Data-Driven Manufacturing enables companies to use production data across the entire manufacturing lifecycle to improve decision-making, optimize operations, enhance machines health and product quality, and increase flexibility in dynamic production environments.
With this service, manufacturing SMEs can experience how to transform their factory into a smart, data-driven production environment by leveraging existing machine data, sensor data, and operational data through an AI-powered automation. The service demonstrates how AI technologies can unlock hidden value in existing data and convert it into actionable insights for production optimization.
The solution builds on advanced AI capabilities and industrial data integration, enabling seamless connectivity between machines, sensors, and relevant IT systems. By applying AI models to real production data, companies gain insights into performance, bottlenecks, and improvement opportunities, supporting continuous optimization of manufacturing processes. This aligns with modern smart factory approaches where interoperability, real-time monitoring, and intelligent decision-making are key drivers of efficiency and scalability
Customer requirements
To ensure effective analysis, accurate AI model application, and meaningful production optimization, customers are expected to provide the following data and information:
1. Production Process Overview
A clear description of the manufacturing environment.
2. Production and Machine Data
Selection of production processes, machines, analysis of available data. where required, selection of suitable AI model (e.g. for defect detection). Both historical and real-time data can be used to train and validate AI models.
3. IT Systems and Data Infrastructure
Information about existing digital systems and data availability. This ensures smooth data integration and interoperability with AI platforms.
4. Use Case Definition and Objectives
A clear definition of the intended use case and expected outcomes.
5. Operational Constraints and Requirements
Important constraints that must be considered.
The outcomes of the experiment are documented in a comprehensive evaluation report, providing insights into:
– Opportunities for production optimization and efficiency gains
– Improved data utilization and visibility across the production process
– Impact of AI-driven decision-making on quality, downtime, and throughput
– Recommendations for further digitalisation and AI adoption
Validate the applicability, scalability, and adaptability of the AI-driven solution across different manufacturing processes. Demonstrate impact of the applied technology
Experimentation Approach
This service is delivered through a structured experimentation process, tailored to the SME’s production environment:
Use Case and Data Assessment Analysis of available production data, machine connectivity, and operational challenges to identify high-impact opportunities for AI-driven optimization.
AI Model Application and Data Integration Deployment of AI-driven models and tools on existing data sources to analyze production performance, detect inefficiencies and anamalies, and support automated decision-making. This may include integration with shopfloor systems, sensors, machines and control platforms.
Smart Factory Demonstration Demonstration of how AI-powered platforms enable real-time monitoring, adaptive control, and improved operational performance within a connected manufacturing environment.
Manufacturing companies that what to unlock potential of data to improve decision making, optimise operations, enhance machine health, with explicit attention to machine-human interaction and scalability.