What I’ve Built
Advanced Solutions is a digital business unit within JSP, a Japanese materials manufacturer operating globally.
I built it from scratch.
The idea took shape in 2014, when I began asking a question that nobody in our industry was asking seriously: what if the data already present in our customers' factories could be made visible, connected, and useful without replacing the people who run them?
The prototypes came before 2017. The first proof of concept was in 2019. Market validation at K-Fair 2022, one of the largest plastics and rubber trade fairs in the world. Formally established as an independent business pillar within JSP in 2024.
Today, Advanced Solutions operates across nine countries: Germany, Sweden, the Netherlands, the Czech Republic, France, Italy, Portugal, the United Kingdom and the United States, serving manufacturers who are ready to move beyond intuition and toward data-driven operations.
The platform is built on proprietary DDE (Dynamic Data Exchange) technology, offering three product lines:
Core — foundational data connectivity and visibility for production environments
Plus — advanced analytics and process intelligence built on top of Core
Platform — full-scale industrial AI integration across the entire production network
“Industrial AI isn’t about replacing people.
It’s about unlocking their potential.”
Industry Leadership
In parallel with building Advanced Solutions, I encouraged the establishment of the OPC UA Particle Foam Companion Specification, a standardised framework for data communication between machines, systems, and software across OEMs and automotive networks.
This was not a project. It was an act of industry architecture, bringing together machine makers, system suppliers, and technology partners to agree on how data should flow in a sector that had never standardised it before.
The specification now serves as the foundation for interoperability across the particle foam manufacturing ecosystem.
Results That Matter
Instant visibility across 25 injection moulding machines
A manufacturing plant running 25 injection moulding machines had no centralised visibility into production status. Operators collected machine data manually, walking the floor, diagnosing issues by proximity and experience alone.
After connecting all machines through DDE and deploying a single operator-facing application, the entire plant became visible from one screen. Operators can now diagnose issues, prioritise incidents, and take action without leaving the control room.
Result: up to 1,000 hours of manual data collection eliminated annually.
100% detection of non-conforming parts before shipment automotive battery production
A factory producing battery solutions for an automotive OEM had no real-time connection between process data and quality outcomes. Non-conforming parts were a latent risk difficult to detect before reaching the customer.
After connecting process signals to quality results in real time, every operator gained access to live indicators of process drift. The system flags anomalies as they occur, enabling intervention before a defective part moves downstream.
Result: 100% of non-conforming parts detected before shipment, eliminating the risk of expensive customer recalls.
OEE reporting automated, downtime decisions accelerated — discrete manufacturing
A production team was spending the equivalent of one full-time employee per month manually collecting and compiling Overall Equipment Effectiveness (OEE) data. Root cause analysis for downtime events took days, by which point the opportunity to intervene had passed.
After automating OEE reporting through DDE and surfacing root cause data directly to the shop floor team, the picture changed. Data collection became instantaneous. Decisions on corrective actions moved from days to minutes.
Result: one month of manual reporting work eliminated annually, 5% improvement in OEE.
€6,000,000 in annual material savings — insulation block production
A manufacturer producing large insulation blocks wanted to reduce raw material consumption without compromising product integrity. The challenge: machine inconsistency was causing parts to run heavy, consuming more material than necessary, with no real-time signal to operators.
After deploying a monitoring solution that tracked part weight against target in real time, operators gained the ability to react immediately when weight drifted upward — adjusting parameters before excess material was consumed.
Result: 3,000 tonnes of material saved annually, representing approximately €6,000,000 in annual savings.
“What seems impossible in factories today will be standard tomorrow. Someone has to build that bridge.”
Next Keynote
Interoperability Summit, 7 May 2026 in Bonn, Germany
Beyond Dashboards: Real-World Industrial AI and the Role of Interoperability in Manufacturing Transformation
Writing
The AI Universe – Thriving Within Civilization’s Next Big Disruption.
Chapter 26: Industrial AI Unlocks Hidden Potential by Robert Pluska
