Industrial processes within logistics require ever greater flexibility from automation solutions that must be able to deal with seasonal influences and the rapid change of the objects to be processed. As a result, there are often processes in which products of unknown size have to be processed within a short time. Viroteq’s systems are able to stack and package these new products within (robotic) automation solutions without prior knowledge of these package characteristics and sequence.
Our AI algorithms are able to recognize patterns that are completely random for humans and use this to make smarter stacking choices. This includes all system and process parameters that can influence this. Examples of this are stability, feasibility by robot, product limitations and customer-specific stacking requirements. With our AI stacking algoritms, it becomes possible to (robotically) automate even the most difficult stacking processes where objects and process require the highest degree of flexibility such as mixed palletizing or binpacking solutions.
Tested and improved by processing millions and millions of stacks and products, our AI stacking models are able to make efficient stacks within flexible mixed stacking and mixed packing processes while optimizing the volumetric filling level. With this we are able to stack more packages within a volume than conventional approaches. On the large volumes within the logistics supply chain, an improvement in transport efficiency of a few percents can already have a great added value from both financial and sustainability perspective.
With the increase of variation in shipped parcels, automation solutions throughout the supplychain need to be able to handle an increasing level of ranges of object dimensions. Where traditional mixed-size stacking systems are able to re-arrange the sequence of the packages that need to be picked and packed, processes nowadays are not suitable for these kind of solutions. For the process-situation where there is a small footprint, a constant stream of unknown parcels with varying dimensions without the possibility to see what other parcels will arrive in the future, a new approach to stack and pack these parcels has to be taken.
With On The Fly mixed stacking and packing, a very powerfull AI decides for each parcel and/or object how it needs to be pickup up and where it needs to be placed down; all happening while the object is ‘On-The-Fly’ by a handling system. There is no need to know what the next package looks like. The Viroteq platform AI is develop to be optimized to handle On The Fly stacking and packing processes and provides a solution to efficiently automate the physically heavy, dull, and monotonous stacking and packing processes throughout the logistic supply chain.
The Viroteq platform is build in a modular way, which enables user to profit from the combined added value of each AI system component to deal with process flexibility challenges.
With smart interfacing and customizability features, the Viroteq platform can be integrated in varying hardware setups where the platform adapts itself to the hardware and component used.
Build on complex but highly performing AI algorithms, the Viroteq platform can outperform confentional systems, enable new automation possibilities and boost process performance.
Easy integration with existing datastream and WMS enables users to fully benefit from the available process and product data that can be used to improve tracability, reduce human errors and optimize productivity.
Cookie | Duur | Omschrijving |
---|---|---|
cookielawinfo-checkbox-analytics | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics". |
cookielawinfo-checkbox-functional | 11 months | The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". |
cookielawinfo-checkbox-necessary | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". |
cookielawinfo-checkbox-others | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. |
cookielawinfo-checkbox-performance | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance". |
viewed_cookie_policy | 11 months | The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data. |