Smart Manufacturing
Built on Data. Driven by Intelligence.

Yes — this is the most common concern we hear, and the answer is yes for virtually all machine types. For modern PLCs and CNC controllers, we implement OPC-UA server configuration on the controller itself — a software change that does not require physical access to the machine during production. For older machines without network connectivity, we deploy non-invasive retrofit sensors (vibration, current clamp, temperature) that capture machine state from external signals without any modification to the machine or its control system. Edge gateways aggregate data from all sources. The only machines that require a production stoppage for connection are those where we need physical access inside the control cabinet — and we schedule that work during planned maintenance windows.

A manufacturing digital twin is a virtual model of a physical asset (machine, production line, or factory) that receives real-time data from the physical asset and can be used to simulate, analyse, and predict behaviour without affecting the real system. Digital twins are valuable for three specific use cases: virtual commissioning (testing machine programmes and process parameters in the virtual model before running them on real equipment), process optimisation (running simulation scenarios to find the optimal operating parameters without production trials), and predictive maintenance (using the digital model to predict when a machine will reach a failure condition based on its current operating signature). Not every manufacturer needs a full digital twin — it depends on your product complexity, changeover frequency, and the cost of physical experimentation. We assess your specific situation and recommend whether digital twin investment is justified for your operation.

SAP integration with shop floor is typically the most complex technical challenge in manufacturing digitalisation — bridging the gap between OT (operational technology: PLCs, SCADA, MES) and IT (SAP). We approach it using SAP's standard interfaces where they exist (PP-PDC for shop floor data collection, QM for quality notifications, PM for maintenance work orders) and building custom integration where they do not. For SAP S/4HANA, we use the SAP Digital Manufacturing integration suite for MES connectivity and RFC/BAPI-based integration for simpler transactions. The critical design decision is where each transaction originates: some data (production confirmations, quality results) should originate in the MES and flow to SAP; other data (work orders, materials, routings) should originate in SAP and flow to the shop floor. We specify this data flow architecture before any development begins.

Planned maintenance (also called preventive or scheduled maintenance) replaces components or services equipment on a fixed time or cycle schedule — regardless of actual condition. It is better than reactive (break-fix) maintenance but wastes resources replacing components before they need it and does not prevent all failures. Predictive maintenance uses condition monitoring data — vibration signatures, temperature, oil analysis, current draw, acoustic emissions — to detect the early signs of developing failures and predict when the failure will occur. This allows maintenance to be scheduled at the optimal time: before failure, but not earlier than necessary. Rackwave implements condition monitoring using sensors on critical assets, ML models trained on your specific equipment's failure signatures, and integration with your CMMS to automatically generate work orders when a prediction threshold is crossed.

Yes — greenfield smart factory design is an area where getting the technology architecture right from the start avoids the retrofit challenges that constrain brownfield implementations. We work with manufacturing clients on greenfield projects at the technology architecture stage — specifying the IIoT connectivity approach, MES/ERP integration design, network infrastructure (OT/IT convergence), control system standards (PLC platform selection, OPC-UA requirement specification), and the data architecture that makes analytics and AI viable from day one. We work alongside your process engineers and automation suppliers, not instead of them — our role is to design the connected data infrastructure that turns the physical factory into a smart factory, and to ensure the technology decisions made during commissioning do not create data silos that require expensive remediation later.