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Four Technologies driving the future of Supply Chain Traceability

How AI, Machine Vision, Smart devices, and Optimized Pixel Density can reduce traceability costs and manual labor expenses
Recent world events, including the COVID-19 outbreak, have highlighted vulnerabilities in the global supply chain. In most cases, these vulnerabilities manifested themselves as tighter labor pools coupled with increased demand. This combination of increased work and decreased labor created the perfect storm for many supply chains leaving them crippled and unable to deliver for their customers. Advanced automation technologies can help mitigate these vulnerabilities for warehouses and distribution centers working to keep up with demand. Specifically, four technologies can improve supply chains by reducing the labor, labor costs, and costs associated with traceability.

Advancing Technologies

In the last two years, the pandemic has driven many of the trends in the supply chain, e-commerce, and the labor force. In short, there has been an enormous increase in online purchasing; likewise, the expectation for end-to-end and last-mile traceability has also increased enormously. Unfortunately, the available labor force to meet these expectations has dramatically decreased. These challenges drive the incorporation of automation into production, packaging, and the supply chain. While this is not a new phenomenon, four new technologies deliver greater benefits to supply chains significantly reducing the cost of labor and traceability:

AI Prevents Problems

Almost every parcel traveling in supply chains is tracked and traced using barcodes. Driven by regulatory requirements, customer expectations, or needs within the enterprise, traceability is crucial to supply chains. Therefore, maintaining a contiguous tracking history is important. In most cases, a tracking history is interrupted by a barcode No Read. No Reads kill the efficiency of traceability systems and wreak havoc on sorting processes. No Reads result in automated systems becoming manual, so how do you prevent this?
Camera technology in traceability devices provides plenty of images for every Read and No Read. When a No Read event occurs, vision algorithms and artificial intelligence can extract value from these images. Using these tools, AI can pinpoint the No Read root cause in an automated way. For example, the label shown here should be recognized as a broken barcode classification.

AI can automatically do this classification along with aggregation and reporting. This is the key to quickly helping understand major No Read causes and promptly define corrective action.

One example of implementing AI to prevent No Read is the No Read Classifier Engine. 

This is software Datasensor has added to the Web Sentinel™ Plus platform. This engine automatically processes images related to No Read, classifying them into different categories. Using this information, reports, and queries can be run on the database. For example, a trend report can show the hourly trend of No Reads on a certain day.

Drilling down into the report could show several parcels of the same shape, from the same customer with labels placed across the seam. Rather than have this continue, corrective action with that customer can be issued. Without the automated classification, this issue may not have ever been identified and the manual work from No Reads would have continued.

There are many ways AI can be used to prevent No Reads. Corrective action, preventative maintenance, and other activities can be done as the AI system sees No Reads and categorizes them. All of this is done in real-time.

AI analysis makes improvements possible without spending large amounts of time manually checking parcel by parcel, image by image. Even at a very low throughput of 4,000 parcels per day, you would need to spend multiple hours per day for manual classification to get to the root of a problem. This technology has a great benefit in keeping automation running and workers focused on real high-value tasks instead of manual rework.

Vision Detection Supply chain applications have become more complex as items of varying shape, size, and type are being purchased and fulfilled through e-commerce. The cardboard box and polybag are not the only type of packaging moving through supply chains. As e-commerce grows, so do the shapes and sizes of parcels. Large or irregularly shaped, non-conveyable items require manual measurement, sorting, and data collection. A new data collection system based on using dynamic vision detection called Mass Flow Detection can automate and eliminate the need for manual sorting for these large, irregularly shaped, non-conveyable items.

Vision systems deliver the ability to collect and do more with data. For example, a parcel with a missing label may still be identified through its shape, color, or optical characteristics. Vision systems can process data by applying algorithms that allow them to detect and decipher items in 3D.

The Mass Flow Detection System can determine if an image shows one irregularly shaped item or multiple parcels touching. The system provides volume data for each item, using advanced real-time image analysis to recognize and separate items. Multiple scanners generate hundreds of images, which are stitched together to provide a high-resolution top view. Scanners collect barcode data from each item, and a color camera takes a multi-sided view of each item running over the belt. The color JPEG image is saved with a bounding box, created by 3D measurement data. The image and the bounding box provide proof of the object’s condition. All the data is aggregated creating a complete picture of the item’s physical characteristics (size, shape, weight), traceability data (barcode), and condition.

The Mass Flow Detection System is revolutionary. The system eliminates the need for manual work that is currently performed to properly size items that do not fit or travel on traditional conveyor systems. Moreover, its high accuracy certifies it for Legal for Trade applications. In applications where clients provide dimensional data for their non-conveyable shipments, the Mass Flow Detection System can audit shipments to ensure the accuracy of the shipment and facilitate revenue recovery when shipment data is incorrect.

Getting Smart with Barcode Detection

Technology advances are driving the creation of smarter devices. This is particularly true for data capture devices, specifically barcode imaging scanners. Most scanners are considered dumb devices, they have one function which they are specifically designed to do. To do this function they require other ancillary devices such as power supplies, proximity sensors, cabling, switches, and programmable logic controllers (PLCs).

A new generation of Smart Scanners is emerging. One example is the AV900 from Datasensor, a Smart Scanner that can be programmed directly and can operate autonomously without much of the ancillary devices traditional scanners require. Smart Scanners reduce the system cost of traceability systems in two ways. First, their built-in intelligence reduces the components required to operate. Second, this same intelligence when coupled with higher-resolution imagers reduces the number of scanners required and makes the solution more effective with enhanced features.

For example, fast and wide conveyor systems would require two standard barcode scanners with 5 megapixels of resolution. These devices would need dimensioners, power supplies, proximity sensors, cabling, switches, or Programmable Logic Controllers (PLCs) to ensure that the right parcel gets to the correct location. A Smart Scanner such as the AV900 delivers 9 megapixels of a resolution allowing one device to replace the traditional units. Importantly, the intelligence within the Smart Scanner directly increases its functionality and effectiveness with new features such as three different focusing modes:

Sequential focus is the most powerful focus mode in the Smart Scanner. In this mode, the scanner can be programmed to look for barcodes at different positions, with different frame rates. The information for sequential focus is sent directly to the Smart Scanner and stored locally. The sequence is executed directly without the need for a computer or PLC. This type of intelligence illustrates how smart scanners do more than just scan barcodes; they cut the cost of traceability systems.

Higher Accuracy with Barcode Scanners

While the high resolution remains an important specification in many industrial applications, how that resolution is utilized can be even more important. Most barcode scanners have a 4:3 aspect ratio, creating a field of view that resembles a square. If one of these scanners is positioned to scan a 2-foot conveyor covering the full width, the field of view is 2 feet x 1.5 feet, with pixels evenly spaced over that area. Most barcodes are detected close to the starting edge of the field of the view, meaning that many of the available pixels go unused, creating serious system inefficiency and problems for barcode detection.

If an application requires an increased field of view, the scanner must be raised to cover the width of the conveyor. Increasing the scanning area reduces pixel density in the field of view and impacts how well the scanner can detect and decode barcodes. Poorly printed, damaged, or low-contrast barcodes can become difficult or impossible to read. A common solution is to add more scanners, but this increases cost and complexity.
Optimizing the aspect ratio is a better way to solve this challenge and it provides a better fit for industrial applications. For example, the Matrix™ 320 from Datasensor delivers a 16:9 aspect ratio, a flatter rectangle than the 4:3 aspect. Changing the aspect ratio provides a wider field of view making it more effective to cover conveyor applications while maintaining high pixel density. The real benefit comes when reading poorly printed, damaged, low-contrast, or multicolor barcodes. The 16:9 aspect has more pixels in the read zone enabling higher read rates.
Often, when a barcode scan fails, a person must intervene to make sure the package continues to its destination. This human involvement is required to ensure the accuracy of a parcel’s traceability data. An optimized aspect ratio provides more pixels in a rectangular field of view, which greatly increases the number of readable labels. Reducing barcode scan failures reduces labor costs. While aspect ratio might seem basic, optimizing it for better detection plays a critical role in reducing labor costs and increasing traceability effectiveness.

Technology to Stay Competitive

Optimizing aspect ratio, smart scanning, vision detection, and machine learning provide tangible, quantifiable advantages for traceability improvement. Moreover, they deliver real business benefits that increase the enterprise’s bottom line. Companies not utilizing these technologies will find it increasingly difficult to stay competitive. Technology is not stagnant, improvements in traceability systems will continue to bring benefits to enterprises willing to adapt while laggards fall farther behind.

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