Sensor Solutions for a More Transparent Supply Chain

Flexibility in production and logistics is possible by implementing smart manufacturing and industry 4.0 solutions, reliable safety sensors for human-robot collaboration, and accurate track and trace systems on conveyors. In an effort to provide highly-efficient solutions for intralogistics applications, SICK has solutions that show how logistics get smart. With these solutions, we provide intelligent sensors for a transparent supply chain.

Taking Industry 4.0 to the Next Level

Industry 4.0 is now in full swing, and SICK has solutions that can help bring logistics processes even further into the Industrial Internet of Things. From deep learning to virtual reality to an industry 4.0 simulation, you can see how logistics get smart with SICK technology.

First, discover how machines can learn to think and advance logistics processes. With deep learning processes, cameras can automatically detect, verify, classify, and localize features or objects by analyzing the learned image base. For example, they can check whether any flats are present in the sorter trays, optimizing sorter cell assignment and increase throughput. Deep learning networks are capable of compiling an enormous amount of data, many of which can be powerful for improving logistics processes.

Second, see Industry 4.0 in action with our dashboard that can simulate sensors and provide a live view of the sensors at work. Decentralized intelligence in the sensors (edge computing) forms the basis for flexible control of production processes in smart logistics processes. Additional cloud intelligence in the form of data analyses and apps allow the use of self-learning mechanisms in the sensors, increasing their adaptability to production-related changes. This process enables indoor localization and asset tracking.

Lastly, learn how to make any facility safe with our virtual reality safety services demo. In this demo, manufacturers can virtually see exactly what may be deemed “unsafe” in a facility and what we recommend as a solution to make it safe. Using a 3D safety concept, a digital twin of your machinery can be created by gathering point cloud data to design technical protective measures. This provides a live view of the risk assessment and risk reduction process necessary to run an efficient and effective supply chain.

Autonomous Vehicles and Mobile Robots

When logistics gets smart, flexibility is an added benefit. The supply chain is being taken over by driverless transport systems (whose autonomous navigation is shown live) along with collision prevention solutions. The data from all the sensors in the smart factory are evaluated on a large display giving visitors a constant overview of logistics and production processes, as well as key performance indicators.

Assets in a facility can be tracked continuously using localization solutions and space-time coordinates can be continuously recorded and stored. Having this data allows for complete transparency to all important movements on the warehouse floor. Today’s analytics tools can already use this data to make connections between different events, presenting an unfiltered look into actual logistics processes.

All the established technologies—ultra wide-band tags, LiDAR Contour Mapping, line guidance sensors, infrastructure sensors—record either their own position or the position of the desired objects. Depending on the application as well as the positioning accuracy and update rate required, the right technology is selected or various technologies are combined with each other.

These mobile robots can have localization implemented to be able to accurately track the route of each vehicle and monitor where assets are in the facility at all times – indoors or outdoors. Outdoor localization is made possible with the first ever outdoor-rated safety laser scanner, SICK’s outdoorScan3. The outdoorScan3 allows automated guided vehicle (AGV) systems to navigate safely through outdoor industrial environments.

Register for our webinar to learn more about how your mobile robots can navigate safely using SICK safety LiDAR.

Accurate and Reliable Track and Trace Solutions for Logistics Processes

Lastly, track and trace solutions are a necessity for maintaining a high throughput in intralogistics environments. SICK has various types of sensors that can be used on conveyors for zone control, dimensioning, and overall quality control.

The detection of polybags and flexible packaging has been a recent challenge in logistics, but leading edge detection can easily be accomplished with the RAY10 and RAY26 photoelectric sensor. The sensor works with a 2D light band that allows the reliable detection of flat and location-tolerant objects on conveying systems. The RAY10 and RAY26 saves up to 50 percent of the installation effort and costs involved in light grid solutions or multiple photoelectric sensors.

Additionally, many find it a challenge to reliably detect 1D, 2D, stacked codes, and plain text. SICK’s Lector image-based code readers can read laser or dot-peened codes perfectly, even in cases of low contrast levels, contamination or poor code quality. This is the perfect solution to transition from using laser scanners to cameras for applications like tote box identification in logistics facilities.

With SICK’s line of track and trace solutions, goods can be continuously tracked throughout a logistics facility. A Dimensioning-Weighing-Scanning (DWS) system allows logistics centers to record all data needed for shipments at the touch of a button. In addition, 2D and 3D vision can be used to enable deep learning on conveyor systems to ensure only the correct materials make it through the system, eliminating any potentially hazardous materials.

To top it all off, spontaneous errors in the track and trace process can be a thing of the past with SICK’s EventCam. This industrial-grade 2D camera brings clarity when unexplained gripping, handling, and positioning problems arise or when unreproducible installation errors or machine downtimes occur. It is specifically designed for the detection and analysis of sporadic errors in industrial processes.

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