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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European Smart Mobility Resource Manager

Client

Funded by the Spanish Government

Partners

The problem

Promote new sustainable and complementary mobility schemes, including dynamic vehicle sharing, real-time carpooling, demand-responsive transport, Electric Vehicles (EVs) sharing, etc to minimise the use of private car.

To do that, it is primal to address the lack of an efficient and seamless integration of complementary, capacity-limited mobility services in the overall urban travel chain, including all transport modes:

    • Electric Vehicles (EVs)
    • Public transport (bus, tram, metro, train)
    • Flexible services such as transport on-demand
    • Mobility sharing schemes (car sharing, motorbike sharing and carpooling)

The solution

Mosaic Factor’s team focus was:

  1. Optimising intermodal journeys with the possibility to combine available transport modes focusing on user needs, habits, and preferences to provide a more personalised service offer. Taking into account that we all have the same rights in terms of mobility and accessibility but we often like different things, and the purpose of our journey may be different.
  2. Defining specific and realistic persona and cluster user behaviours and patterns, taking into account the privacy restrictions and challenges associated with mobility data.

A mobile application was designed to transform personal mobility in urban regions by integrating all available transportation options into a single, seamless journey planning solution.

Mosaic Factor’s team role in MyWay project was to develop components and an integrated application to facilitate sustainable mobility, combining all sorts of transport services, public and private, regular and flexible, providing personalised service, adapting route and modality suggestions in real-time, monitoring the parameters affecting service offering performance and gathering and maintaining users’ feedback about the quality of services to assist service improvement.

MyWay app provides travel suggestions optimised to the user’s perspective:

    • Considering preferences and needs: including requirements of time, cost, and comfort
    • Integrated ticket information and real-time updates,
    • Displaying of all integrated transport modes available
    • Encouraging the use of cleaner modes of transport.

Data

MyWay investigated, developed, validated, and integrated the platform, the European Smart Mobility Resource Manager, including cloud-based services and facilities to support community-supplied information collection and processing.

  • connecting transport service providers and travellers in the provision and combined use of several mobility services, both individual (car-sharing, scooter-sharing, pooled taxis,), collective (busses, demand responsive busses, local trains) and related to (small) goods services.

  • supporting and personalising travel options search and composition, travel execution, continuous proactive on-trip user aid (information and events, re-planning, suggestion of alternatives, etc.).

  • integrating crowd-sourcing and social sharing facilities into travel offer, planning and execution.

  • based on cloud technologies and compatible with Future Internet infrastructures and services (Generic and Specific Enablers).

  • enhancing current intermodal public transport journey planning services with integration of personal mobility services (flexible car/scooter-sharing schemes, taxis/shared taxis, demand responsive transport, parking search/booking).

  • supporting the full operational chain of travel options searching, planning, composition, booking and payment.

  • monitoring user travelling through mobile Apps and interaction with the transport service infrastructure (e.g. NFC, payment, booking).

  • collecting user feedback and evaluating travelling experience.

  • building up and maintenance over time of a set of service planning rules balancing users’ profiles and preferences with operators service provision goals.

  • integrating of social sharing and on-trip traveller communities support in the process of searching, planning, execution and adaptation of personal trips.

  • mining and analysis of users’ feedback for service operators and city planners for adaptation and improvement of transport provisions.
  • deployment of core components of MYWAY platform on Cloudwatt™ infrastructure provided by Thales / Orange.

  • validation and evaluation of technical, operational and business impacts of cloud-based solutions in the provision of advanced ICT services enabling sustainable personal mobility service offering and usage.

Results

The platform was tested in three ‘Living Labs’ that have deployed innovative measures to improve sustainable mobility:

  1. Barcelona and Catalonia Region (ES): involving multiple public transport modes (from classical ones to more innovative as Electrical Bike sharing or Bus-on-demand) as well as individual or collective private options in a major urban area together with a complete region (Catalonia).
  2. Berlin (DE): specific scenarios were simulated to observe and measure the impact of different mobility strategies.
  3. Trikala (GR): validation of the MYWAY environment for promoting the use of car sharing, demand responsive bus transport, and cycling in real conditions in a medium-size urban area.

The combination of large and dense cities along with smaller MyWay test sites reflects the ambition for the European Smart Mobility Resource Manager to be tested in varying urban conditions and produce more in-depth data analysis.

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