picking systems, we can expect to see significant improvements in the efficiency and effectiveness of supply chain operations, resulting in increased competitiveness and customer satisfaction. The use of inventory tracking for order-picking automation has enormous potential and is critical to the growth of modern warehouse management.
The Easyecom solution is user-friendly, scalable, and cost-effective, enabling precise order fulfillment with a unified platform that streamlines processes and boosts efficiency. As more e-commerce companies adapt to these shifts, automation will undoubtedly continue to revolutionize the supply chain industry going forward.
1) What are the three order-picking systems?
The three basic order-picking methods are wave picking, batch picking, and zone picking. Most warehouses use one or a combination of these three methods.
2) How is AI used in warehousing?
Artificial Intelligence is used in assistive and wearable forms to optimize man efforts across the warehouse. Some applications include
1. Natural language processing to activate voice-picking in areas inaccessible to workers. They can work hands free without having to enter hazardous areas.
2. Smart glasses that are camera-equipped and use computer vision to automatically recognize barcodes.
3.Machine learning employs algorithms to "learn from experience" and make practical warehouse decisions. It detects sensor data patterns and recommends faster replenishment of nearly out-of-stock items, shorter walking routes, and better inventory positioning.
3) What are methods used in order picking?
Order picking methods are defined in terms of
(a) pickers per order - the number of pickers that work on a single order at one time;
(b) lines per pick - the number of orders a single item is picked for at one time;
(c) periods per shift - the frequency of order scheduling during one shift