Data Enhanced Products

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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

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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

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

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Descriptive Models

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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Pattern Exploration

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

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Trustworthy AI

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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

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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

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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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Hannover Messe: Exploring the Future of Manufacturing

Last week, Hannover Messe showcased groundbreaking innovations shaping the future of manufacturing. The focus on Digital Twins, Robotics, and AI highlighted their growing importance across industries.

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Here are the key trends and developments that stood out.

Digital Twin Innovations

The fair featured numerous Digital Twin solutions, emphasizing virtual commissioning and real-time manufacturing services:

  • Delta Electronics presented a Digital Twin for virtual commissioning of robotic gluing cells, enhancing automation efficiency.
  • An open-source initiative by IDTA (Eclipse Foundation) garnered attention for advancing accessibility in Digital Twin technology.
  • Circularity emerged as a critical theme, with contributions from Fraunhofer or Schneider promoting sustainable manufacturing practices. These projects spanned sectors such as food production and automotive component recycling.
  • Siemens showcased a wind tunnel simulation using Digital Twin and AI technologies, it demonstrates the potential for advanced testing methods.

AI driving innovation

AI applications in manufacturing were prominently displayed, extending beyond traditional machine learning:

  • The importance of Explainable AI (XAI) and Certified AI was highlighted, reflecting the industry’s commitment to transparency and reliability.
  • Schneider utilised AI in plant-based milk production to optimise processes, ensuring consistent sugar content—an example of machine learning automating complex tasks.
  • DFKI‘s AI solutions included temperature configuration analysis for heating systems and quality assurance for car body construction, combining image analysis, sensors, and Digital Twin technology.
  • Robotics-integrated AI applications also introduced explainability for tasks such as detecting assembly errors and automating quality checks.

Robotics and AI convergence

The robotics exhibits presented diverse applications:

  • Project ROX demonstrated process automation.
  • Siemens unveiled projects combining robots with virtual PLCs, powered by AI and large language models (LLMs). One application involved assembling toys based on customer instructions via a digital interface.

Hannover Messe proved that the synergy of Digital Twins, AI, and Robotics is transforming manufacturing. These technologies not only enhance efficiency but also pave the way for sustainable and innovative production solutions.