Jakub Kaczyński

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can process — yet much of its potential remains locked inside disconnected systems, legacy integrations, and outdated architectures.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.

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 pharmaceutical manufacturer visualised real-time IIoT data on Microsoft HoloLens, but operators stayed with the standard HMI. See how redefining the business problem with operators improved adoption, and four lessons for scaling augmented reality in pharma manufacturing.

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.

A real example of root cause analysis in manufacturing: machine learning linked unstable casting quality at an automotive plant to an HVAC outlet near the cooling zone.

Real-time manufacturing dashboards improve KPIs only when operators use them. See how a global maker of electrical accessories for automation turned shop floor data into a tool for production optimisation.

A paperless shop floor only works when operators use it. See how a glass packaging manufacturer fixed low adoption of digital production reporting with a UX audit.

Machine downtime tracking improves KPIs only when downtime data carries context. A food and beverage manufacturer running semi-automated production lines already calculated availability, performance and quality from PLC data, yet its KPIs kept falling short. Once TT PSC correlated every downtime event with shift and operator data, the root cause became visible: unreported stoppages on […]

A predictive maintenance implementation at an aerospace and defence manufacturer with 70+ CNC machining centres shows why data collection and storage must come before AI/ML models.

Industry 4.0 is the use of connected machines, real-time data and software to run a factory on facts rather than assumptions. Here is what it means for a plant with existing equipment, and where to start.