The e-commerce giant said it expects the technology to cut the number of damaged items sent out, speed up picking and packing, and eventually play a critical role in the company’s efforts to automate more of its fulfillment operations.

Introduction

In its ongoing pursuit of operational excellence and customer satisfaction, Amazon has turned to the power of artificial intelligence (AI) to tackle a significant challenge: identifying damaged goods within its vast inventory. By implementing AI technology, the e-commerce giant aims to reduce the number of damaged items shipped out, expedite the picking and packing processes, and ultimately drive the automation of its fulfillment operations.

The Current Challenge

Amazon's warehouse workers play a crucial role in ensuring the quality of goods by inspecting them for signs of wear and tear while handling the picking, packing, and storage tasks. However, this process can be time-consuming, particularly given the large volume of items and the fact that most products are in impeccable condition. Jeremy Wyatt, director of applied science at Amazon Robotics, emphasizes that the cognitive demand of identifying rare instances of damage can divert workers' attention from their primary responsibilities.

The Significance of Addressing Damaged Goods

Though Amazon estimates that less than one in 1,000 items it handles is damaged, the sheer scale of the company's operations means that even a small percentage amounts to a substantial number of affected products. With Amazon handling approximately 8 billion packages annually, minimizing the occurrence of damaged goods is crucial for enhancing the customer experience and maintaining the retailer's reputation for high-quality service.

Utilizing AI to Improve Quality Control

In response to these challenges, Amazon has embarked on an AI-driven solution. The implementation involves training the AI system using a vast array of images depicting undamaged items as well as damaged ones, teaching the technology to discern between the two and flag any products that do not meet the criteria for perfection. Christoph Schwerdtfeger, a software development manager at Amazon, reveals that the AI system has proven to be three times as effective as human workers in identifying damaged items.

How the AI System Works

During the picking and packing process, goods are carefully selected for individual orders and placed into bins that traverse an imaging station. This station acts as the AI system's "eyes," employing computer vision algorithms to evaluate whether the items exhibit any damage. If the AI detects a broken or flawed item, the bin is diverted to a worker who conducts a more detailed inspection. However, if everything appears fine, the order progresses seamlessly towards being packed and shipped to the customer.

Scaling Up AI Implementation

Amazon has already implemented this AI technology at two of its fulfillment centers and plans to roll out the system at an additional 10 sites across North America and Europe. This expansion signifies Amazon's commitment to leveraging cutting-edge technologies to enhance its logistics operations and streamline supply chains. The e-commerce giant joins a growing number of companies across various industries that are embracing AI to optimize workflows and decision-making in the logistics sector.

Automation's Impact on Warehouse Operations

As automation continues to transform warehouse operations, logistics operators face the challenge of developing technologies that can seamlessly perform tasks traditionally handled by human workers. Rueben Scriven, research manager for the warehouse automation sector at Interact Analysis, emphasizes the importance of automating the identification of damaged items, which is typically a straightforward task for humans but requires a specialized approach for machines.

Conclusion

Amazon's adoption of AI technology to detect damaged goods is a significant step towards improving the quality control process and enhancing the customer experience. By leveraging the power of AI, the e-commerce giant aims to reduce the incidence of damaged items, streamline its fulfillment operations, and ultimately provide a more efficient and reliable service to its customers. As AI continues to revolutionize the logistics industry, we can expect to see further advancements in automating warehouse operations and optimizing supply chains across the corporate world.

“That’s cognitively demanding because obviously you’re looking for something that’s rare and it’s not your primary job,” Wyatt said.

Amazon trained the AI using photos of undamaged items compared with damaged items, teaching the technology the difference so it can flag a product when it doesn’t look perfect, Schwerdtfeger said.

Amazon so far has implemented the AI at two fulfillment centers and plans to roll out the system at 10 more sites in North America and Europe. The company has found the AI is three times as effective at identifying damage as a warehouse worker, said Christoph Schwerdtfeger, a software development manager at Amazon. 

For warehouse workers today, “when you’re decanting a case into totes, it’s a manual process,” Scriven said. “It’s very easy for someone to check if this is damaged or not. But if you wanted to automate that process, you then have to also find a way to identify where damaged items are.”