Manufacturing & Industrial

OT/IT integration is approached read-first—we extract data from OT systems passively through historian connections or OPC-UA subscriptions before any bidirectional integration is attempted. This avoids any risk to OT system stability during the early phases of a programme. Security architecture follows the Purdue model as a baseline—clear demarcation between OT and IT networks, with data crossing the boundary through purpose-built edge nodes or data diodes depending on sensitivity. Any changes to OT systems are validated in staging environments that mirror production conditions before any production changes, and we coordinate closely with the plant engineering and automation teams who own OT system continuity.

The right starting point is reliable data, not models. Many predictive maintenance programmes fail because they attempt to build ML models before they have consistent, high-quality sensor data with accurate failure labels. We begin by assessing what sensor data already exists and what gaps need to be filled, what historical failure records are available and how well they can be linked to sensor readings, and which assets represent the highest business value target for reduced unplanned downtime. A focused pilot on one or two asset classes—with clear success metrics and a production integration path for alerts—delivers faster, more credible results than a broad programme that tries to cover all equipment simultaneously.

Yes. AlgoDomain supports manufacturing clients on SAP S/4HANA programmes—specifically in the integration, data migration, and custom development workstreams that are consistently the most complex and risk-prone parts of an S/4HANA transition. We help clients assess and rationalise custom ABAP development before migration, design the integration architecture for connecting S/4HANA to MES, WMS, and quality systems, and build the data migration tooling needed to cleanse, transform, and load master data and transaction history. We work alongside SAP system integrators rather than displacing them—focusing on the engineering workstreams where specialist capability adds the most value.

The protocol diversity of manufacturing environments—OPC-UA, MQTT, Modbus, EtherNet/IP, proprietary historian interfaces, HL7 adaptations for process environments—is handled through an edge layer that abstracts protocol specifics before data reaches the integration or analytics platform. We use OPC-UA as the preferred standard where equipment supports it, supplemented by protocol-specific adapters for legacy equipment. On the platform side, contextualisation of raw sensor data—mapping time-series signals to asset hierarchy, location, and production context—is handled through an asset model that is maintained independently of the ingestion pipelines, so the analytical layer works with contextualised data rather than raw machine signals.

Reliable manufacturing analytics require: a consistent data ingestion layer from relevant source systems (MES, OT historians, ERP, QMS); a defined asset hierarchy and equipment taxonomy that allows metrics to be aggregated from machine through line to plant level; a contextualisation layer that enriches raw data with production context (shift, product, order, operator); and a storage architecture suited to the query patterns involved—typically a combination of time-series storage for high-frequency sensor data and a relational or lakehouse store for contextualised production records. We assess the current state of these foundations during a discovery phase before committing to an analytics build—because the analytics are only as good as the data infrastructure underneath them.