Digital Manufacturing

Kepware Server connects to virtually every PLC, RTU and IIoT device on the plant floor through 150+ certified drivers, exposing the data through a single OPC UA, MQTT or REST interface. This guide walks through the full driver catalogue, the Communication Suites, the Kepware+ enterprise subscription and the configuration steps a system integrator needs to scope and deploy a project.

TPG Capital has acquired PTC’s entire IIoT portfolio including ThingWorx and Kepware. Raising understandable questions for manufacturers relying on these platforms. Here’s a quick, clear explanation of what happened and what this change truly means for your operations

High energy costs, complex tariffs and tighter regulations are reshaping UK manufacturing. This guide explains how Energy Management Systems help manufacturers reduce energy costs, manage compliance and improve long-term energy performance.

Energy costs are no longer a fixed overhead in manufacturing. This guide explains what an Energy Management System is, how it works in a manufacturing environment, and why real-time energy data is becoming critical for cost control, operational efficiency, and compliance.

Unified Namespace with MQTT provides a scalable, event-driven foundation for industrial OT/IT data integration.
This article explains proven MQTT patterns, topic design, state management, and common pitfalls when building a production-ready UNS.

What is Unified Name Spac and why is it quickly becoming the “central nervous system” of modern manufacturing?

If your IT and OT worlds are still tied together with brittle point-to-point integrations, siloed data, and reports that are always a step behind, it’s time for a different approach. In this practical guide, we explain What is Unified Name Space, how it differs from the Purdue model, and how to design a scalable, real-time data layer using a consistent hierarchy, naming conventions, and governance. You’ll also learn the core implementation steps with MQTT/Sparkplug B or OPC UA—so every system can publish and subscribe to the same contextual, live source of truth.

Industrial data is growing faster than most manufacturers can process — yet much of its potential remains locked inside disconnected systems, legacy integrations, and outdated architectures. As a result, critical insights arrive too late, operational decisions lose accuracy, and digital initiatives struggle to scale. This article explains why traditional data management can no longer support modern industrial needs and how a Unified Namespace approach helps eliminate silos, unify data flows, and create the real-time foundation required for Industry 4.0, advanced analytics, OEE optimization, and energy efficiency.

Choosing the right OEE (Overall Equipment Efficiency) system is crucial for optimizing production performance. In this article, we highlight the key factors to consider when selecting an OEE system to effectively monitor machine efficiency, minimize downtime, and improve production quality. Learn how to choose a system that supports your factory’s growth and delivers real, measurable benefits.

Global food and beverage manufacturers face constant pressure to innovate faster, maintain quality, and streamline production. This case study shows how one of the world’s largest producers modernized recipe management across its European plants using a Unified Namespace (UNS) and Industrial IoT platform — turning fragmented data into a single, reliable source of truth. The result? Real-time collaboration, smarter operations, and readiness for AI-driven manufacturing.

Article based on the report: Technology Evaluation of Augmented and Mixed Reality Systems in Shipbuilding Processes Preliminary Report The technology was designed for laser welding, a high-energy process requiring precise path, speed, and angle control. While automation is common, recent advances in handheld laser welding demand tools to help “manual” welders maintain accuracy. To address […]

How an aerospace and defence manufacturer scaled its digital transformation with a Continuous Innovation Programme, PLM integration, cloud-based supply chain orchestration and a shift from proof of concept to proof of value.

A semiconductor manufacturer wanted to predict product quality from machine and quality control data, but its first AI model was not accurate enough. See how physics-based modelling, with machine learning working on top, made the predictions reliable.