Trustworthy AI

When using AI models in environments where compliance standards are important, Mosaic Factor can help your company be on top of data governance by applying trustworthy AI solutions.

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Synthetic Data

Synthetic data is artificial data generated from original data using a model trained to reproduce its characteristics and structure.

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Descriptive Models

Descriptive models aim to describe patterns, relationships, and structures within data. They don’t predict future outcomes but provide insights into existing phenomena.

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Predictive Models

Predictive modelling, also known as predictive analytics, is a discipline that uses statistical, mathematical and artificial intelligence techniques to predict future outcomes based on historical data.

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LLMs

At Mosaic Factor, we focus on the creation of domain specific LLMs (or light Large Language Models) for our client organisations.

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Digital Twins

To allow your business to monitor and optimise your assets in real-time Mosaic Factor uses Digital Twins. They can predict failures, detect inefficiencies, and improve decision-making through the use of data.

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Data Enhanced Products

Through different data sources (ie. physical tests) and ML models and usually in combination with our digital twin solutions, our data enhancement solution can learn, predict, and simulate outcomes to provide automatic product configurations that result in product and component improvement during the development process.

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Data As a Service Products

Data as a Service (DaaS) is a cloud-based model that allows companies to access, manage, and analyse data on demand, without the need for extensive on-premise infrastructure.

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Predictive Maintenance

For Predictive maintenance models, we use historical and real-time data to anticipate equipment failures or maintenance needs. By analysing sensor data, maintenance logs, and other relevant information, we can schedule maintenance proactively, reduce downtime, and extend the lifespan of your machinery.

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Demand Cost Forecasting

Our predictive models help businesses forecast demand for products or services. By analysing historical sales data, seasonality, economic factors, and external events we can optimise inventory levels, allocate resources efficiently, and minimise overstock or stockouts.

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Quality Analytics

We identify patterns that correlate with defects or quality issues, allowing your business to take corrective actions early and maintain high-quality standards.

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Inventory Management

We use predictive models to optimise inventory levels by considering factors such as lead time, demand variability, and storage costs.

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Supply Chain Management

We can use historical and real-time data analytics to manage the supply chain, optimise transportation and ensure on-time product delivery.

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Market Understanding

Our descriptive AI models provide valuable insights for decision-making and understanding complex systems of your organisation.

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Pattern Exploration

Our descriptive AI models provide valuable insights for decision-making and understanding complex systems of your organisation.

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Logistics

Logistics

Mosaic Factor’s higher priority in Logistics is sharing key data across different Supply Chain players to optimise performance while managing sustainability by mitigating the impact of these operations.

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Automotive

B:SM Tram Parquímetre

Mosaic Factor’s apply AI solutions in various aspects of the automotive industry, usually by enhancing vehicles and its components during its development.

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Mobility

Mobility

Mosaic Factor’s higher priority in Mobility is to optimise transport systems to people’s mobility while improving overall security and sustainability of transport solutions.

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Corporate Services

Corporate Services

Our machine learning and complex algorithms help organisations manage compliance and customer service to increase the service level of your organization while optimising resolution time for several processes.

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Manufacturing

Manufacturing

Mosaic Factor’s higher priority in Manufacturing is aid our clients decrease costs, increase sustainability while streamlining the production chain.

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Healthcare

Healthcare

Mosaic Factor’s higher priority in Healthcare is making use of data to improve patient care and monitoring in a safe manner to optimise healthcare systems resources and assisting healthcare professionals.

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Projects

Automotif

Simulations for shunting operations

Client

Partners

The problem

Automating railway operations by using the terminal as an intermodal hub through the use of autonomous locomotives, cranes, and ground vehicles.

This will result in seamless transfers between maritime, road, and rail transport, supporting a more efficient and interconnected logistics chain, reducing delays and improving overall operational fluidity.

AutoMoTIF focuses on the development of strategies, business and governance models, regulatory recommendations and synergies that enable the integration and interoperability of automated transport systems and solutions towards the operational automation of multimodal cargo flows and logistics supply chains in the intra-European network.

It will list the gaps – both regulatory and technological – that are currently identified in automated transport technologies and logistics operations between modes and hubs through the analysis of automation demand and supply in multimodal transportation (users vs market).

The solution

AutoMoTIF follows an inclusive model to ensure that user and community needs are properly addressed, and innovations are aligned with their expectations. UCs and scenarios will focus on real challenges and gaps in seamless automated logistics that will be simulated in real settings and different geographical locations.

MOSAIC FACTOR focuses the automation of shunting, marshalling, and loading/unloading of railcars to:

    • Enhance efficiency
    • Reduce bottlenecks
    • Environmentally friendly terminal management

The simulation environment accurately replicates real-world operations, considering factors like train schedules, container movements, and coordination between autonomous systems.

Data

During simulations, the model replicates the behaviour of autonomous systems based on their predefined algorithms and responses to external stimuli. 

It factors in real-world constraints, such as train schedules, container destinations, and resource availability, to mirror rail terminal’s operational complexity accurately.

The simulation runs iteratively, simulating multiple scenarios to analyse the impact of automation on key performance indicators (KPIs) such as modal share increase, efficiency gains, emission reduction, safety performance, reliability, cost savings, and intermodal coordination.

    • Historical data
    • Operational plans
    • Designed scenarios to test terminal’s performance
    • Synthetic data to augment available data, capturing different operational conditions and challenges

Results

The results of four use cases will be used to set up a master scenario addressing the end-to-end delivery of goods using highest degree of automation possible. The outcomes from current practices will be analysed, leading to the assessment of benefits that can be gained by exploiting automated transport means to seamless multimodal automatic cargo transport and vice versa. These benefits will be defined and analysed based on their social, environmental and economic impact, such as decreased emissions and congestion, improved working conditions and safety, as well as reduced logistics and freight transport costs, with the SSH aspects being a priority. 

AutoMoTIF contributes to the enhancement of synergies among sectors and stakeholders, following European priorities, strategic partnerships, such as CCAM, Zero Emission Waterborne Transport and EU Rail JU to ensure that transferability of expected outcomes.

Do you have any questions?

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