Digitalization in manufacturing?
Without operational risk.

Through zone models aligned with IEC 62443, least-privilege access, audit logs and clearly defined deployment processes. PLCs and SCADA stay in the OT zone; the connection to IT goes through a DMZ with unidirectional gateways or OPC UA reverse connect.

With data contracts at every integration point, quality gates (plausibility, completeness, range checks) and a clear owner per data product. Sensor and machine data is validated at the source – invalid values end up in quarantine, not on the KPI dashboard. As a result, metrics such as OEE, scrap rate and cycle time become traceable and audit-proof across shifts and locations.

A semantic KPI layer centrally defines how each metric is calculated – for example OEE = availability × performance × quality, with consistent time windows and downtime reason codes. All reports and dashboards (shift log, plant manager cockpit, group reporting) then deliver consistent numbers, even when raw data comes from different systems.

We start by mapping all relevant data sources (MES, ERP, SCADA, LIMS, Excel islands), score them by quality and business relevance and prioritize the most critical integrations. Instead of a big-bang rebuild, we connect silos step by step via a central integration model – using adapters, API facades and a semantic data model. This turns isolated systems into a connected data ecosystem without replacing the existing applications.

Through an event-driven integration layer: production orders flow from ERP (e.g. SAP, Odoo, Microsoft Dynamics) into the MES, and confirmations (quantities, times, scrap) flow back automatically. We use proven standards such as OPC UA, REST/JSON or message brokers (Kafka, RabbitMQ) and implement dead-letter queues, retries and monitoring – so data loss is ruled out, even during network glitches or maintenance windows.

Yes. For legacy controllers (Siemens S5/S7, older Allen-Bradley systems, proprietary machine protocols) we build adapters and API facades that read data via Modbus, Profibus, serial interfaces or OPC Classic and translate it into modern formats. Even 20-year-old machines can be integrated into a central MES or IoT system without replacing the controller itself.

We start read-only: in the first phase, data is only extracted and mirrored. Write access only goes live after extensive testing in staging environments that mirror the actual plant setup. Rollouts happen in maintenance windows, with a rollback plan and live monitoring. Critical interfaces run redundantly, so a failure of the integration layer cannot affect machine control.

After integration, the real value emerges: connected data reveals KPIs, trends and anomalies that were invisible before. We combine data analytics, Power BI or comparable tools and – where it makes sense – AI models for forecasting (predictive maintenance, quality prediction). This way, raw machine data becomes decision support for plant management and executives.

Industry 4.0 is not a single mega-project, it is a principle: connected machines, end-to-end data flows and decisions based on data rather than gut feeling. For mid-sized manufacturers we typically start with a manageable use case – for example a live OEE dashboard for one line or automated material replenishment – and grow the platform step by step. That keeps investment predictable and value measurable from the start.

Sensor data (vibration, temperature, current draw, pressure) is captured continuously and compared with historical patterns. Depending on maturity, we use rule-based thresholds, statistical methods or machine learning models. The goal: detect wear or failures before they cause downtime – typical effects are fewer unplanned outages and longer maintenance intervals. A clean data foundation (see data quality) is the prerequisite.

There is no universal answer – we choose based on requirements, existing systems and budget. Commonly used: AWS IoT Core, Azure IoT Hub, Siemens MindSphere/Insights Hub, Bosch IoT Suite or open-source stacks (EMQX, Node-RED, InfluxDB/Grafana). We evaluate vendor lock-in, data sovereignty (GDPR, EU hosting), TCO and future extensibility. For many mid-sized manufacturers a hybrid approach works best: edge processing in the plant plus a central cloud platform for analytics.

We recommend edge computing for time-critical processing directly at the machine (millisecond response) and cloud or on-premise platforms for aggregated analytics, reporting and ML training. Sensitive machine data and recipes stay local, while metrics and trends are analyzed centrally. The exact hosting setup and provider choice are decided together, with GDPR, protection requirements and IT strategy in mind.

With a multi-layered approach: network segmentation aligned with IEC 62443 (Purdue model), firewalls and a DMZ between OT and IT, patch management that respects maintenance windows, endpoint hardening and continuous monitoring for anomalies in machine traffic. External access (e.g. remote maintenance by equipment vendors) is allowed only via controlled jump servers with multi-factor authentication and full session recording.

Since 2024, the NIS2 directive obliges many mid-sized manufacturers (mechanical engineering, food, chemicals, critical supply chains) to apply stricter security measures, risk management and incident reporting. We help with a current-state assessment, identify gaps across OT and IT and implement technical and organizational measures – including supplier management and incident response plans.

We apply the 3-2-1 rule to production data as well: three copies, two media types, one offsite. Controller backups (PLC programs, HMI configurations) are versioned automatically and restore procedures are tested regularly. For critical MES and database systems we set up active failover clusters so hardware or software failures do not stop production.

We start with a structured workshop (2–3 days on-site) where we map data flows, prioritize pain points and create an integration map. From there we build a backlog of clear use cases. In iterative 2- to 4-week sprints we deliver runnable increments and test them directly in the plant. After each milestone we re-evaluate the business case – so the project stays steerable and the budget remains transparent.

Often within a few weeks: transparency dashboards (e.g. live OEE for one line), monitoring alerts and the first automations work fast and create acceptance in the team. More demanding building blocks like the semantic KPI layer, predictive maintenance or ERP/MES integration are added on top and also delivered in small, productive steps – instead of a big-bang go-live after 18 months.

Entry via workshop and integration map starts at approx. €5,000. Quick wins (e.g. a dashboard with 2–3 live KPIs) are typically achievable from €15,000. A complete MES/ERP integration with KPI layer and real-time events tends to range from €40,000 to €150,000, depending on the number of machines, data volume and existing systems. We deliver concrete offers after the workshop; an initial estimate is also possible with our automation ROI calculator.

Yes. Many mid-sized manufacturers do not have a dedicated OT or IoT team – we either run the entire project or work hand in hand with your existing IT or your equipment vendor. After go-live we offer managed services with defined response times so you can focus on production.

Your data belongs exclusively to you – this applies to raw data as well as derived models. We develop Made in Germany at our location in Leer (East Frisia), host by default in German or European data centers and put all necessary contracts in place (GDPR DPA, NDA, source code ownership). Your data sovereignty remains intact, even when the project ends.