Here you will have the opportunity to see how partners from our network have successfully implemented data analysis and evaluation.
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Analyze machine data, recognize patterns, automate decisions. For analyzing production data or predictive maintenance, you will find tried-and-tested solutions from our network here. Scalable, understandable, and ready to use.
The technologies and applications presented here come from experienced partners in our network. They have been tested in industrial practice and can be flexibly integrated into your existing system landscape.
achtBytes is a german corporate startup of the STEGO Group. It is a software company with the focus on...
ALD Vacuum Technologies designs, engineers and produces advanced vacuum metallurgy, heat treatment and...
autosen offers sensors, automation technology and realizes potentials of the IIoT for customers. With...
b.telligent is a technology-independent consultancy with a focus on optimizing digital and data-driven...
We are CREM SOLUTIONS, a provider of software solutions for real estate management. Every day, we...
Cumulocity offers an enterprise-grade AIoT platform that connects & manages assets efficiently,...
DeDeNet supports companies in the digitization of business processes and specializes in paperless,...
doubleSlash stands for value creation with digital solutions and develops software-based products in...
Endress+Hauser is a leading supplier of products, solutions and services for industrial process...
ifm electronic gmbh with headquarters in Essen develops, produces and distributes sensors, controls,...
The world is in constant motion. And where movement happens, that's where we come in: improve what...
in.hub GmbH is a specialist for condition monitoring in the industrial environment. The goal of in.hub...
More and more industrial companies are collecting and visualizing their machine, process, or energy data. This is an important first step toward greater transparency. However, simply displaying data is often not enough to solve operational problems or make well-informed decisions.
Data analysis takes a crucial step further. It makes it possible to identify patterns in historical and real-time data, predict failures, prevent quality losses, and optimize processes in a targeted way. In this way, data visibility turns into real understanding, and reactive control becomes proactive operation.
Data analysis and data visualization are closely connected. Without clear and accessible presentation, many analysis results remain unused. For those who want to dive deeper, you can find more information about data visualization here.
Here you will have the opportunity to see how partners from our network have successfully implemented data analysis and evaluation.
With IXON Cloud, mechanical engineering company Pantec is not only optimizing its remote service, but also gaining new insights for product development and efficiency improvements through targeted data analysis—including tools such as the Foil Optimizer for reducing operating costs.
A leading bearing manufacturer uses Itanta Analytics to achieve real-time transparency of energy consumption—without writing a single line of code. The no-code platform provides layer-based dashboards and automated reports for rapid decision-making and sustainable efficiency gains.
WIKA and Asystom demonstrate how wireless multisensors monitor mud pumps in real time on offshore drilling platforms. AI-powered analysis detects metallic friction early on and enables planned maintenance—before unplanned downtime leads to costly consequences.
in.hub equips a long-established manufacturer with an IIoT platform that monitors machines of different generations across manufacturers. The MaDoW app is used to record, classify, and analyze downtime—for data-driven production optimization.
Companies in our network repeatedly face a number of critical challenges with their partners. These impair their efficiency, profitability, and future viability. Without advanced data analysis and evaluation, these problems often remain unsolved, resulting in significant costs.
Unplanned downtime disrupts production and service schedules and causes immense follow-up costs.
Without clear data, energy waste remains undetected and optimization potential untapped.
A lack of automation and fragmented data streams delay responses and tie up resources.
Identifying sources of error early on is crucial to preventing scrap and rework.
Proprietary interfaces and technical limitations prevent the efficient expansion of modern systems.
If expertise is not shared, risks arise for operational safety and efficiency.
Good analysis starts with data acquisition. Machines, sensors, and controllers provide different types of data. A capable solution can integrate both modern and legacy interfaces. In addition, data is cleaned and time-aligned to allow for accurate comparisons.
For data to create real value within a company, it must be integrated into existing systems. This includes connections to ERP, MES, or BI systems. The best way to achieve this is through standardized interfaces or specialized cloud platforms.
Raw data can already be preprocessed or buffered at the point of origin. Centrally, the data is collected on an IoT platform and automatically analyzed.
Because industrial environments vary widely, the solution needs to be flexible. It can be deployed in the cloud, on-premises, or at the edge. Equally important are simple, user-friendly interfaces that anyone can operate.
The core of any solution is the analysis itself, often supported by artificial intelligence (AI). This includes functions such as predictive maintenance to prevent downtime, predictive quality to ensure product consistency, and energy efficiency analyses to reduce costs. Data analysis and remaining useful life predictions are also part of this.
Cybersecurity is especially important in industrial environments. The solution must comply with GDPR, protect access to sensitive data, and enable a quick response in case of security incidents.
A powerful data analysis and evaluation solution not only creates transparency but also delivers concrete operational value, from cost savings to the development of new business models. The following advantages are based on real-world use cases from the IoT environment:
By continuously monitoring equipment conditions, companies can reduce time spent on repairs. Aurubis, for example, monitors cooling water temperatures to detect early signs of inductor downtime. This helps prevent costly production interruptions.
GELSENWASSER saves over 35,000 euros annually by analyzing its power consumption with the help of AI. SICK uses sensors to measure compressed air consumption in real time, helping to identify leaks that often translate into hidden costs.
WAGO uses sensors and data analysis to trigger filter cleaning in a material distribution system only when necessary. This prevents unnecessary maintenance and improves product quality.
Tracto-Technik monitors machines to detect issues at an early stage. This reduces service visits and increases plant availability. IXON provides dashboards for continuous insight into OEE, cycle times, and other key performance indicators.
Real-time and long-term data make it possible to forecast production volumes, resource requirements, and maintenance cycles more accurately – and to visualize them automatically using systems such as Power BI or moneo IIoT.
igus uses sensors and algorithms to identify unusual vibrations in energy chains, preventing damage to crane systems. GELSENWASSER automatically detects irregular consumption patterns in its energy monitoring.
Autosen provides fill-level data to enable a “Disposal as a Service” approach, turning sensor technology into the foundation for new business models. BestSecret aggregates product data in Kafka systems to make data-driven decisions about assortments and supply chains.
Confluent Cloud processes massive amounts of data in real time – a key component for performance and scalability in analytics processes. Platforms such as those from CREM SOLUTIONS or KUNBUS offer open interfaces for flexible data integration.
Many solutions – for example autinityHub or IXON – are specifically designed to store data locally or in compliance with GDPR within the EU. This is particularly important for sensitive application areas.
Whether it’s predictive maintenance, energy management, or quality assurance, every data-driven decision starts with the right technical foundation. Here you can learn which components work together and how to gradually integrate them into your existing environment – even in brownfield settings.
In industrial environments, data analysis ranges from simple status evaluations to predictive, automated decision-making, always based on reliable data. Below is an overview of the three main types of analysis, including practical examples.
Descriptive analysis provides transparency about current or past conditions. Typical applications include dashboards for machine status, production metrics, or energy consumption.
Examples from real-world practice include:
By using mathematical models and machine learning, it becomes possible to identify patterns in data and predict future events before failures or quality losses occur.
Examples from practice include:
AI systems identify complex relationships, propose actions, and can even implement them automatically. They learn from data, adapt continuously, and provide actionable recommendations.
Typical areas of application include:
The implementation of Industrial IoT (IIoT) and analytics solutions in manufacturing environments is a structured process that spans from data collection to continuous process optimization. Various sources describe slightly different but complementary steps, which are summarized below.
Before analysis can begin, the relevant data sources must be identified and connected. Measurements of condition variables such as temperature, vibration, current consumption, or process pressure are taken directly at the machine via sensors. Existing control systems (PLCs) provide additional operational data. Data connection is established using modern protocols such as OPC UA or MQTT, or via protocol converters. The collected data is then transferred to an edge gateway, such as the Revolution Pi or the ifm edgeGateway. At this stage, data can be buffered and preprocessed.
One example is the company igus, which measures shear forces and vibrations on energy chains and transmits the data locally via edge technology for further analysis.
At the edge or within the IoT platform, data is time-synchronized, cleaned, normalized, and converted into a unified format. Only then can it be meaningfully analyzed and combined with other data sources. It should be noted that many solutions make use of scalable platforms such as Confluent Cloud, Autosys Cloud, Elastic, or Peakboard Hub.
An example is SICK’s use of sensors with integrated value-added logic. These sensors provide preconfigured data that can be used directly on the platform without requiring separate conversion.
With tools such as Power BI, moneo, or dedicated analytics modules, relevant metrics are calculated, trends are made visible, and KPIs are visualized in dashboards. AI models help automatically detect anomalies and enable proactive maintenance, quality forecasting, or predictive energy monitoring.
Example: WAGO detects blocked material flows through pressure curve analysis. GELSENWASSER uses a dashboard to avoid load peaks and optimize energy flows in a targeted way.
Once patterns have been identified, corresponding actions can be triggered automatically or manually. These may include alerts for anomalies, automated maintenance orders in SAP, or the shift from reactive to predictive processes.
Example: Tracto-Technik integrates machine data into its service process to enable targeted remote maintenance. Autosen uses fill-level data to carry out dynamic emptying planning – fully automated and without manual intervention.
Successful systems are continuously improved through practical feedback, dashboard refinements, the integration of new data sources, or scaling to additional locations. Standardized interfaces and cloud-based solutions enable flexible expansion and the development of data-driven business models.
Example: BestSecret uses a central Kafka system for company-wide data aggregation and forecasting of inventory and logistics processes.
Real insights from the network: how leading companies from our network implement data-driven solutions for their partners in practice
In this episode, IMS and MP-Sensor demonstrate how smart connectors and sensors detect cable damage at an early stage and transmit measurement signals wirelessly. The combination of measurement technology, Bluetooth, and AI-based analysis enables predictive maintenance, reduces downtime, and simplifies the retrofitting of existing systems.
Aurubis and azeti show how targeted data collection and analysis in production facilities help prevent downtime and enable predictive maintenance planning. The IoT platform consolidates machine, sensor, and control data to support fast and informed decision-making.
An intelligent material distribution system at the WAGO plant ensures the smooth transport of granulate to the injection molding machines. An AI system analyzes data, calculates the optimal time for filter cleaning, and detects errors more quickly. The result: less waste and downtime, continuous production, and reduced effort.
This episode is about one of the largest train washing facilities in Europe and how Luxembourg’s CFL, together with igus, has digitalized its system. Intelligent sensor technology and smart energy chains make maintenance work predictable, prevent downtime, and reduce operating costs.
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.
Want to start or optimize your data analysis? Get in touch—we will support you with implementation, tech stack selection, and finding the right partner from our network.
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