Industry

Main challenges of the industrial sector in Industry 4.0 .|

Prediction of machinery maintenance needs

Automated quality inspection

Single data management among all the agents involved.

Automation of administrative processes

Detection of anomalies in the production line

Improved remote work capabilities of line personnel

Reduction of technician training times

Leverage organizational knowledge

Automated control of working conditions

Simulation of manufacturing and prototyping conditions

Monitoring and control of construction machinery

Optimization of planning and inventories

What technologies are transforming the industry? .|

Business Intelligence
& Data Analytics

Artificial
intelligence

Extended
Realities

Robotic
Process Automation

IoT

Digital
Twins

Data analysis and business intelligence.|

Data strategy in the industrial sector.|

.| The industrial sector generates huge amounts of data at every node of the value chain.

.| It is essential that this data flows and converges between all participating agents to ensure transparency and process optimization.

.| The incorporation of Internet of Things (IoT) technology has generated additional needs in terms of infrastructure and data management.

.| The exploitation of data provides an enormous capacity for optimization at many levels of the chain.

Objectives to drive the industrial sector in Industry 4.0 .|

To discover the knowledge hidden in the data, from any source.

Do it in a collaborative way among all participating agents.

Enable the organization to make decisions efficiently.

Employ AI capabilities to access data visualizations in an intuitive manner

Reduce complex reporting and solution development times

Power BI
The platform that makes it easy for you to understand your business data

Industries

Industrial Vision Solution.|

Machine vision solution that provides automatic image-based inspection and analysis for applications such as automatic inspection, process control, robot guidance, etc.

Vision Solution for the industrial sector.|

Thanks to Machine Learning and Deep Learning algorithms it is possible to analyze visual images and extract information to be able to make subsequent decisions. These algorithms can be executed in the Cloud or on the devices themselves, depending on the needs of the company.

Through images, algorithms, and processing devices we obtain valuable information for decision-making.

Industrial Vision Solution

Industrial Vision Solution for anomaly detection and quality control.|

Industrial vision solution for safety.|

Benefits of Vision Solution in the industrial sector.|

Increases employee productivity. Thanks to this solution, processes that used to require human supervision can be automated.

Cost optimization because the quality of products can be improved, focusing employees on other different tasks.

Guaranteed safety, since we will be able to detect any desired object to prevent accidents.

Immediate presentation of validation results, without delays in the production chain.

Improved quality thanks to algorithms that will allow us to verify the assemblies in a chain to detect problems before they happen, among others.

Speed and automatic learning, the algorithms used are automatically improved as they are used.

Predictive maintenance of production equipment with AI .|

Continuous real-time data collection

Build predictive models/learning algorithms

Real-time data analysis to detect failures and root cause patterns

Sending alerts/notifications to take action

Improve equipment maintenance plan

Optimize spare parts inventories and costs

The Internet of Things

Predictive maintenance of production equipment with AI .|

.I Production equipment in any factory, as well as auxiliary equipment for transport, and material movement, among others, provide, through sensorization, a large volume of data on their operation.

.I I Prediction of deadlines for possible failure events or the proximity to the life limit of each component, thus being able to improve equipment maintenance plans and optimize spare parts purchase cycles and associated inventories.

.I I Reduction of equipment downtime, thus optimizing the production of each piece of equipment and, in general, of the entire factory.

Mixed Reality and its tools.|

Microsoft Dynamics 365 Remote Assist

Microsoft Dynamics 365 Remote Assist enables technicians to collaborate and troubleshoot with remote collaborators using Dynamics 365 Remote Assist for mobile devices or Microsoft Teams.

Microsoft Dynamics 365 Guides 

Microsoft Dynamics 365 Guides is a mixed reality tool that enables employees to learn through interactive instructions.

Benefits of implementing Mixed Reality with HoloLens in the industrial sector .|

Increases employee productivity by up to 25% through the use of available tools.

Improved training and preparation for complex tasks that require several steps to execute.

Ensures quality by offering a better service and with a much better-trained staff.

Optimization of direct costs by up to 15%, thanks to remote assistance and employee training.

Remote Monitoring .|

The first step many companies take to begin implementing their Factory of the Future is to connect their equipment (production assets) for complete visibility.

Remote monitoring allows companies to continuously monitor the health and performance of individual assets, as well as entire factories to predict potential problems and manage changing customer demand in the industrial sector.

Visualize operational data in real-time
Collect and analyze data from any device or sensor
Ingest and store data for real-time analysis to drive decisions
Deliver analytics that will improve factory performance and efficiency.
Monitor production parameters.
Digital twins.|
Simulations of material movement in plant
Raw material stock control
Machine production simulations

Production and supply chain .I

.| Environmental conditions incidence analysis

.| Simulation of production impact of equipment failure

.| Production balancing between lines

.| Optimization of stock levels in the face of demand variations

.| Identification of bottlenecks in production lines.

.| Definition of production buffers

Deployment of Digital Twins with Bravent.|

Criticality assessment

Definition of the progressive digitalization plan

Cost assessment

Detailed definition of the process

Evaluation of the expected return

Deployment of Digital Twins with Bravent.|

Development of the digitization process

Connection with IoT systems

Training of AI and ML models

Deployment and testing

Putting into production

Continuous improvement

Do you have an idea for an Industry 4.0 project?