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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Optimisation Models

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

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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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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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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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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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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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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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Modelling Platform

Client

Partners

The problem

GreenLog accelerates systemic changes in last mile delivery ecosystems for economically, environmentally and socially sustainable city logistics. The project establishes city platforms comprising of inclusive stakeholder Urban Living Labs for nurturing social innovation, designing and deploying innovative delivery solutions while allowing the most effective exchange of ideas, the development of robust, harmonized regulatory and policy frameworks, and cooperative business models that build upon effective public/private-sector collaboration and joint investments.

The Green-Log approach provides an innovative simulation environment for scenario building combining different solutions that allow the integration of last-mile delivery interventions with the highest possible impact on environmental sustainability and traffic reduction, while considering their financial viability. The approach is deployed and validated in four cities and areas: Athens, Barcelona, Oxfordshire and Ispra.

The solution

Mosaic Factor focused in the development of the Augmented Intelligence Modelling Platform that integrates the following tools and algorithms:

    • the demand prediction,
    • the optimisation and the simulation tool,
    • the development of user interfaces for configuring high-level modelling scenarios.

The integrated GreenLog design platform constitutes a constellation of modules acting as connectors to the outputs of the tools mentioned above and is implemented in the form of a microservices platform for seamless integration of additional services, dynamic customisation of workflows, improved security and resilience and modular deployment in different environments.

The developed microservices are categorised according to their domain of operation which varies to user-related (user access, user information delivery, etc.), data-related (data transformer, data connector,etc.) and function-related (location optimisation, scheduling, etc.), allowing for more efficient access control and compartmentalised implementation and testing.

Data

The types of data sources or datasets used for this project are:

    • Order datasets,
    • parcel datasets,
    • maps,
    • demand datasets,
    • microhubs and microcenter datasets,
    • amongst others.

Results

Check our new on the first project deliverable on our Augmented Intelligence Modelling Platform.

Do you have any questions?

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





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