How to reduce picking complexity

Picking may sound an easy activity: taking an item, box or container from a shelf and bringing it to a loading bay, workstation or pallet area.

However, it is a complex operation as it requires both immediate item identification and very accurate picking and handling skills.

There are several factors that increase picking complexity, including:

• Increasing numbers of codes to be handled in the warehouse, both due to an ever-higher level of product customisation and an increase in product obsolescence rates.
• Variation in picking list profile: while the number of references to be managed increases, at the same time the size of articles is constantly decreasing.
• Need to serve the market through multiple distribution channels - each with its own unique requirements.
• Reduction of the average order lead time expected by the customer.
• Increase in urgent deliveries and increased volatility of the number of daily orders due to high peaks and sales promotions.
• Increasing amount of information associated to products to be tracked during order processing (batches, deadlines, etc.) and to be communicated to customers with data interchange flows.

Picking is a critical process in warehouse operations, and in this article we will look at some ideas that can help you reduce the complexity.

A revolutionary scenario

The exponential growth of the e-commerce sector is revolutionising warehouse picking operations, as it requires much faster and more accurate picking of any type of item, even the smallest item.

DHL, estimated that 80% of warehouses worldwide do not adopt any type of automation and only 5% use innovative automated systems.

Among the most advanced organisations are the large logistics and e-commerce market leaders, who have the resources and financial interests to invest in automation.

Amazon, in 2020 already, had installed over 200 thousand AMRs and is currently developing together with NVIDIA a new generation of autonomous systems, where object recognition is aided by Artificial Intelligence.

Specifically, using artificial intelligence, can significantly accelerate picking process automation, improving efficiency, accuracy and speed.

AI is used to recognise objects, labels, barcodes or the classic QR codes needed to guide robots to pick items.

It is also used to develop advanced algorithms that can plan and optimise the AMR systems' paths in the warehouse, reducing the time and distance needed to pick items.

Finally, artificial intelligence enables the robots to navigate safely within the warehouse, avoiding obstacles and interacting with their surroundings.

How AMRs can reduce picking complexity

AMR robots (Autonomous Mobile Robots) are systems that can reduce the picking complexity in several ways:

1. AMR robot-assisted picking: AMR robots can work in cooperation with operators to simplify the picking process. They can transport trolleys or racks containing the required items directly to the operators. In this way, operators no longer have to physically move between different picking points, reducing travel distance and time spent picking.
2. Fully automated picking: AMR robots can also be used to perform picking completely autonomously. Through advanced navigation algorithms, the robots can locate, pick and transfer items to the desired destinations. This reduces the need for operator intervention in the picking process, simplifying overall handling.
3. Route optimisation: AMR robots can be programmed to optimise picking routes. Using route planning algorithms, the robots can determine the most efficient route to pick items from the warehouse. This helps to reduce the distance travelled by the robots and accelerates the overall picking time.
4. Integration with warehouse management system (WMS): Integrating AMR robots with a warehouse management system (WMS) improves picking efficiency. The WMS can send instructions to the robots to indicate which items to pick and where to deliver them. The AMR robots, in turn, communicate with the WMS to update the picking status and request new tasks. This integration enables a smoother workflow and optimised management of picking tasks.
5. Data monitoring and analysis: AMR robots can collect data on the performance of the picking process, such as time spent, throughput and error rate. This data can be used to monitor overall performance and identify areas for improvement. Through data analysis, modifications and optimisations can be made to further reduce picking complexity.

Conclusioni

One of the most critical activities within the warehouse is picking, as it requires a considerable amount of resources, both technological and human, and above all, strongly affects customer satisfaction, in terms of accuracy and timeliness in receiving items.

The direction of corporate policies, oriented towards cost reduction and improved order fulfilment service, has further complicated picking activities, generating a considerable increase in deliveries, urgent requests and customisation.

For this reason, companies today are focusing on the development of modular solutions and technologies to improve picking speed and accuracy.

Flexible, scalable and accessible systems that simplify overall management, interact and work side by side with operators, can optimise the overall operational efficiency of the warehouse.

Sources

• I sistemi avanzati di picking: quali sono e quando utilizzarli – Logistica Efficiente
• L’insospettabile complessità del picking. Claudio Corbetta. “Logistica, organizzazione, sistemi e metodi per la Supply Chain”. Anno LIII – maggio 2023, pag. 63.