In intralogistics, e-commerce and returns management, automating standard sorting tasks and checking the machine capability of items are important criteria for improving system availability. Increasing the speed of the system, and therefore achieving a higher throughput, makes systems more efficient and cost-effective – an urgently needed advantage in an extremely competitive market. FRAMOS Imaging Systems offers a range of plug-and-play imaging sensor solutions and adaptable identification technologies for automating logistics functions. Marketed under the name of “Sorting Intelligence”, they can be seamlessly integrated into both new and existing systems.
Sorting intelligence technologies can be used in a wide range of different ways to automate the logistics chain. Optimising freight costs for e-commerce retailers, using machines for sorting accuracy in empty goods logistics, ensuring the machine capability of parcels before they reach package openers in returns management and checking curvature to ensure shuttle compatibility in shuttle warehouse systems are just some of the challenges that the FRAMOS sorting intelligence technology can help to overcome. “As a technology that is used across a variety of industries, imaging offers sorter manufacturers, OEMs, suppliers of material handling solutions and system integrators in the logistics industry an intelligent way of automating and controlling their systems.” says Dr. Simon Che’Rose, Director of FRAMOS Imaging Systems.
“This can lead to simplified processes for end customers in industry, effective system utilisation with reduced downtimes and improved cost and resource efficiency.”
When performing standard sorting tasks, the sorters in logistics facilities need clear sorting criteria in order to correctly classify the items. Imaging specialist FRAMOS offers support in this area with its extensive expertise in designing algorithms and many years of project experience. Rules-based or learning algorithms are used, depending on the specific application and the nature of the sorted goods. A rules-based process is an appropriate solution if the products can be clearly differentiated and separated. A learning sorting algorithm is a better option when clear and constant basic criteria such as shape or size are not sufficient to ensure reliable classification. Higher-level variations, such as traces of use, damage, label residues or colour deviations require enhanced criteria definitions that have the ability to learn in order to ensure consistent, error-free sorting.
Dr Simon Che’Rose describes the typical course of a project: “As a technology partner, we take the customer’s individual needs into account and work with them to create a catalogue of criteria for the project. On this basis, we then develop an appropriate sensor system, including the necessary software, that transmits the required results to the controller for the subsequent stages of the process. Control systems can take several forms, including light barriers, 2D or 3D camera systems and laser scanners combined with code readers and weighing scales, depending on the application in question and the needs of the customer. Our systems fit seamlessly into automation processes in new and existing logistics systems alike and are characterised by their long MTBF. The services we offer also include flexible and efficient communication with ERP and WMS systems, as well as material flow computers and a direct connection to conveyor and PLC systems.”
For OEMs and system integrators, the benefits lie in the outsourced development and the added value for the customer as a result of the industry partners’ expanded service portfolio. As an expert in imaging and sensors, FRAMOS can also supply the required system intelligence. With years of applications experience, we help our partners to create smart logistics solutions which are intelligent and easy to use and which, because of their low level of complexity, can be integrated easily and with very little risk.
Example: Optimising freight costs
The aim was to improve the shipping costs for an e-commerce retailer. After the picking process, the FRAMOS VLG dimension measurement system automatically classifies the outgoing packages according to size and weight. The data collected is used to select the best shipping partner and the correct label is applied using label applicators. A downstream sorter allocates the packages to the correct consigner deliveries. The savings in shipping costs ensure a quick return on investment for the sorting intelligence system.
Example: Empty goods logistics
In foodstuffs logistics, fresh goods are usually transported in reusable containers, although each supplier will prefer different types of container. The solutions from FRAMOS use image recognition and self-learning algorithms to automate the processing of these containers. When the fresh goods are delivered, the container types are automatically recognised so that they can be appropriately separated and stored. After the supermarkets return the containers to the empty goods centre, they are organised into groups of a single type, before they either move on to the washer or are returned to the corresponding supplier.
Example: Bulge check for shutlle warehouse
Unwanted variations (such as bulges) in the shape of transported objects can quickly become a problem for many shuttles in automated warehouses. If the permitted tolerances are exceeded, the shuttle system stops and the warehouse comes to a standstill. To prevent costly downtime and maintenance, a curvature measurement is conducted beforehand using sorting intelligence technology. This allows items that are not compatible with the shuttle to be removed and ensures maximum system availability.
Example: Machine capability in returns management
Most e-commerce retailers have to deal with very high levels of returns. This process needs to be designed as efficiently as possible in order to keep the associated costs down. The use of automated package openers is one potential option here. In the pre-sorting phase, a system of sensors based on sorting intelligence technology decides whether the package in question can be processed by the machine or needs to be opened manually due to its nature.