About Dr.
My professional career has taken me through various leadership roles at Siemens, where I helped shape forward-looking fields such as the Internet of Things, SaaS solutions, and augmented reality. While pursuing my Ph.D. in Business Informatics—a program I began concurrently during my time at Siemens—I founded my own company together with my brothers. We specialize in training, consulting, and the implementation of generative AI and digitalization solutions. As co-host of the IoT Use Case Podcast, I bring the connection between industrial innovation, technology, and science to the table.
Podcast Episodes
Asset Administration Shell in the Model Factory: Reconfiguring Production Lines Flexibly
The starting point is a familiar problem: new machines are hard to integrate into existing lines, because line PLCs are rigidly programmed and data breaks across subsystems that use different semantics. The Asset Administration Shell acts as a standardized data interface – within the plant and across company boundaries. In the model factory, every machine and every module is given an Asset Administration Shell. Using an Asset Orchestration Platform, Bosch Rexroth models the line with business logic rather than fixed PLC programming, and reconfigures flexibly between AGVs and machines. XITASO supports the standardized creation of these shells; an early example is the digital nameplate at WITTENSTEIN. The Asset Administration Shell remains one technology alongside OPC UA and MQTT – what matters is choosing the right one for each use case. The biggest hurdle is creating the Asset Administration Shell itself. Krause advises starting small, at the presumed bottleneck machine, and establishing data transparency before orchestrating the line. Knoblich shifts the focus to OT/IT and a shared target vision: in the end, it is the people you have to bring along. What you'll take away The main barrier to entry is creating the Asset Administration Shell itself – for brownfield machines, XITASO offers a PLC function block for this purpose. Based on the Asset Administration Shell, a line can be modeled with business logic through an Asset Orchestration Platform instead of being rigidly programmed into the line PLC. The Asset Administration Shell is one technology among several; MQTT is suited to motion commands, OPC UA to sensor signals. To improve OEE, start at the presumed bottleneck machine: first make the losses transparent, then orchestrate.
Retrofit Utility Networks: Wireless Condition Data, Not Inspection Trips
Many utilities run decades-old legacy networks whose condition has so far only been captured through manual inspections. Two employees drive out to a chamber, climb up to six metres underground, and read off an analogue pressure gauge. That yields nothing more than a snapshot – leaks, flooded chambers, or failing insulation on steel jacket pipes go undetected between inspection trips. Lausberger walks through the path from sensor to control room: battery-powered measurement technology transmitting wirelessly via LoRaWAN, mioty, or mobile networks such as NB-IoT and LTE-M, connected through standardized interfaces like REST API, MQTT, and OPC UA. The key trade-off is scalability. Homegrown, isolated solutions quickly hit their limits, which is why he favours cloud-based network servers and a reliable partner over running an in-house platform. To show how the data improves network operation, he points to a pressure control valve that was identified, after weeks of searching, as the cause of an oscillating network, as well as to long-term data on pressure peaks for network planning. At under 100 euros per measuring point, he considers the approach viable for smaller municipalities too. Key takeaways The condition of many district heating, water, and gas networks is still captured only through manual chamber inspections; IoT delivers continuous data instead of snapshots. Battery-powered wireless sensors (LoRaWAN, mioty, NB-IoT, LTE-M) achieve lifetimes of up to ten years and can be retrofitted without power or cable installation. Homegrown, isolated solutions scale poorly; according to Lausberger, a cloud-based network server and a reliable partner are the more sustainable route. Chambers six metres deep call for alternatives such as detachable antennas, because otherwise the wireless signal never reaches the surface.
180km Steam Network: Wireless Condition Monitoring with LoRaWAN
CHEMPARK operates around 1,000 kilometers of pipelines, roughly 180 kilometers of which is steam network. The network was historically designed for different load profiles – demand and offtake points have changed significantly since then. Conventional temperature measurement is barely economically viable out in the field without extensive cabling. Steam traps – components that drain liquid condensate from the steam system – were previously inspected manually; as a result, defects went unnoticed for months. Currenta Conneqtive has built a LoRaWAN network that covers the entire CHEMPARK with just a few outdoor antennas. Battery-powered sensors capture temperatures at network nodes and monitor the status of the steam traps via Condition Monitoring. The goal is to reduce response times for defective traps from months to hours – and to gradually optimize the network design based on better data. In parallel, an AI-powered dynamic simulation model is being developed that maps the network's condition even between measurement points. Because external service providers couldn't deliver the necessary combination of ML expertise and thermodynamic process knowledge, the model is being developed in-house. CHEMPARK serves as a stress test here – what runs reliably here is intended to be offered as a standardized SaaS product for external industrial customers. Your key takeaways LoRaWAN covers expansive industrial sites cost-effectively with just a few antennas – without extensive cabling infrastructure. Condition Monitoring of the steam traps reduces response times for defects from months to hours. Live sensor data from the field improve network simulations and enable better-informed investment decisions in steam network operation. AI-powered simulation in a process environment requires ML expertise and thermodynamic domain knowledge in equal measure – without combining both, you will fail at the model. A LoRaWAN network as shared infrastructure can be used for multiple use cases simultaneously and gradually expanded with new applications.
Vibe-Coding on the Shopfloor: Building MES Applications In-House Without System Integrators
UMH positions itself as an open-source platform for industrial data management. The core idea: anyone who wants to use vibe coding — the AI-assisted generation of frontend applications — on the shopfloor needs a clean, structured data foundation for it. UMH provides this through a Unified Namespace: an event-based architecture that normalizes machine data from a wide range of sources and makes it available via REST APIs and real-time streams. Krüger explains why AI needs the same data semantics as a human production manager — without clear context, it hallucinates just the same. Things get especially concrete on the topic of MES. Traditional implementations cost hundreds of thousands of euros, require system integrators, and are barely modifiable. With a clean data backend and vibe coding, Krüger says, such applications can now be built in-house within a few weeks and at a fraction of the former cost. A Forrester study with UMH customers shows: 5% energy savings, 14% fewer unplanned stoppages, and an ROI of over 400%. Your key takeaways Vibe coding on the shopfloor only works on a clean data foundation — the Unified Namespace provides the necessary semantics and structure. AI needs the same context as a human: without structured data, it hallucinates just as reliably as it would with unclear work instructions. Infrastructure as code makes connecting machines dramatically faster — days turn into minutes when the AI works with configuration files instead of UI clicks. Build vs. buy is shifting fundamentally: MES applications can now be built in-house at a fraction of the former cost. UMH is fully open source — just download it, validate your own use cases, and get started.
No Cables, No PLC, No IT Project: Simply Connecting Decentralized Assets
autosen's answer is the minion: a modular sensor system with an integrated cellular gateway and battery that works without any existing infrastructure. The underlying approach is deliberately radically simple — control systems are left untouched; instead, critical points in the field are monitored. From unboxing to the first data in the cloud: 30 minutes at most. Dennis Jansen explains why autosen relies on LTE-M and NB-IoT rather than 5G — energy efficiency beats bandwidth when a battery needs to last two years. And he makes clear who the system is designed for: not the end user themselves, but system manufacturers who want to offer their customers standalone IoT services — for example with industrial fans, level monitoring, or access control. Key takeaways Plug & play in the IIoT is possible if you leave control systems aside and deploy standalone devices for critical points LTE-M and NB-IoT beat 5G for battery-powered sensors, because energy efficiency comes before bandwidth The minion system targets system manufacturers as an enabler — not end customers directly
Direct Air Capture: From Pilot to Autonomous Industrial Scale
Greenlyte is transferring a direct air capture technology, originally validated in the lab, into real-world, scalable systems—from a 50 t CO₂/year pilot plant in Duisburg to a 1,500 t/year first-of-a-kind facility in Marl. The key challenge lies less in the core idea and more in its industrial implementation: fluctuating availability of renewable energy, varying environmental conditions such as temperature and humidity, and the combination of classical process engineering (absorption) with electrochemistry (desorption) require highly dynamic and robust process control. In addition, practical aspects such as reliable sensor performance under real-world conditions—e.g. foam formation or changing media properties—play a crucial role. From a technical perspective, Greenlyte relies on end-to-end digitalization from an early stage: sensors are connected via IO-Link, while parameterization and data access are handled remotely via ifm moneo. Centralized data management, reuse of parameter sets, and structured FAT/SAT testing enable rapid iteration and scaling. This is complemented by revision-based plant engineering, where changes are often implemented via configuration rather than code rollouts. The use case demonstrates how standardized field connectivity, remote service, and data-driven optimization help stabilize prototypes more quickly, accelerate commissioning, and lay the foundation for scalable plant fleets and efficient maintenance strategies.
No laptop at the control cabinet: scaling device & application management in OT
Live from the Hannover Messe, Portainer.io and WAGO discuss how container-based application and device management makes operating and scaling edge solutions on the shopfloor practical. At the core is the transfer of proven IT principles into OT – without requiring OT teams to become IT specialists. These modern approaches meet typical challenges: heterogeneous hardware (32/64-bit architectures), lack of transparency around software versions (“golden USB stick”), time-consuming 1:1 updates directly at the control cabinet, and the organizational IT/OT gap with strictly defined update windows. The technical foundation consists of standardized Docker images (e.g., Node-RED), distributed via a control plane and deployed automatically through CI/CD pipelines. Rollbacks ensure that systems can quickly return to stable versions when needed. AI use cases can also be integrated seamlessly: edge hardware can be extended with AI accelerators to run applications such as anomaly detection or specialized smaller LLMs (tiny LLMs) directly on-site – without breaking the container-based stack. For decision-makers, this results in clear benefits: faster and more predictable deployments across distributed locations, reduced travel and integration costs, increased modularity through additional containers, and greater independence from individual vendors.
LIVE: Making sensors network-ready: shopfloor data directly into IT
Many IIoT projects fail not because of AI or analytics, but due to limited access to shopfloor data. Especially in heterogeneous legacy environments, data often only reaches IT systems with significant engineering effort. The main causes include multi-layered OT architectures, cyclic fieldbus communication, and unstructured raw data lacking sufficient semantic context. Even small changes at the sensor level, such as adjustments to measurement ranges, can make data difficult to interpret without context and require extensive normalization. Perinet addresses this challenge by making sensors and actuators directly network-capable. Relevant information is transmitted via IP in an event-based manner, parallel to existing PLC communication, directly into IT systems. One of the key technologies is Single Pair Ethernet, which brings Ethernet connectivity down to the field device level and enables retrofit in existing systems—without requiring additional infrastructure such as new control cabinets. The result is a streamlined data path with reduced integration effort, lower data volume, and significantly higher data quality. This enables applications such as AI, OEE, and condition monitoring to be implemented more efficiently—while also meeting security requirements in the context of the EU Cyber Resilience Act.
Product digitalization for OEMs: modular development of complex field products
OEMs want to digitalize machines and vehicles in the field while ensuring that functions operate reliably, even though the devices are often only rarely physically accessible. In the podcast, Tobias Maier and Mirko Spitalny explain, using the example of the “digital freight train” with Knorr-Bremse, how early prototypes are turned into robust IoT products. Three recurring challenges are at the center of the discussion: a missing or unpredictable energy supply, reliable data transmission in demanding environments with a lot of metal, and the ruggedization of the product for field deployment. To address these challenges, an energy-autonomous edge system with solar cells and batteries is implemented. For the safety-critical digital brake test, wagon-to-wagon wireless networking in the sub-gigahertz range is used, while LTE is employed as a complementary connection and only occasionally for functions such as firmware upgrades or position transmission. The foundation for this approach is a modular IoT toolkit with reusable hardware and software modules, for example for energy harvesting, battery systems, BLE for commissioning, wireless communication and device management. For IT and OT decision-makers, the episode shows how development effort can be reduced, iterations accelerated and digital products operated reliably over long life cycles.
Earlier flood detection with radar and NB-IoT
Municipalities need reliable real-time data during heavy rain and flooding in order to identify critical developments earlier and respond more quickly. That is exactly what this podcast episode with Felix Brühl from Endress+Hauser is about. The focus is on autonomous water level and soil moisture sensors that must operate reliably even at remote measuring points without a power supply. The episode shows which requirements matter in practice: long battery life, stable data transmission despite fluctuating network coverage, and valid measured values even in cases of frost, vegetation growth, vandalism, or changes in sensor position. It also discusses how water level sensors work with event-based transmission intervals and why soil moisture and soil temperature provide important additional information for early warning. In addition, diagnostic data such as battery status, tilt angle, and sensor status play an important role in ensuring measurement quality over the long term. The episode also looks at how the data is integrated into existing system environments, for example via APIs, webhooks, or OPC UA, and at what municipalities and operators of critical infrastructure should consider when selecting, operating, and scaling such solutions.
IO-Link in Manufacturing: Condition Monitoring and Digital Services
In many plants, IO-Link-capable sensors and actuators are installed, but often only switching and analog outputs are used; diagnostic and condition data remains untapped. In projects, value creation fails less because of the technology than because of unclear goals (e.g., alarming, reports, OEE, energy) and because the right stakeholders from OT and IT are not brought to the table early enough. nass magnet addresses the field level with an IO-Link-capable Smart Connector (plug) for solenoids and valves, as well as an Ethernet master that forwards measurement data in an IoT-ready way. in.hub integrates the master into SIINEOS: devices and IO-Link devices can be configured without programming, data is normalized, stored at the edge, and provided via services such as dashboards, alarming, and cloud and SQL connections. IODD interpretation and structured handover (e.g., via OPC UA/MQTT) support semantics toward the IT world. This enables faster retrofit projects, lower load on the control system, and a scalable data basis for condition monitoring as well as OEE and energy analyses.
Digital Product Passport: Making Product Data Interoperable with ECLASS and AAS
Many industrial companies need to provide product data for procurement, engineering, and service across multiple systems (ERP/PLM/PIM) — and in the future additionally in the context of the EU Digital Product Passport (DPP). The podcast demonstrates how missing semantics and inconsistent terms (e.g. “height” vs. “depth” depending on the CAD/CAE context) lead to interpretation effort, errors, and manual rework. The approach involves standardized data containers and clear feature definitions: the Asset Administration Shell (AAS) structures the exchange, while ECLASS provides the semantics for classification and features. WAGO, together with Neoception, uses a mapping of source system data to ECLASS (Advanced) to provide information consistently and, in the future, deliver it to various targets (including CAD portals, the website, and the DPP). For IT/OT decision-makers, this creates a scalable way to reduce integration costs, maintain data sovereignty, and meet regulatory requirements using reusable data building blocks.
From Condition Monitoring to Proactive Service in Mechanical Engineering
SPALECK, a machine builder for vibratory conveyors and vibrating screens used in recycling, chemical, and food processing plants, integrates condition monitoring into its long-lasting machines to avoid downtime in linked process lines. The challenge was less about data collection than about operational responsiveness: local traffic-light indicators were overlooked, and rigid thresholds are not reliable early-warning signals when products and operating modes change. To derive service decisions from machine data in time, machines were quickly connected via edge routers from IXON and data was made available in the cloud. aiXbrain added ML models for predictive maintenance as an integrated app (Dataray) — including in-platform labeling, model comparison (false positives/negatives), and automated retraining. The solution is deliberately kept open via interfaces such as OPC UA, PROFINET, and APIs, so operators can integrate the data into their own plant dashboards. Benefits for IT/OT decision-makers: fast rollout, secure remote access, a scalable data pipeline, earlier fault detection (several days of lead time), and service-ready processes with clear alerts instead of additional tool overhead.
IoT Low-Code on the Shopfloor: Machine Data via OPC UA into ERP
GIESSER Messer started with a simple shopfloor visualization to make production KPIs visible for the first time on a daily basis and directly at the machine. The core challenges included low user acceptance, heterogeneous data sources (ERP, machines, energy), missing standards in machine interfaces, and the requirement to operate production-critical visualizations reliably – without a large-scale IT project or mandatory cloud dependency. This was implemented using a decentralized IoT low-code architecture: applications are created in the Peakboard Designer using drag-and-drop (with optional scripting), data is connected via OPC UA, Modbus, and SQL/ERP systems, and deployed to local Peakboard Boxes. The Peakboard Hub can optionally be used for centralized device management, while shopfloor logic continues to run independently even if the central instance is unavailable. Use cases include machine and production data acquisition (MDE/BDE), downtime tracking, low-paper order control based on pull production principles, as well as energy monitoring up to load management. For IT and OT decision-makers, the use case demonstrates how iterative scaling with low initial effort enables fast integration of existing OT and IT systems, increased transparency, and robust operational reliability on the shopfloor.
Exact Irrigation System: Saving > 60% Water through Energy Chains and Monitoring
Dercks Gartenbau irrigates potted plants in open fields at an industrial scale using an exact irrigation system that delivers water and fertilizer directly into the center of each pot. The starting point was recurring waterlogging, rising water and fertilizer consumption, and the need to use cultivation areas more efficiently. Key challenges included a travel distance of more than 200 meters, reliable supply of media and data in outdoor operation, obstacles along the travel path, cost pressure, and the requirement to avoid unplanned downtime. Technically, the irrigation system is supplied via an igus energy chain instead of dragging hoses across the field. Sensor technology (smart plastics) monitors push and pull forces for condition monitoring and serves as the basis for predictive maintenance. The solution combines energy chains, media lines, and condition monitoring sensors connected to monitoring and notification systems. The results are measurable: 60–70% water savings (down to roughly one third of previous consumption), reduced fertilizer runoff into groundwater, approximately 50% lower energy costs, and 9–10% improved land utilization. For IT and OT decision-makers, this use case demonstrates how IoT-based monitoring secures mechanical systems, enables predictable maintenance, and measurably reduces operating costs and environmental impact.
OPC UA Forge: Unified Namespace for Legacy Factory Data
Casa Mendes Gonçalves (Portugal) started its industrial digitalization with fragmented machine data and a largely manual, outsourced setup that made scaling to additional legacy and new equipment difficult. Key challenges were heterogeneous “machine dialects”, unreliable monitoring/alerting, and the lack of a unified view of OT data for operations, quality, and energy management—while keeping costs under control and retaining flexibility for own dashboards. The team implemented an OPC UA–based architecture using Prosys OPC UA Forge as an aggregation server to build a unified namespace, bringing data from multiple systems into one OPC UA server and exposing it to Grafana. Data is collected at one‑minute intervals (≈500 data points/min) to enable dashboards, real-time alerts (e.g., cooling chambers, fermentation temperatures), and remote monitoring via VPN. The setup also supports next steps toward semantic information modeling to improve interoperability and prepare data for AI use cases. For IT/OT decision-makers, the outcome is measurable: faster integration of heterogeneous assets, stable data access for analytics, and cost reductions such as identifying compressed-air leaks worth ~€28k/year—plus a roadmap toward internal GenAI/chatbot solutions based on reliable, contextualized factory data.
Digital Intralogistics: From Visual Management to IoT
How can intralogistics processes become more efficient, safer, and more transparent—without complex, large-scale IT projects? In this episode, ORGATEX and the IoT Use Case Podcast provide a practical look into the digital transformation of intralogistics. The challenge: In many manufacturing companies, production processes are highly optimized—yet bottlenecks often occur in intralogistics: missing empty containers, long search times, unclear order status, and safety risks in dynamic warehouse environments. At the same time, IT resources are often lacking to implement individual digitalization projects. The solution: ORGATEX develops modular, user-oriented solutions that work without traditional project complexity. Key building blocks include the OX-Label as a digital production order, Kanban and location sensors for automated demand detection, and intelligent spot projections to improve shopfloor safety. The portfolio is complemented by a cloud platform with device management, role and rights concepts, and predefined use cases. From a technology perspective, ORGATEX relies on low-maintenance communication via Narrowband and Thread, over-the-air updates, and a strong focus on scalability. The vision: the “smart home of intralogistics,” where users can install, configure, and expand systems themselves.
Private 5G in Heavy Industry: Practical Insights from a Steel Plant
How can a driverless transport system with a total weight of 100 tons, coils heated to up to 650 °C, and constantly changing indoor and outdoor environments be operated safely? In this episode, Julian Altevogt from Salzgitter Flachstahl GmbH and Daniel Mai from Siemens AG explain why a private 5G network forms the technological foundation for this use case. The challenge: Large plant areas, harsh environmental conditions, real-time safety communication, and growing requirements for data volumes and availability. Traditional Wi-Fi infrastructures quickly reach their limits due to interference, high installation effort, and limited scalability. The solution: A private 5G campus network using licensed spectrum, clearly defined access control, and high radio stability. It enables reliable safety communication, live transmission of AI-based camera streams, and a robust connectivity foundation for autonomous vehicles. At the same time, Wi-Fi remains in use where single applications or smaller areas can be sufficiently covered. The result: A stable operation that can be scaled step by step – from an initial driving distance of 85 meters to future expansion across larger areas. The episode provides practical insights into how industrial companies approach 5G as infrastructure, which organizational and regulatory aspects must be considered, and why private mobile networks are a key enabler for automation, AI, and secure industrial digitalization. An episode for decision-makers and OT and IT leaders who want to understand real-world 5G applications beyond buzzwords.
IT/OT Integration in the Pharmaceutical Industry: Scaling in a GMP-Compliant Way
How can digitalization in the pharmaceutical industry succeed under GMP conditions? In this episode, Vetter Pharma and soffico demonstrate how IT/OT integration can be implemented sustainably even in highly regulated environments. The starting point: paper-based GMP documentation, high manual effort, heterogeneous machine and laboratory systems, and strict requirements for data integrity, validation, and auditability. At the same time, a scalable data foundation for future use cases was missing. The approach: a central data integration architecture based on the Orchestra platform from soffico. Instead of point-to-point connections, Vetter relies on a standardized, three-layer architecture covering the OT level, aggregation layer, and IT level. QA, validation, and operations were integrated from the very beginning. Automation using Kubernetes and CI/CD enables scalability while maintaining compliance. The result: fewer manual checks through “review by exception,” consistent data for MES, LIMS, and analytics applications, and a robust foundation for data-driven optimization — all the way to AI-based use cases. The episode is aimed at managers in regulated industries who want to implement IT/OT integration strategically and for the long term.
Plan connectivity from the start – Edge Layer integrating OT and IT
The guests explain why many production plants—especially those built around 2010—exhibit highly heterogeneous landscapes of controllers, interfaces, and protocols, leading to significant integration effort, performance bottlenecks, and risks related to update capability. A key focus of the episode is how connectivity can be considered early in plant design to better manage later requirements around security, software maintenance, NIS2 compliance, CRA regulations, and scalability. Based on real project experience, the experts demonstrate how an edge layer standardizes the connection of machines from different generations, provides consistent data, and enables secure update rollouts during ongoing operations. The discussion also covers the technical criteria that must be considered when implementing OPC UA in high-cycle environments—such as latency limits below 100 milliseconds or typical fluctuations into the seconds range depending on the implementation. The guests share insights into integration scenarios where FabEagle®Connect is deployed as a Docker-based component within the Kontron Grid, serving as a reliable data source for MES systems, control systems, and data lake environments. Looking ahead, both speakers outline how a well-designed edge layer forms the foundation for digital twins, AI-based analytics, and scalable production data management. This enables companies to integrate additional lines and sites step by step without having to re-implement existing interfaces.
Successfully Monetizing IoT Services with doubleSlash
IoT as a Revenue Driver: How Companies Launch New Business Models with Digital Services Many industrial companies have established initial connectivity and data integrations. But how do you turn connectivity into actual revenue? And how do you make digital services scalable, secure and profitable? Jonas and Marina from doubleSlash provide concrete, real-world insights into exactly these questions. The challenges: Heterogeneous data sources, lack of scalability in the IT architecture, fragmented stakeholders, and complex billing and tax logic in an international context. On top of that, predictive maintenance requires reliable data histories that need to be built up first. The solutions: doubleSlash relies on a consistent three-step approach: Connect – secure connectivity for machines, data standardization, and update capability, among other things with regard to the Cyber Resilience Act. Make Smart – AI and machine learning for predictive maintenance, remote services, and efficient knowledge usage through generative AI. Monetize – building scalable billing systems, digital service products in vehicles and industry, partner ecosystems, and modular software components for fast implementation. The result: From new revenue models and recurring income streams to reduced service costs, this episode shows how IoT becomes economically viable – step by step, without overwhelming the organization.
Passive OT Monitoring: Detect attacks before they become critical
OT cybersecurity in brownfield environments. How Rhebo protects industrial networks through passive monitoring In this episode, Jan Fischer shows how companies can raise their OT security to a new level in a pragmatic way without putting production networks or critical infrastructure at risk. The starting point is historically grown brownfield networks that include legacy protocols such as Profibus or Modbus, unencrypted HTTP communication, forgotten printers or Raspberry Pis on the network, and security components with severely delayed updates. Rhebo’s solution is based on passive monitoring. The software taps into OT network traffic, distinguishes typical from atypical behavior patterns, and reports anomalies at an early stage. An assessment examines the existing infrastructure in depth. Warning signs include unexpected DHCP servers, the appearance of new protocols, data flows leaving the country, or compromised systems following social engineering attacks. A forensics and diagnostics team evaluates the findings and derives concrete actions, from closing security gaps to targeted security upgrades. Jan also addresses current developments such as NIS2, the Cyber Resilience Act, and the growing demand for European on-prem solutions, while explaining the limits of AI in OT security. The episode is aimed at operators of critical infrastructure, manufacturing and logistics companies, and OT leaders who want to harden their networks and detect real attacks early.
Implementing IoT Successfully: Use Cases, Impact, Partnerships
Implementing IoT Successfully: Why Purpose, Partnerships and Small Use Cases Matter In this episode, Ing. Madeleine Mickeleit, Dr. Peter Schopf and Jens Petri discuss the key success factors in IoT projects. At the center is the question of why so many initiatives stall and how companies can shift their focus from a technology driven start to a clear value driven approach. A defined purpose becomes the foundation for decisions related to sensor technology, data models and platforms. With concrete examples from steel plants and machine building, Jens shows how small and well defined use cases create fast and measurable impact, for example through early detection of wear or by monitoring critical components. These approaches build trust, simplify scaling and prevent costly missteps. Another focus is the importance of partnerships. IoT projects thrive on interoperable solutions. Sensor technology, connectivity, gateways and software must work together. Examples such as the Franconian alliance around GMN illustrate how technological cooperation enables real innovation and softens traditional competitive boundaries. Listeners also learn why Dr. Peter Schopf will take over as the host of the podcast and which perspectives and experiences he brings to the role. This episode offers clear guidance for everyone who wants to implement IoT projects pragmatically, in a scalable way and with a strong focus on real business value.
