From Retail to Dining: How AI Image Recognition Solves Checkout Challenges for Unpackaged Goods

September 25, 2024

The checkout process for unpackaged goods has long been a major challenge in the retail and hospitality industry. Traditional barcode scanning simply does not work for unpackaged items like bakery goods, food or fresh produce, making the process slower and more prone to errors.

AI-powered image recognition technology offers a fresh solution for unpackaged shopping and food service. Leveraging artificial intelligence, machine learning, deep learning, and computer vision, AI can simultaneously and accurately identify multiple unpackaged items. Whether it’s a meal with varying components or baked goods with similar appearances, AI can easily differentiate them. Compared to manual recognition, AI not only significantly enhances checkout efficiency but also reduces the likelihood of human error.

Here are the key benefits of AI image recognition for unpackaged goods at checkout:

1. Improved Checkout Efficiency

Unpackaged goods often emphasize freshness, which means they come without fixed packaging or barcodes, such as bread, cakes, or fresh produce. AI image recognition technology can quickly and accurately identify different products based on the visual characteristics of each item. This significantly reduces manual input and customer wait time, boosting overall checkout speed.

2. Accurate Product Identification

AI image recognition systems can learn to detect subtle differences between similar products. For example, different flavors of buns may come with varied prices, and bakeries often design these items to have distinct appearances so that staff can place them correctly on display and ensure accurate pricing at checkout. As long as there are differences in appearance, the AI system can accurately identify each flavor, ensuring error-free transactions and a smooth checkout process.

3. Maximizing Workforce Value and Reducing Employee Workload

Generally speaking, checkout for unpackaged goods relies on employees to differentiate products, which can be time-consuming and lead to mistakes. AI image recognition takes over the manual identification, not to replace staff, but to assist them. This reduces staff’s burden, allowing them to focus on more valuable tasks.

If store layout and staffing permit, a self-checkout system can even be implemented, offering fast checkout options during peak hours while still providing customer assistance when needed. Additionally, AI models are constantly improving, minimizing the need for manual intervention over time.

4. Enhancing Customer Experience

The speed and convenience of the checkout process directly impact the overall shopping experience. With AI image recognition, staff no longer need to scan barcodes or manually input product information for unpackaged goods. The system automatically identifies the items and pulls up the correct prices from the POS, quickly calculating the total. This shortens wait times and enhances customer satisfaction.

Similarly, applying AI image recognition to self-checkout means customers do not need to individually scan items or navigate through complex lists. The system automatically identifies items and calculates the total, making the process faster, smoother, and more accurate, reducing the hassle of manual inputs.

5. Environmental Sustainability

One of the goals of promoting unpackaged goods is to reduce the use of plastics and packaging materials, supporting environmental sustainability. With AI image recognition, retailers can efficiently manage products and checkout without relying on barcodes or other packaging markers. This encourages more businesses to adopt unpackaged strategies, further cutting down on packaging waste.

Case Study 1: I Jy Sheng (Bakery Chain in Taiwan)

Take the well-known bakery chain I Jy Sheng’s store at Taoyuan Airport’s Terminal 1 as an example: AI has effectively replaced the repetitive task of manual identification and frequent POS clicks during checkout.

Statistics show that AI helps this store’s employees save over 3,500 POS clicks every day, enabling each transaction to be completed in just 30 seconds to 1 minute. This gives employees breathing room even in a busy airport setting, allowing them to focus more on customer service and store management, thereby improving overall efficiency and service quality.

Case Study 2: Hi Noodle by Haidilao

While “meals” are not typically considered retail products, they can also be seen as unpackaged goods. Hi Noodle, a noodle shop under Singapore’s Haidilao brand, has implemented AI image recognition technology to create a self-checkout system.

Freshly prepared noodles are represented by cards, while pre-prepared side dishes are placed in the self-checkout area. Customers simply place the side dishes and noodle cards under the AI camera, and the system quickly completes the transaction. This allows customers to immediately enjoy the side dishes while waiting for their freshly prepared noodles, offering a convenient and innovative dining experience.

AI image recognition in unpackaged goods checkout not only improves efficiency but also optimizes human resources, enhances customer satisfaction, and promotes environmental sustainability. As this technology continues to mature and spread, it is poised to be a key driver of digital transformation in retail, helping businesses streamline operations while reducing environmental impact.