Real examples from the network illustrate how companies use data standardization to reduce complexity, cut costs, and enable new services.
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Discover how others are already scaling successfully with proven strategies, standards, and partner solutions from our IIoT network. Avoid isolated solutions and rely on approaches that have already proven their value.
Our platform brings together leading technology partners and solution providers who can help you implement your data standardization. Benefit from tried-and-tested solutions and experienced specialists from the IIoT community who know which transmission technologies work reliably in demanding industrial environments.
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In many companies, valuable data remains unused even though it already exists. The problem is that it cannot be leveraged across systems. The reason lies in a fragmented communication landscape without a common language.
Production environments that have evolved over time consist of a wide range of devices, machines, and controllers using different communication protocols. The result is data silos, integration gaps, and high engineering effort.
Systems often lack a unified programming language. As a result, data must be transferred, converted, or maintained manually and repeatedly. This complicates OT/IT integration and reduces overall data quality.
Without standardized interfaces, digital projects quickly reach their limits, especially when connecting multiple sites, plants, or external partners.
Custom interfaces or isolated solutions for legacy systems require significant effort in development, testing, and maintenance. Projects quickly turn into patchwork solutions instead of scalable infrastructures.
For data-driven services such as predictive maintenance, energy optimization, or live monitoring, fast and reliable data streams are essential. Incompatible systems, however, prevent such use.
Operating multiple networks in parallel for motion control, safety, image processing, and other applications leads to redundancy, higher error risk, and rising operational costs.
The foundation for creating measurable value lies in using a common language, not only between machines and systems but also among people.
This is precisely where data standardization comes into play. It establishes binding structures for content, formats, and semantics, forming the backbone of scalable IIoT architectures.
Retrofit projects, digital twins, predictive maintenance, or AI-based optimization—all rely on standardized data formats, protocols, and models to ensure seamless integration, analysis, and automation of processes.
Companies that take this step early benefit from reduced integration costs and shorter project timelines. They also secure a sustainable competitive edge in the data-driven production landscape of the future.
Consistent, standardized data communication reduces complexity, lowers costs, and builds the foundation for scalable, secure, and future-ready IIoT architectures across sites, devices, and systems.
IoT systems generate billions of data points every day. Without unified formats, protocols, and semantics, this data flood does not become information but turns into an expensive blind flight.
Operational technology infrastructures that have evolved over time rely on a wide range of fieldbuses, protocols, and proprietary data models. Without proper standardization, connecting new systems can lead to errors, significant effort, and high costs.
Operational technology infrastructures that have evolved over time rely on a wide range of fieldbuses, protocols, and proprietary data models. Without proper standardization, connecting new systems can lead to errors, significant effort, and high costs.
Use cases such as predictive maintenance, anomaly detection, or track & trace require reliable data that is available quickly, securely, and in a structured manner. Standardized, low-latency protocols such as MQTT or OPC UA TSN are essential for this.
The separation between shopfloor and cloud is outdated. Achieving convergence between machine environments and IT systems requires unified interfaces and semantic models. Standardization forms the foundation for this connection.
Pandemics, the semiconductor crisis, and geopolitical tensions have made resilience one of the key challenges. Standardized data models create transparency across the supply chain, identify bottlenecks early, and accelerate response times.
Real examples from the network illustrate how companies use data standardization to reduce complexity, cut costs, and enable new services.
At VWN’s Hanover plant, the SYNAOS Intralogistics Management Platform coordinates the world’s largest manufacturer-independent VDA 5050 robot fleet. Standardized communication orchestrates more than 135 AGVs and automates just-in-sequence material flows with around 9,000 transports per day.
This example demonstrates how the Digital Product Passport (DPP), part of the EU Green Deal, standardizes and provides product-relevant data in a machine-readable format. The foundation is built on ECLASS (semantics) and the Asset Administration Shell (AAS). The first mandatory application will be the Battery Passport starting in 2027, with additional product groups to follow.
Learn how the Asset Administration Shell (AAS) standardizes the digital twin in production and links asset data with business processes. Discover how the Neoception Digital Twin Infrastructure dynamically generates AAS in real time, uses ECLASS semantics, and integrates customer systems and apps in a scalable and GDPR-compliant way.
Learn how Neoception and ECLASS created a common data language for machines to lay the foundation for the digital twin and enable new competitive advantages.
Many IIoT projects fail not because of the idea, but because of the implementation: a lack of scalability, high operating costs and unclear requirements lead to expensive rework and a failed business case.
On our platform, you will find tried-and-tested technologies, best practices from real industry projects and the collective knowledge of our community. We show you how to avoid typical mistakes with the right technology stack – from data acquisition to AI evaluation – and how to set up your IIoT project economically and future-proof.
Discover how leading companies from our network successfully structure their projects – modular, interoperable and data-secure.
Data acquisition forms the solid foundation of your IoT application. Whether machine, operating or sensor data - accurate and reliable data acquisition enables precise analyses and data-based decisions. Modern solutions capture data directly at the machine, standardized and in real time.
Reliable data transmission is essential for every IoT process. Choose between wired (e.g. Ethernet) and wireless technologies (e.g. 5G, LoRaWAN) based on your requirements in order to optimally combine stability and flexibility.
The efficient preparation and pre-processing of your raw data ensures that it can be used immediately. Whether edge computing or local pre-processing - reduce the amount of data and significantly improve the performance of your IoT systems.
Data standardization creates the basis for efficient, cross-manufacturer communication and consistent use of data throughout the entire life cycle. Whether through protocols such as OPC UA over MQTT for secure transmission or standardized product data and digital twins - your IIoT projects remain flexible, scalable and economical.
The convergence of production technology (OT) and information technology (IT) enables you to achieve a transparent data flow without media disruptions. This allows you to eliminate data silos, speed up decision-making and optimize your operational processes in the long term.
IoT platforms form the central nervous system of your digital infrastructure. As PaaS or SaaS solutions - for example in the form of customer portals for manufacturers - they store, visualize and manage IoT data. As a result, you always have a comprehensive overview of your processes and can make well-founded, data-based decisions.
Protecting sensitive industrial and process data is a top priority. Modern security concepts ensure that your data is transmitted and stored in encrypted form and that your systems always comply with current regulatory requirements.
Efficient management of networked IoT devices is a key component of successful digitalization strategies. From commissioning and updates to decommissioning - structured device management reduces operating costs and significantly increases the security of your IoT infrastructure.
Visualized data enables faster and more precise decisions in your company. Modern dashboards and graphical presentations transform complex data streams into clear, real-time displays and create transparency at all levels of the company.
The systematic analysis of your IoT data uncovers hidden correlations and identifies optimization potential in your processes. From descriptive statistics to complex analysis procedures - gain valuable insights from your operational data for well-founded business decisions.
Data analysis is the basis for data-driven IIoT applications - from process optimization to AI-supported predictions. While traditional evaluations work with fixed rules, AI algorithms independently recognize patterns and anomalies - for predictive maintenance or quality forecasts, for example. Both approaches complement each other and optimize the potential of your IoT data.
Industry-specific IoT applications address concrete challenges with preconfigured functionalities. From production optimization to asset tracking — these specialized solutions offer rapid time-to-value and can be flexibly adapted to individual requirements.
Our community brings together industry experts who have already implemented successful IIoT projects – openly, practically and on an equal footing. Gain insights into how other companies have solved challenges, share your use cases and discover new ideas and concrete solutions for your business.
Every innovation starts with an idea. Discover proven use cases that support your digital transformation — from predictive maintenance to worker safety.
Real-time monitoring of machine and sensor data to reduce downtime.
Data-driven maintenance to detect failures early and cut costs.
Seamless tracking of assets and material flows in production and logistics.
Automated collection and management of production and operational data.
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