Waldkirch/Düsseldorf, November 2022 – At the SPS 2022 trade fair in Nuremberg on November 8–10, 2022, SICK is presenting innovative complete solutions for digitalized intralogistics. One of the highlights is the Master Data Analyzer Vision for the automated capturing of master data in the goods receipt area of industrial and trading companies. Depending on the shipping volume, incoming parcels are manually or automatically digitalized and can, in conjunction with the Automated Goods Receipt (AGR) software solution from SICK, be directly integrated into automated logistic processes and seamlessly documented. The same applies to the automated classification of goods carriers such as pallets with the help of the PACS pallet classification system. A further highlight at the SICK booth (Hall 7A, Booth 340) is the Asset Analytics software solution. It makes it possible, with the help of localization information on intralogistics assets, to visualize, evaluate and optimize material flow processes and, using process mining, to achieve greater efficiency, availability and profitability.
Complete real time transparency in logistics requires a full digitalization of the material flow processes from goods receipt to dispatch. The goal is to gather, evaluate and transform into information all data throughout the intralogistic process and value adding chain. When developing and implementing digital application solutions, SICK draws on its decades of experience in all areas of logistics automation: There is hardly any challenge the logistics experts in the company cannot master by combining complete solutions comprising sensors and sensor systems – for example the Master Data Analyzer Vision – with software solutions such as Asset Analytics to create a digitalized intralogistics.
Master Data Analyzer Vision: More transparency and efficiency in goods receipt
The Master Data Analyzer Vision is the latest in a number of application solutions SICK has developed to digitalize material flows and supply chains. The track and trace system is used in the goods receipt area of industrial and trading companies to capture master data and digitalize incoming shipments. In a single work operation, the system captures in just a few seconds not only the dimensions, weight, and 1D or 2D labeling of a delivered parcel but also takes a 3D color image of the parcel at the same time using an integrated snapshot vision sensor to document the condition of the shipment. In this way the Master Data Analyzer Vision creates a digital double of each incoming shipped item, the data of which can be used for automated material flow processes and in higher level ERP systems or applications via suitable host interfaces.
PACS pallet classification system: Automated classification of pallets
Using the deep learning algorithms-based system for classifying pallets from SICK – PACS for short – customers can now automate the previously time-consuming and manual process of classifying pallets and differentiating those pallets for which a deposit has paid and those with no deposit. This reduces costs and increases transparency in pallet handling. The deep learning algorithms-based system also provides information on which pallets are in circulation. PACS has a modular design and consists of a combination of hardware and software components from SICK. Depending on requirements, one or more midiCam color cameras are used for image recording. The SIM1012 Sensor Integration Machine handles the processing and evaluation of the captured data, the execution of the trained neural network, and the communication with the controller. The users can independently train the neural network with the help of the dStudio web service, which is part of the SICK AppSpace Eco-System, even without in-depth image processing or programming expertise in the area of machine learning. Once trained with representative images, the system can independently perform the classification directly on the SIM1012 using artificial intelligence. This enables new object classes to be quickly and easily added.
Process mining using Asset Analytics
Localization solutions such as the Tag-LOC System from SICK or the smaRTLog track and trace solution jointly developed with SAP deliver a wide range of digital information about the material flow, for example localization data and holding time details for products, transportation aids, and transport vehicles, the recording of routes, or the occupation of virtual geozones. The Asset Analytics software platform from SICK provides a basis for linking the data from the SICK localization system with the process data from the application and for analyzing that data. The aim of process mining is to statistically evaluate the entirety, chronology, and interdependencies of the intralogistic business data in order to be able to draw verifiable conclusions on the efficiency of processes in the overall material flow as well as at critical points. The objective is to achieve more efficiency, profitability, and process reliability, for example to optimize inventory management or delivery performance. At the same time, Asset Analytics supports a wide variety of technologies and IT applications used in connection with material flow digitalization projects. In addition to supporting process mining and digitalization, Asset Analytics – through real time event management and the associated user-defined actions – also makes optimizations possible while the system is operation.
SICK is one of the world’s leading solutions providers for sensor-based applications in the industrial sector. Founded in 1946 by Dr.-Ing. e. h. Erwin Sick, the company with headquarters in Waldkirch im Breisgau near Freiburg ranks among the technological market leaders. With more than 50 subsidiaries and equity investments as well as numerous agencies, SICK maintains a presence around the globe. In the 2021 fiscal year, SICK had more than 11,000 employees worldwide and a group revenue of around EUR 2 billion. Additional information about SICK is available on the Internet at http://www.sick.com or by phone on +49 (0)7681202-4183.