Compliance Considerations for Healthcare ERP Projects

Connecting an ERP system to real-time shop-floor data is one of the more technically demanding integrations in manufacturing, largely because the two systems operate on completely different data rhythms. Shop-floor systems generate real-time data from machines and sensors, while ERP systems are built around transactional records, and bridging the two requires careful handling of data volume and timing.

We typically use a middleware layer that aggregates high-frequency machine data into meaningful summaries before it reaches the ERP, rather than flooding it with raw sensor readings.

This gives production managers real-time visibility in the ERP without overwhelming it, and lays the groundwork for predictive maintenance features later.

The mismatch in data rhythm is the core engineering challenge: a machine sensor might emit readings several times per second, while an ERP is designed around discrete transactional events like a completed production run or a material consumption record. Feeding raw sensor streams directly into the ERP would overwhelm its database and offer no real business value, since nobody needs a per-second log of vibration readings inside their financial system.

The middleware layer solves this by aggregating and summarising: converting a stream of raw readings into meaningful events, such as "machine ran for 47 minutes at 94% efficiency" or "temperature exceeded threshold for 3 minutes," which is the level of detail a production manager or the ERP actually needs to act on.

A common pitfall is underestimating network reliability on the shop floor itself, where older machines, intermittent connectivity, and industrial electrical interference are all more common than in a typical office IT environment. We design the middleware layer to buffer and retry data delivery rather than assuming a constant, reliable connection between the floor and the ERP.

Once this aggregated data pipeline is reliably flowing, it becomes the foundation for more advanced capabilities later, particularly predictive maintenance, where trends in the aggregated machine data can flag a likely failure before it actually causes unplanned downtime, but we always recommend proving out the basic visibility layer first before layering predictive models on top.