Manufacturing Software Development

We build across the main categories of manufacturing software: MES (manufacturing execution systems) for real-time shop floor data collection, work order management, and OEE measurement; ERP integration projects that connect shop floor data to SAP, Oracle, or Microsoft Dynamics; IoT and sensor data pipelines that pull data from PLCs, SCADA systems, and sensors into modern software; predictive maintenance platforms using machine learning on sensor data to predict equipment failure before it happens; quality control automation with computer vision inspection, SPC dashboards, and non-conformance tracking; and supply chain visibility platforms with supplier portals, MRP integration, and demand forecasting.

An ERP (enterprise resource planning system) manages your business data at the planning level, production orders, bills of materials, purchasing, inventory, and costing. It reflects what should happen. A MES (manufacturing execution system) manages the production process at the shop floor level, capturing what is actually happening in real time. Work orders, operator instructions, machine status, production counts, quality results, and material consumption are all recorded as they happen. A MES connects the planned world of the ERP to the actual world of the shop floor and sends the actual results back up. Most manufacturers have an ERP. Fewer have a MES, which is why the ERP data is often hours behind actual production.

We connect to existing industrial equipment using standard industrial protocols. OPC-UA is the preferred modern protocol for most PLC and SCADA integration; it provides a standardised interface that most modern automation controllers support. For older equipment, Modbus TCP and Modbus RTU are the common protocols for direct PLC connections. Where proprietary protocols are in use (Siemens S7, Allen-Bradley EtherNet/IP), we use the appropriate OPC server or protocol adapter to normalise data to a standard format before it enters the software pipeline. We don't replace your control systems. We add the software layer above them that makes their data usable.

Predictive maintenance AI involves training machine learning models on historical sensor data (vibration, temperature, current draw, pressure, acoustic emissions) to recognise the patterns that precede equipment failure. The models run continuously on live sensor streams and generate alerts when they detect anomaly patterns. The value is in what happens after the alert. We connect predictive maintenance alerts to your CMMS (computerised maintenance management system) or maintenance workflow, so a predicted failure automatically creates a maintenance work order, assigns it to the right team, and tracks completion. An alert that nobody acts on because it goes to a dashboard nobody checks is not maintenance, it's noise.

A focused manufacturing software project, MES for a single production line or an ERP integration covering one data flow, typically runs $60,000--$100,000. Full manufacturing platforms covering MES, ERP integration, IoT pipelines, and predictive maintenance run $100,000--$200,000. Projects requiring computer vision quality inspection or significant legacy SCADA integration sit toward the higher end of that range. Pricing is fixed cost based on scoped features, you know the number before development starts.

Legacy SCADA systems typically expose data via OPC-DA (older) or OPC-UA (newer) interfaces. Where OPC is available, we deploy an OPC-to-MQTT bridge or OPC-UA client that pulls data from the SCADA historian and forwards it to a modern message broker (MQTT, Kafka) for processing. Where OPC isn't available, we use Modbus or the SCADA vendor's own API if one exists. In cases where the legacy system has no API at all, we work with your automation team to add a data export layer at the PLC level, pulling from the source rather than screen-scraping the SCADA UI. We've connected Wonderware, AVEVA, Ignition, and GE iFIX systems.

AI in manufacturing software covers three practical categories. Predictive maintenance: machine learning on historical and live sensor data to predict equipment failure, reducing unplanned downtime. Quality control: computer vision models that inspect products on the production line and classify defects at camera speed, replacing or supplementing manual visual inspection. Process optimisation: models that analyse production data, machine parameters, material batches, and environmental conditions to recommend process adjustments that improve yield or reduce waste. All three require clean, well-labelled historical data to train on. We assess your available data during discovery and design the AI integration around what you actually have, not what would theoretically be ideal.