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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.

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.

Optimisation Models

Optimisation AI models allow our client to improve processes, reduce costs and increase competitiveness.

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.

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.

LLMs

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

Synthetic Data

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

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.

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.

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.

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.

Inventory Management

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

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.

Market Trends

Our predictive models analyse market data, consumer behavior, and external factors to understand patters, identify trends and anticipate shifts.

Market Understanding

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

Pattern Exploration

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

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.

Projects

PORT OF ANTWERP-BRUGES

Optimisation Models, Predictive Models

Cargo Flow Predictor

Client

Partners

The problem

As part of the PIONEERS project funded by the European Union, the Cargo Flow Predictor for the Port of Antwerp-Bruges (PoAB) explores how predictive analytics can improve the logistical efficiency of the port, enable modal shift, and contribute to the goals of climate-neutral and smart mobility within the framework of the European Green Deal.

The solution

Mosaic Factor’s team developed an AI-powered forecasting tool for port logistics that predicts cargo flows, demonstrating the power of predictive analytics to enhance operational decision-making at the Port of Antwerp-Bruges.

With continuous access to data and collaboration among stakeholders, the solution is well-positioned to transition from pilot to production, contributing to a smarter, greener, and more efficient port ecosystem.

The digital solution predicts two critical events for containerized cargo:

The Next Mode of Transport (NMOT), i.e., road, rail, or inland waterway
The Departure Moment (terminal pickup day)

This fosters more proactive and sustainable logistical planning and paves the way for future integration into PoAB’s digital ecosystem, enabling:

An improvement in operational efficiency and the reduction of port congestion.
Promotion of modal shift towards sustainable transport (rail, inland waterway).
Increase data-driven decision-making capacity for terminal and transport operators.
Finally, demonstrate the viability of AI-driven predictive tools in a real port environment.

Results

This pilot project directly contributes to the EU policy objectives through:

Supporting the modal shift agenda by providing advanced visibility on inland transport modes.
Improving resource efficiency at the terminal level through optimized stacking.
Demonstrating how AI and machine learning can drive sustainable digital innovation in ports.

The project has also delivered a scalable dashboard prototype that can be expanded, improved with new data, and potentially transferred to other ports or intermodal nodes.

Do you have any questions?

We are always ready to help you and answer your questions.