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Reference
Reference
Examples of the use of our professional services for organizations operating in a highly dynamic environment
Adaptive management of the production of precision tools for the production of watches at Aguna
Adaptive management of the production of forging press parts in the Šmeral company
Adaptive management of motorcycle parts production at Blata
Adaptive management of the production of go-kart parts at Berg Factory Czech
Solution design for the development of a real-time predictive energy optimization system in buildings using artificial intelligence at Synerga
We partner with Quintiq to implement a Self-Learning Supply Chain
We are certified in Machine Learning and AI by Andrew Ng, Stanford University, one of the current AI gurus
Adaptive management of the production of precision tools for the production of watches at Aguna
Adaptive control of the production of precision tools for the production of watches at Aguna
- Construction of a production plant in the industrial zone of Brno, Černovické terasy, Czech Republic
Supply of precision tools for the parent Swiss company and control of precision CNC production within geometric tolerances of a thousandth of a millimeter
Implementation of an adaptive production control system including real-time operational data collection and advanced detailed APS production planning using genetic algorithms to produce and deliver precise watchmaking tools exactly according to promised delivery dates.
Main benefits
Real-time event management, reverse optimization and decision support
Complete overview, measurement and regulation of operations and main KPI performance parameters
Continuous, real-time optimized plans in a dynamic environment
Improving service, delivery times and customer satisfaction
Reduction of operating, storage, maintenance and transport costs
Maximizing resource utilization and productivity
Improving the level of fulfillment of contractual agreements
Adaptive management of the production of forging press parts in the Šmeral company
Adaptive control of the production of forging press parts in the Šmeral company
Implementation of an adaptive production control system, including operational data collection and advanced detailed production planning of APS using genetic algorithms , enabling the production and delivery of precise parts for the production of forging presses exactly according to the promised delivery dates.
Main benefits
Real-time event management, reverse optimization and decision support
Complete overview, measurement and regulation of operations and main KPI performance parameters
Continuous, real-time optimized plans in a dynamic environment
Improving service, delivery times and customer satisfaction
Reduction of operating, storage, maintenance and transport costs
Maximizing resource utilization and productivity
Improving the level of fulfillment of contractual agreements
Adaptive management of motorcycle parts production at Blata
Adaptive management of motorcycle parts production at Blata
Implementation of an adaptive production control system including operational data collection and advanced detailed production planning of APS using genetic algorithms to produce and deliver precise parts for motorcycle production exactly according to the promised delivery dates.
Main benefits
Real-time event management, reverse optimization and decision support
Complete overview, measurement and regulation of operations and main KPI performance parameters
Continuous, real-time optimized plans in a dynamic environment
Improving service, delivery times and customer satisfaction
Reduction of operating, storage, maintenance and transport costs
Maximizing resource utilization and productivity
Improving the level of fulfillment of contractual agreements
Adaptive management of the production of go-kart parts at Berg Factory Czech
Adaptive management of the production of go-kart parts at Berg Factory Czech
- Construction of a production facility in the industrial area of Brno, Vienna, Czech Republic
Supply of go-cart parts for the Dutch parent company and production management
Implementation of an adaptive production control system enabling the production and delivery of go-cart parts precisely according to the promised delivery dates.
Main benefits
Real-time event management, reverse optimization and decision support
Complete overview, measurement and regulation of operations and main KPI performance parameters
Continuous, real-time optimized plans in a dynamic environment
Improving service, delivery times and customer satisfaction
Reduction of operating, storage, maintenance and transport costs
Maximizing resource utilization and productivity
Improving the level of fulfillment of contractual agreements
Solution design for the development of a real-time predictive energy optimization system in buildings using artificial intelligence at Synerga
Solution proposal for the development of a real-time predictive energy optimization system in buildings using artificial intelligence at Synerga, a system integrator of intelligent buildings
The system uses artificial intelligence technology to proactively optimize the energy consumption of one of the biggest sources of climate change in buildings.
The system supports a self-service building that does not require human intervention. Using machine learning and cloud-based data processing, this solution autonomously optimizes existing heating, ventilation and air conditioning (HVAC) control systems for maximum energy savings resulting in:
25-35% reduction in total energy costs
60% improvement in tenant comfort
20-40% reduction in carbon footprint
The artificial intelligence core of the system is designed to deliver significant savings and dramatically reduce carbon emissions, enabling a self-operating building and moving from reactive to preventative HVAC control in three steps:
Step 1
Learning the behavior of the building
The system identifies your building's specific operating behavior and energy flow by collecting data from both the building and external sources. It thus creates an energy profile of the building to make predictions about future energy flow.
The system collects hundreds of thousands of real-time data points such as outdoor temperature, fan speed, duct pressure, indoor temperature, humidity level, tenant concentration and more.
Step 2
Optimizing the power flow
Using artificial intelligence algorithms working in real time, the system will instruct your existing HVAC to work smarter and more efficiently.
This process is similar to the auto-pilot in a self-driving car.
Step 3
Continuous improvement
The system constantly analyzes all data generated by the building, optimizes operational efficiency and reveals the behavior of the building in areas such as:
How do changes in occupancy levels affect energy consumption?
Which HVAC units are the most energy efficient in cold or humid climates?
We partner with Quintiq to implement a Self-Learning Supply Chain
We partner with Quintiq to implement the Self-Learning Supply Chain
- The self-learning supply chain represents the next major frontier of supply chain innovation.
- Supply chain systems, using artificial intelligence (AI), can analyze existing supply chain strategies and data to determine what factors lead to supply chain failure.
- These AI-driven systems then use this knowledge to predict future supply chain issues and proactively prescribe or autonomously implement solutions.
- Artificial intelligence algorithms are driving the supply chains of the future. They are able to analyze all combinations of factors, determine which ones are predictive of service failure, and develop risk mitigation strategies that help organizations serve customers faster, with higher order fulfillment, on time, and at the lowest cost.
We are certified in Machine Learning and AI by Andrew Ng, Stanford University, one of the current AI gurus
We are certified in Machine Learning and AI by Andrew Ng, Stanford University, one of the current AI gurus
Andrew Ng taught artificial intelligence at Stanford, led the Google Brain project, and is among the pioneers of deep learning—the use of large neural networks in AI. He is also one of the leading experts on how AI is used by real businesses.
Andrew Ng has become an evangelist for what he calls "data-centric AI." The underlying premise is that state-of-the-art AI algorithms are ubiquitous thanks to open source repositories and publishing of cutting-edge AI research.
Companies such as toolmaker Stanley Black & Decker, electronics maker Foxconn, and auto parts maker Denso, which work with Andrew Ng, have access to and use the same software code as Google or NASA.
The real difference between businesses that succeed in AI and those that don't lies in the data: What data is used to train the algorithm, how is it collected and processed? Data-centric AI, says Andrew Ng, is the most important shift businesses need to make today to take full advantage of AI.