Many of us may think that computer vision tech has perhaps reached its zenith by providing facial recognition capabilities, augmenting autonomous cars, and helping warehouse robots to “see”. Indeed that is not the case, at least for supply chains, where there are unlimited business use cases for computer vision systems. This is especially true as AI, the brains behind most visioning systems, continues to mature and expand, offering even more capabilities In fact, we are only limited by our imagination in how computer vision AI systems can transform the way supply chains operate. 

In this article, I’ll first explain exactly what a computer vision AI system is and how it works. Moreover, I’ll look at 14 compelling supply chain use cases. Without a doubt, I believe these 14 use cases represent just a fraction of computer vision tech’s real potential. Indeed, this technology has unlimited possibilities to empower supply chains by providing unparalleled visibility and actionable intelligence.

What is Computer Vision AI?

Now, most of us are familiar with the basic capabilities of computer vision. For instance, news media has popularized facial recognition which is one use case of a computer vision system. However, computer vision tech is rapidly evolving where there are now countless use cases for its use. So to understand its unlimited potential, let’s first start with a definition of computer vision. See below: 

“Computer vision is a field of artificial intelligence (AI) that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images, videos and other visual inputs—and to make recommendations or take actions when they see defects or issues.”

IBM

One thing to note with this definition is that computer vision is a form of Artificial Intelligence (AI). Basically, the AI component of computer vision tech is the “intelligence” that assesses and provides insights about the image or video depending on the business use case. Further as this AI technology continues to advance, we can expect to see computer vision AI playing an increasingly pivotal role in shaping the future of supply chain operations.

What Are the Primary Components of a Machine Vision AI System?

While the concept of computer vision AI might seem complex, the components required to make it a reality are relatively straightforward. At the heart of any computer vision system is a camera or other imaging device that captures visual data of the environment or objects. Then, basically from there the computer vision AI system evaluates what it “sees”. Once it assesses the images, it then provides insights, and even recommendations, based on the business use case. See below for the basic processing steps used by a typical computer vision system. 

Basic Processing Steps of a Computer Vision System

1. Acquire Image. First, the system needs to capture a digital image. This image can come from a general-purpose camera or from a specialty 2D or 3D camera. Also, this can be a sensor that provides image frames.
2. Pre-Process Raw Image. Here, the system optimizes the raw image in preparation for the subsequent computer vision tasks. This preprocessing step can include noise reduction, contrast enhancement, re-scaling, or image cropping.
3. AI Algorithm Evaluates Image. Next, the AI evaluates the image to extract information from the image or video frames. Specifically, AI tasks can include image recognition, object detection, image segmentation, and image classification to name a few.
4. Applies Automation Logic. Lastly, depending on the use case, the computer vision system will do final processing of the image information that was provided in the previous AI evaluation step. With this last step, the computer logic will provide specific conclusions or recommendations based on the use case. For example, these results could be a pass or fail for automatic inspection applications. In another use case, it could generate an alert for human review such as in identifying a product defect.

For more information on how computer vision systems work, see visio.ai’s article, What is Computer Vision? Next, let’s look at specific supply chain use cases for computer vision AI systems.

14 Use Cases in Supply Chains for Computer Vision AI.

From order fulfillment through final delivery, there are an increasing number of use cases for leveraging computer vision AI within supply chains. In some cases, such as robotics and parcel scanning, tech vendors are fully leveraging computer vision AI today. In fact, many large logistics organizations are already benefiting from this type of visioning tech. Also, in the area of safety and security there are many computer vision-enabled solutions available. However, there are still many emerging computer vision solutions that are just starting to emerge. To detail, below I’ll provide 14 examples of emerging computer vision solutions that can help every size of business across all supply chain functions.

1. Read Anything at the Receiving Dock for Check-In: This includes Barcode, QR Code, Labels, Documents, and more.

For example, a computer vision system can instantly scan a pallet’s barcode, QR code, and characters on labels, automatically registering the shipment without manual data entry. Indeed, this functionality goes far beyond just barcode reading as the system can use optical character recognition (OCR) to read every bit of text on the label or even the container. See PackageX’s article, Computer Vision and Logistics: From Image Acquisition to Decision Making, for more information on this particular solution.

2. Automate Damage Assessment for Claims Processing.

For instance at the receiving dock of a warehouse, the AI vision system can autonomously detect a damaged box. From there it can automatically assess the extent of the damage, and even fill out a claims form to expedite the claims process. For more details, see Arvist’s computer vision solutions for improved quality control.

3. Optimize Routing in Warehouses Using Heat Maps and Traffic Monitoring.

In this case, a computer vision system can help direct traffic within a congested warehouse using heat maps and historical warehouse traffic patterns. As a result, it can optimize both robots and forklift routes to reduce congestion.

4. Monitor Real-Time for Efficiency, Security, and Safety Anytime, Anywhere.

Here, there are numerous use cases. Generally, computer vision-powered surveillance systems can track warehouse activity 24/7, alerting managers to potential security breaches and safety hazards. For example with safety and compliance, see Arvist‘s computer vision AI solutions.

5. Track Movable Objects to Manage Assets.

For example, a computer vision system can use existing warehouse cameras to track the movement of forklifts and pallet jacks in real-time. As a result, this minimizes theft and assists with optimizing equipment utilization.

6. Streamline Package Sorting and Routing.

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This particular use case for computer vision systems is widely deployed within the sorting facilities of large parcel carriers. Here, the system scans packages on rapidly moving conveyor belts, automatically reading labels and helping to direct packages to the correct sorting lanes.

7. Facilitate Autonomous Forklifts and Warehouse Robotics.

Here, computer vision systems are a key sub-system for warehouse robotics. For instance with these systems, robots can navigate warehouses independently, avoiding obstacles and locating items for retrieval.

8. Enable Predictive Maintenance for Equipment Failure Detection.

For instance, computer vision-powered sensors as well as robotics can oversee equipment and even monitor gauges. As a result, these systems can detect subtle visual signs of wear and tear to predict when maintenance will be required.

9. Autonomous Inventory Tracking and Counting.

In this case, drones equipped with computer vision can fly through warehouses, automatically scanning and counting inventory without human intervention. Further, computer vision systems can use stationary cameras to detect when products enter or leave storage areas. These systems can also count the quantity of different products such at the dock door or staging area..

10, Automated Visual Inspections of Products and Containers.

Here, a computer vision system can inspect products at a warehouse picking station, detecting defects or anomalies in real-time. As a result, the system can flag mistakes and generate alerts.

11. Optimize Robotics Container Packing and Data Capture of Volumetric Properties.

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For instance, computer vision guides robots to optimally pack items within containers. Also, the system can capture precise shipment container dimensions to work with carriers for accurate billing. As a result, this keeps shipping costs low and minimizes surprise invoice charges from carriers.

12. Enable Assisted and Automated Pallet Building.

As an example, a computer vision system can provide real-time guidance to warehouse staff on how to optimally build pallets, or it can automate the pallet building process working with robots. See Optioryx’s article, 3D Bin Packing: The Tetris of Logistics, for more information on the use of computer vision tech and “bin” packing.

13. Provides Visibility for Yard Management.

Here, computer vision systems can use cameras to track the location and status of trailers within a yard. Thus, providing real-time visibility to optimize loading and unloading operations.

14. Captures Proof of Shipping through Delivery for Enhanced Visibility.

In this case, it is becoming commonplace for delivery drivers to use computer vision-enabled smartphones to capture images of packages upon delivery. Thus, providing instant proof of delivery. Additionally, more and more shipping departments are using computer vision systems to capture images of shipments prior to handoff to their carriers. This not only provides proof of damage-free shipments, but also can help verify physical box count against order fulfillment system box count. This use case has unlimited possibilities to include end-to-end shipment visibility. Further for shipment tracking, computer vision AI can drastically reduce data Integration efforts for exchanging shipment status. For more information, see my article, How to Make End-to-End Shipment Tracking the Best Using Computer Vision AI.

How to Make End-to-End Shipment Tracking the Best Using Computer Vision AI

Conclusion.

These 14 supply chain use cases demonstrate the transformative power behind computer vision AI tech. Moreover, AI, the brains behind these visioning systems, continues to both mature and expand to offer even more capabilities. In fact, we are only limited by our imagination in how these AI systems can revolutionize the way supply chains operate. Undoubtedly, we will find even more innovative ways to leverage computer vision AI in the future. Indeed, this technology has unlimited possibilities to empower supply chains by providing unparalleled visibility and actionable intelligence. For more references on computer vision AI tech, see below:

• Computer Vision Guide. See visio.ai’s article, What is Computer Vision?. 
• Use Cases for Computer Vision. See Kibsi’s article, 9 Ways to Optimize Supply Chain Operations with Computer Vision Technology, DevisionX’s article, Top 10 AI Computer Vision Applications Used in Supply-Chain and Logistics, and visio.ai’s article, 25+ Applications of Computer Vision in Logistics,
• Use Case for Supply Chain Visibility. See my article, Picture-Perfect Tech for Supply Chain Visibility: The Use Of Emerging Computer Vision AI To Find Out What Happened for leveraging image-based tracking status messages.

Picture-Perfect Tech for Supply Chain Visibility: The Use Of Emerging Computer Vision AI To Find Out What Happened

For more from SC Tech Insights, see the latest articles on IoT, AI, Augmented Reality, ITS, and Warehousing.