Object properties such as identification, dimension, weight, and condition – are fundamental building blocks for the smooth and efficient automation of material flow.
Yet not all attributes are static and constant; many vary throughout the intralogistical chain between goods receipt, warehouse storage, and goods issue. High-performance logistics automation must be able to generate this data throughout development. System solutions are available for just this purpose – but their journey into the intralogistics process is only just beginning.
The variability of object properties is just one of the challenges we face. Ambiguous master data and the detection, storage, retrieval, and real-time provision of what are normally large data volumes throughout the process also prove to be both costly and burdensome without the appropriate technology. But at the same time, experience has shown us that deviations from the expected condition may have huge negative ramifications for logistical and automated processes.
What happens when object properties change?
The intralogistics lifecycle of goods normally begins at goods receipt and ends at goods issue. In a very small number of cases, the object properties, and therefore the data relevant to logistics, remain unaltered. Object data at goods receipt can be either not known at all or known only at a basic level because of the preceding electronic data interchange (EDI). Therefore, it either has to be entered as new data or matched up and updated – and this process can exclude the part number which is necessary from a logistics point of view. The part number usually remains the same, however, the properties relevant to logistics and handling may undergo considerable changes. As a result, storage sizes and containers cannot be used as planned, incorrect storage space suggestions can trigger stock transfers, and shipping units can vary regarding size and weight. It can also become critical when a company uses the handling units of the supplier (e.g., shipping cartons) to store goods in their warehouse. The condition of the handling unit; in terms of its suitability for the conveyor system, storage capabilities in the automated warehouse, stackability, and ease of handling, are of fundamental importance. These object attributes do not at first constitute the core master data of the product in question, but they do add properties relevant to logistics and are key to smooth processing regarding storage and the conveyor system.
New business models increase the intralogistics complexity
E-commerce, multi-channel sales, cross-selling, and far-reaching supply chains also increase the complexity of processes. This not only increases requirements regarding logistical performance: faster, punctual, and more cost-efficient all at once. This trend also means that a sharp increase in returned goods (e.g., from e-commerce) must be taken into consideration when planning logistical systems. Moreover, influencing factors such as batch size 1, the volatility of markets, flexible business models, demographic change, and, increasingly, Industry 4.0 mean that logistics must transform itself from a conventional field of business to one of the drivers of innovation. This, in turn, requires even more extensive, timely, and machine-readable a priori knowledge about object properties – particularly in intralogistics process stages in which master data or object properties have previously been collected and updated in a highly randomised way, meaning they are not always up to date as a whole. Only constant and continual analysis of the relevant master data can prevent logistical errors occurring in high-performance material flow systems. Over the coming years, the degree of automation will steadily increase as a result. Only then will the balance between flexibility, throughput, and quality within a given budget guideline be possible and become the benchmark for successful approaches to logistics. The knowledge required for this to happen is incredibly difficult to obtain or to generate in processes, but there are already suitable system solutions available today which can tackle this challenge.
System solutions for high-performance material flow
SICK offers a variety of system solutions – dubbed track-and-trace systems – which enable the automated collection, plausibility evaluation, and storage of product master data and object properties. This involves both static and dynamic solutions, e.g., for manual and automated goods receipt processes. The systems also draw on state-of-the-art technologies such as barcode scanners, RFID, vision sensors, and multidimensional laser measurement technology for identifying objects and determining their geometries, contours, overruns, and weights. All systems are designed for “plug & play”, easy operation, high reliability and availability, as well as simple maintenance. Among other features, the solutions differ in terms of the minimum and maximum size and weight of objects they can detect. In addition, they may differ in terms of technology depending on the surface condition of the objects. Some systems are also able to provide additional 2D-image or 3D-object information in order to carry out analyses of data that are relevant to logistics and handling. For example, regarding optimal gripping points for robots, the geometric centres of containers, their suitability for the conveyor system, and the occurrence of bulges, as well as container counting and optical character recognition. With this information, the key logistical master data can be updated in the ERP, MES, and warehouse management systems. The logistics chain benefits from improved processes such as those for storage location determination, packaging suggestions, or forecasting shipping prices.
With the track-and-trace systems from SICK, appropriate solutions ensuring consistent availability of master data are available on the market today as state-of-the-art solutions. In the future, collection of object information in large data centres along with modern capabilities for processing these large data volumes virtually in real time will create entirely new concepts which will strike a balance between efficient, high-performance material flow systems on the one hand and maximum flexibility on the other, at an affordable cost.
Intralogistics: Perfect object data for greater performance
Q: What intralogistical processes will necessitate greater knowledge of the properties of objects in the future?
Volker Glöckle: Modern automated warehouses will have to evolve more and more into handling centres in order to remain competitive and flexible. In order to establish reliable processes such as palletising unsorted pallets or “bin picking” as added value in terms of logistics, it is essential to produce information such as gripping points, stackability, or load capability to a reliable standard of quality. This will make further object attributes relevant, alongside the conventional master data. In particular, when independent agents initiate machining, transport, and further processing for automation concepts within the context of Industry 4.0, this information becomes significant.
Q: To what extent is it useful or necessary to use such comprehensive object properties both internally and within a supply chain?
Bernd von Rosenberger: Master data plays an important role in logistics handling at many stages along a supply chain. In this respect, it is obviously desirable to have consistent standards for storing and archiving object data even across company boundaries so that this data can always be accessed by each link in the supply chain, e.g., via the cloud. But in order to do this, it will be necessary to establish rules regarding the origin, real-time provision, and alteration of data, as well as standardization and security of master data archives, among other things. Industry 4.0 can make an important contribution in this regard, since its processes are particularly reliant on data that is consistent, secure, and quickly available.
Q: How will entering new object properties affect the formation of intralogistical information systems?
Volker Glöckle: In order to manage the data of objects, it is normally allocated to them using their part number, e.g., EAN. A query sent to the host system with this identifier returns the desired data back into the process. Particularly when part numbers are not unique, additional identification technologies will be required in the future such as fingerprint or matching methods, in addition to the barcode, RFID, and vision sensors. The data storage of 2D and 3D information relating to objects in large data centres and the possibility of processing these large data volumes in real time will create entirely new approaches to logistics and automation.
Q: Is this still a pipe dream, or has the future already begun?
Bernd von Rosenberger: Today, the awareness of object properties already enables smooth automation and therefore higher quality of logistics, while optimizing process costs. This is based on intelligent sensors and system solutions – already available today – which collect the master data at the required standard of quality. So in one sense the future has already begun, but it still offers a lot of potential for further automation of intralogistical tasks if we are able to develop technologies further and make large data volumes manageable and usable in a secure way.
Thank you for talking to us!