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The overall performance of an automated line depends on an extremely high number of variables, the behaviour of which is influenced by the performance of the individual devices or machines that make up the line, from the planning of production set by the end customer, to scheduled maintenance and extraordinary interventions (to name just a few of the key factors that have the most influence on the system).
Imagine a system in which the information collected in the past across various automated lines that have already been implemented, makes it possible to identify the perfect mix of machinery and devices that ensure the overall performance expected by the customer:
All this is possible thanks to the Brick Machine Learning solution.
Dashboards through which the user can, for each individual production line process, define the best “asset mix” and OEE (Overall Equipment Effectiveness) indicator.The solution makes it possible to simulate various configurations used in automated production lines, in order to recommend the optimal mix of devices required to achieve the overall performance requested by the end customer.
Automated production line manufacturers have to contend with the variety of automation devices available. One of their key goals is to identify the perfect mix of devices and machinery needed to optimise production lines, making full use of their potential and predicting their productivity. Their customers want to know in advance the expected overall performance of the new automated line they have purchased.
The monitoring capability offered by the Brick platform facilitates the real time collection of all data from assembly lines that have already been installed. Brick Machine Learning (BML) controls, cleans and models the collected data, in order to predict the performance of each new machinery combination. The new solution is designed to define the best combination of machinery for a specific production setup.
Overall performance estimates are typically based on complex simulation models driven by discrete events and by the expertise and experience of design engineers:
A Simulation and a Machine Learning module has been developed, which facilitates the configuration and the execution of simulations designed to predict the overall efficiency of an automated line by integrating with Hermes Reply’s Brick platform (in particular with its Monitoring component).
Through front-end web development users can:
By integrating with Microsoft's Azure Machine Learning cloud services, the platform:
The Brick Machine Learning technology solution consists of a web application published on the Microsoft Azure cloud platform and integrated with a Machine Learning algorithm implemented on Azure and consumable through a web-service.
The application architecture is implemented using the following components:
Automated Invoice is the solution, also available as a service, which facilitates the automated management of the accounts payable process, from the posting phase to the reconciliation between invoices and purchase orders/delivery notes/receipts, highlighting the differences identified.
Employee Monthly Expenses is the solution that facilitates the automated creation of expense reports starting from the underlying cost items, quickly and without the need for manual intervention.
Know your Orders is the solution that makes it possible to create a simple interface which can be consulted by users using a natural language, thus facilitating access to information while ensuring consistency and accuracy.