Industrial data systems for Australian food and beverage manufacturers. From raw PLC signals through to OEE dashboards, batch genealogy and MES platforms, built on a data architecture that supervisors and engineers can trust.
Industrial data systems for Australian food and beverage manufacturers. From raw PLC signals through to OEE dashboards, batch genealogy and MES platforms, built on a data architecture that supervisors and engineers can trust.
OEE, downtime, batch reporting and historian. Built on a controls layer that feeds MES platforms including Sepasoft and Ignition.
Reliable analytics starts with disciplined data architecture. We identify the critical data points from PLCs, SCADA and instruments, then model them with naming standards, units, quality states and event context. Collection paths typically include OPC UA collectors, MQTT brokers and historian connectors into SQL or cloud storage layers. Edge buffering is deployed where network reliability is variable, which prevents data loss during outages. Equipment commonly includes industrial PCs, virtualised historian servers and secure gateway appliances. Clear source-of-truth models and validation checks give downstream dashboards, alerts and optimisation initiatives a dependable foundation.
Reporting layers are designed to answer operational questions. Common outputs include OEE by line and shift, downtime Pareto, changeover performance, utility intensity and batch genealogy. Where MES functions are required, we implement workflows for electronic work instructions, lot tracking, quality holds and production declarations. Standards such as ISA-95 help structure data flow between control, operations and business domains. On a recent food plant project, full traceability from raw ingredient intake to pallet dispatch was integrated across multiple lines, so daily investigations and reviews now run from a single reporting model.
Digital programs succeed when delivery is staged and linked to measurable value. Initiatives are prioritised by business impact, implementation effort, data readiness and change management capacity. Roadmaps often begin with foundational tasks such as historian cleanup and alarm rationalisation, then progress to predictive analytics or closed-loop optimisation once data confidence is established. Governance for ownership, data quality monitoring and cybersecurity obligations is defined so systems remain supportable. Proof-of-value pilots run with clear acceptance criteria, then successful patterns scale across additional lines or sites. This is the alternative to a risky, all-at-once platform replacement.
Even a few weeks of clean, contextual data can provide value for downtime and quality analysis. Longer history improves seasonality and trend analysis, but early wins usually come from better event quality and tagging discipline.
Yes. We design segmented architectures with edge buffering, controlled outbound data paths and strong authentication. This allows reporting access while keeping critical control assets isolated from unnecessary inbound exposure.
Yes. We run practical training on dashboard interpretation, data quality ownership and root-cause workflows so teams can turn insights into sustained operational improvements.
Speak directly with an engineer about scope, timing and technical constraints.
